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Toxics, Volume 10, Issue 5 (May 2022) – 76 articles

Cover Story (view full-size image): Heavy metals have been recognized as potent biological poisons, influencing water quality parameters and aquatic life in general due to their toxicity, persistence, bioaccumulation, and biomagnification. The histopathology of teleosts can be used to detect pollutant-induced stress. The focus of this research is determining how heavy metals affect the morphology of Boops boops kidney and gills, with emphasis on melanomacrophage centers (MMCs) and rodlet cells (RCs) as environmental biomarkers. MMCs and RCs are reliable biomarkers of prospective aquatic environmental changes reflected in fish fauna according to our findings. The use of RCs and MMCs in cytological studies should help researchers to better comprehend teleosts’ complex immune system. View this paper
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17 pages, 5964 KiB  
Article
The Anti-Rheumatic Drug, Leflunomide, Induces Nephrotoxicity in Mice via Upregulation of TGFβ-Mediated p53/Smad2/3 Signaling
by Alhanouf A. Aljohani, Yasmeen S. Alqarni, Maram N. Alrashidi, Maha H. Aljuhani, Shaimaa A. Shehata, Mohamed K. El-Kherbetawy, Kousalya Prabahar, Reem Alshaman, Abdullah Alattar, Ahmed M. N. Helaly, Hayam Ateyya, Ezzat A. Ismail and Sawsan A. Zaitone
Toxics 2022, 10(5), 274; https://doi.org/10.3390/toxics10050274 - 23 May 2022
Cited by 3 | Viewed by 3226
Abstract
Recent studies indicated renal toxicity and interstitial nephritis in patients receiving leflunomide (LEFN), but the exact mechanism is still unknown. The transforming growth factor β (TGFβ)/p53/Smad2/3 pathway crucially mediates renal fibrosis. We aimed to assess the nephrotoxic effect of LEFN in mice and [...] Read more.
Recent studies indicated renal toxicity and interstitial nephritis in patients receiving leflunomide (LEFN), but the exact mechanism is still unknown. The transforming growth factor β (TGFβ)/p53/Smad2/3 pathway crucially mediates renal fibrosis. We aimed to assess the nephrotoxic effect of LEFN in mice and the possible role of TGFβ-stimulated p53/SMAD2/3 signaling. The study design involved distributing sixty male albino mice into four groups: (i) vehicle-treated mice, (ii) LEFN (2.5 mg/kg), (iii) LEFN (5 mg/kg), and (iv) LEFN (10 mg/kg). The drug was given orally every 48 h and continued for 8 weeks. Blood samples were then taken from mice for the determination of kidney function parameters. Right kidneys were used for histopathologic staining and immunohistochemistry, whereas left kidneys were frozen and used for Western blot analysis of the target proteins, p-p53 and Smad2/3. Results indicated that chronic administration of LEFN in mice resulted in a four- and nine-fold increase in serum urea and creatinine levels, respectively. Kidney specimens stained with hematoxylin and eosin or periodic acid–Schiff showed significant histopathological manifestations, such as cellular irregularity, interstitial congestion, and moderate lymphocytic inflammatory infiltrate in mice treated with LEFN. Western blotting indicated upregulation of the p-p53/Smad2/3 proteins. LEFN, especially in the highest dose (10 mg/kg), produced prominent nephrotoxicity in mice. This toxicity is mediated through stimulating fibrotic changes through TGFβ-stimulated p53/Smad2/3 signaling and induction of glomerular and tubular apoptosis. An improved understanding of LEFN-induced nephrotoxicity would have great implications in the prediction, prevention, and management of leflunomide-treated rheumatic patients, and may warrant further clinical studies for following up these toxidromes. Full article
(This article belongs to the Section Toxicology)
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Graphical abstract

Graphical abstract
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<p>Network protein–protein interaction analysis for TGFβ and Smad proteins as presented by the STRING database.</p>
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<p>TGFβ-induced Smad signaling, created via Reactome and KEGG bioinformatic data bases.</p>
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<p>Effect of LEFN on kidney function parameters in adult mice. (<b>A</b>) Serum creatinine, (<b>B</b>) BUN. Data are mean ± SD, and analysis was performed by applying one-way ANOVA and Bonferroni’s test at <span class="html-italic">p</span> &lt; 0.05. †: versus the vehicle group, ‡: versus the LEFN 2.5 mg/kg group, and Σ: versus the LEFN 5 mg/kg group. BUN: blood urea nitrogen.</p>
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<p>Histopathologic scores for hematoxylin and eosin-stained kidney specimens in mice treated with LEFN. Panel (<b>A1</b>,<b>A2</b>) show sections of kidney tissue in the vehicle group, showing regular tubules (dashed arrow) lined by epithelial cells with intact eosinophilic cytoplasm and regular nuclei, and glomeruli (arrowhead) showing capillary tuft with a thin wall and thin patent Bowman’s space and mesangial cells. Interstitium showed thin blood vessels (curved arrow) and loose intervening stroma. Panel (<b>B1</b>,<b>B2</b>) are kidneys from the LEFN 2.5 mg/kg group showing mild focal tubular (dashed arrow) hydropic degeneration of tubular epithelial cells, glomeruli (arrowhead) showing average normal cellularity, and stroma showing focal minimal lymphocytic inflammatory infiltrate (arrow). Panel (<b>C1</b>,<b>C2</b>) are for kidneys in the LEFN 5 mg/kg group showing moderate tubular (dashed arrow) hydropic and vacuolar degeneration of tubular epithelial cells, glomeruli (arrowhead) showing a slight increase in cellularity, and stroma showing focal mild congestion (curved arrow) and focal mild lymphocytic inflammatory infiltrate (arrow). Panel (<b>D1</b>,<b>D2</b>), the LEFN 10 mg/kg group showed moderate hydropic degeneration of tubular epithelial cells (dashed arrow), and glomeruli showed a moderate increase in cellularity with irregularity and focal shrinkage (arrowhead). There is interstitial congestion and focal hemorrhage (curved arrow) with moderate lymphocytic inflammatory infiltrate (arrow), ×100 and ×400, respectively.</p>
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<p>Histopathologic scores for hematoxylin and eosin-stained kidney specimens in mice treated with LEFN. Scores for (<b>A</b>) lymphocytic infiltrate, (<b>B</b>) congestion and hemorrhage, (<b>C</b>) glomerular degeneration, and (<b>D</b>) tubular degeneration. A score from 0–5 was given to each sample, and scores are presented as medians and quartiles, and compared by Kruskal–Wallis ANOVA and Dunn’s test at <span class="html-italic">p</span> &lt; 0.05. *: versus the vehicle group.</p>
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<p>Periodic acid–Schiff staining for the kidney specimen. (<b>A</b>) Sections in kidney tissue from vehicle group showing (left panel) glomerulus with thin basement membranes (black arrow) and patent vascular lumen, and tubules showing (right panel) a preserved brush border (black arrow) of tubular epithelial cells with average cytoplasm (red arrow). (<b>B</b>) Sections in kidney tissue from the LEFN 2.5 mg/kg group showing (left panel) glomerulus with thin basement membranes (black arrow) and patent vascular lumen, and tubules showing (right panel) a focally disrupted and effaced brush border (black arrow) of tubular epithelial cells and early cytoplasmic vacuoles (red arrow). (<b>C</b>) Sections in kidney tissue from the LEFN 5 mg/kg group showing (left panel) glomerular irregularities of vascular lumens with focal increased cellularity (red arrow), but with thin basement membranes (black arrow) (right panel) showing a disrupted and effaced brush border (black arrow) of tubular epithelial cells with moderate cytoplasmic vacuolation (red arrow). (<b>D</b>) Sections in kidney tissue from the LEFN 10 mg/kg group showing (left panel) a mild glomerular increase in cellularity (red arrow) with thin basement membranes (black arrow); the right panel shows multiple intratubular eosinophilic hyaline casts filling tubules (black arrow), with marked disruption of lining epithelial cells’ brush borders.</p>
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<p>Masson’s trichrome staining for the kidneys of mice treated with LEFN. (<b>A</b>, left panel) Sections in kidney tissue from the vehicle group showing glomerulus and proximal tubules with interstitial tissue showing no deposition of green staining fibers in between, and no fibrosis. (<b>A</b>, right panel) shows distal tubules and a vessel in the center with no perivascular or peritubular collagen deposition (arrow). Sections from the kidneys of the LEFN 2.5 mg/kg group showing (<b>B</b>, left panel) no glomerular deposition of collagen fibers (arrow) (<b>B</b>, right panel) shows areas of congested vessels (arrow) with no or faint perivascular green staining, indicating no fibrosis from the kidneys of the LEFN 2.5 mg/kg group. (<b>C</b>, left panel) Sections in kidney tissues from the LEFN 5 mg/kg group showing peritubular and interstitial mild focal deposition of thin green staining collagen fibers (arrow). (<b>C</b>, right panel) shows areas of perivascular, green-stained collagen fibers deposition (arrow), indicating more localization to perivascular areas in the kidneys of the LEFN 5 mg/kg group. (<b>D</b>, left panel) Sections in kidney tissue from the LEFN 10 mg/kg group showing peritubular and interstitial inflammatory infiltrate with deposition of thin green staining collagen fibers (arrow). (<b>D</b>, right panel) shows wider areas of perivascular, green-stained collagen fibers deposition (arrow), indicating more localization to perivascular areas from the kidneys of the LEFN 10 mg/kg group. (<b>E</b>) Column chart for the mean ± SD of the fibrosis area % as measured in each group kidney sepecimens. †: versus the vehicle group, ‡: versus the LEFN 2.5 mg/kg group, and Σ: versus LEFN the 5 mg/kg group at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Immunohistochemistry for TGFβ in kidney specimens from LEFN-treated mice. (<b>A</b>) Sections from the vehicle group show focal weak TGFβ-staining in normal kidneys, localized to periglomerular and peritubular areas (in left panel), and focal minimal staining in perivascular areas (right panel). (<b>B</b>) Sections from the LEFN 2.5 mg/kg group show faint focal staining of TGFβ in periglomerular areas (left panel) and minimal focal weak staining in peritubular areas (arrow in right panel). (<b>C</b>) Sections from the LEFN 5 mg/kg group show moderate staining of TGFβ encircling periglomerular areas (left panel), and this is also seen in perivascular areas or focally in peritubular areas (right panel). (<b>D</b>) Sections from the LEFN 10 mg/kg group show moderate to strong staining of TGFβ, especially in areas surrounded by inflammatory cells infiltrate (left panel), and perivascular and peritubular areas (right panel), arrows in all images indicate positive staining. (<b>E</b>) Column chart for mean ± SD of the stained area % in kidney specimens from the experimental groups. †: versus the vehicle group, ‡: versus the LEFN 2.5 mg/kg group, and Σ: versus the LEFN 5 mg/kg group at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Immunohistochemistry for p53 in the kidney specimens. (<b>A</b>) Images from the vehicle group show focal weak p53 staining, localized to periglomerular cells (in left panel) and focal minimal staining in tubular cells (right panel). (<b>B</b>) Images from the LEFN 2.5 mg/kg group show a very faint focal staining of p53 in glomerular areas (left panel) and weak staining in peritubular areas (arrow in right panel). (<b>C</b>) The LEFN 5 mg/kg group showed focal moderate or weaker staining of p53 encircling periglomerular areas (left panel), and this is also seen in peritubular and perivascular areas (right panel). (<b>D</b>) The LEFN 10 mg/kg group revealed moderate to strong staining of p53, with most staining in periglomerular tubules (left panel) and in areas surrounded by inflammatory cells infiltrate (right panel), arrows in all images indicate positive staining (<b>E</b>) Column chart for mean ± SD of the stained area % in kidney specimens from the experimental groups and data were analyzed using one-way ANOVA. †: versus the vehicle group, ‡: versus the LEFN 2.5 mg/kg group, and Σ: versus the LEFN 5 mg/kg group at <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Western blot analysis for the target proteins. (<b>A</b>) The Western blot gels for p-p53, SMAD2/3, and SMA compared to β-ACTIN. (<b>B</b>,<b>C</b>) Column charts for the p-p53 and SMAD2/3 in the experimental groups. Data are mean ± SD. †: versus the vehicle group, ‡: versus the LEFN 2.5 mg/kg group, and Σ: versus the LEFN 5 mg/kg group at <span class="html-italic">p</span> &lt; 0.05.</p>
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15 pages, 1505 KiB  
Article
The Human Health Risk Assessment of Heavy Metals Impurities (Cd and Pb) in Herbal Medicinal Products as Menthae piperitae tinctura (Mentha × piperita L., folium) Available in Pharmacies from Poland
by Kamil Jurowski and Mirosław Krośniak
Toxics 2022, 10(5), 273; https://doi.org/10.3390/toxics10050273 - 23 May 2022
Cited by 4 | Viewed by 2342
Abstract
Appropriate human health risk assessment (HHRA) is desire in modern regulatory toxicology, especially for elemental impurity studies. The aim of this article is the comprehensive HHRA of two heavy metals impurities—Cd and Pb in herbal medicinal products (HMP) as Menthae piperitae tinctura ( [...] Read more.
Appropriate human health risk assessment (HHRA) is desire in modern regulatory toxicology, especially for elemental impurity studies. The aim of this article is the comprehensive HHRA of two heavy metals impurities—Cd and Pb in herbal medicinal products (HMP) as Menthae piperitae tinctura (Mentha × piperita L., folium) available in Polish pharmacies. These phytopharmaceuticals registered in EU are very common and usually applied OTC products by adults and also children/adolescents. For this purpose, we applied double regulatory approach, including: (1) requirements of ICH Q3D R1 guideline about elemental impurities and (2) additionally margin of exposure (MoE)-based concept to cover also specific population groups. Raw results shows that Cd and Pb were present in all analyzed HMP with Mentha × piperita L., folium (PTM1–PTM10) available in Polish pharmacies. In all samples, Cd impurities (in the range: 0.305–0.506 µg/L) were greatly lower than Pb impurities (in the range: 1.122–4.4921 µg/L). The HHRA of Cd and Pb impurities considering ICH Q3D R1 guideline-based approach made it possible to conclude that all results were below the permissible limit set by FAO/WHO for medicinal herbs and plants in different countries (300 µg/kg for Cd and 10,000 µg/kg for Pb). Additionally, the estimated daily intake of investigated elemental impurities compared to the PDE value confirm all samples safety. The second approach, an MoE-based strategy, indicated that the obtained values of MoE for Cd and Pb in daily dose for each samples were above 10,000; hence, exposure to these elemental impurities would not cause a health risk for all investigated population groups (children, adolescents, and adults). To the best our knowledge, this article is the first study about heavy metals impurities level in final HMPs as Menthae piperitae tinctura (Mentha × piperita L., folium) available in Polish pharmacies. Full article
(This article belongs to the Special Issue Advances in Risk Assessment and Management)
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Figure 1
<p>The graphic summary of the applied strategies in comprehensive human health risk assessment (HHRA) of Cd and Pb impurities in HMP with <span class="html-italic">Mentha × piperita</span> L., folium available in Polish pharmacies.</p>
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<p>The elemental impurities profile for Cd and Pb in investigated herbal medicinal products with <span class="html-italic">Mentha × piperita</span> L., folium (PTM1–PTM10).</p>
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<p>(<b>A</b>) The range of Cd and Pb impurities levels in all investigated herbal medicinal products with <span class="html-italic">Mentha × piperita</span> L., folium (PTM1–PTM10) as the plotbox (log scale). (<b>B</b>) The violin plot showing values of Cd and Pb impurities level (log scale) in all investigated herbal medicinal products with <span class="html-italic">Mentha × piperita</span> L., folium (PTM1–PTM10).</p>
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12 pages, 2488 KiB  
Article
Environmental Toxicity Assessment of Sodium Fluoride and Platinum-Derived Drugs Co-Exposure on Aquatic Organisms
by Davide Di Paola, Fabiano Capparucci, Giovanni Lanteri, Rosalia Crupi, Ylenia Marino, Gianluca Antonio Franco, Salvatore Cuzzocrea, Nunziacarla Spanò, Enrico Gugliandolo and Alessio Filippo Peritore
Toxics 2022, 10(5), 272; https://doi.org/10.3390/toxics10050272 - 23 May 2022
Cited by 23 | Viewed by 3039
Abstract
Pharmaceuticals are widely acknowledged to be a threat to aquatic life. Over the last two decades, the steady use of biologically active chemicals for human health has been mirrored by a rise in the leaking of these chemicals into natural environments. The aim [...] Read more.
Pharmaceuticals are widely acknowledged to be a threat to aquatic life. Over the last two decades, the steady use of biologically active chemicals for human health has been mirrored by a rise in the leaking of these chemicals into natural environments. The aim of this work was to detect the toxicity of sodium fluoride (NaF) exposure and platinum-derived drugs in an ecological setting on aquatic organism development. From 24 to 96 h post-fertilization, zebrafish embryos were treated to dosages of NaF 10 mg/L−1 + cisplatin (CDDP) 100 μM, one with NaF 10 mg/L−1 + carboplatin (CARP) 25 μM, one with NaF 10 mg/L−1 + CDDP 100 μM + CARP 25 μM. Fluoride exposure in combination with Cisplatin and Carboplatin (non-toxic concentration) had an effect on survival and hatching rate according to this study. Additionally, it significantly disturbed the antioxidant defense system and increased ROS in zebrafish larvae. NaF 10 mg/L−1 associated with CDDP 100 μM and CARP 25 μM, increased the production of apoptosis-related proteins (caspase 3, bax, and bcl-2) and the downregulation of acetylcholinesterase (AChE) activity, while no effect was seen for the single exposure. Full article
(This article belongs to the Section Exposome Analysis and Risk Assessment)
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<p>The morphological abnormalities in zebrafish caused by different NaF associations:NaF 10 mg/L<sup>−1</sup> + CDDP 25 μM; NaF 10 mg/L<sup>−1</sup> + CARP 100 μM and NaF 10 mg/L<sup>−1</sup> + CDDP 25 μM + CARP 100 μM (<b>A</b>), survival rate (<b>B</b>), and hatching rate (<b>C</b>). Images were taken from the lateral view under a dissecting microscope (magnification 25). Scale bar, 500 mm.</p>
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<p>Effects of Naf 10 mg/L<sup>−1</sup> + CDDP/NaF 10 mg/L<sup>−1</sup> + CARP/NaF 10 mg/L<sup>−1</sup> + CDDP + CARP exposure on activities of SOD (<b>A</b>), CAT (<b>B</b>) MDA (<b>C</b>), in the larval zebrafish. Embryonic zebrafish was exposed to these solutions from 24 to 96 hpf. Data are expressed as the mean ± SEM of three replicates (about 20 larvae per replicate). The asterisk denotes a statistically significant difference when compared with the CTRL: *** <span class="html-italic">p</span>&lt; 0.0001 versus control.</p>
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<p>The NaF associations groups exposure effects on cell death zebrafish embryos. Related gene expression levels of apoptotic pathway in zebrafish embryos exposed to NaF 10 mg/L + CDDP 25 μM (A); NaF 10 mg/L<sup>−1</sup> + CARP 100 μM (B); NaF 10 mg/L<sup>−1</sup> + CDDP 25 μM + CARP 100 μM (C). The fold change from the CTRL group is considered to reflect the mRNA expression levels. *** <span class="html-italic">p</span> &lt; 0.001 versus control.</p>
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<p>Changes in acetylcholinesterase (AChE) activity in zebrafish larvae after 96 hpf exposure to NaF 10 mg/L + CDDP 25 μM; NaF 10 mg/L−1 + CARP 100 μM; NaF 10 mg/L−1 + CDDP 25 μM + CARP 100 μM Each bar represents 3 replicates (each replicate contained 30 larvae) and expresses as average ± SEM. * <span class="html-italic">p</span> ≤ 0.05, *** <span class="html-italic">p</span> &lt; 0.001 indicate significant differences between exposure groups and the control group.</p>
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12 pages, 2319 KiB  
Article
Glycine Betaine Relieves Lead-Induced Hepatic and Renal Toxicity in Albino Rats
by Farid Abdelrazek, Dawlat A. Salama, Afaf Alharthi, Saeed A. Asiri, Dina M. Khodeer, Moath M. Qarmush, Maysa A. Mobasher and Mervat Ibrahim
Toxics 2022, 10(5), 271; https://doi.org/10.3390/toxics10050271 - 23 May 2022
Cited by 7 | Viewed by 2590
Abstract
Lead (Pb) is a widespread and nondegradable environmental pollutant and affects several organs through oxidative mechanisms. This study was conducted to investigate the antioxidant protective effect of glycine betaine (GB) against Pb-induced renal and hepatic injury. Male albino rats (n = 45) [...] Read more.
Lead (Pb) is a widespread and nondegradable environmental pollutant and affects several organs through oxidative mechanisms. This study was conducted to investigate the antioxidant protective effect of glycine betaine (GB) against Pb-induced renal and hepatic injury. Male albino rats (n = 45) were divided into three groups: G1 untreated control, G2 Pb-acetate (50 mg/kg/day), and G3 Pb-acetate (50 mg/kg/day) plus GB (250 mg/kg/day) administered for 6 weeks. For G3, Pb-acetate was administered first and followed by GB at least 4 h after. Pb-acetate treatment (G2) resulted in a significant decrease in renal function, including elevated creatinine and urea levels by 17.4% and 23.7%, respectively, and nonsignificant changes in serum uric acid levels. Serum aspartate aminotransferase (AST), alanine aminotransferase (ALT), and alkaline phosphates (ALP) activities were significantly increased with Pb treatment by 37.6%, 59.3%, and 55.1%, respectively. Lipid peroxidation level was significantly increased by 7.8 times after 6 weeks of Pb-acetate treatment. The level of reduced glutathione (GSH-R) significantly declined after Pb-acetate treatment. Pb-acetate treatment also reduced the activities of superoxide dismutase (SOD), glutathione-S-transferase (GST), and glutathione peroxidase (GSH-PX) by 74.1%, 85.0%, and 40.8%, respectively. Treatment of Pb-intoxicated rats with GB resulted in a significant reduction in creatinine, urea, ALT, AST, and lipid peroxidation, as well as a significant increase in the level of GSH-R and in the activities of ALP, SOD, GST, and GSH-PX. The molecular interaction between GB and GSH-PX indicated that the activation of GSH-PX in Pb-intoxicated rats was not the result of GB binding to the catalytic site of GSH-PX. The affinity of GB to bind to the catalytic site of GSH-PX is lower than that of H2O2. Thus, GB significantly mitigates Pb-induced renal and liver injury through the activation of antioxidant enzymes and the prevention of Pb-induced oxidative damage in the kidney and liver. Full article
(This article belongs to the Special Issue Heavy Metal Contamination in Soil and Health Risks)
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Figure 1
<p>Glycine betaine inhibits Pb-induced lipid peroxidation and leads to reduced glutathione levels in albino rats. (<b>A</b>) Lipid peroxidation is expressed as malondialdehyde concentration in serum after 6 weeks of lead-acetate ingestion with or without glycine betaine. (<b>B</b>) Serum with reduced glutathione levels after 6 weeks of lead acetate administration in the presence or absence of glycine betaine. Data expressed as the mean of six replicates ± SE. Bars with a different letter are significantly different (<span class="html-italic">p</span> &lt; 0.05) as assessed by the LSD test.</p>
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<p>Liver enzymes activities after 6 weeks of exposure to lead acetate (50 mg/kg bw) or lead acetate (50 mg/kg bw) with glycine betaine (250 mg/kg bw). (<b>A</b>) Serum aspartate transaminase, AST (IU/L); (<b>B</b>) serum alanine transaminase, ALT (IU/L); and (<b>C</b>) serum alkaline phosphates, ALP (IU/L). Data expressed as the mean of six replicates ± SE. Bars with a different letter are significantly (<span class="html-italic">p</span> &lt; 0.05) different as assessed by the LSD.</p>
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<p>Serum biomarkers of renal function after 6 weeks of exposure to lead acetate (50 mg/kg bw) or lead acetate (50 mg/kg bw) with glycine betaine (250 mg/kg bw). (<b>A</b>) Serum creatinine level (mg/dL), (<b>B</b>) serum urea concentration (mg/dL), and (<b>C</b>) serum uric acid (mg/dL). Data expressed as the mean of six replicates ± SE. Bars with a different letter are significantly different (<span class="html-italic">p</span> &lt; 0.05) as assessed by the LSD test.</p>
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<p>Glycinebetaine improves antioxidant-defense systems in Pb-intoxicated rats: (<b>A</b>) % of antioxidant enzyme activities and reduced glutathione levels in Pb-intoxicated rats. (<b>B</b>) % of recovery in untreated control rats. Data expressed as the mean of six replicates ± SE. Bars with a different letter are significantly different (<span class="html-italic">p</span> &lt; 0.05) as assessed by the LSD test.</p>
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<p>Molecular interaction between glutathione peroxidase catalytic site and GB. (<b>A</b>) The molecular structure of glutathione peroxidase 4 from the mouse shown as a cartoon and the catalytic shown as magenta sticks. (<b>B</b>) Interaction between glycine betaine and the catalytic site of glutathione peroxidase4, ligand in sticks, residues with polar interaction with ligands in blue lines, hydrogen bonds in yellow dash, and residues with hydrophobic interaction with ligands in red lines. The structure of glutathione peroxidase 4 was cited from [<a href="#B41-toxics-10-00271" class="html-bibr">41</a>].</p>
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<p>Schematic model for the effect of GB administration on the Pb-induced oxidative toxicity in rats. GB treatment reduced lipid peroxidation and liver enzymes such as AST and ALT in serum, serum urea, and creatinine, and this treatment increased ALP activity, uric acid, and antioxidant enzymes’ activities in serum (SOD, GST, and GSH-PX). GB interacts with the binding site of GSH-PX, which may result in enzyme activation and the mitigation of Pb-induced toxicity.</p>
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22 pages, 1834 KiB  
Review
Nanoplastics: Status and Knowledge Gaps in the Finalization of Environmental Risk Assessments
by Andrea Masseroni, Cristiana Rizzi, Chiara Urani and Sara Villa
Toxics 2022, 10(5), 270; https://doi.org/10.3390/toxics10050270 - 23 May 2022
Cited by 14 | Viewed by 3609
Abstract
Nanoplastics (NPs) are particles ranging in size between 1 and 1000 nm, and they are a form of environmental contaminant of great ecotoxicological concern. Although NPs are widespread across ecosystems, they have only recently garnered growing attention from both the scientific community and [...] Read more.
Nanoplastics (NPs) are particles ranging in size between 1 and 1000 nm, and they are a form of environmental contaminant of great ecotoxicological concern. Although NPs are widespread across ecosystems, they have only recently garnered growing attention from both the scientific community and regulatory bodies. The present study reviews scientific literature related to the exposure and effects of NPs and identifies research gaps that impede the finalization of related environmental risk assessments (ERAs). Approximately 80 articles published between 2012 and 2021 were considered. Very few studies (eight articles) focused on the presence of NPs in biotic matrices, whereas the majority of the studies (62 articles) assessed the lethal and sublethal effects of NPs on aquatic and terrestrial organisms. Whilst many studies focused on nude NPs, only a few considered their association with different aggregates. Amongst NPs, the effects of polystyrene are the most extensively reported to date. Moreover, the effects of NPs on aquatic organisms are better characterized than those on terrestrial organisms. NP concentrations detected in water were close to or even higher than the sublethal levels for organisms. An ERA framework specifically tailored to NPs is proposed. Full article
(This article belongs to the Special Issue Recent Frontiers in Research on Nanoplastics)
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<p>Global plastic production from 1950 to 2020 [<a href="#B5-toxics-10-00270" class="html-bibr">5</a>].</p>
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<p>Graphical representation of plastic debris of macro, micro, and nano size (please note that the images representing plastic particles are not in scale).</p>
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<p>Lethal values (EC<sub>50</sub>) expressed in mg·L<sup>−1</sup> of PS for different freshwater and marine taxa.</p>
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<p>Tools for exposure and effect assessments as part of the general environmental risk assessment framework for NPs.</p>
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24 pages, 1584 KiB  
Article
Development, Optimization, and Validation of Forensic Analytical Method for Quantification of Anticholinesterase Pesticides in Biological Matrices from Suspected Cases of Animal Poisoning
by André Rinaldi Fukushima, Juliana Weckx Peña-Muñoz, Luís Antônio Baffile Leoni, Maria Aparecida Nicoletti, Glaucio Monteiro Ferreira, Jan Carlo Morais Oliveira Bertassoni Delorenzi, Esther Lopes Ricci, Marlos Eduardo Brandão, Lorena de Paula Pantaleon, Vagner Gonçalves-Junior, Paula Andrea Faria Waziry, Paulo Cesar Maiorka and Helenice de Souza Spinosa
Toxics 2022, 10(5), 269; https://doi.org/10.3390/toxics10050269 - 23 May 2022
Cited by 2 | Viewed by 2833
Abstract
Anticholinesterase pesticides are a main cause of the intentional or accidental poisoning of animals. Anticholinesterases include several substances that cause the overstimulation of both central and peripheral acetylcholine-dependent neurotransmission. Forensic analyses of poisoning cases require high levels of expertise, are costly, and often [...] Read more.
Anticholinesterase pesticides are a main cause of the intentional or accidental poisoning of animals. Anticholinesterases include several substances that cause the overstimulation of both central and peripheral acetylcholine-dependent neurotransmission. Forensic analyses of poisoning cases require high levels of expertise, are costly, and often do not provide reliable quantitative information for unambiguous conclusions. The purpose of the present study was to develop and validate a method of high-performance liquid chromatography with diode array detector (HPLC–DAD) for the identification and quantitation of n-methyl carbamates, organophosphates and respective metabolites from biological samples of animals that were suspected of poisoning. HPLC–DAD is reliable, fast, simplistic and cost-effective. The method was validated for biological samples obtained from stomach contents, liver, vitreous humor and blood from four different animal species. The validation of the method was achieved using the following analytical parameters: linearity, precision, accuracy, selectivity, recovery, and matrix effect. The method showed linearity at the range of 25–500 μg/mL, and the correlation coefficient (r2) values were >0.99 for all matrices. Precision and accuracy were determined by the (a) coefficient of variation (CV), (b) relative standard deviation low-quality control (LQC), (c) medium-quality control (QCM), and (d) high-quality control (QCA). The indicated parameters were all less than 15%. The recovery of analytes ranged from 31 to 71%. The analysis of results showed no significant interfering peaks due to common xenobiotics or matrix effects. The abovementioned method was used to positively identify pesticide analytes in 44 of the 51 animal samples that were suspected of poisoning, demonstrating its usefulness as a forensic tool. Full article
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<p>Flowchart for extraction and preparation of samples for analysis.</p>
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<p><b>Chromatogram of carbamates by HPLC–DAD</b>. Simultaneous analysis of carbamates shows distinct separation of peaks that correspond to each tested carbamate. Notice the high resolution for each principal component and the absence of background noise.</p>
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<p>(<b>A</b>–<b>C</b>). Chromatograms of white samples represented in figure (<b>A</b>), zero (white sample added to the internal standard) represented in figure (<b>B</b>) and standards (aldicarb–sulfoxide, aldicarb–sulfone, aldicarb, 3-OH-cabrofuran, carbofuran, internal standard and phorate) represented in figure (<b>C</b>). The pattern was added to a pool of all matrices evaluated.</p>
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<p>(<b>A</b>–<b>C</b>). Chromatograms of white samples represented in figure (<b>A</b>), zero (white sample added to the internal standard) represented in figure (<b>B</b>) and standards (aldicarb–sulfoxide, aldicarb–sulfone, aldicarb, 3-OH-cabrofuran, carbofuran, internal standard and phorate) represented in figure (<b>C</b>). The pattern was added to a pool of all matrices evaluated.</p>
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<p>(<b>A</b>,<b>B</b>). Chromatograms of the white sample added from the internal standard and the aldicarb figure (<b>A</b>). Standard was added in a pool of all the matrices evaluated and the actual sample figure (<b>B</b>).</p>
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<p>(<b>A</b>,<b>B</b>). Chromatograms of the white sample added from the internal standard and the aldicarb figure (<b>A</b>). Standard was added in a pool of all the matrices evaluated and the actual sample figure (<b>B</b>).</p>
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20 pages, 3645 KiB  
Article
Individual and Combined Effects of Paternal Deprivation and Developmental Exposure to Firemaster 550 on Socio-Emotional Behavior in Prairie Voles
by Sagi Enicole A. Gillera, William P. Marinello, Mason A. Nelson, Brian M. Horman and Heather B. Patisaul
Toxics 2022, 10(5), 268; https://doi.org/10.3390/toxics10050268 - 22 May 2022
Cited by 9 | Viewed by 2608
Abstract
The prevalence of neurodevelopmental disorders (NDDs) is rapidly rising, suggesting a confluence of environmental factors that are likely contributing, including developmental exposure to environmental contaminants. Unfortunately, chemical exposures and social stressors frequently occur simultaneously in many communities, yet very few studies have sought [...] Read more.
The prevalence of neurodevelopmental disorders (NDDs) is rapidly rising, suggesting a confluence of environmental factors that are likely contributing, including developmental exposure to environmental contaminants. Unfortunately, chemical exposures and social stressors frequently occur simultaneously in many communities, yet very few studies have sought to establish the combined effects on neurodevelopment or behavior. Social deficits are common to many NDDs, and we and others have shown that exposure to the chemical flame retardant mixture, Firemaster 550 (FM 550), or paternal deprivation impairs social behavior and neural function. Here, we used a spontaneously prosocial animal model, the prairie vole (Microtus ochrogaster), to explore the effects of perinatal chemical (FM 550) exposure alone or in combination with an early life stressor (paternal absence) on prosocial behavior. Dams were exposed to vehicle (sesame oil) or 1000 µg FM 550 orally via food treats from conception through weaning and the paternal absence groups were generated by removing the sires the day after birth. Adult offspring of both sexes were then subjected to open-field, sociability, and a partner preference test. Paternal deprivation (PD)-related effects included increased anxiety, decreased sociability, and impaired pair-bonding in both sexes. FM 550 effects include heightened anxiety and partner preference in females but reduced partner preference in males. The combination of FM 550 exposure and PD did not exacerbate any behaviors in either sex except for distance traveled by females in the partner preference test and, to a lesser extent, time spent with, and the number of visits to the non-social stimulus by males in the sociability test. FM 550 ameliorated the impacts of parental deprivation on partner preference behaviors in both sexes. This study is significant because it provides evidence that chemical and social stressors can have unique behavioral effects that differ by sex but may not produce worse outcomes in combination. Full article
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<p>Study design.</p>
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<p>Open-field outcomes. (<b>A</b>) Three-way ANOVA <span class="html-italic">p</span>-values for main effects of sex, FM 550 exposure, and paternal care and (<b>B</b>) Two-way ANOVA within sex <span class="html-italic">p</span>-values for each endpoint. Significant effects are highlighted in black and suggestive effects (<span class="html-italic">p</span> ≤ 0.10) are highlighted in grey. (<b>C</b>) FM 550 females spent less time and made fewer entries (<b>D</b>) in the center than unexposed females. (<b>C</b>) FM 550 males spent more time in the center than the unexposed males. Both the unexposed and exposed PD females spent less time in the center (<b>C</b>) with a suggestive but not significant decrease in center entries (<b>D</b>) than the BPC unexposed females. (<b>E</b>) Similarly, unexposed and exposed paternally deprived males took longer to enter the center than BPC unexposed males. (<b>F</b>) The main effect of FM 550 was found for males on distance traveled. Graphs depict mean ± SEM. For each dose (<span class="html-italic">n</span> = 13–25), * denotes statistically significant difference between groups within sex, while ψ denotes significant sex differences between BPC controls. A single symbol represents <span class="html-italic">p</span><sup>(</sup>*<sup>,<span class="html-italic">ψ</span>)</sup> ≤ 0.05 and a double symbol represents <span class="html-italic">p</span><sup>(</sup>**<sup>)</sup> ≤ 0.01.</p>
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<p>Sociability test. (<b>A</b>) Arena schematic depicting placement of each element. (<b>B</b>) Three-way ANOVA <span class="html-italic">p</span>-values for main effects of sex, FM 550 exposure, and paternal care and (<b>C</b>) Two-way ANOVA <span class="html-italic">p</span>-values for each endpoint. Significant effects are highlighted in black and suggestive effects (<span class="html-italic">p</span> ≤ 0.10) are highlighted in grey. Main effects were primarily driven by paternal deprivation (<b>B</b>) and primarily in females on the exploratory endpoints in the task (<b>C</b>). The sociability index revealed no preference for either cup in any group, however, a suggested lower sociability index PD + FM 550 males compared to BPC males (<b>D</b>). No significant differences in duration with strangers were found in either sex (<b>E</b>) however, there was a suggestive paternal care effect in males. PD females traveled more (<b>G</b>) with more entries with the stranger (<b>H</b>) and empty cup (<b>I</b>) than BPC females. PD + FM 550 males visited (<b>I</b>) and spent more time with the empty cup than BPC males (<b>F</b>). Graphs (<b>E</b>–<b>I</b>) depict mean ± SEM and (<b>D</b>) depicts mean ± 95% CI. For each dose (<span class="html-italic">n</span> = 15–28); * denotes a statistically significant difference between groups within sex, while ψ denotes significant sex differences between BPC controls. A single symbol represents <span class="html-italic">p</span><sup>(</sup>*<sup>,<span class="html-italic">ψ</span>)</sup> ≤ 0.05 and a double symbol represents <span class="html-italic">p</span><sup>(</sup>**<sup>)</sup> ≤ 0.01. For the sociability index, a significant difference from chance (SI = 0, solid line). A sociability index of 1.0 indicates preference for stranger animal and an index of −1.0 indicates preference for the empty cup.</p>
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<p>Social endpoints in the partner preference test. (<b>A</b>) Schematic depicting the placement of the partner and stranger animals in the three-chamber arena. (<b>B</b>) Three-way ANOVA <span class="html-italic">p</span>-values for main effects of sex, exposure, paternal deprivation, and interaction for each endpoint. Significant effects are highlighted in black and suggestive effects (<span class="html-italic">p</span> ≤ 0.10) are highlighted in grey. (<b>C</b>) Two-way ANOVA <span class="html-italic">p</span>-values within sex. (<b>D</b>) Partner preference index, calculated over the entire 2 hrs, was sexually dimorphic and paternal deprivation significantly lowered PPI in both unexposed groups (male and female). Only the PD males did not show a partner preference. (<b>E</b>) Time spent with the stranger was sexually dimorphic and PD females and males spent more time with the stranger than their BPC counterparts. FM 550 PD females spent significantly less time with the stranger than PD only females. Similar findings were found for partner duration (<b>F</b>), with PD males and females spending less time with their partners than BPC controls of the same sex. Endpoints were also binned into 30-min intervals (<b>G</b>–<b>I</b>) to explore PP behavior over time. Notably, PPI and partner duration tended to increase with time in the BPC groups, but this pattern was not seen in the PD males. Graphs (<b>E</b>–<b>I</b>) depict mean ± SEM and (<b>D</b>) depicts mean ± 95% CI. For each dose (<span class="html-italic">n</span> = 14–23), * denotes statistically significant exposure effects within sex, while ψ denotes significant sex differences between unexposed BPC animals. For PPI (<b>D</b>), a significant difference from equal preference (PPI = 0, solid line) is indicated by ρ. A lack of significant difference is indicated by ns. A partner preference index of 1.0 indicates a maximal preference for a partner, while an index of −1.0 indicates a maximal preference for the stranger. For binned data (<b>G</b>–<b>I</b>), significant group differences at individual time points are depicted by letters (a: BPC + FM 550; b: PD; c: PD + FM 550); circles with solid lines = BPC, triangles with dashed lines = PD, black = unexposed, grey = FM 550 exposed. A single symbol represents <span class="html-italic">p</span><sup>(</sup>*<sup>,<span class="html-italic">ρ</span>,<span class="html-italic">ψ</span>,<span class="html-italic">a</span>,<span class="html-italic">b</span>)</sup> ≤ 0.05, a double symbol represents <span class="html-italic">p</span><sup>(</sup>**<sup>,<span class="html-italic">ρρ</span>,<span class="html-italic">bb</span>)</sup> ≤ 0.01, a triple symbol represents <span class="html-italic">p</span><sup>(<span class="html-italic">bbb</span>)</sup> ≤ 0.001, and four symbols represent <span class="html-italic">p</span><sup>(<span class="html-italic">ρρρρ</span>)</sup> ≤ 0.0001.</p>
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<p>PP exploratory behavior in the partner preference test. (<b>A</b>) Three-way ANOVA <span class="html-italic">p</span>-values for main effects of sex, exposure, paternal deprivation, and interaction for each endpoint. Significant effects are highlighted in black and suggestive effects (<span class="html-italic">p</span> ≤ 0.10) are highlighted in grey. (<b>B</b>) Two-way ANOVA <span class="html-italic">p</span>-values within sex. Main effects of exposure were only seen in males, while the main effects of PD were only observed in females. (<b>C</b>) Effects on distance traveled were influenced by paternal care in females but exposure in males, with co-exposed females traveling more than any other female group. Similar findings were found for bouts with the stranger (<b>D</b>) and partner (<b>E</b>) animals, where a main effect of paternal care was found in females but exposure in males. Endpoints were also binned into 30-min intervals (<b>F</b>–<b>H</b>) to explore PP behavior over time. Overall activity decreased over time as the animals habituated to the task. PD females were more active early in the task, while FM 550 exposed male activity was higher than unexposed male activity towards the end. Graphs (<b>C</b>–<b>H</b>) depict mean ± SEM. For each dose (<span class="html-italic">n</span> = 14–23), * denotes statistically significant exposure effects within sex, while ψ denotes significant sex differences between unexposed BPC animals. For binned data (<b>F</b>–<b>H</b>), significant group differences at individual time points are depicted by letters (a: BPC + FM 550; b: PD; c: PD + FM 550); circles with solid lines = BPC, triangles with dashed lines = PD, black = unexposed, grey = FM 550 exposed. A single symbol represents <span class="html-italic">p</span><sup>(</sup>*<sup>,<span class="html-italic">ψ</span>,<span class="html-italic">a</span>,<span class="html-italic">b</span>,<span class="html-italic">c</span>)</sup> ≤ 0.05, a double symbol represents <span class="html-italic">p</span><sup>(</sup>**<sup>,<span class="html-italic">ψψ</span>,<span class="html-italic">aa</span>,<span class="html-italic">bb</span>,<span class="html-italic">cc</span>)</sup> ≤ 0.01, a triple symbol represents <span class="html-italic">p</span><sup>(</sup>***<sup>,<span class="html-italic">aaa</span>,<span class="html-italic">ccc</span>)</sup> ≤ 0.001, and four symbols represent <span class="html-italic">p</span><sup>(<span class="html-italic">cccc</span>)</sup> ≤ 0.0001.</p>
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16 pages, 1947 KiB  
Article
Biomonitoring of Exposure to Urban Pollutants and Oxidative Stress during the COVID-19 Lockdown in Rome Residents
by Flavia Buonaurio, Francesca Borra, Daniela Pigini, Enrico Paci, Mariangela Spagnoli, Maria Luisa Astolfi, Ottavia Giampaoli, Fabio Sciubba, Alfredo Miccheli, Silvia Canepari, Carla Ancona and Giovanna Tranfo
Toxics 2022, 10(5), 267; https://doi.org/10.3390/toxics10050267 - 21 May 2022
Cited by 4 | Viewed by 3211
Abstract
Background: The objective of this study is to evaluate the effects of traffic on human health comparing biomonitoring data measured during the COVID-19 lockdown, when restrictions led to a 40% reduction in airborne benzene in Rome and a 36% reduction in road traffic, [...] Read more.
Background: The objective of this study is to evaluate the effects of traffic on human health comparing biomonitoring data measured during the COVID-19 lockdown, when restrictions led to a 40% reduction in airborne benzene in Rome and a 36% reduction in road traffic, to the same parameters measured in 2021. Methods: Biomonitoring was performed on 49 volunteers, determining the urinary metabolites of the most abundant traffic pollutants, such as benzene and PAHs, and oxidative stress biomarkers by HPLC/MS-MS, 28 elements by ICP/MS and metabolic phenotypes by NMR. Results: Means of s-phenylmercaputric acid (SPMA), metabolites of naphthalene and nitropyrene in 2020 are 20% lower than in 2021, while 1-OH-pyrene was 30% lower. A reduction of 40% for 8-oxo-7,8-dihydroguanosine (8-oxoGuo) and 8-oxo-7,8-dihydro-2-deoxyguanosine (8-oxodGuo) and 60% for 8-oxo-7,8-dihydroguanine (8-oxoGua) were found in 2020 compared to 2021. The concentrations of B, Co, Cu and Sb in 2021 are significantly higher than in the 2020. NMR untargeted metabolomic analysis identified 35 urinary metabolites. Results show in 2021 a decrease in succinic acid, a product of the Krebs cycle promoting inflammation. Conclusions: Urban pollution due to traffic is partly responsible for oxidative stress of nucleic acids, but other factors also have a role, enhancing the importance of communication about a healthy lifestyle in the prevention of cancer diseases. Full article
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<p>Average monthly traffic data in Italy in March 2019, February and March 2020, and March 2021. IMR (Indice Mobilità Rilevata) is a mobility index expressed as the mean number of vehicles/day, calculated by ANAS S.p.A.</p>
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<p>Weekly trend of the concentration of airborne benzene in the years 2019, 2020 and 2021. The weeks 11–21, marked in red, are the lockdown weeks in year 2020.</p>
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<p>PLS-DA score plot (<b>A</b>) and significant regression coefficients (<b>B</b>). Blue represents 2020 and red 2021.</p>
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<p>Boxplots of (<b>A</b>) succinic acid, (<b>B</b>) 8-oxoGua, (<b>C</b>) 8-oxoGuo and (<b>D</b>) 8-oxodGuo.</p>
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<p>Boxplots of (<b>A</b>) boron, (<b>B</b>) cobalt and (<b>C</b>) copper.</p>
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14 pages, 1101 KiB  
Article
Enhanced Cd Phytoextraction by Solanum nigrum L. from Contaminated Soils Combined with the Application of N Fertilizers and Double Harvests
by Wei Yang, Huiping Dai, Lidia Skuza and Shuhe Wei
Toxics 2022, 10(5), 266; https://doi.org/10.3390/toxics10050266 - 19 May 2022
Cited by 1 | Viewed by 1784
Abstract
It is very important to increase phytoremediation efficiency in practice in suitable climatic conditions for plant growth through multiple harvests. Solanum nigrum L. is a Cd hyperaccumulator. In the present experiment, after applying different types of N fertilizers (NH4HCO3, [...] Read more.
It is very important to increase phytoremediation efficiency in practice in suitable climatic conditions for plant growth through multiple harvests. Solanum nigrum L. is a Cd hyperaccumulator. In the present experiment, after applying different types of N fertilizers (NH4HCO3, NH4Cl, (NH4)2SO4, CH4N2O), root and shoot biomasses and Cd phytoextraction efficiency of S. nigrum effectively improved (p < 0.05). Shoot biomasses of S. nigrum harvested at the first florescence stage plus the amounts at the second florescence stage were higher than those harvested at the maturation stage, which indicates that S. nigrum Cd phytoaccumulation efficiency was higher in the former compared to the latter as there was no clear change in Cd concentration (p < 0.05). The pH value and extractable Cd contents showed no changes, regardless of whether N fertilizer was added or not at different growth stages. In addition, after N fertilizer was applied, H2O2 and malondialdehyde (MDA) contents in S. nigrum in vivo were lower compared to those that had not received N addition (CK); similarly, the concentration of proline was decreased as well (p < 0.05). The activity of the antioxidant enzyme catalase (CAT), harvested at different growth periods after four types of N fertilizer applications, obviously decreased in S. nigrum shoots, while peroxidase (POD) and superoxide dismutase) (SOD) activities increased (p < 0.05). Our study demonstrated that (NH4)2SO4 treatment exerted the most positive effect and CH4N2O the second most positive effect on S. nigrum Cd phytoremediation efficiency in double harvests at florescence stages, and the growth conditions were better than others. Full article
(This article belongs to the Special Issue Safety Utilization and Remediation of Heavy Metal Polluted Farmland)
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Figure 1
<p>Effects of different types of N fertilizers on proline concentration (<b>a</b>) and the activity of CAT (<b>b</b>), POD (<b>c</b>) and SOD (<b>d</b>) by comparing single and double harvests of <span class="html-italic">S. nigrum</span> shoots (means with different letters in the panel are significantly different among treatments at <span class="html-italic">p</span> &lt; 0.05. Error bars reported in figures are means of 3 replicates with standard deviation).</p>
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<p>Effects of different types of N fertilizers on proline concentration (<b>a</b>) and the activity of CAT (<b>b</b>), POD (<b>c</b>) and SOD (<b>d</b>) by comparing single and double harvests of <span class="html-italic">S. nigrum</span> shoots (means with different letters in the panel are significantly different among treatments at <span class="html-italic">p</span> &lt; 0.05. Error bars reported in figures are means of 3 replicates with standard deviation).</p>
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<p>Effects of different types of N fertilizers on extractable Cd concentration by comparing single and double harvests of <span class="html-italic">S. nigrum</span> (means with different letters in the panel are significantly different among treatments at <span class="html-italic">p</span> &lt; 0.05. Error bars reported in figures are means of 3 replicates with standard deviation).</p>
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<p>Effects of different types of N fertilizers on pH in soil as demonstrated by comparing single and double harvests of <span class="html-italic">S. nigrum.</span> (means with different letters in the panel are significantly different among treatments at <span class="html-italic">p</span> &lt; 0.05. Error bars reported in figures are means of 3 replicates with standard deviation).</p>
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19 pages, 1837 KiB  
Review
Adverse Effects of Perfluorooctane Sulfonate on the Liver and Relevant Mechanisms
by Pingwei Wang, Dongge Liu, Shuqi Yan, Jiajing Cui, Yujun Liang and Shuping Ren
Toxics 2022, 10(5), 265; https://doi.org/10.3390/toxics10050265 - 19 May 2022
Cited by 28 | Viewed by 5630
Abstract
Perfluorooctane sulfonate (PFOS) is a persistent, widely present organic pollutant. PFOS can enter the human body through drinking water, ingestion of food, contact with utensils containing PFOS, and occupational exposure to PFOS, and can have adverse effects on human health. Increasing research shows [...] Read more.
Perfluorooctane sulfonate (PFOS) is a persistent, widely present organic pollutant. PFOS can enter the human body through drinking water, ingestion of food, contact with utensils containing PFOS, and occupational exposure to PFOS, and can have adverse effects on human health. Increasing research shows that the liver is the major target of PFOS, and that PFOS can damage liver tissue and disrupt its function; however, the exact mechanisms remain unclear. In this study, we reviewed the adverse effects of PFOS on liver tissue and cells, as well as on liver function, to provide a reference for subsequent studies related to the toxicity of PFOS and liver injury caused by PFOS. Full article
(This article belongs to the Topic Hazard Assessment of Endocrine Disrupting Chemicals)
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<p>Outline diagram of PFOS entry into the body and excretion from the body.</p>
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<p>PFOS affects the metabolism of cholesterol and bile acid via CYP7A11.</p>
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<p>Summary diagram of inflammation-related pathways mediated by PFOS.</p>
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<p>Outline diagram of ROS-related pathways mediated by PFOS.</p>
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<p>Summary diagram of PFOS-induced apoptotic pathways.</p>
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21 pages, 5539 KiB  
Article
Simulating PM2.5 Concentrations during New Year in Cuenca, Ecuador: Effects of Advancing the Time of Burning Activities
by René Parra, Claudia Saud and Claudia Espinoza
Toxics 2022, 10(5), 264; https://doi.org/10.3390/toxics10050264 - 19 May 2022
Cited by 4 | Viewed by 2086
Abstract
Fine particulate matter (PM2.5) is dangerous to human health. At midnight on 31 December, in Ecuadorian cities, people burn puppets and fireworks, emitting high amounts of PM2.5. On 1 January 2022, concentrations between 27.3 and 40.6 µg m−3 [...] Read more.
Fine particulate matter (PM2.5) is dangerous to human health. At midnight on 31 December, in Ecuadorian cities, people burn puppets and fireworks, emitting high amounts of PM2.5. On 1 January 2022, concentrations between 27.3 and 40.6 µg m−3 (maximum mean over 24 h) were measured in Cuenca, an Andean city located in southern Ecuador; these are higher than 15 µg m−3, the current World Health Organization guideline. We estimated the corresponding PM2.5 emissions and used them as an input to the Weather Research and Forecasting with Chemistry (WRF-Chem 3.2) model to simulate the change in PM2.5 concentrations, assuming these emissions started at 18:00 LT or 21:00 LT on 31 December 2021. On average, PM2.5 concentrations decreased by 51.4% and 33.2%. Similar modeling exercises were completed for 2016 to 2021, providing mean decreases between 21.4% and 61.0% if emissions started at 18:00 LT. Lower mean reductions, between 2.3% and 40.7%, or even local increases, were computed for emissions beginning at 21:00 LT. Reductions occurred through better atmospheric conditions to disperse PM2.5 compared to midnight. Advancing the burning time can help reduce the health effects of PM2.5 emissions on 31 December. Full article
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<p>Location of: (<b>a</b>) Ecuador; (<b>b</b>,<b>c</b>) Cuenca; (<b>d</b>) urban area of Cuenca and the air quality stations (red dots) with PM<sub>2.5</sub> sensors (name, nomenclature): Colegio Carlos Arízaga, CCA; Terminal Terrestre, TET; Municipio, MUN; Condamine, CON, Cebollar, CEB; Escuela Ignacio Escandón, EIE. Orange dots indicate industries.</p>
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<p>(<b>a</b>) Hourly emissions from 31 December 2020 to 1 January 2022 from background sources (on-road traffic, industries, and power facility) and New Year’s burning activities. (<b>b</b>) Gridded emissions on 1 January 2022 at 00:00 LT from background sources. (<b>c</b>) Gridded emissions on 1 January 2022 at 00:00 LT from background sources and New Year’s burning activities.</p>
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<p>View of the city of Cuenca: (<b>a</b>) during the afternoon of 31 December 2021; (<b>b</b>) approximately 5 min before the midnight of 31 December 2021; (<b>c</b>) after the first 30 min of 1 January 2022. Hourly PM<sub>1</sub> and PM<sub>2.5</sub> concentrations on 31 December 2021 and 1 January 2022 measured at the CEB (<b>d</b>), CON (<b>e</b>), and TER (<b>f</b>) stations. (<b>g</b>) Hourly PM<sub>2.5</sub> concentrations on 31 December 2021 and 1 January 2022.</p>
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<p>Modeled maps of hourly PM<sub>2.5</sub> concentrations from 31 December 2021 at 22:00 LT to 1 January 2022 at 03:00 LT. Reference scenario: New Year’s emissions beginning on 1 January 2022 at 00:00 LT.</p>
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<p>Hourly PM<sub>2.5</sub> records and modeled concentrations from 31 December 2021 to 1 January 2022: the (<b>a</b>) CCA, (<b>b</b>) CEB, (<b>c</b>) TER, (<b>d</b>) MUN, (<b>e</b>) CON, and (<b>f</b>) EIE stations.</p>
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<p>Maximum 24 h mean PM<sub>2.5</sub> records and modeled concentrations from 31 December 2021 to 1 January 2022. Dots and nomenclature correspond to the six stations measuring PM<sub>2.5</sub> concentrations. The black line indicates the linear fit between records and modeled values. The red line corresponds to a perfect fit.</p>
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<p>Modeled maps of temperature and planetary boundary layer (PBL) depth for selected hours: 31 December 2021 at 18:00 LT (<b>a</b>,<b>b</b>), 31 December 2021 21:00 LT (<b>c</b>,<b>d</b>), and 1 January 2022 at 00:00 LT (<b>e</b>,<b>f</b>).</p>
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<p>Modeled maps of PM<sub>2.5</sub> and planetary boundary layer (PBL) depth. Reference scenario: New Year’s emissions starting on 1 January 2021 at 00:00 LT (<b>a</b>,<b>b</b>). Scenario with New Year’s emissions starting on 31 December 2020 at 21:00 LT (<b>c</b>,<b>d</b>).</p>
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<p>Hourly meteorological records versus modeled values on 31 December 2021 and 1 January 2022 (MUN station): (<b>a</b>) temperature and (<b>b</b>) wind speed.</p>
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<p>Modeled maps of hourly PM<sub>2.5</sub> concentrations from 31 December 2021 at 19:00 LT to 1 January 2022 at 00:00 LT. Scenario assuming that New Year’s emissions began on 31 December 20221 at 21:00 LT.</p>
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<p>Modeled maps of hourly PM<sub>2.5</sub> concentrations from 31 December 2021 at 16:00 LT to 31 December 2021 at 21:00 LT. Scenario assuming that New Year’s emissions began on 31 December 20221 at 18:00 LT.</p>
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15 pages, 2256 KiB  
Article
Insights into the Endocrine Disrupting Activity of Emerging Non-Phthalate Alternate Plasticizers against Thyroid Hormone Receptor: A Structural Perspective
by Torki A. Zughaibi, Ishfaq Ahmad Sheikh and Mohd Amin Beg
Toxics 2022, 10(5), 263; https://doi.org/10.3390/toxics10050263 - 19 May 2022
Cited by 17 | Viewed by 3715
Abstract
Many endocrine-disrupting chemicals (EDCs) have a ubiquitous presence in our environment due to anthropogenic activity. These EDCs can disrupt hormone signaling in the human and animal body systems including the very important hypothalamic-pituitary-thyroid (HPT) axis causing adverse health effects. Thyroxine (T4) and triiodothyronine [...] Read more.
Many endocrine-disrupting chemicals (EDCs) have a ubiquitous presence in our environment due to anthropogenic activity. These EDCs can disrupt hormone signaling in the human and animal body systems including the very important hypothalamic-pituitary-thyroid (HPT) axis causing adverse health effects. Thyroxine (T4) and triiodothyronine (T3) are hormones of the HPT axis which are essential for regulation of metabolism, heart rate, body temperature, growth, development, etc. In this study, potential endocrine-disrupting activity of the most common phthalate plasticizer, DEHP, and emerging non-phthalate alternate plasticizers, DINCH, ATBC, and DEHA against thyroid hormone receptor (TRα) were characterized. The structural binding characterization of indicated ligands was performed against the TRα ligand binding site employing Schrodinger’s induced fit docking (IFD) approach. The molecular simulations of interactions of the ligands against the residues lining a TRα binding pocket, including bonding interactions, binding energy, docking score, and IFD score were analyzed. In addition, the structural binding characterization of TRα native ligand, T3, was also done for comparative analysis. The results revealed that all ligands were placed stably in the TRα ligand-binding pocket. The binding energy values were highest for DINCH, followed by ATBC, and were higher than the values estimated for TRα native ligand, T3, whereas the values for DEHA and DEHP were similar and comparable to that of T3. This study suggested that all the indicated plasticizers have the potential for thyroid hormone disruption with two alternate plasticizers, DINCH and ATBC, exhibiting higher potential for thyroid dysfunction compared to DEHA and DEHP. Full article
(This article belongs to the Topic Hazard Assessment of Endocrine Disrupting Chemicals)
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<p>Two-dimensional structures of diisononyl hexahydrophthalate (DINCH), acetyl tributyl citrate (ATBC), di(2-ethylhexyl) phthalate (DEHP), thyroid receptor (TRα) native ligand, triiodothyronine (T3), and di-(2-ethylhexyl) adipate (DEHA).</p>
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<p>The molecular interactions of non-phthalate plasticizers (<b>a</b>) diisononyl hexahydrophthalate (DINCH), (<b>b</b>) acetyl tributyl citrate (ATBC), (<b>c</b>) di-(2-ethylhexyl) adipate (DEHA), and (<b>d</b>) di (2-ethylhexyl) phthalate (DEHP) with residues lining the thyroid receptor (TRα) ligand-binding pocket.</p>
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<p>The molecular interactions of a triiodothyronine (TRα) native ligand and triiodothyronine (T3) with residues lining TRα ligand-binding pocket.</p>
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0 pages, 518 KiB  
Systematic Review
Impacts of Cigarette Smoke (CS) on Muscle Derangement in Rodents—A Systematic Review
by Aaron W. J. He, Shirley P. C. Ngai, Kwok Kuen Cheung, Benson W. M. Lau, Dalinda-Isabel Sánchez-Vidaña and Marco Y. C. Pang
Toxics 2022, 10(5), 262; https://doi.org/10.3390/toxics10050262 - 18 May 2022
Cited by 2 | Viewed by 2143 | Correction
Abstract
Cigarette smoke (CS) is the major risk factor for chronic obstructive pulmonary disease (COPD) and can induce systemic manifestations, such as skeletal muscle derangement. However, inconsistent findings of muscle derangement were reported in previous studies. The aim of the present study was to [...] Read more.
Cigarette smoke (CS) is the major risk factor for chronic obstructive pulmonary disease (COPD) and can induce systemic manifestations, such as skeletal muscle derangement. However, inconsistent findings of muscle derangement were reported in previous studies. The aim of the present study was to consolidate the available evidence and assess the impact of CS on muscle derangement in rodents. A comprehensive literature search of five electronic databases identified ten articles for final analysis. Results showed that the diaphragm, rectus femoris, soleus, and gastrocnemius exhibited significant oxidative to glycolytic fiber conversions upon CS exposure. In contrast, the extensor digitorum longus (EDL), plantaris, and tibialis did not exhibit a similar fiber-type conversion after CS exposure. Hindlimb muscles, including the quadriceps, soleus, gastrocnemius, and EDL, showed significant reductions in the CSA of the muscle fibers in the CS group when compared to the control group. Changes in inflammatory cytokines, exercise capacity, and functional outcomes induced by CS have also been evaluated. CS could induce a shift from oxidative fibers to glycolytic fibers in high-oxidative muscles such as the diaphragm, rectus femoris, and soleus, and cause muscle atrophy, as reflected by a reduction in the CSA of hindlimb muscles such as the quadriceps, soleus, gastrocnemius, and EDL. Full article
(This article belongs to the Section Exposome)
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<p>Selection procedure for articles.</p>
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3 pages, 219 KiB  
Editorial
Statistical Assessment, Modeling, and Mitigation of Water and Soil Pollution
by Lucica Barbeş and Alina Bărbulescu
Toxics 2022, 10(5), 261; https://doi.org/10.3390/toxics10050261 - 18 May 2022
Viewed by 1401
Abstract
Nowadays, ambient air pollution levels and trends have become a topic of interest worldwide because primary atmospheric pollutants (APPs) are risk factors for the population and ecosystems [...] Full article
18 pages, 2731 KiB  
Article
Impact of Particle Size on Toxicity, Tissue Distribution and Excretion Kinetics of Subchronic Intratracheal Instilled Silver Nanoparticles in Mice
by Fernanda Rosário, Jan Creylman, Geert Verheyen, Sabine Van Miert, Conceição Santos, Peter Hoet and Helena Oliveira
Toxics 2022, 10(5), 260; https://doi.org/10.3390/toxics10050260 - 18 May 2022
Cited by 12 | Viewed by 3592
Abstract
The unique physicochemical properties of silver nanoparticles (AgNPs) make them useful in a wide range of sectors, increasing their propensity for human exposure, as well as the need for thorough toxicological assessment. The biodistribution of silver, hematological parameters and GSH/GSSG levels in the [...] Read more.
The unique physicochemical properties of silver nanoparticles (AgNPs) make them useful in a wide range of sectors, increasing their propensity for human exposure, as well as the need for thorough toxicological assessment. The biodistribution of silver, hematological parameters and GSH/GSSG levels in the lung and liver were studied in mice that were intratracheally instilled with AgNP (5 and 50 nm) and AgNO3 once a week for 5 weeks, followed by a recovery period of up to 28 days (dpi). Data was gathered to build a PBPK model after the entry of AgNPs into the lungs. AgNPs could be absorbed into the blood and might cross the physiological barriers and be distributed extensively in mice. Similar to AgNO3, AgNP5 induced longer-lasting toxicity toward blood cells and increased GSH levels in the lung. The exposure to AgNP50 increased the GSH from 1 dpi onward in the liver and silver was distributed to the organs after exposure, but its concentration decreased over time. In AgNP5 treated mice, silver levels were highest in the spleen, kidney, liver and blood, persisting for at least 28 days, suggesting accumulation. The major route for excretion seemed to be through the urine, despite a high concentration of AgNP5 also being found in feces. The modeled silver concentration was in line with the in vivo data for the heart and liver. Full article
(This article belongs to the Special Issue Impacts of Nanomaterials in the Environment)
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<p>Schematic overview of the experimental protocol. Black lines mean exposure time and dashed lines mean recovery time.</p>
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<p>Mice body weight changes during a 28-day exposure via intratracheal instillation of saline (control), AgNO<sub>3</sub>, AgNP5 and AgNP50 and a 28-day recovery time. * means significant differences vs. last day of exposure (one-way ANOVA; Holm–Sidak <span class="html-italic">p</span> ≤ 0.05) (<span class="html-italic">n</span> = 4).</p>
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<p>Mice relative organ weights over a 28-day recovery time after repeated intratracheal instillations of saline (control), AgNO<sub>3</sub>, AgNP5 and AgNP50. (<b>a</b>) WBC; (<b>b</b>) lymphocytes; (<b>c</b>) neutrophils; (<b>d</b>) monocytes; (<b>e</b>) eosinophils; (<b>f</b>) basophils.</p>
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<p>Differential mice blood cell counts for hemoglobin concentration (<b>a</b>), RBC (<b>b</b>) and MCV (<b>c</b>) over a 28-day recovery time after repeated intratracheal instillations of saline (control), AgNO<sub>3</sub>, AgNP5 and AgNP50. * means significant differences between the control and treatments (one-way ANOVA; Dunn’s test <span class="html-italic">p</span> &lt; 0.05) (<span class="html-italic">n</span> = 4).</p>
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<p>Mice hematology values during a 28-day recovery time after repeated intratracheal instillations of saline (control), AgNO<sub>3</sub>, AgNP5 and AgNP50. (<b>a</b>) WBC; (<b>b</b>) lymphocytes; (<b>c</b>) neutrophils; (<b>d</b>) monocytes; (<b>e</b>) eosinophils; (<b>f</b>) basophils. * means significant differences between the control and treatments (one-way ANOVA; Dunn’s test <span class="html-italic">p</span> &lt; 0.05) (<span class="html-italic">n</span> = 4).</p>
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<p>GSH and GSSG levels (µg/mg protein) and GSSG:GSH ratio analysis during a 28-day recovery time after repeated intratracheal instillations of saline (control), AgNO<sub>3</sub>, AgNP5 and AgNP50. (<b>a</b>) Lung GSSG:GSH ratio; (<b>b</b>) lung total GSH; (<b>c</b>) liver GSSG:GSH ratio; (<b>d</b>) liver total GSH. * means significant differences between the control and treatments (one-way ANOVA; Dunn’s test <span class="html-italic">p</span> &lt; 0.05) (<span class="html-italic">n</span> = 4).</p>
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<p>Silver concentrations (ng/mg tissue fresh weight) during a 28-day recovery time after repeated intratracheal instillations of saline (control), AgNO<sub>3</sub>, AgNP5 and AgNP50. (<b>a</b>) Heart; (<b>b</b>) spleen; (<b>c</b>) kidney; (<b>d</b>) lung; (<b>e</b>) brain; (<b>f</b>) blood; (<b>g</b>) liver. Silver PBPK data are represented as the lighter bars (<span class="html-italic">n</span> = 4).</p>
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<p>Excreted silver concentrations (ng/mL or ng/mg) during a 28-day recovery time after repeated intratracheal instillations of saline (control), AgNO<sub>3</sub>, AgNP5 and AgNP50. (<b>a</b>) Urine; (<b>b</b>) feces (<span class="html-italic">n</span> = 4).</p>
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15 pages, 1033 KiB  
Article
Multi-Component Passivators Regulate Heavy Metal Accumulation in Paddy Soil and Rice: A Three-Site Field Experiment in South China
by Shouping Zhao, Xuezhu Ye, De Chen, Qi Zhang, Wendan Xiao, Shaofu Wu, Jing Hu, Na Gao and Miaojie Huang
Toxics 2022, 10(5), 259; https://doi.org/10.3390/toxics10050259 - 18 May 2022
Cited by 2 | Viewed by 1972
Abstract
To fulfill sustainability principles, a three-site field experiment was conducted to screen suitably mixed passivators from lime + biochar (L + C, 9000 kgha−1 with a rate of 1:1) and lime + biochar + sepiolite (L + C + S, 9000 kg [...] Read more.
To fulfill sustainability principles, a three-site field experiment was conducted to screen suitably mixed passivators from lime + biochar (L + C, 9000 kgha−1 with a rate of 1:1) and lime + biochar + sepiolite (L + C + S, 9000 kg ha−1 with a rate of 1:1:1), in Yuecheng (YC), Zhuji (ZJ), and Fuyang (FY), where there are typical contaminated soils, in South China. Treated with passivators in soil, DTPA-extractable Cd, Crand Pb in soil were decreased by 9.87–26.3%, 37.2–67.5%, and 19.0–54.2%, respectively; Cd, Cr, and Pb in rice were decreased by 85.9–91.5%, 40.0–76.5%, and 16.4–45.4%, respectively; and these were followed by slightly higher efficacy of L + C + S than L + C. The differences between L + C and L + C + S mainly lie in soil microbial communities, enzymes, and fertility. In YC, treatment with L + C + S increased microbial carbon and activities of urease (EC3.5.1.5) and phosphatase (EC3.1.3.1) by 21.0%, 85.5%, and 22.3%; while treatment with L + C decreased microbial carbon and activities of phosphatase and sucrose (EC3.2.1.26) by 1.31%, 34.9%, and 43.4%, respectively. Moreover, the treatment of FY soils with L + C + S increased microbial carbon and activities of urease, phosphatase, and sucrase by 35.4%, 41.6%, 27.9%, and 7.37%; and L + C treatment only increased the microbial carbon and the activity of phosphatase by 3.14% and 30.3%, respectively. Furthermore, the organic matter and available nitrogen were also increased by 8.8–19.0% and 7.4–14.6% with L + C + S treatments, respectively. These suggested that the combination of L + C + S stimulated the growth of soil microbial communities and increased the activity of soil enzymes. Therefore, the L + C + S strategy can be a practical and effective measure for safe rice production as it was more suitable for the remediation of heavy metals in our experimental sites. Full article
(This article belongs to the Special Issue Safety Utilization and Remediation of Heavy Metal Polluted Farmland)
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<p>Cd (<b>a</b>), Cr (<b>b</b>), and Pb (<b>c</b>) in rice treated by control with no passivators (CK), 9000 kg ha<sup>−1</sup> of lime + biochar with a rate of 1:1 (L + C), and 9000 kg ha<sup>−1</sup> lime + biochar + sepiolite with a rate of 1:1 (L + C + S) in paddy soil. Values (means ± SD, <span class="html-italic">n</span> = 3) with different letters indicate significant differences between objects in the respective sites (separately for ZJ (Zhuji), YC (Yuecheng), or FY (Fuyang)).</p>
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<p>Paddy soil pH treated by control with no passivators (CK), 9000 kg ha<sup>−1</sup> of lime + biochar with a rate of 1:1 (L + C), and 9000 kg ha<sup>−1</sup> lime + biochar + sepiolite with a rate of 1:1 (L + C + S). Values (means ± SD, <span class="html-italic">n</span> = 3) with different letters indicate significant differences between objects in the respective sites (separately for ZJ (Zhuji), YC (Yuecheng), or FY (Fuyang)).</p>
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<p>DTPA-extractable Cd (<b>a</b>), Cr (<b>b</b>), and Pb (<b>c</b>) in paddy soil treated by control with no passivators (CK), 9000 kg ha<sup>−1</sup> of lime + biochar with a rate of 1:1 (L + C), and 9000 kg ha<sup>−1</sup> lime + biochar + sepiolite with a rate of 1:1 (L + C + S). Values (means ± SD, <span class="html-italic">n</span> = 3) with different letters indicate significant differences between objects in the respective sites (separately for ZJ (Zhuji), YC (Yuecheng), or FY (Fuyang)).</p>
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<p>Soil microbial carbon (<b>a</b>) and activities of urease (<b>b</b>), phosphatase (<b>c</b>), and sucrose (<b>d</b>) in paddy soil treated by control with no passivators (CK), 9000 kg ha<sup>−1</sup> of lime + biochar with a rate of 1:1 (L + C), and 9000 kg ha<sup>−1</sup> lime + biochar + sepiolite with a rate of 1:1 (L + C + S). Values (means ± SD, <span class="html-italic">n</span> = 3) with different letters indicate significant differences between objects in the respective sites (separately for ZJ (Zhuji), YC (Yuecheng), or FY (Fuyang)).</p>
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21 pages, 2447 KiB  
Systematic Review
Towards Reference Values for Malondialdehyde on Exhaled Breath Condensate: A Systematic Literature Review and Meta-Analysis
by Veronica Turcu, Pascal Wild, Maud Hemmendinger, Jean-Jacques Sauvain, Enrico Bergamaschi, Nancy B. Hopf and Irina Guseva Canu
Toxics 2022, 10(5), 258; https://doi.org/10.3390/toxics10050258 - 18 May 2022
Cited by 11 | Viewed by 2648
Abstract
Many pathological conditions and certain airway exposures are associated with oxidative stress (OS). Malondialdehyde (MDA) is an end-product of the oxidation of lipids in our cells and is present in all biological matrices including exhaled breath condensate (EBC). To use MDA as a [...] Read more.
Many pathological conditions and certain airway exposures are associated with oxidative stress (OS). Malondialdehyde (MDA) is an end-product of the oxidation of lipids in our cells and is present in all biological matrices including exhaled breath condensate (EBC). To use MDA as a biomarker of OS in EBC, a reference interval should be defined. Thus, we sought to summarize reference values reported in healthy adult populations by performing a systematic review and meta-analysis using a standardized protocol registered in PROSPERO (CRD42020146623). Articles were retrieved from four major databases and 25 studies with 28 subgroups were included. Defining the distribution of MDA measured in reference populations with a detection combined with a separation technique still represents a challenge due to the low number of studies available, different analytical methods used, and questionable methodological qualities of many studies. The most salient methodological drawbacks have been in data collection and reporting of methods and study results by the researchers. The lack of compliance with the recommendations of the European Respiratory Society and American Thoracic Society was the major limitation in the current research involving EBC. Consequently, we were unable to establish a reference interval for MDA in EBC. Full article
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<p>Flowchart of the article selection process.</p>
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<p>MDA concentration in EBC stratified according to: (<b>a</b>) Collection device; (<b>b</b>) duration of collection (minutes); (<b>c</b>) gender group (no papers with females only). All presented results exclude the outliers and units of the GMs are ng/mL.</p>
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<p>MDA concentration in EBC stratified according to: (<b>a</b>) Collection device; (<b>b</b>) duration of collection (minutes); (<b>c</b>) gender group (no papers with females only). All presented results exclude the outliers and units of the GMs are ng/mL.</p>
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<p>Forest plots of MDA in EBC concentration quantified in healthy adult participants by age group: population with a mean age less than 30 years [<a href="#B28-toxics-10-00258" class="html-bibr">28</a>,<a href="#B43-toxics-10-00258" class="html-bibr">43</a>,<a href="#B46-toxics-10-00258" class="html-bibr">46</a>,<a href="#B58-toxics-10-00258" class="html-bibr">58</a>]; between 30 and 40 years [<a href="#B25-toxics-10-00258" class="html-bibr">25</a>,<a href="#B31-toxics-10-00258" class="html-bibr">31</a>,<a href="#B34-toxics-10-00258" class="html-bibr">34</a>,<a href="#B41-toxics-10-00258" class="html-bibr">41</a>,<a href="#B42-toxics-10-00258" class="html-bibr">42</a>,<a href="#B49-toxics-10-00258" class="html-bibr">49</a>,<a href="#B51-toxics-10-00258" class="html-bibr">51</a>,<a href="#B59-toxics-10-00258" class="html-bibr">59</a>], between 40 and 50 years [<a href="#B32-toxics-10-00258" class="html-bibr">32</a>,<a href="#B36-toxics-10-00258" class="html-bibr">36</a>,<a href="#B55-toxics-10-00258" class="html-bibr">55</a>] and above 50 years [<a href="#B24-toxics-10-00258" class="html-bibr">24</a>,<a href="#B26-toxics-10-00258" class="html-bibr">26</a>,<a href="#B36-toxics-10-00258" class="html-bibr">36</a>,<a href="#B46-toxics-10-00258" class="html-bibr">46</a>,<a href="#B50-toxics-10-00258" class="html-bibr">50</a>].</p>
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<p>Forest plots of MDA in EBC concentration quantified in healthy adult participants considering their smoking habits: non-smokers exclusively [<a href="#B24-toxics-10-00258" class="html-bibr">24</a>,<a href="#B25-toxics-10-00258" class="html-bibr">25</a>,<a href="#B31-toxics-10-00258" class="html-bibr">31</a>,<a href="#B32-toxics-10-00258" class="html-bibr">32</a>,<a href="#B34-toxics-10-00258" class="html-bibr">34</a>,<a href="#B36-toxics-10-00258" class="html-bibr">36</a>,<a href="#B42-toxics-10-00258" class="html-bibr">42</a>,<a href="#B43-toxics-10-00258" class="html-bibr">43</a>,<a href="#B46-toxics-10-00258" class="html-bibr">46</a>,<a href="#B50-toxics-10-00258" class="html-bibr">50</a>,<a href="#B58-toxics-10-00258" class="html-bibr">58</a>]; mixed population of smokers and non-smokers [<a href="#B41-toxics-10-00258" class="html-bibr">41</a>,<a href="#B49-toxics-10-00258" class="html-bibr">49</a>,<a href="#B55-toxics-10-00258" class="html-bibr">55</a>], smokers exclusively [<a href="#B24-toxics-10-00258" class="html-bibr">24</a>,<a href="#B26-toxics-10-00258" class="html-bibr">26</a>,<a href="#B36-toxics-10-00258" class="html-bibr">36</a>,<a href="#B46-toxics-10-00258" class="html-bibr">46</a>,<a href="#B51-toxics-10-00258" class="html-bibr">51</a>] and unknown smoking status [<a href="#B28-toxics-10-00258" class="html-bibr">28</a>,<a href="#B44-toxics-10-00258" class="html-bibr">44</a>,<a href="#B59-toxics-10-00258" class="html-bibr">59</a>].</p>
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27 pages, 2396 KiB  
Article
Development of a 96-Well Electrophilic Allergen Screening Assay for Skin Sensitization Using a Measurement Science Approach
by Elijah J. Petersen, Richard Uhl, Blaza Toman, John T. Elliott, Judy Strickland, James Truax and John Gordon
Toxics 2022, 10(5), 257; https://doi.org/10.3390/toxics10050257 - 17 May 2022
Cited by 4 | Viewed by 5110
Abstract
The Electrophilic Allergen Screening Assay (EASA) has emerged as a promising in chemico method to detect the first key event in the adverse outcome pathway (AOP) for skin sensitization. This assay functions by assessing the depletion of one of two probe molecules (4-nitrobenzenethiol [...] Read more.
The Electrophilic Allergen Screening Assay (EASA) has emerged as a promising in chemico method to detect the first key event in the adverse outcome pathway (AOP) for skin sensitization. This assay functions by assessing the depletion of one of two probe molecules (4-nitrobenzenethiol (NBT) and pyridoxylamine (PDA)) in the presence of a test compound (TC). The initial development of EASA utilized a cuvette format resulting in multiple measurement challenges such as low throughput and the inability to include adequate control measurements. In this study, we describe the redesign of EASA into a 96-well plate format that incorporates in-process control measurements to quantify key sources of variability each time the assay is run. The data from the analysis of 67 TCs using the 96-well format had 77% concordance with animal data from the local lymph node assay (LLNA), a result consistent with that for the direct peptide reactivity assay (DPRA), an OECD test guideline (442C) protein binding assay. Overall, the measurement science approach described here provides steps during assay development that can be taken to increase confidence of in chemico assays by attempting to fully characterize the sources of variability and potential biases and incorporate in-process control measurements into the assay. Full article
(This article belongs to the Section Toxicology)
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<p>Chemical structure of probe molecules.</p>
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<p>Flow chart outlining the main steps for the EASA method.</p>
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<p>96 well plate design.</p>
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<p>Cause-and-effect analysis of the EASA method. The four main branches indicate factors that are expected to have the greatest potential to cause variability in assay results.</p>
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<p>Values obtained from NBT (<b>A</b>), PDA absorbance (<b>B</b>), and PDA fluorescence (<b>C</b>) percentage depletion for test compounds. Data points and error bars indicate the mean and 95% confidence intervals determined using Bayesian modeling. Each data point represents the data for a <span class="html-italic">TC</span> in a particular run. Asterisks (indicated by *) indicate statistically significant results where the 95% confidence interval does not overlap with zero. Data is arranged from lowest to highest percentage depletion. Compounds with negative percentage depletion values less than −20% or percentage depletion values greater than 110% are excluded.</p>
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35 pages, 11678 KiB  
Article
Developmental Neurotoxicity and Behavioral Screening in Larval Zebrafish with a Comparison to Other Published Results
by Kimberly A. Jarema, Deborah L. Hunter, Bridgett N. Hill, Jeanene K. Olin, Katy N. Britton, Matthew R. Waalkes and Stephanie Padilla
Toxics 2022, 10(5), 256; https://doi.org/10.3390/toxics10050256 - 17 May 2022
Cited by 13 | Viewed by 3588
Abstract
With the abundance of chemicals in the environment that could potentially cause neurodevelopmental deficits, there is a need for rapid testing and chemical screening assays. This study evaluated the developmental toxicity and behavioral effects of 61 chemicals in zebrafish (Danio rerio) [...] Read more.
With the abundance of chemicals in the environment that could potentially cause neurodevelopmental deficits, there is a need for rapid testing and chemical screening assays. This study evaluated the developmental toxicity and behavioral effects of 61 chemicals in zebrafish (Danio rerio) larvae using a behavioral Light/Dark assay. Larvae (n = 16–24 per concentration) were exposed to each chemical (0.0001–120 μM) during development and locomotor activity was assessed. Approximately half of the chemicals (n = 30) did not show any gross developmental toxicity (i.e., mortality, dysmorphology or non-hatching) at the highest concentration tested. Twelve of the 31 chemicals that did elicit developmental toxicity were toxic at the highest concentration only, and thirteen chemicals were developmentally toxic at concentrations of 10 µM or lower. Eleven chemicals caused behavioral effects; four chemicals (6-aminonicotinamide, cyclophosphamide, paraquat, phenobarbital) altered behavior in the absence of developmental toxicity. In addition to screening a library of chemicals for developmental neurotoxicity, we also compared our findings with previously published results for those chemicals. Our comparison revealed a general lack of standardized reporting of experimental details, and it also helped identify some chemicals that appear to be consistent positives and negatives across multiple laboratories. Full article
(This article belongs to the Special Issue Developmental Exposure to Environmental Contaminants)
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<p><b>Experimental Design</b>. Detailed timeline of the experimental process from spawning to analyses. The diagram is divided into sections for each critical time period, then further subdivided for event.</p>
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<p><b>Comparison of the Developmental Chlorpyrifos Effect When Tested on Either the DanioVision or Tower System.</b> Upper (<b>A</b>) shows the results when larvae treated with chlorpyrifos during development were tested on either the DanioVision system (<b>left</b> panel) or Tower system (<b>right</b> panel). Using an ANOVA with chlorpyrifos treatment and the system tested as independent variables, and locomotor activity as the dependent variable, it was found that there was an overall effect of chlorpyrifos (<span class="html-italic">p</span> &lt; 0.0001) and of the system used (<span class="html-italic">p</span> &lt; 0.0001), but that there was no interaction between those two variables (<span class="html-italic">p</span> = 0.22). Because the effect of the chlorpyrifos did not depend on the system that was used for testing, the data from both systems were combined, expressed as a percent of control and analyzed to delineate the effect of chlorpyrifos (<b>B</b>). In this case the data were analyzed using an ANOVA (chlorpyrifos or Light/Dark period were independent variables and locomotor activity was the dependent variable). This analysis showed that there was an overall effect of chlorpyrifos (<span class="html-italic">p</span> &lt; 0.0001), Light/Dark period (<span class="html-italic">p</span> &lt; 0.0001), and that there was an interaction between the two (<span class="html-italic">p</span> = 0.0003), meaning that the effect of chlorpyrifos was different depending on whether the animals were tested in the Light or Dark period. Using an ANOVA and testing each period separately, it was first determined whether there was an overall effect of chlorpyrifos concentration (<span class="html-italic">p</span> &lt; 0.0001 in either the Light or Dark) and then a Fisher’s PLSD <span class="html-italic">post hoc</span> test was conducted to determine which chlorpyrifos concentration was different from control in either the Light or Dark period. Those concentrations that were different from control are indicated by an asterisk. In the Light period the 0.3 µM (<span class="html-italic">p</span> = 0.03), 1.0 µM (<span class="html-italic">p</span> &lt; 0.0001) and the 3.0 µM (<span class="html-italic">p</span> &lt; 0.0001) chlorpyrifos were all different from control, while in the Dark period, only the highest concentration 3.0 µM (<span class="html-italic">p</span> &lt; 0.0001) was different from control. For the DanioVision system testing, the sample sizes were 63 controls, 65 at 0.3 µM, 63 at 1.0 µM, and 61 at 3.0 µM, and for the Tower system, the sample sizes were 69 controls, 62 at 0.3 µM, 69 at 1.0 µM and 54 at 3.0 µM. The sample sizes for (<b>B</b>) were a combination of each of those sample sizes for each system at each concentration.</p>
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<p>Behavioral Nonparametric Statistics Results. Results of the Kruskal-Wallis Nonparametric test for each chemical. A Bonferroni correction was applied to the overall effect to account for the Light and Dark periods, resulting in α = 0.025. The Wilcoxon-Mann-Whitney post-hoc test (α = 0.05) compared each concentration to the control for that chemical. The circle with the slash symbol (<span class="html-fig-inline" id="toxics-10-00256-i001"> <img alt="Toxics 10 00256 i001" src="/toxics/toxics-10-00256/article_deploy/html/images/toxics-10-00256-i001.png"/></span>) indicates developmental toxicity: that concentration was not included in behavioral analyses due to the number of dead, malformed and uninflated swim bladders exceeding 25%. Overall effect is listed under the chemical name followed by the sample size and results for each concentration, with the Dark period shaded gray. Statistically significant results are highlighted with the light-yellow shading in the Light period and dark-yellow shading in the Dark period.</p>
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<p>Behavioral Nonparametric Statistics Results. Results of the Kruskal-Wallis Nonparametric test for each chemical. A Bonferroni correction was applied to the overall effect to account for the Light and Dark periods, resulting in α = 0.025. The Wilcoxon-Mann-Whitney post-hoc test (α = 0.05) compared each concentration to the control for that chemical. The circle with the slash symbol (<span class="html-fig-inline" id="toxics-10-00256-i001"> <img alt="Toxics 10 00256 i001" src="/toxics/toxics-10-00256/article_deploy/html/images/toxics-10-00256-i001.png"/></span>) indicates developmental toxicity: that concentration was not included in behavioral analyses due to the number of dead, malformed and uninflated swim bladders exceeding 25%. Overall effect is listed under the chemical name followed by the sample size and results for each concentration, with the Dark period shaded gray. Statistically significant results are highlighted with the light-yellow shading in the Light period and dark-yellow shading in the Dark period.</p>
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<p>Behavioral Nonparametric Statistics Results. Results of the Kruskal-Wallis Nonparametric test for each chemical. A Bonferroni correction was applied to the overall effect to account for the Light and Dark periods, resulting in α = 0.025. The Wilcoxon-Mann-Whitney post-hoc test (α = 0.05) compared each concentration to the control for that chemical. The circle with the slash symbol (<span class="html-fig-inline" id="toxics-10-00256-i001"> <img alt="Toxics 10 00256 i001" src="/toxics/toxics-10-00256/article_deploy/html/images/toxics-10-00256-i001.png"/></span>) indicates developmental toxicity: that concentration was not included in behavioral analyses due to the number of dead, malformed and uninflated swim bladders exceeding 25%. Overall effect is listed under the chemical name followed by the sample size and results for each concentration, with the Dark period shaded gray. Statistically significant results are highlighted with the light-yellow shading in the Light period and dark-yellow shading in the Dark period.</p>
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<p><b>Histograms of the Distribution of the Control Activity for the Light and Dark Periods of Testing.</b> The sum of the activity of the control animals (n = 1851) was plotted as a histogram to visualize the non-normal distribution of the data. Note that the activity intervals are different for the Light and Dark periods. Plots and data calculations were performed using SigmaPlot.</p>
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<p><b>Behavioral Concentration Response for Each Chemical Presented in a Box Plot with Developmental Toxicity as a Line Graph in the Inset.</b> Box plots show locomotor activity for both the Light and Dark (gray background) periods. The box represents the interquartile range (middle 50%), the top of the box to the top error bar is the upper quartile (75th percentile) while the bottom of the box to lower error bar is the lower quartile (25th percentile). The solid line in the middle of the box is the median and the dotted line in the middle of the box is the mean. The top whisker/error bar indicates the maximum and the bottom whisker/error bar indicates the minimum. The developmental toxicity inset shows the percent of normal larvae for the control and for each concentration. The dotted red line is the 75% line and concentration groups that fall below are considered developmentally toxic and not included in behavioral analyses. The triangle represents control data, and the gray circles indicate results at each concentration. All concentrations are in micromolar (µM).</p>
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<p><b>Behavioral Concentration Response for Each Chemical Presented in a Box Plot with Developmental Toxicity as a Line Graph in the Inset.</b> Box plots show locomotor activity for both the Light and Dark (gray background) periods. The box represents the interquartile range (middle 50%), the top of the box to the top error bar is the upper quartile (75th percentile) while the bottom of the box to lower error bar is the lower quartile (25th percentile). The solid line in the middle of the box is the median and the dotted line in the middle of the box is the mean. The top whisker/error bar indicates the maximum and the bottom whisker/error bar indicates the minimum. The developmental toxicity inset shows the percent of normal larvae for the control and for each concentration. The dotted red line is the 75% line and concentration groups that fall below are considered developmentally toxic and not included in behavioral analyses. The triangle represents control data, and the gray circles indicate results at each concentration. All concentrations are in micromolar (µM).</p>
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<p><b>Behavioral Concentration Response for Each Chemical Presented in a Box Plot with Developmental Toxicity as a Line Graph in the Inset.</b> Box plots show locomotor activity for both the Light and Dark (gray background) periods. The box represents the interquartile range (middle 50%), the top of the box to the top error bar is the upper quartile (75th percentile) while the bottom of the box to lower error bar is the lower quartile (25th percentile). The solid line in the middle of the box is the median and the dotted line in the middle of the box is the mean. The top whisker/error bar indicates the maximum and the bottom whisker/error bar indicates the minimum. The developmental toxicity inset shows the percent of normal larvae for the control and for each concentration. The dotted red line is the 75% line and concentration groups that fall below are considered developmentally toxic and not included in behavioral analyses. The triangle represents control data, and the gray circles indicate results at each concentration. All concentrations are in micromolar (µM).</p>
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<p><b>Behavioral Concentration Response for Each Chemical Presented in a Box Plot with Developmental Toxicity as a Line Graph in the Inset.</b> Box plots show locomotor activity for both the Light and Dark (gray background) periods. The box represents the interquartile range (middle 50%), the top of the box to the top error bar is the upper quartile (75th percentile) while the bottom of the box to lower error bar is the lower quartile (25th percentile). The solid line in the middle of the box is the median and the dotted line in the middle of the box is the mean. The top whisker/error bar indicates the maximum and the bottom whisker/error bar indicates the minimum. The developmental toxicity inset shows the percent of normal larvae for the control and for each concentration. The dotted red line is the 75% line and concentration groups that fall below are considered developmentally toxic and not included in behavioral analyses. The triangle represents control data, and the gray circles indicate results at each concentration. All concentrations are in micromolar (µM).</p>
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<p><b>Behavioral Concentration Response for Each Chemical Presented in a Box Plot with Developmental Toxicity as a Line Graph in the Inset.</b> Box plots show locomotor activity for both the Light and Dark (gray background) periods. The box represents the interquartile range (middle 50%), the top of the box to the top error bar is the upper quartile (75th percentile) while the bottom of the box to lower error bar is the lower quartile (25th percentile). The solid line in the middle of the box is the median and the dotted line in the middle of the box is the mean. The top whisker/error bar indicates the maximum and the bottom whisker/error bar indicates the minimum. The developmental toxicity inset shows the percent of normal larvae for the control and for each concentration. The dotted red line is the 75% line and concentration groups that fall below are considered developmentally toxic and not included in behavioral analyses. The triangle represents control data, and the gray circles indicate results at each concentration. All concentrations are in micromolar (µM).</p>
Full article ">Figure 5 Cont.
<p><b>Behavioral Concentration Response for Each Chemical Presented in a Box Plot with Developmental Toxicity as a Line Graph in the Inset.</b> Box plots show locomotor activity for both the Light and Dark (gray background) periods. The box represents the interquartile range (middle 50%), the top of the box to the top error bar is the upper quartile (75th percentile) while the bottom of the box to lower error bar is the lower quartile (25th percentile). The solid line in the middle of the box is the median and the dotted line in the middle of the box is the mean. The top whisker/error bar indicates the maximum and the bottom whisker/error bar indicates the minimum. The developmental toxicity inset shows the percent of normal larvae for the control and for each concentration. The dotted red line is the 75% line and concentration groups that fall below are considered developmentally toxic and not included in behavioral analyses. The triangle represents control data, and the gray circles indicate results at each concentration. All concentrations are in micromolar (µM).</p>
Full article ">Figure 5 Cont.
<p><b>Behavioral Concentration Response for Each Chemical Presented in a Box Plot with Developmental Toxicity as a Line Graph in the Inset.</b> Box plots show locomotor activity for both the Light and Dark (gray background) periods. The box represents the interquartile range (middle 50%), the top of the box to the top error bar is the upper quartile (75th percentile) while the bottom of the box to lower error bar is the lower quartile (25th percentile). The solid line in the middle of the box is the median and the dotted line in the middle of the box is the mean. The top whisker/error bar indicates the maximum and the bottom whisker/error bar indicates the minimum. The developmental toxicity inset shows the percent of normal larvae for the control and for each concentration. The dotted red line is the 75% line and concentration groups that fall below are considered developmentally toxic and not included in behavioral analyses. The triangle represents control data, and the gray circles indicate results at each concentration. All concentrations are in micromolar (µM).</p>
Full article ">Figure 5 Cont.
<p><b>Behavioral Concentration Response for Each Chemical Presented in a Box Plot with Developmental Toxicity as a Line Graph in the Inset.</b> Box plots show locomotor activity for both the Light and Dark (gray background) periods. The box represents the interquartile range (middle 50%), the top of the box to the top error bar is the upper quartile (75th percentile) while the bottom of the box to lower error bar is the lower quartile (25th percentile). The solid line in the middle of the box is the median and the dotted line in the middle of the box is the mean. The top whisker/error bar indicates the maximum and the bottom whisker/error bar indicates the minimum. The developmental toxicity inset shows the percent of normal larvae for the control and for each concentration. The dotted red line is the 75% line and concentration groups that fall below are considered developmentally toxic and not included in behavioral analyses. The triangle represents control data, and the gray circles indicate results at each concentration. All concentrations are in micromolar (µM).</p>
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<p><b>Comparison of Nonparametric Statistical Results and Percent Change Calculations.</b> Comparison of the nonparametric statistical results (from <a href="#toxics-10-00256-f003" class="html-fig">Figure 3</a>) and percent change calculations showing the degree of change in each concentration group compared to the controls. The middle column lists the chemical name, the outside columns show the nonparametric results for the Light and Dark periods, with the percent change columns next to them. Chemical concentrations are listed at the top of each column. Colored shading represents the following: light gray = concentration not tested; blue = decrease in activity; green = no effect; yellow = increase in activity; red = developmental toxicity. The percent change value is indicated in each cell and the data are color coded by 50% increments.</p>
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<p><b>Comparison of the Present Behavioral Results with Previous Studies from the Literature that Included Similar Experimental Conditions Testing the Same Chemicals.</b> To be included, all studies met the following criteria: chemical exposure began during 0–3 dpf and lasted at least 24 h; behavior was tested 5–7 dpf, included an acclimation period prior to testing and at least one transition from Light to Dark during the testing protocol. This figure was populated based on information reported by other researchers; the results were not interpreted or inferred. Some studies only reported the lowest effect dose and did not report results for other concentrations that may also have had an effect. Effects may have occurred in the acclimation, Light or Dark periods. Colored shading represents the following: blue = decrease in activity; yellow = increase in activity; blue with yellow center = both decrease and increase in activity; purple = direction of the effect could not be determined; gray = chemical concentration was tested, but results were unclear and effect could not be determined; red = developmental toxicity for the current study only. Superscript refers to publication number in the reference section of this manuscript. The results for the current study are on the first line for each chemical, with bold text. Chlorpyrifos was both a test chemical and a positive control in our study; the results for the positive control are indicated by the (+) in this figure. All concentrations are in micromolar (µM).</p>
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<p><b>Comparison of the Present Behavioral Results with Previous Studies from the Literature that Included Similar Experimental Conditions Testing the Same Chemicals.</b> To be included, all studies met the following criteria: chemical exposure began during 0–3 dpf and lasted at least 24 h; behavior was tested 5–7 dpf, included an acclimation period prior to testing and at least one transition from Light to Dark during the testing protocol. This figure was populated based on information reported by other researchers; the results were not interpreted or inferred. Some studies only reported the lowest effect dose and did not report results for other concentrations that may also have had an effect. Effects may have occurred in the acclimation, Light or Dark periods. Colored shading represents the following: blue = decrease in activity; yellow = increase in activity; blue with yellow center = both decrease and increase in activity; purple = direction of the effect could not be determined; gray = chemical concentration was tested, but results were unclear and effect could not be determined; red = developmental toxicity for the current study only. Superscript refers to publication number in the reference section of this manuscript. The results for the current study are on the first line for each chemical, with bold text. Chlorpyrifos was both a test chemical and a positive control in our study; the results for the positive control are indicated by the (+) in this figure. All concentrations are in micromolar (µM).</p>
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<p><b>Comparison of the Present Behavioral Results with Previous Studies from the Literature that Included Similar Experimental Conditions Testing the Same Chemicals.</b> To be included, all studies met the following criteria: chemical exposure began during 0–3 dpf and lasted at least 24 h; behavior was tested 5–7 dpf, included an acclimation period prior to testing and at least one transition from Light to Dark during the testing protocol. This figure was populated based on information reported by other researchers; the results were not interpreted or inferred. Some studies only reported the lowest effect dose and did not report results for other concentrations that may also have had an effect. Effects may have occurred in the acclimation, Light or Dark periods. Colored shading represents the following: blue = decrease in activity; yellow = increase in activity; blue with yellow center = both decrease and increase in activity; purple = direction of the effect could not be determined; gray = chemical concentration was tested, but results were unclear and effect could not be determined; red = developmental toxicity for the current study only. Superscript refers to publication number in the reference section of this manuscript. The results for the current study are on the first line for each chemical, with bold text. Chlorpyrifos was both a test chemical and a positive control in our study; the results for the positive control are indicated by the (+) in this figure. All concentrations are in micromolar (µM).</p>
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14 pages, 2537 KiB  
Article
Acute and Chronic Toxicity of Binary Mixtures of Bisphenol A and Heavy Metals
by Jun Yang, Anqi Liao, Shulin Hu, Yiwen Zheng, Shuli Liang, Shuangyan Han and Ying Lin
Toxics 2022, 10(5), 255; https://doi.org/10.3390/toxics10050255 - 17 May 2022
Cited by 8 | Viewed by 2426
Abstract
Bisphenol A (BPA) and heavy metals are widespread contaminants in the environment. However, the combined toxicities of these contaminants are still unknown. In this study, the bioluminescent bacteria Vibrio qinghaiensis Q67 was used to detect the single and combined toxicities of BPA and [...] Read more.
Bisphenol A (BPA) and heavy metals are widespread contaminants in the environment. However, the combined toxicities of these contaminants are still unknown. In this study, the bioluminescent bacteria Vibrio qinghaiensis Q67 was used to detect the single and combined toxicities of BPA and heavy metals, then the joint effects of these contaminants were evaluated. The results show that chronic toxicities of chromium (Cr), cadmium (Cd), lead (Pb), arsenic (As), mercury (Hg), nickel (Ni), and BPA were time–dependent; in fact, the acute toxicities of these contaminants were stronger than the chronic toxicities. Furthermore, the combined toxicities of BPA and heavy metals displayed BPA + Hg > BPA + Cr > BPA + As > BPA + Ni > BPA + Pb > BPA + Cd in the acute test and BPA + Hg > BPA + Cd > BPA + As > BPA + Cd in the chronic test, which suggested that the combined toxicity of BPA and Hg was stronger than that of other mixtures in acute as well as chronic tests. Additionally, both CA and IA models underestimated the toxicities of mixtures at low concentrations but overestimated them at high concentrations, which indicates that CA and IA models were not suitable to predict the toxicities of mixtures of BPA and heavy metals. Moreover, the joint effects of BPA and heavy metals mainly showed antagonism and additive in the context of acute exposure but synergism and additive in the context of chronic exposure. Indeed, the difference in the joint effects on acute and chronic exposure can be explained by the possibility that mixtures inhibited cell growth and luminescence in chronic cultivation. The chronic toxicity of the mixture should be considered if the mixture results in the inhibition of the growth of cells. Full article
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Figure 1
<p>The acute (<b>A</b>) and chronic (<b>B</b>) single toxicity of BPA and Cr, Cd, Pb, As, Hg, and Ni. The concentration–inhibition data were fitted using the logistic and dose–response models. The error bars indicate the standard deviations from three independent experiments.</p>
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<p>Toxicities of mixtures of BPA and heavy metals. (<b>A</b>) BPA and heavy metals at equitoxic ratios in acute toxicity tests; (<b>B</b>,<b>C</b>) BPA and Cd, BPA, and As at non–equitoxic ratios in the acute toxicity test, respectively; (<b>D</b>) BPA and heavy metals at equitoxic ratios in the chronic toxicity test; (<b>E</b>,<b>F</b>) BPA and Cd, BPA, and As at non–equitoxic ratios in the chronic toxicity test, respectively. The error bars indicate the standard deviations from three independent experiments.</p>
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<p>Acute toxicity of mixtures of BPA and Cr (<b>A</b>), Cd (<b>B</b>), Pb (<b>C</b>), As (<b>D</b>), Hg (<b>E</b>), and Ni (<b>F</b>) compared with the predicted value by the CA and IA models. The black circles are the experimental mean values of the toxicity of mixtures of BPA and heavy metals, and the black solid line is the fitting curve using the mathematical mode. The red shadow indicates the 95% confidence band. The red and blue dotted lines suggest the values predicted by the CA and IA models, respectively. The error bars indicate the standard deviations from three independent experiments.</p>
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<p>Joint effect of BPA and heavy metals. (<b>A</b>–<b>C</b>) BPA and heavy metals at equitoxic or non–equitoxic ratios in the acute toxicity test; (<b>D</b>–<b>F</b>) BPA and heavy metals at equitoxic or non–equitoxic ratios in a chronic toxicity test. The error bars indicate the standard deviations from three independent experiments.</p>
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<p>The impact of the mixtures of BPA and heavy metals (equitoxic ratio) on the growth and luminescence of <span class="html-italic">V. qinghaiensis</span> Q67. The concentrations of mixtures used in this work are the EC<sub>50</sub> values of the mixtures in the test of the chronic toxicity of the mixtures, and the concentrations of BPA and heavy metals correspond to their individual concentrations in mixtures. The error bars indicate the standard deviations from three independent experiments. Impact of the individual contaminant on growth (<b>A</b>). The impact of mixtures on the growth and luminescence (<b>B</b>).</p>
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13 pages, 3080 KiB  
Article
Lanthanides Release and Partitioning in Municipal Wastewater Effluents
by Patrice Turcotte, Shirley Anne Smyth, François Gagné and Christian Gagnon
Toxics 2022, 10(5), 254; https://doi.org/10.3390/toxics10050254 - 17 May 2022
Cited by 4 | Viewed by 1871
Abstract
The use of lanthanides is increasing in our society, whether in communication technologies, transportation, electronics or medical imaging. Some lanthanides enter urban wastewater and flow through municipal wastewater treatment plants (WWTPs). However, little is known about the effectiveness of treatment processes to remove [...] Read more.
The use of lanthanides is increasing in our society, whether in communication technologies, transportation, electronics or medical imaging. Some lanthanides enter urban wastewater and flow through municipal wastewater treatment plants (WWTPs). However, little is known about the effectiveness of treatment processes to remove these elements and the concentrations released in effluents to receiving waters. The main objective of this study was to investigate the fate of lanthanides in various wastewater treatment processes. A secondary objective was to better understand the fate of medical gadolinium (Gd) complexes; anthropogenic inputs were differentiated from geological sources using an approach based on concentration normalization with respect to chondrite Post-Archean Australian Shale (PAAS). The hypothesis was that most lanthanides, especially of geological origin, are associated with the particulate phase and could be efficiently removed by WWTPs. To monitor these elements in different WWTPs, various urban influents and effluents from simple aerated lagoons to advanced treatments were sampled in Canada. The results showed that the rates of lanthanide removal by treatment processes decrease with their atomic number; from 95% for cerium (Ce) to 70% for lutetium (Lu), except for Gd, which was minimally removed. The normalization approach permitted the determination of the origin of Gd in these wastewaters, i.e., medical application versus the geological background. By distinguishing the geogenic Gd fraction from the anthropogenic one, the removal efficiency was evaluated according to the origin of the Gd; nearly 90% for geogenic Gd and a rate varying from 15% to 50% in the case of anthropogenic Gd. The processes using alum as the flocculating agent had the highest removal efficiency from wastewater. Full article
(This article belongs to the Special Issue Fate of Metals Released from Wastewater Effluents)
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<p>Dissolved proportion of lanthanides in effluents (<span class="html-italic">n</span> = 3) from various municipalities in Canada.</p>
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<p>Dissolved proportion of lanthanides in St. Lawrence River (St-L), Athabasca River (AR) and the mean effluents from various municipalities in Canada.</p>
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<p>Ratio of the dissolved concentration in influent (INF D) and effluent (EFF D) for each lanthanides in various treatment plants.</p>
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<p>Removal efficiency of total lanthanides in various municipal wastewater treatment plants.</p>
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24 pages, 4765 KiB  
Article
Influence of Urban Informal Settlements on Trace Element Accumulation in Road Dust and Their Possible Health Implications in Ekurhuleni Metropolitan Municipality, South Africa
by Innocent Mugudamani, Saheed A. Oke and Thandi Patricia Gumede
Toxics 2022, 10(5), 253; https://doi.org/10.3390/toxics10050253 - 17 May 2022
Cited by 7 | Viewed by 3304
Abstract
The study was aimed at assessing the influence of urban informal settlement on trace element accumulation in road dust from the Ekurhuleni Metropolitan Municipality, South Africa, and their possible health implications. The concentration of major and trace elements was determined using the wavelength [...] Read more.
The study was aimed at assessing the influence of urban informal settlement on trace element accumulation in road dust from the Ekurhuleni Metropolitan Municipality, South Africa, and their possible health implications. The concentration of major and trace elements was determined using the wavelength dispersive XRF method. The major elements in descending order were SiO2 (72.76%), Al2O3 (6.90%), Fe2O3 (3.88%), CaO (2.71%), K2O (1.56%), Na2O (0.99%), MgO (0.94%), MnO (0.57%), TiO2 (0.40%), and P2O5 (0.16%), with SiO2 and P2O5 at above-average shale values. The average mean concentrations of 17 trace elements in decreasing order were Cr (637.4), Ba (625.6), Zn (231.8), Zr (190.2), Sr (120.2), V (69), Rb (66), Cu (61), Ni (49), Pb (30.8), Co (17.4), Y (14.4), Nb (8.6), As (7.2), Sc (5.8), Th (4.58), and U (2.9) mg/kg. Trace elements such as Cr, Cu, Zn, Zr, Ba, and Pb surpassed their average shale values, and only Cr surpassed the South African soil screening values. The assessment of pollution through the geo-accumulation index (Igeo) revealed that road dust was moderately to heavily contaminated by Cr, whereas all other trace elements were categorized as being uncontaminated to moderately contaminated. The contamination factor (CF) exhibited road dust to be very highly contaminated by Cr, moderately contaminated by Zn, Pb, Cu, Zr, and Ba, and lowly contaminated by Co, U, Nb, Ni, As, Y, V, Rb, Sc, Sr, and Th. The pollution load index (PLI) also affirmed that the road dust in this study was very highly polluted by trace elements. Moreover, the results of the enrichment factor (EF) categorized Cr as having a significant degree of enrichment. Zn was elucidated as being minimally enriched, whereas all other trace elements were of natural origin. The results of the non-carcinogenic risk assessment revealed a possibility of non-carcinogenic risks to both children and adults. For the carcinogenic risk, the total CR values in children and adults were above the acceptable limit, signifying a likelihood of carcinogenic risk to the local inhabitants. From the findings of this study, it can be concluded that the levels of trace elements in the road dust of this informal settlement had the possibility to contribute to both non-carcinogenic and carcinogenic risks, and that children were at a higher risk than the adult population. Full article
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<p>Study area and the location of the sampling site.</p>
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<p>Geological map of the study area.</p>
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<p>Trace element contamination levels in road dust based on Igeo values.</p>
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<p>Contamination factor and pollution load index of individual trace elements in road dust.</p>
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<p>Enrichment factor values of trace elements in road dust.</p>
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<p>Hazard index for exposure to trace elements via various pathways.</p>
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<p>Contribution of trace elements to non-carcinogenic risk.</p>
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<p>Contribution of exposure pathway carcinogenic risks.</p>
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<p>Contribution of trace elements to cancer risks.</p>
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<p>Contribution of trace elements to lifetime carcinogenic risks.</p>
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17 pages, 964 KiB  
Article
Urinary Levels of Sirtuin-1, π-Glutathione S-Transferase, and Mitochondrial DNA in Maize Farmer Occupationally Exposed to Herbicide
by Supakit Khacha-ananda, Unchisa Intayoung, Klintean Wunnapuk, Kanyapak Kohsuwan, Pitchayuth Srisai and Ratana Sapbamrer
Toxics 2022, 10(5), 252; https://doi.org/10.3390/toxics10050252 - 17 May 2022
Cited by 1 | Viewed by 2069
Abstract
Epidemiologic studies have suggested an association between agrochemical exposure and risk of renal injury. Farmers face great risks to developing adverse effects. The most appropriate biomarker related to renal injury needs to be developed to encounter earlier detection. We aim to study the [...] Read more.
Epidemiologic studies have suggested an association between agrochemical exposure and risk of renal injury. Farmers face great risks to developing adverse effects. The most appropriate biomarker related to renal injury needs to be developed to encounter earlier detection. We aim to study the association between early renal biomarker and occupational herbicide exposure in maize farmers, Thailand. Sixty-four farmers were recruited and interviewed concerning demographic data, herbicide usage, and protective behavior. Two spot urines before (pre-work task) and after (post-work task) herbicide spraying were collected. To estimate the intensity of exposure, the cumulative herbicide exposure intensity index (cumulative EII) was also calculated from activities on the farm, type of personal protective equipment (PPE) use, as well as duration and frequency of exposure. Four candidate renal biomarkers including π-GST, sirtuin-1, mitochondrial DNA (mtDNA) were measured. Most subjects were male and mostly sprayed three herbicides including glyphosate-based herbicides (GBH), paraquat, and 2,4-dichlorophenoxyacetic acid (2,4-D). A type of activity in farm was mixing and spraying herbicide. Our finding demonstrated no statistical significance of all biomarker levels between pre- and post-work task urine. To compare between single and cocktail use of herbicide, there was no statistical difference in all biomarker levels between pre- and post-work task urine. However, the urinary mtDNA seems to be increased in post-work task urine. Moreover, the cumulative EII was strongly associated with change in mtDNA content in both ND-1 and COX-3 gene. The possibility of urinary mtDNA as a valuable biomarker was promising as a noninvasive benchmark for early detection of the risk of developing renal injury from herbicide exposure. Full article
(This article belongs to the Topic Air Pollution and Occupational Exposure)
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<p>The geography of Thung Lang subdistrict, Long district, Phrae province, Thailand. This area is mainly mountainous and plain where residential and agricultural zone.</p>
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<p>Comparison of urinary biomarkers (<b>A</b>) sirtuin-1, (<b>B</b>) π-glutathione S-transferase (π-GST), (<b>C</b>) <span class="html-italic">NADH-ubiquinone oxidoreductase chain 1</span> (<span class="html-italic">ND-1</span>), and (<b>D</b>) <span class="html-italic">cytochrome c oxidase subunit III</span> (<span class="html-italic">COX-3</span>) between pre- and post-work task urine sample. The data represented as median and 95% confidence interval. ng: nanogram; mg: milligram; Cr: creatinine; Ct: threshold cycle.</p>
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<p>Comparison of urinary biomarkers (<b>A</b>) sirtuin-1, (<b>B</b>) π-glutathione S-transferase (π-GST), (<b>C</b>) <span class="html-italic">NADH-ubiquinone oxidoreductase chain 1</span> (<span class="html-italic">ND-1</span>), and (<b>D</b>) <span class="html-italic">cytochrome c oxidase subunit III</span> (<span class="html-italic">COX-3</span>) between pre- and post-work task sample of farmers who sprayed single or cocktail use of herbicide. The data represented as median and 95% confidence interval. ng: nanogram; mg: milligram; Cr: creatinine; Ct: threshold cycle.</p>
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22 pages, 1067 KiB  
Review
Effects of Phthalate Mixtures on Ovarian Folliculogenesis and Steroidogenesis
by Endia J. Fletcher, Ramsés Santacruz-Márquez, Vasiliki E. Mourikes, Alison M. Neff, Mary J. Laws and Jodi A. Flaws
Toxics 2022, 10(5), 251; https://doi.org/10.3390/toxics10050251 - 16 May 2022
Cited by 26 | Viewed by 6032 | Correction
Abstract
The female reproductive system is dependent upon the health of the ovaries. The ovaries are responsible for regulating reproduction and endocrine function. Throughout a female’s reproductive lifespan, the ovaries undergo continual structural changes that are crucial for the maturation of ovarian follicles and [...] Read more.
The female reproductive system is dependent upon the health of the ovaries. The ovaries are responsible for regulating reproduction and endocrine function. Throughout a female’s reproductive lifespan, the ovaries undergo continual structural changes that are crucial for the maturation of ovarian follicles and the production of sex steroid hormones. Phthalates are known to target the ovaries at critical time points and to disrupt normal reproductive function. The US population is constantly exposed to measurable levels of phthalates. Phthalates can also pass placental barriers and affect the developing offspring. Phthalates are frequently prevalent as mixtures; however, most previous studies have focused on the effects of single phthalates on the ovary and female reproduction. Thus, the effects of exposure to phthalate mixtures on ovarian function and the female reproductive system remain unclear. Following a brief introduction to the ovary and its major roles, this review covers what is currently known about the effects of phthalate mixtures on the ovary, focusing primarily on their effects on folliculogenesis and steroidogenesis. Furthermore, this review focuses on the effects of phthalate mixtures on female reproductive outcomes. Finally, this review emphasizes the need for future research on the effects of environmentally relevant phthalate mixtures on the ovary and female reproduction. Full article
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<p>The process of folliculogenesis. The schematic shows that primordial germ cells form germ cell nests, which subsequently break down to form a finite pool of primordial follicles, starting the process of folliculogenesis. During folliculogenesis, primordial follicles grow and mature into primary follicles, then preantral follicles, and finally antral follicles.</p>
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<p>Effects of phthalates on female reproduction. The schematic shows that exposure to single phthalates as well as mixtures of phthalates affects several similar ovarian and female reproductive outcomes in mice.</p>
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18 pages, 3316 KiB  
Article
CeO2-Zn Nanocomposite Induced Superoxide, Autophagy and a Non-Apoptotic Mode of Cell Death in Human Umbilical-Vein-Derived Endothelial (HUVE) Cells
by Mohd Javed Akhtar, Maqusood Ahamed and Hisham Alhadlaq
Toxics 2022, 10(5), 250; https://doi.org/10.3390/toxics10050250 - 16 May 2022
Cited by 7 | Viewed by 2067
Abstract
In this study, a nanocomposite of cerium oxide-zinc (CeO2-Zn; 26 ± 11 nm) based on the antioxidant rare-earth cerium oxide (CeO2) nanoparticles (NPs) with the modifier zinc (Zn) was synthesized by sintering method and characterized. Its bio-response was examined in [...] Read more.
In this study, a nanocomposite of cerium oxide-zinc (CeO2-Zn; 26 ± 11 nm) based on the antioxidant rare-earth cerium oxide (CeO2) nanoparticles (NPs) with the modifier zinc (Zn) was synthesized by sintering method and characterized. Its bio-response was examined in human umbilical-vein-derived endothelial (HUVE) cells to get insight into the components of vascular system. While NPs of CeO2 did not significantly alter cell viability up to a concentration of 200 µg/mL for a 24 h exposure, 154 ± 6 µg/mL of nanocomposite CeO2-Zn induced 50% cytotoxicity. Mechanism of cytotoxicity occurring due to nanocomposite by its Zn content was compared by choosing NPs of ZnO, possibly the closest nanoparticulate form of Zn. ZnO NPs lead to the induction of higher reactive oxygen species (ROS) (DCF-fluorescence), steeper depletion in antioxidant glutathione (GSH) and a greater loss of mitochondrial membrane potential (MMP) as compared to that induced by CeO2-Zn nanocomposite. Nanocomposite of CeO2-Zn, on the other hand, lead to significant higher induction of superoxide radical (O2•−, DHE fluorescence), nitric oxide (NO, determined by DAR-2 imaging and Griess reagent) and autophagic vesicles (determined by Lysotracker and monodansylcadeverine probes) as compared to that caused by ZnO NP treatment. Moreover, analysis after triple staining (by annexin V-FITC, PI, and Hoechst) conducted at their respective IC50s revealed an apoptosis mode of cell death due to ZnO NPs, whereas CeO2-Zn nanocomposite induced a mechanism of cell death that was significantly different from apoptosis. Our findings on advanced biomarkers such as autophagy and mode of cell death suggested the CeO2-Zn nanocomposite might behave as independent nanostructure from its constituent ones. Since nanocomposites can behave independently of their constituent NPs/elements, by creating nanocomposites, NP versatility can be increased manifold by just manipulating existing NPs. Moreover, data in this study can furnish early mechanistic insight about the potential damage that could occur in the integrity of vascular systems. Full article
(This article belongs to the Section Toxicology)
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<p>Size, shape and crystallinity characterization of CeO<sub>2</sub>-Zn nanocomposite. (<b>A</b>) TEM image captured at 50 nm of resolution and (<b>B</b>) captured at 5 nm of resolution (i.e., High-res image). Lighter areas represent Zn whereas relatively darker and larger areas represent CeO<sub>2</sub> NP in high-res image B. XRD of nanocomposite is given in (<b>C</b>).</p>
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<p>Potential cytotoxicity in HUVECs due to CeO<sub>2</sub>-Zn nanocomposite at concentrations indicated (<b>A</b>). Cell morphology under phase contrast in (<b>B</b>) and calcein-AM fluorescence (green images in (<b>B</b>)) is provided as direct observation of cell damages caused by designated treatments. Calcein-AM fluorescence intensity from calcein-AM images present in <a href="#toxics-10-00250-f002" class="html-fig">Figure 2</a>B is plotted as bar diagram and is given as Figure (<b>C</b>). In all subsequent figures of the bar diagram and image, CeO<sub>2</sub> and CeO<sub>2</sub> NP both refer to 100 µg/mL of CeO<sub>2</sub> NPs. NC and nanocomposite both refer to IC50 of CeO<sub>2</sub>-Zn nanocomposite. ZnO and ZnO NP both refer to IC50 of ZnO NPs. In horizontal axis of each bar diagram given, NPs of CeO<sub>2</sub> and ZnO were denoted simply as CeO<sub>2</sub> and ZnO, respectively, while the nanocomposite of CeO<sub>2</sub>-Zn as NC due to limited space there. In images, names are in full form. Merge refers to the images obtained after color merging of phase-contrast and calcein-AM images. IC50s calculations were performed using the online IC50 calculator (<a href="https://www.aatbio.com/tools/ic50-calculator" target="_blank">https://www.aatbio.com/tools/ic50-calculator</a>, accessed on 16 January 2022) provided by AAT Bioquest, Inc. (Sunnyvale, CA, USA). Scale bar represents 25 µm which is only provided in control images as a matter of convention. Each image was captured by a 40× objective under uniform conditions of illumination intensity, time and other variables. Each experiment was performed in triplicates (<span class="html-italic">n</span> = 3). * statistically significant difference from the control (<span class="html-italic">p</span> &lt; 0.05). # statistically significant difference in between treatments of CeO<sub>2</sub>-Zn nanocomposite and ZnO NP (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Potential generation of lipid peroxidation (LPO) in the membrane of HUVEC cells due to various NP treatments was evaluated by BODIPY imaging under fluorescence microscopy (<b>A</b>), BODIPY green fluorescence intensity quantification (<b>B</b>), TBARS quantification biochemically (<b>C</b>) and LDH enzyme release in surrounding cell culture media (<b>D</b>). Only green fluorescence is plotted as a graph as it is the fluorescence which is modulated in proportion to the amount of LPO occurring in membranes (red fluorescence is uniform in control and treated cells and signals about its quality of staining in cells). Merge refers to the images obtained after color merging of red and green images of BODIPY in ImageJ. Scale bar represents 50 µm and provided only in control images. Each image was captured by a 20× objective under uniform conditions of illumination intensity, time and other variables. Each experiment was performed in triplicates (<span class="html-italic">n</span> = 3). * statistically significant difference from the control (<span class="html-italic">p</span> &lt; 0.05). # statistically significant difference in between treatments of CeO<sub>2</sub>-Zn nanocomposite and ZnO NP (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Potential generation of ROS in HUVEC cells due to various NP treatments was evaluated by DCFH-DA (<b>A</b>), DHE imaging (red images in (<b>B</b>)) and its fluorescence quantification (<b>C</b>). MMP was determined by Rh123 imaging (green images in (<b>B</b>)) and its fluorescence quantification (<b>D</b>). Merge refers to the images obtained after color merging of DHE and Rh123 images in ImageJ. Scale bar represents 25 µm and was provided only in the control images. Each image was captured by a 40× objective under uniform conditions of illumination intensity, time and other variables. Each experiment was performed in triplicates (<span class="html-italic">n</span> = 3). * statistically significant difference from the control (<span class="html-italic">p</span> &lt; 0.05). # statistically significant difference in between treatments of CeO<sub>2</sub>-Zn nanocomposite and ZnO NP (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Potential generation of NO in HUVEC cells due to various NP treatments was evaluated by DAR-2 imaging in infra-red region (see images under DAR-2 in Figure (<b>A</b>)) and its quantification (<b>B</b>). NO was also determined by indirectly quantifying nitrites by Griess reagent (<b>C</b>). Scale bar represents 25 µm. Each image was captured by a 40× objective under uniform conditions of illumination intensity, time and other variables. Each experiment was performed in triplicates (<span class="html-italic">n</span> = 3). * statistically significant difference from the control (<span class="html-italic">p</span> &lt; 0.05). # statistically significant difference in between treatments of CeO<sub>2</sub>-Zn nanocomposite and ZnO NP (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Autophagic vesicles were visualized by LTR (red fluorescence images) and MDC (blue fluorescence images) co-staining in HUVE cells (<b>A</b>) treated with NPs for 24 h. Fluorescence quantifications for LTR and MDC, respectively, are given in (<b>B</b>,<b>C</b>). Merge refers to the images obtained after color merging of LTR and MDC images in ImageJ. Antioxidant GSH modulation due to NPs is given in (<b>D</b>) where BSO stands for buthionine-(S,R)-sulfoximine which is an inhibitor of an enzyme involved in GSH synthesis. Scale bar represents 25 µm (40× objective). Each experiment was performed in triplicates (<span class="html-italic">n</span> = 3). * statistically significant difference from the control (<span class="html-italic">p</span> &lt; 0.05). # statistically significant difference in between treatments of CeO<sub>2</sub>-Zn nanocomposite and ZnO NP (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>HUVEC cells treated with mentioned NPs for 24 h and potential induction of apoptosis/necrosis was determined by triple staining (<b>A</b>), caspase 9 activity (<b>B</b>) and caspase 3 activity (<b>C</b>). Cells were stained with Hoechst 33442 (blue color) that stains the nucleus of live or dead cells, PI (red color) that stains the nucleus of only dead or dying cells and annexin-V (green color) that preferentially stains apoptotic cells. Dying or dead cells that are stained with PI but lack annexinV-FITC fluorescence clearly undergo a mode of cell death that is not apoptosis. Merge refers to the images obtained after color merging of triple images of Hoechst, annexinV-FITC and PI, respectively, in ImageJ. Scale bar represents 50 µm and is only provided in control images. Each image was captured by a 20× objective under uniform conditions of illumination intensity, time and other variables. Each experiment was performed in triplicates (<span class="html-italic">n</span> = 3). * statistically significant difference from the control (<span class="html-italic">p</span> &lt; 0.05). # statistically significant difference in between treatments of CeO<sub>2</sub>-Zn nanocomposite and ZnO NP (<span class="html-italic">p</span> &lt; 0.05).</p>
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15 pages, 784 KiB  
Review
Toxic Metals in a Paddy Field System: A Review
by Yuanliang Duan, Qiang Li, Lu Zhang, Zhipeng Huang, Zhongmeng Zhao, Han Zhao, Jun Du and Jian Zhou
Toxics 2022, 10(5), 249; https://doi.org/10.3390/toxics10050249 - 16 May 2022
Cited by 8 | Viewed by 3020
Abstract
The threat of toxic metals to food security and human health has become a high-priority issue in recent decades. As the world’s main food crop source, the safe cultivation of rice has been the focus of much research, particularly the restoration of toxic [...] Read more.
The threat of toxic metals to food security and human health has become a high-priority issue in recent decades. As the world’s main food crop source, the safe cultivation of rice has been the focus of much research, particularly the restoration of toxic metals in paddy fields. Therefore, in this paper, we focus on the effects of toxic metals on rice, as well as the removal or repair methods of toxic metals in paddy fields. We also provide a detailed discussion of the sources and monitoring methods of toxic metals pollution, the current toxic metal removal, and remediation methods in paddy fields. Finally, several important research issues related to toxic metals in paddy field systems are proposed for future work. The review has an important guiding role for the future of heavy metal remediation in paddy fields, safe production of rice, green ecological fish culture, and human food security and health. Full article
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<p>The relationships between toxic metals and the growth, development, metabolism, and nutrient composition of rice.</p>
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11 pages, 1028 KiB  
Article
Transfer of Pesticide Residues from Grapes (Vitis vinifera) into Wine—Correlation with Selected Physicochemical Properties of the Active Substances
by Arno Kittelmann, Carola Müller, Sascha Rohn and Britta Michalski
Toxics 2022, 10(5), 248; https://doi.org/10.3390/toxics10050248 - 16 May 2022
Cited by 11 | Viewed by 2840
Abstract
The concentration of pesticide residues in agricultural products at harvest can change during subsequent processing steps. This change, commonly expressed as Processing Factor (PF), is influenced by the raw agricultural commodity, and the processing conditions, as well as the properties of the substances. [...] Read more.
The concentration of pesticide residues in agricultural products at harvest can change during subsequent processing steps. This change, commonly expressed as Processing Factor (PF), is influenced by the raw agricultural commodity, and the processing conditions, as well as the properties of the substances. As it is not possible to conduct processing studies for all possible combinations of pesticide × process × product, new approaches for determining processing factors are needed. Wine was chosen as the object of the present study because it is a widely consumed product. Furthermore, it is already known that the concentration of pesticide residues can change considerably during the processing of grapes into wine, substantiating the need for PFs for a large number of pesticides. The aim of the present study was to investigate the correlation between selected physicochemical properties and PFs. In addition, the influence of different winemaking processes was explored. For this purpose, 70 processing studies conducted by pesticide manufacturers in the framework of regulatory procedures were evaluated in detail and PFs were derived for 20 pesticides. For wine, a good correlation between the PF and the octanol-water partition coefficient of the substances was found, depending on the specific production methods used. Exemplarily, the coefficient of determination for white wine was 0.85, and 0.81 for red wine, when thermovinification was applied. These results can serve as the basis for a predictive model to be validated further with future winemaking studies for pesticides. Full article
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<p>Flowcharts of the processes used in the investigated studies to prepare white wine and red wine (mash fermentation). The red wine production with thermovinification was analogous to the white wine production, with the only difference that the mash was heated to over 60 °C before pressing.</p>
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<p>Correlation of processing factors with octanol-water partition coefficients of pesticide active substances for white wines. Each dot represents one of the pesticides in <a href="#toxics-10-00248-t002" class="html-table">Table 2</a>.</p>
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<p>Predicted processing factor (PF) for pesticides in white wines in correlation with measured values. For the multiple linear regression, the log K<sub>OW</sub> was taken into account and whether fining was carried out during winemaking.</p>
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<p>Correlation of median processing factors with octanol-water partition coefficients of pesticide active substances for red wines. Each dot represents one of the pesticides in <a href="#toxics-10-00248-t003" class="html-table">Table 3</a>.</p>
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<p>Correlation of median processing factors with octanol-water partition coefficients of pesticides for red wines produced by thermovinification.</p>
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13 pages, 1391 KiB  
Article
Association between Ambient Air Pollution and Emergency Room Visits for Pediatric Respiratory Diseases: The Impact of COVID-19 Pandemic
by Chi-Yung Cheng, Yu-Lun Tseng, Kuo-Chen Huang, I-Min Chiu, Hsiu-Yung Pan and Fu-Jen Cheng
Toxics 2022, 10(5), 247; https://doi.org/10.3390/toxics10050247 - 14 May 2022
Cited by 9 | Viewed by 2608
Abstract
The level and composition of air pollution have changed during the coronavirus disease 2019 (COVID-19) pandemic. However, the association between air pollution and pediatric respiratory disease emergency department (ED) visits during the COVID-19 pandemic remains unclear. The study was retrospectively conducted between 2017 [...] Read more.
The level and composition of air pollution have changed during the coronavirus disease 2019 (COVID-19) pandemic. However, the association between air pollution and pediatric respiratory disease emergency department (ED) visits during the COVID-19 pandemic remains unclear. The study was retrospectively conducted between 2017 and 2020 in Kaohsiung, Taiwan, from 1 January 2020 to 1 May 2020, defined as the period of the COVID-19 pandemic, and 1 January 2017 to 31 May 2019, defined as the pre-COVID-19 pandemic period. We enrolled patients under 17 years old who visited the ED in a medical center and were diagnosed with respiratory diseases such as pneumonia, asthma, bronchitis, and acute pharyngitis. Measurements of particulate matter (PM) with aerodynamic diameters of <10 μm (PM10) and < 2.5 μm (PM2.5), nitrogen dioxide (NO2), and Ozone (O3) were collected. During the COVID-19 pandemic, an increase in the interquartile range of PM2.5, PM10, and NO2 levels was associated with increases of 72.5% (95% confidence interval [CI], 50.5–97.7%), 98.0% (95% CI, 70.7–129.6%), and 54.7% (95% CI, 38.7–72.6%), respectively, in the risk of pediatric respiratory disease ED visits on lag 1, which were greater than those in the pre-COVID-19 pandemic period. After adjusting for temperature and humidity, the risk of pediatric respiratory diseases after exposure to PM2.5 (inter p = 0.001) and PM10 (inter p < 0.001) was higher during the COVID-19 pandemic. PM2.5, PM10, and NO2 may play important roles in pediatric respiratory events in Kaohsiung, Taiwan. Compared with the pre-COVID-19 pandemic period, the levels of PM2.5 and PM10 were lower; however, the levels were related to a greater increase in ED during the COVID-19 pandemic. Full article
(This article belongs to the Topic Air Pollution and Occupational Exposure)
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<p>Restricted cubic spline for (<b>A</b>) temperature and (<b>B</b>) relative humidity.</p>
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<p>Odds ratios (ORs) and 95% confidence intervals (CIs) for pediatric respiratory disease-related ED visits associated with IQR increments in each air pollutant during the study period, with adjustments for temperature and humidity. ED, emergency department; IQR, interquartile range.</p>
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<p>Odds ratios (ORs) for IQR increments in (<b>A</b>) PM<sub>2.5</sub>, (<b>B</b>) PM<sub>10</sub>, and (<b>C</b>) NO<sub>2</sub> on lag 1 after adjustment for temperature and humidity. Int p, interaction <span class="html-italic">p</span>-value.</p>
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<p>Odds ratios (ORs) for IQR increments in (<b>A</b>) PM<sub>2.5</sub>, (<b>B</b>) PM<sub>10</sub>, and (<b>C</b>) NO<sub>2</sub> on lag 1 after adjustment for temperature and humidity. Int p, interaction <span class="html-italic">p</span>-value.</p>
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10 pages, 612 KiB  
Study Protocol
In Vivo Estimation of the Biological Effects of Endocrine Disruptors in Rabbits after Combined and Long-Term Exposure: Study Protocol
by Vasiliki Karzi, Manolis N. Tzatzarakis, Athanasios Alegakis, Elena Vakonaki, Irene Fragkiadoulaki, Konstantinos Kaloudis, Christina Chalkiadaki, Paraskevi Apalaki, Maria Panagiotopoulou, Aikaterini Kalliantasi, Demetrios Kouretas, Anca Oana Docea, Daniela Calina and Aristidis Tsatsakis
Toxics 2022, 10(5), 246; https://doi.org/10.3390/toxics10050246 - 12 May 2022
Cited by 8 | Viewed by 2547
Abstract
Recently, an increasing number of chemical compounds are being characterized as endocrine disruptors since they have been proven to interact with the endocrine system, which plays a crucial role in the maintenance of homeostasis. Glyphosate is the active substance of the herbicide Roundup [...] Read more.
Recently, an increasing number of chemical compounds are being characterized as endocrine disruptors since they have been proven to interact with the endocrine system, which plays a crucial role in the maintenance of homeostasis. Glyphosate is the active substance of the herbicide Roundup®, bisphenol A (BPA) and di (2-ethylhexyl) phthalate (DEHP) are used as plasticizers, while triclosan (TCS), methyl (MePB), propyl (PrPB), and butyl (BuPB) parabens are used as antimicrobial agents and preservatives mainly in personal care products. Studies indicate that exposure to these substances can affect humans causing developmental problems and problems in the endocrine, reproductive, nervous, immune, and respiratory systems. Although there are copious studies related to these substances, there are few in vivo studies related to combined exposure to these endocrine disruptors. The aim of the present pilot study is the investigation and assessment of the above substances’ toxicity in rabbits after twelve months of exposure to glyphosate (both pure and commercial form) and to a mixture of all the above substances at subtoxic levels. The lack of data from the literature concerning rabbits’ exposure to these substances and the restrictions of the 3Rs Principle will result in a limited number of animals available for use (four animals per group, twenty animals in total). Full article
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<p>Groups of administration (substances-doses). ADI: Acceptable Daily Intake.</p>
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<p>(<b>A</b>). Sacrifice procedure: obtained tissues and applied tests (<b>B</b>). Blood sampling and applied tests.</p>
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12 pages, 1557 KiB  
Article
Pyrolytic Remediation and Ecotoxicity Assessment of Fuel-Oil-Contaminated Soil
by Byeongwook Choi, Jin-Seo Yu, Gu-Young Kang, Tae-Yong Jeong, Eun Hea Jho and Sung-Jong Lee
Toxics 2022, 10(5), 245; https://doi.org/10.3390/toxics10050245 - 12 May 2022
Cited by 1 | Viewed by 2279
Abstract
Oil-contaminated soil is a major societal problem for humans and the environment. In this study, the pyrolysis method was applied to oil-contaminated soil used as a landfill and gas station site in Korea. The removal efficiency of the main components of oil-contaminated soils, [...] Read more.
Oil-contaminated soil is a major societal problem for humans and the environment. In this study, the pyrolysis method was applied to oil-contaminated soil used as a landfill and gas station site in Korea. The removal efficiency of the main components of oil-contaminated soils, such as total petroleum hydrocarbons (TPH), polyaromatic hydrocarbons (PAHs), unresolved complex mixture (UCM), and alkylated PAHs (Alk-PAHs) were measured, and the effect of temperature, treatment time, and moisture content on pyrolysis efficiency was studied. In order to evaluate the risk of soil from which pollutants were removed through pyrolysis, integrated ecotoxicity was evaluated using Daphnia magna and Allivibrio fischeri. The chemical and biological measurements in this study include contaminants of emerging concerns (CECs). Results showed that the pyrolysis was more efficient with higher treatment temperatures, moisture content, and treatment times. In addition, toxicity was reduced by 99% after pyrolysis, and the degree of toxicity was evaluated more sensitively in Allivibrio fischeri than in Daphnia magna. This study shows that weathered oil-contaminated soil can be effectively treated in a relatively short time through pyrolysis, as well as provides information on efficient conditions and the assessment of ecotoxicity. Full article
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<p>Example of pyrolysis reactor used in this study.</p>
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<p>(<b>a</b>) Post-pyrolysis TPH concentration by operational time, temperature, and soil moisture content; (<b>b</b>) post-pyrolysis UCM concentration by operational time, temperature, and soil moisture content (* <span class="html-italic">p</span>-value &lt; 0.05).</p>
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<p>GC chromatograms of the TPH and UCM. (<b>a</b>) Moisture content 10%, temperature 200 °C; (<b>b</b>) moisture content 20%, temperature 200 °C. The green, red, and blue lines indicate the chromatograms of the raw soil, 30 min treated soil, and 60 min treated soil, respectively. The area under the hump is considered as the UCM.</p>
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<p>(<b>a</b>) Post-pyrolysis PAHs’ concentration by operational time, temperature, and soil moisture content; (<b>b</b>) post-pyrolysis Alk-PAHs’ concentration by operational time, temperature, and soil moisture content.</p>
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<p>(<b>a</b>) Post-pyrolysis PAHs’ concentration by operational time, temperature, and soil moisture content; (<b>b</b>) post-pyrolysis Alk-PAHs’ concentration by operational time, temperature, and soil moisture content.</p>
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<p>Content of fluorane, naphthalene, and other compounds in raw soil and treated soil.</p>
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