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14 pages, 2182 KiB  
Article
Plant Root Secretion Alleviates Carbamate-Induced Molecular Alterations of Dissolved Organic Matter
by Zihan Niu, Chao Chen, Qijun Ruan, Yingming Duan, Shuqin Liu and Da Chen
Toxics 2024, 12(9), 654; https://doi.org/10.3390/toxics12090654 - 5 Sep 2024
Viewed by 436
Abstract
Studying the interaction between pesticide contamination in the plant system and the dissolved organic matter (DOM) composition is important to understand the impact of pesticides and plants on the ecological function of DOM. The present study investigated the effects of DOM on the [...] Read more.
Studying the interaction between pesticide contamination in the plant system and the dissolved organic matter (DOM) composition is important to understand the impact of pesticides and plants on the ecological function of DOM. The present study investigated the effects of DOM on the bioaccumulation and biotransformation of carbamates in plants, carbamate exposure on DOM composition, and plant root secretion on the interaction between DOM and carbamates. The concentrations of carbamates and their metabolites in living cabbage plants were continuously tracked through an in vivo analytical method. The presence of DOM was found to reduce the highest bioconcentrations and shorten the time it took to reach the highest bioaccumulated amounts of isoprocarb and carbofuran in plants, while it showed no significant effect on the uptake behavior of carbaryl. DOM profiling results indicated that carbamate exposure substantially decreased the number and molecular diversity of DOM. Notably, plant root secretion alleviated carbamate-induced DOM molecular alterations by inducing a higher turnover rate of DOM compared to that in the uncontaminated group, highlighting the role of plants in mitigating the effects of exogenous pesticide exposure on DOM composition and maintaining DOM molecular homeostasis. Full article
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<p>Molecular structures of carbamates and carbamate metabolites, and the concentrations of carbamates and carbamate metabolites in living cabbage plant stems of treatments with and without HA addition. (<b>A<sub>1</sub></b>,<b>A<sub>2</sub></b>) Concentrations of isoprocarb and o-cumenol, (<b>B<sub>1</sub></b>,<b>B<sub>2</sub></b>) concentrations of carbofuran and carbofuran phenol, and (<b>C<sub>1</sub></b>,<b>C<sub>2</sub></b>) concentrations of carbaryl and 1-naphthalenol. Significant difference between the two plant groups is represented as * <span class="html-italic">p</span> ≤ 0.05.</p>
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<p>Relative amount of DOM molecules in different treatments according to (<b>A<sub>1</sub></b>–<b>D<sub>1</sub></b>) elemental compositions and (<b>A<sub>2</sub></b>–<b>D<sub>2</sub></b>) compound groups.</p>
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<p>(<b>A</b>) Venn diagrams exhibiting the production, persistence, and removal of DOM molecules of different treatments between Day 0 and Day 21. The (<b>B</b>) molecular numbers and (<b>C</b>) relative amount of the persistent, removed, and produced DOM molecules in different treatments.</p>
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<p>Difference in the root exudates and the inhibited DOM components of the HA-Plant and the HA/Carbamate-Plant groups during the 3-week experiment. The numbers of (<b>A</b>,<b>B</b>) the root exudates and (<b>C</b>,<b>D</b>) the inhibited DOM components in the four subcategories (CHO, CHON, CHONS, and CHOS) of the HA-Plant and the HA/Carbamate-Plant groups.</p>
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16 pages, 6113 KiB  
Article
Toxic Effects of Carbaryl Exposure on Juvenile Asian Seabass (Lates calcarifer)
by Junhua Huang, Zhengyi Fu, Wei Yu, Zemin Bai and Zhenhua Ma
J. Xenobiot. 2024, 14(3), 923-938; https://doi.org/10.3390/jox14030051 - 10 Jul 2024
Viewed by 647
Abstract
This study examines the physiological and immunological effects of 0.5 ppm carbaryl exposure on juvenile Asian seabass (Lates calcarifer) over 12 h to 72 h. Notable results include decreased activities of liver enzymes catalase (CAT), lactate dehydrogenase (LDH), and glutathione peroxidase [...] Read more.
This study examines the physiological and immunological effects of 0.5 ppm carbaryl exposure on juvenile Asian seabass (Lates calcarifer) over 12 h to 72 h. Notable results include decreased activities of liver enzymes catalase (CAT), lactate dehydrogenase (LDH), and glutathione peroxidase (GSH-PX), while superoxide dismutase (SOD) levels remained stable, with the lowest activities of CAT and GSH-PX observed at 72 h. Serum biochemistry revealed increased alkaline phosphatase (AKP) and acid phosphatase (ACP) at 24 h, with declining aspartate aminotransferase (AST) and a peak in creatinine at 48 h. Histopathological analysis showed carbaryl-induced necrosis in liver and spleen cells, and increased melanomacrophage centers in both organs. Additionally, immune gene expression analysis indicated an upregulation of heat shock proteins and consistent elevation of complement component C3 and interleukin-8 (IL-8). These findings suggest that carbaryl exposure significantly impairs organ function and modulates immune responses in L. calcarifer, underlining the need for further research on protective strategies against pesticide impacts in aquaculture. Full article
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<p>Changes in antioxidant enzyme activities in the liver of <span class="html-italic">L. calcarifer</span> juveniles exposed to 0.5 ppm carbaryl ((<b>A</b>): Catalase (CAT) activity; (<b>B</b>): Lactate Dehydrogenase (LDH) activity; (<b>C</b>): glutathione peroxidase (GSH-PX) activity; (<b>D</b>): superoxide dismutase (SOD) activity. Symbols in the figure represent statistical significance: “*” for <span class="html-italic">p</span> &lt; 0.05, “**” for <span class="html-italic">p</span> &lt; 0.01, and “***” for <span class="html-italic">p</span> &lt; 0.001). Different letters indicate statistically significant differences.</p>
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<p>Changes in serum biochemical parameters of <span class="html-italic">L. calcarifer</span> juveniles exposed to 0.5 ppm carbaryl ((<b>A</b>): alkaline phosphatase (AKP) activity; (<b>B</b>): acid phosphatase (ACP) activity; (<b>C</b>): malondialdehyde (MDA); (<b>D</b>): aspartate aminotransferase (AST) activity; (<b>E</b>): creatinine. Symbols in the figure represent statistical significance: “*” for <span class="html-italic">p</span> &lt;0.05, “**” for <span class="html-italic">p</span> &lt;0.01, and “***” for <span class="html-italic">p</span> &lt; 0.001). Different letters indicate statistically significant differences.</p>
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<p>Histological sections of the liver tissue in juvenile <span class="html-italic">L. calcarifer</span> exposed to 0.5 ppm carbaryl. (<b>A</b>): Control group; (<b>B</b>): experimental group at 12 h; (<b>C</b>): experimental group at 24 h; (<b>D</b>): experimental group at 48 h; (<b>E</b>): experimental group at 72 h (red arrow: hepatocyte swelling; green arrow: hepatocyte necrosis; black arrow: nuclear shrinkage; scale bar: 50 μm).</p>
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<p>Histological sections of the head kidney tissue in juvenile <span class="html-italic">L. calcarifer</span> exposed to 0.5 ppm carbaryl. (<b>A</b>): Control group; (<b>B</b>): experimental group at 12 h; (<b>C</b>): experimental group at 24 h; (<b>D</b>): experimental group at 48 h; (<b>E</b>): experimental group at 72 h; (red arrow: melanomacrophage centers, MMCs; scale bar: 50 μm).</p>
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<p>Histological sections of the spleen tissue in juvenile <span class="html-italic">L. calcarifer</span> exposed to 0.5 ppm carbaryl. (<b>A</b>): Control group; (<b>B</b>): experimental group at 12 h; (<b>C</b>): experimental group at 24 h; (<b>D</b>): experimental group at 48 h; (<b>E</b>): experimental group at 72 h; (red arrow: melanomacrophage centers, MMCs; black arrow: lymphocytes; green arrow: necrotic spleen cells; scale bar: 50 μm).</p>
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<p>Immune genes in the liver of juvenile <span class="html-italic">L. calcarifer</span> were expressed following exposure to 0.5 ppm carbaryl. ((<b>A</b>): HSP90 gene; (<b>B</b>): HSP70 gene; (<b>C</b>): Complement C3 gene; (<b>D</b>): IL-8 gene. Symbols in the figure represent statistical significance: “*” for <span class="html-italic">p</span> &lt; 0.05, “**” for <span class="html-italic">p</span> &lt; 0.01, and “***” for <span class="html-italic">p</span> &lt; 0.001). Different letters indicate statistically significant differences.</p>
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19 pages, 1681 KiB  
Article
Study of Physicochemical Quality and Organic Contamination in Algerian Honey
by Sofiane Derrar, Vincenzo Lo Turco, Ambrogina Albergamo, Benedetta Sgrò, Mohamed Amine Ayad, Federica Litrenta, Mohamed Said Saim, Angela Giorgia Potortì, Hebib Aggad, Rossana Rando and Giuseppa Di Bella
Foods 2024, 13(9), 1413; https://doi.org/10.3390/foods13091413 - 4 May 2024
Cited by 1 | Viewed by 1120
Abstract
Honey is a natural product extensively consumed in the world for its nutritional and healthy properties. However, residues of pesticides and environmental contaminants can compromise its quality. For this reason, the physicochemical parameters, and the organic contamination of monofloral and multifloral honey from [...] Read more.
Honey is a natural product extensively consumed in the world for its nutritional and healthy properties. However, residues of pesticides and environmental contaminants can compromise its quality. For this reason, the physicochemical parameters, and the organic contamination of monofloral and multifloral honey from three regions of Algeria (Tiaret, Laghouat, and Tindouf) were monitored to evaluate the quality of the honey and its safety for consumers. In general, the results obtained from the physicochemical analyses were in line with the EU standards. In terms of contamination, pesticides authorised and used in Algerian agriculture (metalaxyl-M and cyromazine), as well as a banned pesticide (carbaryl), were found in almost all the samples. However, only the concentration of cyromazine was higher than the relative EU maximum residue levels. PCB 180, PCB 189, anthracene, fluorene, and phenanthrene were mainly detected. All the honey shows traces of DiBP, DBP, DEHP, and DEHT, but no traces of bisphenols were found. Moreover, according to the dietary exposure assessment, a small amount of Algerian honey can be safely consumed. Overall, the data from this study should motivate the Algerian government to enhance their monitoring activities in beekeeping and to find solutions for implementing more sustainable agricultural practices harmonising with international legislation. Full article
(This article belongs to the Section Food Quality and Safety)
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<p>Geographical origin of honey samples considered for the study.</p>
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<p>Box-plots of PAH (<b>a</b>), PCB (<b>b</b>), and pesticide (<b>c</b>) concentrations found in Algerian honey.</p>
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<p>Box-plot illustrating the concentrations of plasticiser residues in Algerian honey.</p>
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<p>Score plot (<b>a</b>) and loading plot (<b>b</b>) of PC1 and PC2, explaining honey samples differentiated by geographical origin. For the analysis, only the data on 10 contaminants detected in at least 60% of the samples were considered.</p>
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<p>Score plot (<b>a</b>) and loading plot (<b>b</b>) of PC1 and PC3, explaining honey samples differentiated by geographical origin. For the analysis, only the data on 10 contaminants detected in at least 60% of the samples were considered.</p>
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14 pages, 5803 KiB  
Article
Sustainable Development of ZnO Nanostructure Doping with Water Hyacinth-Derived Activated Carbon for Visible-Light Photocatalysis
by Sucheewan Krobthong, Tipawan Rungsawang, Naphatson Khaodara, Napat Kaewtrakulchai, Kanit Manatura, Khewika Sukiam, Donchida Wathinputthiporn, Sawitree Wongrerkdee, Chatdanai Boonruang and Sutthipoj Wongrerkdee
Toxics 2024, 12(3), 165; https://doi.org/10.3390/toxics12030165 - 21 Feb 2024
Cited by 3 | Viewed by 1426
Abstract
Water hyacinth (Wh) is an aquatic weed considered a nuisance in agricultural and fishing activities. Therefore, this study proposed repurposing this plant into activated carbon (AC). First, the ZnO-AC was precipitated and applied as a photocatalyst for degrading methylene blue. The preliminary photocatalytic [...] Read more.
Water hyacinth (Wh) is an aquatic weed considered a nuisance in agricultural and fishing activities. Therefore, this study proposed repurposing this plant into activated carbon (AC). First, the ZnO-AC was precipitated and applied as a photocatalyst for degrading methylene blue. The preliminary photocatalytic test under UV irradiation identified the optimum ZnO-AC photocatalyst to degrade methylene blue (MB). The ZnO-AC photocatalyst recorded the highest degradation rate constant of 11.49 × 10−3 min−1, which was almost two-fold higher than that of ZnO (5.55 × 10−3 min−1). Furthermore, photocatalytic degradation of MB and carbaryl under sunlight irradiation by ZnO-AC demonstrated degradation rate constants of 74.46 × 10−3 min−1 and 8.43 × 10−3 min−1, respectively. To investigate the properties of ZnO-AC, several techniques were performed. ZnO-AC and ZnO exhibited similar results in morphology, crystalline structure, and Raman characteristics. However, ZnO-AC presented smaller pore diameters than those of ZnO, which enlarged pore surface area, and the presence of carbon-related groups implied the presence of AC on ZnO-AC surfaces. This can be attributed to the presence of AC on the ZnO surface, increasing the capture of surrounding toxic molecules and elevating the reaction density. This mechanism is attributed to promoting the degradation of toxic molecules. Therefore, using Wh as a carbon source for the transformation of AC can alternatively solve the problems of aquatic weed management and carbon storage strategies, and the application of AC in ZnO-AC photocatalysts can enhance photocatalysis. Full article
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Graphical abstract
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<p>Absorbance of MB after photocatalytic activity at varying intervals of UV-irradiation time using different photocatalysts: (<b>a</b>) ZnO, (<b>b</b>) ZnO-AC1%, (<b>c</b>) ZnO-AC3%, (<b>d</b>) ZnO-AC5%, (<b>e</b>) ZnO-AC10%, and (<b>f</b>) blank.</p>
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<p>Analysis of photocatalytic degradation of MB under UV irradiation: (<b>a</b>) C<sub>t</sub>/C<sub>0</sub> ratio, (<b>b</b>) degradation efficiency, and (<b>c</b>) degradation rate constant.</p>
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<p>Photocatalytic degradation analysis under natural sunlight irradiation: absorbance of MB using (<b>a</b>) ZnO and (<b>b</b>) ZnO-AC3%, absorbance of CBR using (<b>c</b>) ZnO and (<b>d</b>) ZnO-AC3%, and degradation rate constant of (<b>e</b>) MB and (<b>f</b>) CBR degradation.</p>
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<p>Photocatalytic degradation analysis under natural sunlight irradiation: absorbance of MB using (<b>a</b>) ZnO and (<b>b</b>) ZnO-AC3%, absorbance of CBR using (<b>c</b>) ZnO and (<b>d</b>) ZnO-AC3%, and degradation rate constant of (<b>e</b>) MB and (<b>f</b>) CBR degradation.</p>
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<p>TEM images of (<b>a</b>) ZnO and (<b>b</b>) ZnO-AC nanostructures.</p>
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<p>XRD patterns of (<b>a</b>) AC, (<b>b</b>) ZnO, and (<b>c</b>) ZnO-AC.</p>
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<p>Raman spectroscopy analysis of (<b>a</b>) AC, (<b>b</b>) ZnO, and (<b>c</b>) ZnO-AC.</p>
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<p>N<sub>2</sub> adsorption–desorption isotherms of ZnO and ZnO-AC.</p>
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<p>FTIR analysis of ZnO and ZnO-AC using (<b>a</b>) full scanning, and (<b>b</b>) functional group scanning.</p>
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<p>XPS analysis of ZnO and ZnO-AC: (<b>a</b>) full scan, (<b>b</b>) Zn 2p, (<b>c</b>) O 1s, and (<b>d</b>) C 1s.</p>
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<p>The mechanism of contaminant degradation using a ZnO-AC photocatalyst.</p>
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25 pages, 3820 KiB  
Article
The Impact of Pesticide Residues on Soil Health for Sustainable Vegetable Production in Arid Areas
by Turki Kh. Faraj, Mohamed Hamza EL-Saeid, Mohamed M. M. Najim and Maha Chieb
Separations 2024, 11(2), 46; https://doi.org/10.3390/separations11020046 - 31 Jan 2024
Cited by 1 | Viewed by 2855
Abstract
The assessment of pesticide residues in agricultural soils is an essential prerogative in maintaining environmental health standards. Intensive vegetable cultivation is practiced in the Al-Kharj area of the eastern Najd region of Saudi Arabia, where excessive applications of agrochemicals are reported to pollute [...] Read more.
The assessment of pesticide residues in agricultural soils is an essential prerogative in maintaining environmental health standards. Intensive vegetable cultivation is practiced in the Al-Kharj area of the eastern Najd region of Saudi Arabia, where excessive applications of agrochemicals are reported to pollute vegetable-growing soils, challenging the sustainable management of soils and groundwater resources. This study aimed to monitor the levels of thirty-two types of pesticide residues in the soils of vegetable fields and the estimated potential health risk for humans due to non-dietary exposure to pesticides in soils in the Al-Kharj region. Pesticide residues were evaluated at 0–10 cm and 10–20 cm depths at 20 sampling sites from Al-Kharj. Gas chromatograph-mass spectrometry, coupled with a quadrupole mass spectrometer with a GC column, was used in the analysis. The results indicated that agrochemical residues show prolonged soil pollution that may cause adverse impacts on human and environment. Herbicides Atrazine, Isoproturpon, and Linuron have been detected in the soils, and these pose many problematic environmental threats. Bromoxynil, Pendimetholin, and Diclofop-methyl could be used as per the recommendations to sustainably manage soil and water resources in the Al-Kharj area. Resmethrin, Methidathion, Ethoprophos, Tetramethrin, Bromophis-methyl, Bifenthion, Permethrin, Fenoxycarb, Cyfluthrin, Phosmet, and Azinophos-methyl can be used safely in the Al-Kharj agricultural area, maintaining sustainable soils and water resources. Applications of Carbaryl require sufficient care, while Endosulfan, Deltamethrin, Lindane, Chlorpyrifos, Chlorpyrifos-methly, Dimethoate, Heptachlor, and Mevinphos, which are detected in soils, require policy guidelines to limit the use to ensure sustainability. Policy interventions need to be formulated to increase the sustainability of soil management and groundwater resources in the Al-Kharj region to ensure the safety of people who are in direct contact with the agrochemicals used and to ensure the safety of agricultural products generated in this region. Full article
(This article belongs to the Section Analysis of Food and Beverages)
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<p>Sampling sites and rock and soil types.</p>
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<p>Dendrogram of the cluster analysis of sites based on the residual level of herbicides in topsoil.</p>
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<p>Dendrogram of the cluster analysis of sites based on the residual level of herbicides in inner soil.</p>
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<p>Dendrogram of the cluster analysis of sites based on the residual level of pesticides in topsoil.</p>
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<p>Dendrogram of the cluster analysis of sites based on the residual level of pesticides in inner soil.</p>
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<p>Dendrogram of the cluster analysis of sites based on the residual level of other agrochemicals in topsoil.</p>
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<p>Dendrogram of the cluster analysis of sites based on the residual level of other agrochemicals in inner soil.</p>
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16 pages, 1425 KiB  
Article
Efficacy of Conventional and Biorational Insecticides against the Invasive Pest Thrips parvispinus (Thysanoptera: Thripidae) under Containment Conditions
by Livia M. S. Ataide, German Vargas, Yisell Velazquez-Hernandez, Isamar Reyes-Arauz, Paola Villamarin, Maria A. Canon, Xiangbing Yang, Simon S. Riley and Alexandra M. Revynthi
Insects 2024, 15(1), 48; https://doi.org/10.3390/insects15010048 - 10 Jan 2024
Viewed by 4193
Abstract
In 2020, the invasive Thrips parvispinus (Karny) was first detected in Florida, United States. In response to the implemented regulatory restrictions, we conducted laboratory experiments under containment conditions. Thrips larvae and adults were exposed to 32 products (conventional and biorational insecticides) either directly [...] Read more.
In 2020, the invasive Thrips parvispinus (Karny) was first detected in Florida, United States. In response to the implemented regulatory restrictions, we conducted laboratory experiments under containment conditions. Thrips larvae and adults were exposed to 32 products (conventional and biorational insecticides) either directly or indirectly. Direct exposure was performed using a Spray Potter Tower, while indirect exposure was conducted by evaluating residue toxicity against the thrips. Water served as a control. We assessed mortality and leaf-feeding damage 48 h post-treatment. Among the conventional insecticides, chlorfenapyr, sulfoxaflor-spinetoram, and spinosad caused high mortality across all stages in both direct and residue toxicity assays. Pyridalyl, acetamiprid, tolfenpyrad, cyclaniliprole-flonicamid, acephate, novaluron, abamectin, cyantraniliprole, imidacloprid, cyclaniliprole, spirotetramat, and carbaryl displayed moderate toxicity, affecting at least two stages in either exposure route. Additionally, chlorfenapyr, spinosad, sulfoxaflor-spinetoram, pyridalyl, acetamiprid, cyclaniliprole, cyclaniliprole-flonicamid, abamectin, and acephate inhibited larvae and adult’s leaf-feeding damage in both direct and residue toxicity assays. Regarding biorational insecticides, mineral oil (3%) and sesame oil caused the highest mortality and lowest leaf-feeding damage. Greenhouse evaluations of spinosad, chlorfenapyr, sulfoxaflor-spinetoram, and pyridalyl are recommended. Also, a rotation program incorporating these products, while considering different modes of action, is advised for ornamental growers to avoid resistance and to comply with regulations. Full article
(This article belongs to the Section Insect Pest and Vector Management)
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<p>Mortality caused by conventional and biorational insecticides 48 h after treating <span class="html-italic">Thrips parvispinus</span>. The figure illustrates the percentage of dead larvae (L1; L2) and adult thrips in both direct (squares) and residue toxicity assays (circles). Blue color indicates that the observed mortality was significantly higher than the control (<span class="html-italic">p</span> ≤ 0.05; GLMM), while red indicates non-significant differences.</p>
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<p>Percent of leaf-feeding damage caused by <span class="html-italic">Thrips parvispinus</span> 48 h after being treated with conventional and biorational insecticides. The figure illustrates the percentage of bean leaf-feeding damaged area caused by larvae (L1; L2) and adult thrips in both direct (squares) and residue toxicity assays (circles). Blue color indicates that the observed leaf-feeding damage was significantly higher than the control (<span class="html-italic">p</span> ≤ 0.05; GLMM), while red indicates non-significant differences.</p>
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33 pages, 4686 KiB  
Article
Definition of the Neurotoxicity-Associated Metabolic Signature Triggered by Berberine and Other Respiratory Chain Inhibitors
by Ilinca Suciu, Johannes Delp, Simon Gutbier, Julian Suess, Lars Henschke, Ivana Celardo, Thomas U. Mayer, Ivano Amelio and Marcel Leist
Antioxidants 2024, 13(1), 49; https://doi.org/10.3390/antiox13010049 - 28 Dec 2023
Cited by 1 | Viewed by 1647
Abstract
To characterize the hits from a phenotypic neurotoxicity screen, we obtained transcriptomics data for valinomycin, diethylstilbestrol, colchicine, rotenone, 1-methyl-4-phenylpyridinium (MPP), carbaryl and berberine (Ber). For all compounds, the concentration triggering neurite degeneration correlated with the onset of gene expression changes. The mechanistically diverse [...] Read more.
To characterize the hits from a phenotypic neurotoxicity screen, we obtained transcriptomics data for valinomycin, diethylstilbestrol, colchicine, rotenone, 1-methyl-4-phenylpyridinium (MPP), carbaryl and berberine (Ber). For all compounds, the concentration triggering neurite degeneration correlated with the onset of gene expression changes. The mechanistically diverse toxicants caused similar patterns of gene regulation: the responses were dominated by cell de-differentiation and a triggering of canonical stress response pathways driven by ATF4 and NRF2. To obtain more detailed and specific information on the modes-of-action, the effects on energy metabolism (respiration and glycolysis) were measured. Ber, rotenone and MPP inhibited the mitochondrial respiratory chain and they shared complex I as the target. This group of toxicants was further evaluated by metabolomics under experimental conditions that did not deplete ATP. Ber (204 changed metabolites) showed similar effects as MPP and rotenone. The overall metabolic situation was characterized by oxidative stress, an over-abundance of NADH (>1000% increase) and a re-routing of metabolism in order to dispose of the nitrogen resulting from increased amino acid turnover. This unique overall pattern led to the accumulation of metabolites known as biomarkers of neurodegeneration (saccharopine, aminoadipate and branched-chain ketoacids). These findings suggest that neurotoxicity of mitochondrial inhibitors may result from an ensemble of metabolic changes rather than from a simple ATP depletion. The combi-omics approach used here provided richer and more specific MoA data than the more common transcriptomics analysis alone. As Ber, a human drug and food supplement, mimicked closely the mode-of-action of known neurotoxicants, its potential hazard requires further investigation. Full article
(This article belongs to the Section Health Outcomes of Antioxidants and Oxidative Stress)
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<p>Overview of the study design and the compounds investigated. The seven compounds studied here were originally identified in an earlier screen, using the NeuriTox assay [<a href="#B4-antioxidants-13-00049" class="html-bibr">4</a>]. For follow-up studies, differentiating LUHMES neurons were exposed on day 2 (d2) to the screen hits for up to 24 h. (<b>A</b>) General assay scheme: pre-differentiated LUHMES cells were seeded on d2 and treated 1 h later with the toxicants. Orange bars indicate the exposure duration. For determining the toxicity threshold (benchmark concentrations), the 24 h exposure was performed using serial dilutions of the test compounds. (<b>B</b>) The overall viability- (V) and neurite area (NA) inhibition were quantified, using the NeuriTox/UKN4 assay, and the concentrations leading to a NA reduction by 25% (EC25<sub>NA</sub>) were determined. Hits are defined as compounds which decreased viability by 25% (EC25<sub>V</sub>) at a concentration at least 4 x higher than the EC25<sub>NA</sub>. (<b>C</b>) The seven previously identified hits were confirmed here as specific neurite outgrowth inhibitors in an assay run with smaller concentration steps than in the screen mode. The V/NA ratios are given for: rotenone (Rot), berberine chloride (Ber), valinomycin (Val), 1-methyl-4-phenylpyridinium (MPP<sup>+</sup>), carbaryl (Car), colchicine (Col), diethylstilbestrol (DES). The EC25<sub>V</sub>/EC25<sub>NA</sub> is indicated below each compound. (<b>D</b>) Graphs show concentration-dependent changes in neurite area (NA—colored circles) and viability (V—empty squares). The dotted line indicates a benchmark response of 25%. The EC25<sub>NA</sub> concentrations are as follows: 4 µM Ber, 30 nM Rot, 1 µM MPP, 4 nM Val, 19 µM Car and 5 µM DES. Data are means ± SEM of averaged data from independent experiments (n = 3; N = 3). (<b>E</b>) Schematic illustration of the three main approaches used for further investigation of the compounds’ mode of action: (i) transcriptomics profiling in a concentration- and time-dependent manner, (ii) assessment of effects on biochemical cell functions and (iii) metabolome profiling after 24 h exposure to each of the complex I inhibitors (MPP, Ber, Rot). Biochemical functions investigated included: mitochondrial oxygen consumption rate (OCR) and changes in the abundance of intracellular amino acids.</p>
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<p>Time-dependent transcriptome changes induced by neurotoxicants. Transcriptome data were obtained from LUHMES cells treated with seven different neurotoxicants for 4 h, 16 h and 24 h, as outlined in <a href="#antioxidants-13-00049-f001" class="html-fig">Figure 1</a>A. The following concentrations were used: 64 µM carbaryl (Car), 40 nM colchicine (Col), 94 µM berberine chloride (Ber), 21 µM diethylstilbestrol (DES), 4.4 µM rotenone (Rot), 10 µM MPP+ and 17 nM valinomycin (Val). As output metrics, we provide the average log2 fold changes (FCs) relative to the solvent control of the respective time point for each transcript, including the standard deviation, and the statistical significance of the change in <a href="#app1-antioxidants-13-00049" class="html-app">Supplementary Table S1</a>. (<b>A</b>) Graphical overview of the data analysis: transcriptome time-series data were used as input for principal component analysis (PCA) and to determine differentially expressed genes (DEGs). The latter were used for more refined downstream analyses, as indicated by subfigure letters. (<b>B</b>) Log2 FC data from all transcriptome conditions (3 times × 8 treatments) were analyzed together in a joint principal component space. The data for berberine (Ber) are shown here, while the complete PCA plot is displayed in <a href="#app1-antioxidants-13-00049" class="html-app">Figure S2A</a>. The “×” denotes untreated cells, the time labels indicate data points after respective exposure times to Ber. The circle diameters correspond to the exposure time. (<b>C</b>) For each compound and gene, the expression change was followed over time, and only the peak data (defined by significance) were kept as a dimensionality-reduced data set. The genes with an adjusted <span class="html-italic">p</span>-value ≤ 0.05 and an absolute FC &gt; 1.5 were considered “consensus genes” and selected for further analysis (<a href="#app1-antioxidants-13-00049" class="html-app">Figure S2B</a>). Those shared by ≥ 2 compounds (561 out of 3255 measured genes) were selected for further analysis. The heat map gives an overview of the clustered gene hits (common to at least two treatments). The color scale spans from blue (four-fold down) over white (no regulation) to red (four-fold up). Two heat map regions (1, 2) were considered interesting for further analysis and more detailed overviews (shown in <a href="#app1-antioxidants-13-00049" class="html-app">Figure S2C</a>). (<b>D</b>) The bar graph shows the number of differentially expressed genes per compound. The proportion of consensus genes is marked in gray. The colored parts of the bars indicate genes specifically affected by the individual compounds. Pool: data on all genes regulated by at least one toxicant (n = 1037). (<b>E</b>) The top 10% up-regulated genes per compound were selected and displayed together in a dot plot, where significance is encoded by circle size. Red colors indicate up-regulation (full red = four-fold). Blue colors (very rare) indicate down-regulations. Genes were mapped to five functional categories, marked by different colors: cell cycle regulation and mitosis (green), mitochondrial stress response orchestrated by ATF4 (orange), oxidative stress response orchestrated by NRF2 (red), heat shock response (blue) and cell death/parkinsonism (purple). (<b>F</b>) The activity of transcription factors (TF) was predicted based on target gene expression [<a href="#B36-antioxidants-13-00049" class="html-bibr">36</a>]. Predicted activity scores of ATF4, HSF1, ATF6 and NRF2 were plotted over time. For ATF4, the individual regulation of its targets used for the prediction is shown for the 16 h time point in <a href="#app1-antioxidants-13-00049" class="html-app">Figure S3</a>. Note that detailed changes are extensively described in <a href="#app1-antioxidants-13-00049" class="html-app">supplementary results 1</a>.</p>
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<p>Concentration-dependent transcriptome changes induced by exposure of neurons to toxicants. Transcriptome data were obtained from LUHMES cells treated for 24 h with seven different toxicants at five different concentrations. The five concentration intervals were anchored to cytotoxicity data. Concentrations corresponded to 2 × EC<sub>10</sub>, EC<sub>10</sub>, 0.25 × EC<sub>10</sub>, 0.015 × EC10, 0.004 × EC10. The following concentrations intervals were used: 0.25–130 µM carbaryl (Car), 0.15–77 nM colchicine (Col), 0.18–94 µM berberine chloride (Ber), 0.08–42 µM diethylstilbestrol (DES), 0.02–8.7 µM rotenone (Rot), 0.02–10 µM MPP+ (MPP) and 0.07–33 nM valinomycin (Val). The vertical dotted line indicates the benchmark concentration corresponding to a 25% reduction in neurite area (EC25<sub>NA</sub>) as derived from <a href="#antioxidants-13-00049-f001" class="html-fig">Figure 1</a>. Differentially expressed genes (DEGs) had to meet the following criteria: adjusted <span class="html-italic">p</span>-value &lt; 0.05 and absolute FC &gt; 1.5. All background data (average log2 fold changes (FC) relative to the solvent control of the respective compound concentration for each transcript, including the standard deviation, and the statistical significance of the change) are found in <a href="#app1-antioxidants-13-00049" class="html-app">Supplementary Table S1</a>. (<b>A</b>) The filled circles indicate the number of DEGs. For clarity, the data point of the EC10<sub>V</sub> is shown with an extra green circle. As alternative approach, benchmark concentrations (BMC) were calculated in BMDExpress for all genes with a monotonic expression over the concentration series. The genes were ranked by potency and assigned a rank number. Then, the gene BMCs were plotted against rank orders to yield the function of the accumulation plot (empty circles, purple). The <span class="html-italic">x</span>-axis represents the concentration on the log molar scale. For Ber, the identity of three exemplary genes of the accumulation plot is indicated by the arrows (<b>B</b>) Concentration-response curves for the 3 exemplary genes shown in A. The <span class="html-italic">y</span>-axis represents fold changes (FC) on the log2 scale. Dotted vertical lines mark the BMC of the corresponding gene (indicated by color code). Data are means ± SD of independent experiments (N = 3). The <span class="html-italic">x</span>-axis represents concentrations on the log M scale.</p>
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<p>Effect of seven toxicants on glycolysis and mitochondrial respiration. Immature neurons (day 2 LUHMES) were exposed for 24 h to developmental neurotoxicants (DNT): 20 µM carbaryl (Car), 100 nM colchicine (Col), 15 µM berberine chloride (Ber), 10 µM diethylsylbestrol (DES), 1 µM rotenone (Rot), 10 µM MPP<sup>+</sup> and 10 nM valinomycin (Val). (<b>A</b>) Bar plots show the neuronal lactate production and (<b>B</b>) the glucose consumption rates, as determined from medium measurements. The ratios between these two parameters are displayed in <a href="#app1-antioxidants-13-00049" class="html-app">Figure S7</a>. Independent replicates are represented as dots. Data are means of three independent replicates ± SD. Statistical differences between the treatments and the control were evaluated by an analysis of variance (ANOVA) followed by Dunnett’s post hoc test (<span class="html-italic">p</span> &lt; 0.05, indicated by #). (<b>C</b>) Effect on mitochondrial respiration was investigated through the “Mito stress” test at the indicated concentrations. (<b>D</b>,<b>E</b>) The compounds showing activity on mitochondrial respiration were further tested in concentration series up to the highest non-cytotoxic concentration (EC10V), from which the basal respiration parameter was derived. Intracellular ATP was measured for the five OCR-modulating compounds and plotted together with viability (V), neurite area (NA), basal respiration (BR) as concentration–response curves. For Val, the coupling efficiency is also displayed. The ATP measurements for the 2 non-mitochondrial toxicants are shown in <a href="#app1-antioxidants-13-00049" class="html-app">Figure S7</a>. Data are means ± SEM of independent replicates. AntiA—antimycin A; DES—diethylstilbestrol; FCCP—carbonyl cyanide-p-trifluoromethoxy-phenylhydrazone; MPP<sup>+</sup>—1-methyl-4-phenylpyridinium.</p>
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<p>Effects of toxicants on tubulin polymerization. A biochemical assay based on purified tubulin protein monomers was used to study tubulin polymerization in the absence or presence of toxicants. (<b>A</b>) The polymerization assay was performed at 30 °C in the presence of the test compounds (20 µM carbaryl (Car), 100 nM colchicine (Col), 15 µM berberine chloride (Ber), 10 µM diethylsylbestrol (DES), 1 µM rotenone (Rot), 10 µM MPP<sup>+</sup> and 10 nM valinomycin (Val)). The reaction was monitored continuously in a spectrophotometer at 340nm (arbitrary units, AU) for 30 min. Nocodazole (Noc, 1 µM) served as a positive control. The negative control treatment contained 0.5% DMSO and was used for background correction. Exemplary curves are shown. (<b>B</b>) The area under the curve (AUC) was calculated for three independent replicates and displayed as bar plots. (<b>C</b>) The concentration-dependent inhibition of microtubule polymerization was assessed for Rot, DES and Col at various concentrations. Data were normalized to the DMSO control, and the concentration leading to a half-maximal inhibition (EC50) was determined from curve fits. Data are means of three independent replicates ± SD. Statistical differences between the treatments and the control were evaluated by ANOVA followed by Dunnett’s post hoc test (<span class="html-italic">p</span> &lt; 0.05, indicated by #). More data are found in <a href="#app1-antioxidants-13-00049" class="html-app">Figure S8</a>. FC—fold change.</p>
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<p>Intracellular amino acid changes in immature neurons exposed to neurodevelopmental toxicants. LUHMES cells (d2) were exposed for 24 h to putative neurodevelopmental toxicants: 10 µM berberine chloride (Ber), 10 µM MPP<sup>+</sup>, 1 µM rotenone (Rot), 20 µM carbaryl (Car), 100 nM colchicine (Col), 10 µM diethylstilbestrol (DES) and 10 nM valinomycin (Val). Cell lysates were prepared and intracellular amino acid levels were measured by HPLC. Data were normalized to the solvent control (DMSO). Changes in intracellular amino acid levels are displayed as fold changes (FC) for selected toxicants (Ber, MPP<sup>+</sup>, Rot and Val). Data are means ± SD from at least three independent experiments.</p>
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<p>Overview of metabolic changes in immature neurons exposed to mitochondrial toxicants. LUHMES cells (d2) were exposed to 12.5 µM berberine (Ber), 10 µM MPP<sup>+</sup> (MPP) or 0.5 µM rotenone (Rot) for 24 h. Intracellular metabolite concentrations were determined by LC-MS. Significant metabolite changes (vs solvent control) were identified for each treatment condition. (<b>A</b>) Principal component analysis (PCA) of the whole data set: the axes are scaled according to the variances covered. Each biological replicate is displayed with a different symbol. (<b>B</b>) Number of up-regulated (yellow) and down-regulated (blue) metabolites reported as % of all (369) detected metabolites (y-axis) and as absolute numbers (above bars). (<b>C</b>) The changes of metabolites triggered by Ber and MPP were related to one another in a scatter plot. All detected metabolites, besides lipids, dipeptides, NA-AA and nucleotides are shown (n = 180). (<b>D</b>) The heatmap displays all metabolites changed significantly by at least two toxicants (n = 85). Note that lipids, dipeptides, NA-AA and nucleotides are separately displayed in <a href="#app1-antioxidants-13-00049" class="html-app">Figure S10</a>. Yellow colors indicate up-regulation (saturated yellow = 4-fold), purple colors indicate down-regulations (saturated purple = 0.25-fold). Asterisks indicate the significance levels. Exact numbers are found in <a href="#app1-antioxidants-13-00049" class="html-app">Supplementary Table S1</a>. (<b>E</b>) Significantly changed metabolites (except lipids) were contrasted against the KEGG database, to find over-represented pathways. All pathways that are listed have <span class="html-italic">p</span>-values &lt; 0.05 and are ordered by significance. After Benjamini–Hochberg correction for multiple testing, only the pathways above the dotted line had an adjusted <span class="html-italic">p</span>-value &lt; 0.05. Pathways were grouped into four categories: classical amino acid (AA) metabolism (purple), AA-related metabolism (green), energy metabolism (orange) and other (black). * adjusted <span class="html-italic">p</span>-value ≤ 0.05; ** adjusted <span class="html-italic">p</span>-value ≤ 0.01; *** adjusted <span class="html-italic">p</span>-value ≤ 0.001; # Nicotinate and nicotinamide metabolism (original KEGG pathway name); fMet—N-formylmethionine; ETC—electron transport chain; GSH—reduced glutathionine; log2 FC—fold-change on a log2 scale; NADH—reduced nicotinamide adenine dinucleotide; N-lac-AA—N-lactoylated-amino acids; TAG—triacylglycerol.</p>
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<p>Consensus transcriptome changes induced by c-I inhibitors in neurons. Transcriptome data were obtained from LUHMES cells treated with berberine (Ber), rotenone (Rot) and MPP (MPP<sup>+</sup>) for 4 h, 16 h and 24 h, as outlined in <a href="#antioxidants-13-00049-f001" class="html-fig">Figure 1</a>A. Differentially expressed genes were identified (see full details in <a href="#app1-antioxidants-13-00049" class="html-app">Supplementary Table S1</a>). For each compound and gene, the expression change was followed over time, and only the peak data (defined by significance) were kept as a dimensionality-reduced data set. Next, the genes with an adjusted <span class="html-italic">p</span>-value ≤ 0.05 and an absolute fold change (FC) &gt; 1.5 (differentially expressed in ≥ two out of the three toxicant treatments) were selected for further analysis. (<b>A</b>) The up-regulated “consensus genes” (sorted in ascending order of the Ber FC values) are visualized in a heatmap. The color scale spans from blue (four-fold down) over white (no regulation) to red (four-fold up). Genes were mapped to functional categories, marked by different colors: cell cycle regulation and mitosis (green), mitochondrial stress response orchestrated by ATF4 (orange), oxidative stress response orchestrated by NRF2 (red-bold), inflammation (blue, i), DNA repair (blue, r), heat shock/toxicity response and disease/death pathways (purple) and carbon metabolism (black dot). The down-regulated “consensus genes” derived from the same analysis are separately displayed in <a href="#app1-antioxidants-13-00049" class="html-app">Figure S11</a>. (<b>B</b>) The biological pathways from the KEGG databank were analyzed for overrepresentation in the gene set shown in A. For the top 10 over-represented pathways, the significance (<span class="html-italic">y</span>-axis: -log2 adjusted <span class="html-italic">p</span>-value) was plotted against the enrichment ratio (<span class="html-italic">x</span>-axis). The dotted horizontal line marks the adjusted <span class="html-italic">p</span>-value of 0.05. (<b>C</b>) Venn diagrams of pathways from B) were used to display the relationships between the overrepresented gene sets. Not shown are the genes assigned to alcoholism (PPP1CC and histones) and systemic lupus (histones and C1S). (<b>D</b>) The levels of total eIF2 alpha protein and of phosphorylated eIF2 alpha-pSer51 were measured by a modified immunoblot (DigiWest). Individual samples are represented as dots; dot symbols indicate paired biological replicates. Data are means ± SD. Statistical analysis was performed vs. untreated controls (ANOVA followed by Dunnett’s post-hoc test); #: <span class="html-italic">p</span> &lt; 0.05.</p>
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<p>Overview of the changes related to the neuronal amino acid metabolism after exposure to c-I inhibitors. The scheme was constructed to visualize potential connections of the altered amino acid metabolism with neurotoxicity (dotted red arrows), with nitrogen elimination (purple, “N”) and with disposal of excess reducing equivalents (yellow, “H”). The metabolites that are indicated were measured after 24 h incubation of LUHMES cells with berberine chloride (12.5 µM, 4 independent experiments). Boxed compounds are proteinogenic amino acids. Red labelling indicates a significant cellular accumulation, blue labelling a depletion (adjusted <span class="html-italic">p</span> &lt; 0.05); black indicates metabolites that were not altered significantly. Metabolites which were not quantified (but help to understand the metabolic map), are depicted in gray. All metabolites that changed &gt; 1.5 fold are marked by “bold” formatting. Essentially similar data were obtained for Rot (0.5 µM) and MPP (10 µM). Details are found in <a href="#app1-antioxidants-13-00049" class="html-app">Supplementary Table S1</a>. (<b>A</b>) Contextualization of reactions and metabolites directly related to neurotoxicity. (<b>B</b>) Focus on the cellular need to dispose of nitrogen. Exemplary compounds that incorporate nitrogen from other amino acids, and that therefore can act as intermediate “nitrogen sink” (or as export vehicles) are shown. Reactions in (<b>A</b>) labelled with “N” would end up in the same nitrogen sink. α-aminoAdip—2-aminoadipate; α-kB—α-ketobutyrate; α-ketoAdip—α-ketoadipate; α-KG—α-ketoglutarate; α-KGM—α-ketoglutaramate; ArgSucc—argininosuccinate; C1—1-carbon metabolism; Cysta—cystathionine; EMA—ethylmalonate; fMet—N-formylmethionine; Fum—fumarate; “H”—NAD(P)H; imidazole-Ac—4-imidazoleacetate; indoleLac—indolelactate; KIC—α-keto-Leu (keto-isocaproate); KIV—α-keto-Val (keto-isovalerate); KMV—α-keto-Ile (keto-methylvalerate); OH-phe-Lac—3-(4-hydroxyphenyl)lactate; OH-phe-Pyr—4-hydroxyphenylpyruvate; 2-OH-glut—2-hydroxyglutarate; 5-oxo-Pro—5-oxoproline; Mal—malate; MetSO—methionine sulfoxide; “N”—-NH3; Orn—ornithine; SAH—S-adenosylhomocysteine; SAM—S-adenosylmethionine; Succ-CoA—succinyl-coenzyme A; T—transaminase.</p>
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<p>Altered TCA entry scenario upon c-I inhibition. LUHMES cells were exposed to three c-I inhibitors at equipotent concentration for 24 h: 12.5 µM berberine (red), 10 µM MPP<sup>+</sup> (light red) and 0.5 µM rotenone (blue). The bar graphs show the percent changes of the respective metabolites (compared to solvent controls). Data are means and individual data from four independent experiments. The arrows indicate the underlying metabolic pathways. The red crosses indicate reactions that seem to be blocked in the presence of c-I inhibitors. The green pluses indicate reactions that seem to be enhanced in the presence of c-I inhibitors. The tree inhibitors trigger “similar” changes, but the typical glycolytic enhancements (NADH and lactate up) are quantitatively different. CoA—coenzyme A; Ac—acetyl; OAA—oxaloacetic acid; TCA—tricarboxylic acid cycle.</p>
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<p>Shift of the primary metabolism of neurons by mitochondrial inhibitors. LUHMES cells (d2) were exposed to 12.5 µM berberine (Ber), 10 µM MPP<sup>+</sup> (MPP) or 0.5 µM rotenone (Rot) for 24 h before intracellular metabolites were extracted, analyzed and quantified. The data displayed refer to the treatment with Ber. Data on MPP and Rot are found in <a href="#app1-antioxidants-13-00049" class="html-app">Supplementary Table S1</a>, and their pattern was largely similar to that shown for Ber. The grey arrows depict standard/canonical cellular metabolism, such as the oxidative tricarboxylic acid (TCA) cycle (bottom), and glycolysis feeding it (top). Strict compartmental separations of pathways are not indicated, as the analysis approach used did not separate compartments. However, the upper part of the diagram represents mainly cytosolic processes, the TCA reactions are in mitochondria, and some of its metabolites are indicated twice in order to make clear that they are also found in the cytosol (transitions indicated in yellow), and may have different concentrations in each compartment. Color codes were used for compounds that were significantly (adjusted <span class="html-italic">p</span>-value ≤ 0.05) up-regulated (red) or down-regulated (blue). Compounds that were not quantified, or for which the concentration was unclear (e.g., mitochondrial malate) are shown in black (the total cellular concentration of malate is indicated elsewhere). Strong and significant regulations (fold change ≥ 2) are marked in bold and very strong changes (fold change ≥ 3) are underlined. Green arrows indicate pathways that get rid of excess reducing equivalents (NADH). Dashed versions indicate reactions leading to these reactions or following from such reactions. Dark pink arrows indicate the pathway of “reductive carboxylation”, which allows conversion of glutamine to citrate and malate/fumarate by using NADH (instead of generating NADH). The purple arrows indicate hypothetical pathways that we suggest to be activated under conditions of electron transfer chain inhibition. The grey triangle indicates reactions performed by a “cytosolic hydride transfer complex” that regenerates NAD+ from NADH. “E/In” indicates a potential import or export across the cell membrane. Three isolated groups of compounds (not linked here to the carbon metabolism network) shown refer to: GSH and its precursors, NADH and its metabolites and the mitochondrial energy buffer creatine phosphate and its metabolites. Dotted green arrows indicate reactions using up reducing equivalents, but that were considered unlikely to take place. 3PG—3-phosphoglycerate; Aco—aconitate; Arg-Suc—argininosuccinate; Cit—citrate; Cit-Glu—citrylglutamate; Citr—citrulline; Cr—creatine; CrN—creatinine; CrP—creatine phosphate; Cys-Gly—cysteinyl-glycine; DHAP: dihydroxyacetonephosphate; Fruc—fructose; Fum—fumarate; G3P—glycerol-3-phosphate; GA: glyceraldehyde; GAP—glyceraldehyde phosphate; GSH—glutathione reduced; Glc—glucose; Ino—myo-inositol; Iso—isocitrate; Lac—lactate; Mal—malate; NA-riboR—nicotinamide-riboside; NAC—N-acetylcysteine; NAD(P)H—reduced nicotinamide adenine dinucleotide (phosphate); NAM—nicotinamide; NMN—nicotinamide ribonucleotide; NO—nitric oxide; OAA—oxaloacetate; OH-Glutar—2-hydroxyglutarate; Orn—ornithine; P-Ser—phosphorylserine; PEP—phosphoenolpyruvate; PPP—pentose phosphate pathway—represented by erythronate; Pyr—pyruvate; Succ—succinate; SuccCoA—succinyl-CoA; αKG—α-ketoglutarate.</p>
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22 pages, 3769 KiB  
Article
Soil Organic Matter Composition and pH as Factors Affecting Retention of Carbaryl, Carbofuran and Metolachlor in Soil
by Irmina Ćwieląg-Piasecka
Molecules 2023, 28(14), 5552; https://doi.org/10.3390/molecules28145552 - 20 Jul 2023
Cited by 6 | Viewed by 1654
Abstract
The majority of studies concerning the environmental behavior of hydrophobic pollutants in soil consider soil organic matter (SOM) content as a main factor influencing chemical retention, whereas the composition of SOM and its individual fraction share are often neglected. In the present paper, [...] Read more.
The majority of studies concerning the environmental behavior of hydrophobic pollutants in soil consider soil organic matter (SOM) content as a main factor influencing chemical retention, whereas the composition of SOM and its individual fraction share are often neglected. In the present paper, carbaryl, carbofuran and metolachlor retention by loamy sand and loam topsoil materials is compared and referred to humic acids (CHA) and the residual carbon (CR) content of SOM. Additionally, the sorption-desorption behavior of agrochemicals in soils was tested at a pH of three to seven. Calculated isothermal parameters point to favorable, spontaneous and physical pesticide sorption. Groundwater ubiquity score (GUS) indexes confirmed the low leaching ability of metolachlor on soils and moderate of carbofuran. The high affinity of carbaryl to CR may explain its pronounced sorption in loam soil and the lowest percolation potential. Carbofuran retention in soils was associated with montmorillonite (Mt) and CR fractions. Meanwhile, metolachlor uptake was related to humic acid and Mt content of the soils. Lower pH enhanced retention of the agrochemicals, except for carbaryl sorption in sandy loam soil. Results of this study highlight that SOM composition and mutual share of individual organic carbon fractions alongside pH may play a crucial role in predicting non-ionic pesticide behavior in soil. Full article
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<p>Adsorption isotherms of carbaryl, carbofuran and metolachlor on soil L (<b>a</b>) and soil C (<b>b</b>). Error bars represent standard deviation of triplicate samples.</p>
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<p>Comparison of the experimental data with the Langmuir, Freundlich, Temkin and Dubinin–Radushkevich isotherms for the adsorption of carbaryl (<b>a</b>,<b>b</b>), carbofuran (<b>c</b>,<b>d</b>) and metolachlor (<b>e</b>,<b>f</b>) in soils L and C.</p>
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<p>Variation of the separation factor (R<sub>L</sub>) in relation to initial concentration range of the studied (<b>a</b>) carbaryl, (<b>b</b>) carbofuran, (<b>c</b>) metolachlor on soils L and C.</p>
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<p>Sorption magnitude of carbaryl (<b>a</b>), carbofuran (<b>b</b>) and metolachlor (<b>c</b>) in soils L and C. Error bars represent standard deviation of triplicate samples. Different letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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<p>Comparison of carbaryl (<b>a</b>,<b>b</b>), carbofuran (<b>c</b>,<b>d</b>) and metolachlor (<b>e</b>,<b>f</b>) mass sorbed and desorbed in relation to different initial pesticide concentrations added to soils L and C. Error bars represent standard deviation of triplicate samples.</p>
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<p>Sorption of non-ionic pesticides: (<b>a</b>) carbaryl, (<b>b</b>) carbofuran, (<b>c</b>) metolachlor, studied under a pH range of three to seven in L and C soils. Results are expressed as mean values ± standard deviation (n = 3). Different letters indicate significant differences (<span class="html-italic">p</span> &lt; 0.05).</p>
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14 pages, 3306 KiB  
Article
Deashed Wheat-Straw Biochar as a Potential Superabsorbent for Pesticides
by Irmina Ćwieląg-Piasecka, Elżbieta Jamroz, Agnieszka Medyńska-Juraszek, Magdalena Bednik, Bogna Kosyk and Nora Polláková
Materials 2023, 16(6), 2185; https://doi.org/10.3390/ma16062185 - 9 Mar 2023
Cited by 14 | Viewed by 3957
Abstract
Biochar activation methods have attracted extensive attention due to their great role in improving sorptive properties of carbon-based materials. As a result, chemically modified biochars gained application potential in the purification of soil and water from xenobiotics. This paper describes changes in selected [...] Read more.
Biochar activation methods have attracted extensive attention due to their great role in improving sorptive properties of carbon-based materials. As a result, chemically modified biochars gained application potential in the purification of soil and water from xenobiotics. This paper describes changes in selected physicochemical properties of high-temperature wheat-straw biochar (BC) upon its deashing. On the pristine and chemically activated biochar (BCd) retention of five pesticides of endocrine disrupting activity (carbaryl, carbofuran, 2,4-D, MCPA and metolachlor) was studied. Deashing resulted in increased sorbent aromaticity and abundance in surface hydroxyl groups. BCd exhibited more developed meso- and microporosity and nearly triple the surface area of BC. Hydrophobic pesticides (metolachlor and carbamates) displayed comparably high (88–98%) and irreversible adsorption on both BCs, due to the pore filling, whereas the hydrophilic and ionic phenoxyacetic acids were weakly and reversibly sorbed on BC (7.3 and 39% of 2,4-D and MCPA dose introduced). Their removal from solution and hence retention on the deashed biochar was nearly total, due to the increased sorbent surface area and interactions of the agrochemicals with unclogged OH groups. The modified biochar has the potential to serve as a superabsorbent, immobilizing organic pollutant of diverse hydrophobicity from water and soil solution. Full article
(This article belongs to the Special Issue Biochar and Carbon-Based Materials: Properties and Applications)
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<p>Infrared absorption spectra of the BC (solid line) and BCd (dashed line) biochars.</p>
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<p>Scanning electron microscopy images amplified 40 times (<b>a</b>,<b>b</b>) and 500 times (<b>c</b>,<b>d</b>) of the pristine (<b>a</b>,<b>c</b>) and deashed biochar (<b>b</b>,<b>d</b>). Ash particles are highlighted in red.</p>
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<p>Comparison of the sorption magnitude of the tested pesticides on biochars before (BC) and after ash removal (BCd). Error bars represent ± values of the standard deviations of triplicate samples. Letters a and b indicate significant differences between sorption magnitude of each pesticide on pristine and deashed biochar (<span class="html-italic">p</span> &lt; 0.05).</p>
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17 pages, 6051 KiB  
Article
Fabrication of High Surface Area TiO2-MoO3 Nanocomposite as a Photocatalyst for Organic Pollutants Removal from Water Bodies
by Fatima Abla, Yehya Elsayed, Nedal Abu Farha, Khaled Obaideen, Ahmed A. Mohamed, Haesung Lee, Changseok Han, Mehmet Egilmez and Sofian Kanan
Catalysts 2023, 13(2), 362; https://doi.org/10.3390/catal13020362 - 7 Feb 2023
Cited by 8 | Viewed by 2077
Abstract
A nanocomposite (NC) of titanium (IV) oxide (TiO2) and molybdenum (VI) oxide (MoO3) was synthesized using a hydrothermal route. Detailed analyses using transmission electron microscopy, X-ray diffraction, X-ray fluorescence (XRF), Brunauer–Emmett–Teller (BET) isotherms, X-ray photoelectron spectroscopy, Raman, and diffuse [...] Read more.
A nanocomposite (NC) of titanium (IV) oxide (TiO2) and molybdenum (VI) oxide (MoO3) was synthesized using a hydrothermal route. Detailed analyses using transmission electron microscopy, X-ray diffraction, X-ray fluorescence (XRF), Brunauer–Emmett–Teller (BET) isotherms, X-ray photoelectron spectroscopy, Raman, and diffuse reflectance infrared Fourier transform spectroscopy were carried out and confirmed the successful formation of pure TiO2-MoO3 (Ti-Mo) NC. The Ti-Mo NC possesses sizes in the range of 150–500 nm. XPS, Raman, and DRIFT shift measurements confirmed the formation of mixed oxide linkage in the form of Ti-O-Mo. Sorption of nitrogen isotherms revealed a significant increase in the number and pore widths of mesopores in the NC. Water sorption isotherms revealed enhanced affinity of the nanocomposites for water relative to the pure metal oxides. The BET surface area for Ti-Mo NC from the nitrogen adsorption isotherm was 129.3 m2/g which is much higher than the pure metal oxides (i.e., 37.56 m2/g for TiO2 and 2.21 m2/g for MoO3). The Ti-Mo NC provided suitable adsorption sites that captured the studied carbamates from the solution and promoted their photodegradation process. The photocatalytic degradation of MB in the presence of the catalyst was enhanced by 2.9 and 5.5 folds upon irradiation with white LED and 302 nm UV light sources, respectively. Full article
(This article belongs to the Special Issue Advances in Photocatalysis for the Degradation of Organic Pollutants)
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<p>(<b>A</b>) SEM image of Ti-Mo NC and (<b>B</b>) a representative EDX spectrum obtained from individual particles.</p>
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<p>(<b>A</b>–<b>C</b>) TEM images of MoO<sub>3</sub>, (<b>E</b>–<b>G</b>) Ti-Mo NC, (<b>D</b>) SAED patterns of MoO<sub>3</sub>, and (<b>H</b>) Ti-Mo NC.</p>
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<p>(<b>A</b>) X-ray diffraction pattern of Ti-Mo NC; inset: zoomed-in view of lower angle ranges for clarification of the peak broadening. The (<b>B</b>,<b>C</b>) show the reference spectra of constituent TiO<sub>2</sub> and MoO<sub>3</sub> powders.</p>
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<p>Surface analyses for Ti-Mo NC (<b>A</b>) XPS and (<b>B</b>) XRF spectra.</p>
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<p>Surface analyses for Ti-Mo NC: (<b>A</b>) Raman spectroscopy, (<b>B</b>) DRIFT spectroscopy, and (<b>C</b>) FTIR spectroscopy for thin film surface exposed to pyridine vapor (I) followed by evacuation for 2 min (II).</p>
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<p>Sorption of nitrogen isotherms for the pure oxides and Ti-Mo NC.</p>
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<p>Pore size distribution of the as-prepared metal oxide samples.</p>
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<p>Sorption of water isotherms for the pure oxides and Ti-Mo NC.</p>
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<p>Correlation between the volumes of water adsorbed at different relative pressures and the total pore volume.</p>
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<p>SSLS of the studied pesticides along with the Langmuir adsorption profile obtained for (<b>A</b>) carbaryl and (<b>B</b>) fenoxycarb.</p>
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<p>MB irradiated with 75 W LED white and 302 UV lights: (<b>A</b>) The SSLS of MB in the presence of Ti−Mo NC irradiated with 302 nm light for various times; (<b>B</b>) UV−Vis spectra of the MB/Ti−Mo NC ir-radiated with 302 nm UV for various times; and (<b>C</b>) photodegradation profile of MB alone irra-diated 75 W LED (a) and 302 nm (b) lights along with the MB/Ti−Mo NC irradiated with LED light (c) and 302 nm UV (d).</p>
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<p>MB irradiated with 75 W LED white and 302 UV lights: (<b>A</b>) The SSLS of MB in the presence of Ti−Mo NC irradiated with 302 nm light for various times; (<b>B</b>) UV−Vis spectra of the MB/Ti−Mo NC ir-radiated with 302 nm UV for various times; and (<b>C</b>) photodegradation profile of MB alone irra-diated 75 W LED (a) and 302 nm (b) lights along with the MB/Ti−Mo NC irradiated with LED light (c) and 302 nm UV (d).</p>
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<p>Photodegradation products obtained for the studied organic pollutants.</p>
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<p>Schematic for the photocatalytic mechanism for MB degradation of the Ti−Mo NC.</p>
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20 pages, 3065 KiB  
Article
Phytoextraction Potential of Sunn Hemp, Sunflower, and Marigold for Carbaryl Contamination: Hydroponic Experiment
by Najjapak Sooksawat, Duangrat Inthorn, Apisit Chittawanij, Alisa Vangnai, Pornpimol Kongtip and Susan Woskie
Int. J. Environ. Res. Public Health 2022, 19(24), 16482; https://doi.org/10.3390/ijerph192416482 - 8 Dec 2022
Cited by 3 | Viewed by 1550
Abstract
The phytoextraction ability and responses of sunn hemp, sunflower, and marigold plants were investigated toward carbaryl insecticide at 10 mg L−1 and its degradative product (1-naphthol). All test plants exhibited significant carbaryl removal capability (65–93%) with different mechanisms. Marigold had the highest [...] Read more.
The phytoextraction ability and responses of sunn hemp, sunflower, and marigold plants were investigated toward carbaryl insecticide at 10 mg L−1 and its degradative product (1-naphthol). All test plants exhibited significant carbaryl removal capability (65–93%) with different mechanisms. Marigold had the highest translocation factor, with carbaryl taken up, translocated and accumulated in the shoots, where it was biotransformed into 1-naphthol. Consequently, marigold had the least observable toxicity symptoms caused by carbaryl and the highest bioconcentration factor (1848), indicating its hyperaccumulating capability. Sunflower responded to carbaryl exposure differently, with the highest carbaryl accumulation (8.7 mg kg−1) in roots within 4 days of cultivation, leading to a partial toxicity effect. Sunn hemp exhibited severe toxicity, having the highest carbaryl accumulation (91.7 mg kg−1) that was biotransformed to 1-naphthol in the sunn hemp shoots. In addition, the different models were discussed on plant hormone formation in response to carbaryl exposure. Full article
(This article belongs to the Section Environmental Science and Engineering)
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<p>Toxicity in sunn hemp after 4 days of cultivation for carbaryl treatments at 0 mg L<sup>−1</sup> (<bold>a</bold>), 5 mg L<sup>−1</sup> (<bold>b</bold>), 10 mg L<sup>−1</sup> (<bold>c</bold>), after 8 days of cultivation for carbaryl at 5 mg L<sup>−1</sup> (<bold>d</bold>) and 10 mg L<sup>−1</sup> (<bold>e</bold>), after 12 days of cultivation for carbaryl at 5 mg L<sup>−1</sup> (<bold>f</bold>), and 10 mg L<sup>−1</sup> (<bold>g</bold>).</p>
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<p>Toxicity in sunflower after 4 days of cultivation for carbaryl treatments at 0 mg L<sup>−1</sup> (<bold>a</bold>), 5 mg L<sup>−1</sup> (<bold>b</bold>), and 10 mg L<sup>−1</sup> (<bold>c</bold>), after 12 days cultivation (control) (<bold>d</bold>), and gray roots for carbaryl at 10 mg L<sup>−1</sup> (<bold>e</bold>,<bold>f</bold>).</p>
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<p>Toxicity in marigold after 4 days of cultivation for carbaryl treatments at 0 mg L<sup>−1</sup> (<bold>a</bold>), 5 mg L<sup>−1</sup> (<bold>b</bold>), and 10 mg L<sup>−1</sup> (<bold>c</bold>), after 12 days of cultivation for carbaryl at 10 mg L<sup>−1</sup> (control) (<bold>d</bold>) and gray roots (<bold>e</bold>).</p>
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<p>Effect of carbaryl on pigment contents of plants after 4 days of cultivation for carbaryl treatments at 0, 5 or 10 mg L<sup>−1</sup>. Note: a, b, c, d, and e indicate significant differences based on ANOVA and Tukey’s HSD test. The data shown are mean and SD.</p>
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<p>Effect of carbaryl on plant physiology after 4 days of cultivation for carbaryl treatments at 0, 5 and 10 mg L<sup>−1</sup>; relative growth rate (<bold>a</bold>), shoot length (<bold>b</bold>), root length (<bold>c</bold>), leaf number (<bold>d</bold>), shoot weight (<bold>e</bold>), and root weight (<bold>f</bold>). Note: a, b, c, and d indicate significant differences based on ANOVA and Tukey’s HSD test. The data shown are mean and SD.</p>
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<p>Carbaryl and 1-naphthol accumulation in plants after 4 days of cultivation for carbaryl treatments at 5 and 10 mg L<sup>−1</sup>; shoot accumulation (<bold>a</bold>), root accumulation (<bold>b</bold>), uptake capacity (<bold>c</bold>), and translocation factor (<bold>d</bold>). Note: a, b, c, d, and e indicate significant differences based on ANOVA and Tukey’s HSD test. The data shown are mean and SD.</p>
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<p>Carbaryl degradation in shoots and roots of sunn hemp, sunflower, and marigold after 4, 8, and 12 days of cultivation; carbaryl in shoots (<bold>a</bold>), 1-naphthol in shoots (<bold>b</bold>); and carbaryl in roots (<bold>c</bold>). Note: a, b, c, d, e, and f indicate significant differences based on ANOVA and Tukey’s HSD test. The data shown are mean and SD.</p>
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<p>Seed germination test for the length of shoot and root of sunn hemp and marigold exposed to carbaryl concentrations of 0, 5 and 10 mg L<sup>−1</sup>. Note: a, b and c indicate significant differences based on ANOVA and a Tukey HSD test. The data shown are mean and SD.</p>
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17 pages, 8391 KiB  
Article
Structural and Optical Properties of Graphene Quantum Dots−Polyvinyl Alcohol Composite Thin Film and Its Potential in Plasmonic Sensing of Carbaryl
by Nurul Illya Muhamad Fauzi, Yap Wing Fen, Faten Bashar Kamal Eddin and Wan Mohd Ebtisyam Mustaqim Mohd Daniyal
Nanomaterials 2022, 12(22), 4105; https://doi.org/10.3390/nano12224105 - 21 Nov 2022
Cited by 6 | Viewed by 2573
Abstract
In this study, graphene quantum dots (GQDs) and polyvinyl alcohol (PVA) composite was prepared and then coated on the surface of gold thin film via the spin coating technique. Subsequently, Fourier transform infrared spectroscopy (FT-IR), atomic force microscopy (AFM), and ultraviolet-visible spectroscopy (UV–Vis) [...] Read more.
In this study, graphene quantum dots (GQDs) and polyvinyl alcohol (PVA) composite was prepared and then coated on the surface of gold thin film via the spin coating technique. Subsequently, Fourier transform infrared spectroscopy (FT-IR), atomic force microscopy (AFM), and ultraviolet-visible spectroscopy (UV–Vis) were adopted to understand the structure, surface morphology, and optical properties of the prepared samples. The FT-IR spectral analysis revealed important bands, such as O–H stretching, C=O stretching, C-H stretching, and O=C=O stretching vibrations. The surface roughness of the GQDs-PVA composite thin film was found to be increased after exposure to carbaryl. On the other hand, the optical absorbance of the GQDs-PVA thin film was obtained and further analysis was conducted, revealing a band gap Eg value of 4.090 eV. The sensing potential of the thin film was analyzed using surface plasmon resonance (SPR) spectroscopy. The findings demonstrated that the developed sensor’s lowest detection limit for carbaryl was 0.001 ppb, which was lower than that previously reported, i.e., 0.007 ppb. Moreover, other sensing performance parameters, such as full width at half maximum, detection accuracy, and signal-to-noise ratio, were also investigated to evaluate the sensor’s efficiency. Full article
(This article belongs to the Section Nanoelectronics, Nanosensors and Devices)
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<p>The schematic preparation of the composite solution.</p>
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<p>Steps involved in thin film preparation process.</p>
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<p>Schematic of surface plasmon resonance setup.</p>
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<p>FTIR spectra of GQDs, PVA, and GQDs-PVA.</p>
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<p>AFM images of GQDs thin film: (<b>a</b>) 3D image and (<b>b</b>) 2D image.</p>
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<p>AFM images of PVA thin film: (<b>a</b>) 3D image and (<b>b</b>) 2D image.</p>
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<p>AFM images of GQDs-PVA thin film: (<b>a</b>) 3D image and (<b>b</b>) 2D image.</p>
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<p>AFM images of GQDs-PVA thin film after contact with carbaryl: (<b>a</b>) 3D image and (<b>b</b>) 2D image.</p>
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<p>UV-Vis absorption spectra of GQDs, PVA, and GQDs-PVA thin films.</p>
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<p>UV-Vis band gap energy of GQDs thin film.</p>
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<p>UV-Vis band gap energy of PVA thin film.</p>
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<p>UV-Vis band gap energy of GQDs-PVA thin film.</p>
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<p>SPR reflectance curve as a function of incidence angle for gold thin film in the detection of different concentrations of carbaryl.</p>
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<p>SPR reflectance curve as a function of incidence angle for GQDs-PVA thin film in the detection of different concentrations of carbaryl.</p>
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<p>Illustration of FWHM, which represents half of the maximum value on the reflectance curve (for deionized water).</p>
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<p>Graph of FWHM and DA versus concentration of carbaryl.</p>
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<p>Graph of SNR versus carbaryl concentration.</p>
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20 pages, 5051 KiB  
Article
Human Exposure to Pesticides in Dust from Two Agricultural Sites in South Africa
by Céline Degrendele, Roman Prokeš, Petr Šenk, Simona Rozárka Jílková, Jiří Kohoutek, Lisa Melymuk, Petra Přibylová, Mohamed Aqiel Dalvie, Martin Röösli, Jana Klánová and Samuel Fuhrimann
Toxics 2022, 10(10), 629; https://doi.org/10.3390/toxics10100629 - 21 Oct 2022
Cited by 11 | Viewed by 2879
Abstract
Over the last decades, concern has arisen worldwide about the negative impacts of pesticides on the environment and human health. Exposure via dust ingestion is important for many chemicals but poorly characterized for pesticides, particularly in Africa. We investigated the spatial and temporal [...] Read more.
Over the last decades, concern has arisen worldwide about the negative impacts of pesticides on the environment and human health. Exposure via dust ingestion is important for many chemicals but poorly characterized for pesticides, particularly in Africa. We investigated the spatial and temporal variations of 30 pesticides in dust and estimated the human exposure via dust ingestion, which was compared to inhalation and soil ingestion. Indoor dust samples were collected from thirty-eight households and two schools located in two agricultural regions in South Africa and were analyzed using high-performance liquid chromatography coupled to tandem mass spectrometry. We found 10 pesticides in dust, with chlorpyrifos, terbuthylazine, carbaryl, diazinon, carbendazim, and tebuconazole quantified in >50% of the samples. Over seven days, no significant temporal variations in the dust levels of individual pesticides were found. Significant spatial variations were observed for some pesticides, highlighting the importance of proximity to agricultural fields or of indoor pesticide use. For five out of the nineteen pesticides quantified in dust, air, or soil (i.e., carbendazim, chlorpyrifos, diazinon, diuron and propiconazole), human intake via dust ingestion was important (>10%) compared to inhalation or soil ingestion. Dust ingestion should therefore be considered in future human exposure assessment to pesticides. Full article
(This article belongs to the Special Issue Environmental Exposure to Toxic Chemicals and Human Health)
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<p>Map of the sampling sites.</p>
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<p>Quantification frequencies of the individual pesticides in air, soil, and dust. Data on pesticides in air and soil were obtained from [<a href="#B68-toxics-10-00629" class="html-bibr">68</a>].</p>
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<p>Temporal variations of pesticide levels in dust samples (in ng g<sup>−1</sup>) collected at Hex River Valley (H) or Grabouw (G) at households living in farms (f), village (v), or at the school (s).</p>
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<p>Spatial variations in dust levels of pesticides (in ng g<sup>−1</sup>) among the different sites and areas from the samples collected on day 7 only (<span class="html-italic">n</span> = 41). H and G denote Hex River Valley and Grabouw, respectively. Some outliers are not shown for better visibility. Boxplots represent the 25–75th percentile, whiskers represent the minimum and maximum values (excluding outliers which are shown as the red crosses) and the line within the box represents the median value.</p>
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<p>Daily intake of pesticides (in pg kg<sup>−1</sup> day<sup>−1</sup>) via dust ingestion for children using the median concentrations measured and the median ingestion rate.</p>
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<p>Contribution of three exposure pathways (dust ingestion, soil ingestion, and inhalation) on the daily uptake of pesticides of children living at farm and village locations at Hex River Valley and Grabouw using the median concentrations. Blank columns corresponds to the cases when a pesticide was not quantified in air, soil, and dust.</p>
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13 pages, 3417 KiB  
Article
A Novel Paper-Based Electrochemical Biosensor Based on N,O-Rich Covalent Organic Frameworks for Carbaryl Detection
by Yawen Xiao, Na Wu, Li Wang and Lili Chen
Biosensors 2022, 12(10), 899; https://doi.org/10.3390/bios12100899 - 20 Oct 2022
Cited by 10 | Viewed by 2178
Abstract
A new N,O-rich covalent organic framework (COFDHNDA-BTH) was synthesized by an amine-aldehyde condensation reaction between 2,6-dialdehyde-1,5-dihydroxynaphthalene (DHNDA) and 1,3,5-phenyltriformylhydrazine (BTH) for carbaryl detection. The free NH, OH, and C=O groups of COFDHNDA-BTH not only covalently couples with acetylcholinesterase (AChE) into [...] Read more.
A new N,O-rich covalent organic framework (COFDHNDA-BTH) was synthesized by an amine-aldehyde condensation reaction between 2,6-dialdehyde-1,5-dihydroxynaphthalene (DHNDA) and 1,3,5-phenyltriformylhydrazine (BTH) for carbaryl detection. The free NH, OH, and C=O groups of COFDHNDA-BTH not only covalently couples with acetylcholinesterase (AChE) into the pores of COFDHNDA-BTH, but also greatly improves the catalytic activity of AChE in the constrained environment of COFDHNDA-BTH’s pore. Under the catalysis of AChE, the acetylthiocholine (ATCl) was decomposed into positively charged thiocholine (TCl), which was captured on the COFDHNDA-BTH modified electrode. The positive charges of TCl can attract anionic probe [Fe(CN)6]3−/4− on the COFDHNDA-BTH-modified electrode to show a good oxidation peak at 0.25 V (versus a saturated calomel electrode). The carbaryl detection can inhibit the activity of AChE, resulting in the decrease in the oxidation peak. Therefore, a turn-off electrochemical carbaryl biosensor based on a flexible carbon paper electrode loaded with COFDHNDA-BTH and AChE was constructed using the oxidation peak of an anionic probe [Fe(CN)6]3−/4− as the detection signal. The detection limit was 0.16 μM (S/N = 3), and the linear range was 0.48~35.0 μM. The sensor has good selectivity, repeatability, and stability, and has a good application prospect in pesticide detection. Full article
(This article belongs to the Special Issue Paper-Based Biosensors)
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<p>(<b>a</b>) SEM image of COF<sub>DHNDA-BTH</sub>. (<b>b</b>) TEM image of COF<sub>DHNDA-BTH</sub>. (<b>c</b>) FTIR spectra of DHNDA, BTH, and COF<sub>DHNDA-BTH</sub>. (<b>d</b>) Experimental and refined XRD pattern as well as the difference.</p>
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<p>(<b>a</b>) Low-resolution and (<b>b</b>) high-resolution SEM image of commercial carbon paper.</p>
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<p>(<b>a</b>) CVs and (<b>b</b>) EIS of GCE (curve a), COF<sub>DHNDA-BTH</sub>/GCE (curve b), and AChE/COF<sub>DHNDA-BTH</sub>/GCE (curve c) in 0.1 M KCl with 5.0 mM [Fe(CN)<sub>6</sub>]<sup>3</sup><sup>−/4−</sup>.</p>
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<p>(<b>a</b>) Effect of COF<sub>DHNDA-BTH</sub> dosage on AChE/COF<sub>DHNDA-BTH</sub>/GCE on peak current density of 5.0 mM [Fe(CN)<sub>6</sub>]<sup>3−/4−</sup>. (<b>b</b>) Plot of inhibition rate versus incubation time. (<b>c</b>) Plot of peak current density versus AChE concentration. (<b>d</b>) Relationship between concentration of ATCl and peak current density in 0.1 M KCl with 5.0 mM [Fe(CN)<sub>6</sub>]<sup>3−/4−</sup>.</p>
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<p>(<b>a</b>) CVs and (<b>c</b>) EIS (inset is its equivalent circuit) of AChE/COF<sub>DHNDA-BTH</sub>/GCE in 0.1 M KCl + 5.0 mM [Fe(CN)6]<sup>3−/4−</sup> with different concentrations of carbaryl. (<b>b</b>,<b>d</b>) Corresponding linear relationship curves.</p>
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<p>(<b>a</b>) CVs of AChE/COF<sub>DHNDA-BTH</sub>/portable paper-based electrode in 0.1 M KCl + 5.0 mM [Fe(CN)6]<sup>3−/4−</sup> with carbaryl different concentrations. (<b>b</b>) Corresponding linear relationship curve.</p>
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<p>(<b>a</b>) Selectivity test of electrochemical carbaryl biosensor. (<b>b</b>) Current response histogram of electrochemical carbaryl biosensor measured for 30 consecutive days of carbaryl. (<b>c</b>) Current response histogram of six AChE/COF<sub>DHNDA-BTH</sub>/GCE to detect carbaryl.</p>
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<p>Schematic diagram of preparation process of COF<sub>DHNDA-BTH</sub> and detection principle of electrochemical sensor based on AChE/COF<sub>DHNDA-BTH</sub>/GCE.</p>
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<p>Preparation process of paper-based electrode and the picture of obtained paper-based electrode.</p>
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13 pages, 1967 KiB  
Article
Development of a Sensitive and Fast Determination Method for Trace Carbaryl Residues in Food Samples Based on Magnetic COF (TpPa-NH2)@Fe3O4 Nanoparticles and Fluorescence Detection
by Juanli Du, Hao Wu, Xu Jing, Yonghe Yu, Zhisheng Yan and Jianhai Zhang
Foods 2022, 11(19), 3130; https://doi.org/10.3390/foods11193130 - 8 Oct 2022
Cited by 6 | Viewed by 2231
Abstract
Developing a simple and effective method for measuring carbaryl residues in food is urgent due to its widespread use and the associated health risks in agriculture, as well as various defects in existing detection techniques. The COF (TpPa-NH2)@Fe3O4 [...] Read more.
Developing a simple and effective method for measuring carbaryl residues in food is urgent due to its widespread use and the associated health risks in agriculture, as well as various defects in existing detection techniques. The COF (TpPa-NH2)@Fe3O4 nanocomposite (amino modification) was synthesized via a two-step method and used as an adsorbent for the extraction of carbaryl from food samples in this study. The results indicated that COF (TpPa-NH2)@Fe3O4 can rapidly and successfully capture carbaryl directly from samples via π–π stacking and hydrophobic interactions, achieving maximum adsorption within 5 min under a small adsorbent quantity using a fluorescence spectrophotometer. Under the optimized conditions, carbaryl exhibited good linearity in the range of 0.2–120 µg·kg1, and the limit of detection was 0.012 µg·kg−1. The recoveries of the samples were 96.0–107.4%. This method has broad application prospects for the monitoring of carbaryl in food. Full article
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<p>Selection of magnetic material. To conclude, COF (TpPa-NH<sub>2</sub>) @Fe<sub>3</sub>O<sub>4</sub> was the best extractant (50 mL of sample solution, c = 100 ng·mL<sup>−1</sup>, extraction time: 5 min, desorption time: 6 min, <span class="html-italic">n</span> = 3).</p>
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<p>Optimization of the MSPE method (50 mL of sample solution, c = 100 ng·mL<sup>−1</sup>, <span class="html-italic">n</span> = 3): (<b>A</b>) effect of sample solution pH; (<b>B</b>) effect of adsorbent amount; (<b>C</b>) effect of the extraction time; and (<b>D</b>) effect of desorption solvent type.</p>
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<p>Reusability of the magnetic COF (TpPa-NH<sub>2</sub>)@Fe<sub>3</sub>O<sub>4</sub> (5 mg COF (TpPa-NH<sub>2</sub>)@Fe<sub>3</sub>O<sub>4</sub>, 50 mL of sample solution; c = 100 ng·mL<sup>−1</sup>; extraction time: 5 min; 0.4 mL acetonitrile as desorption solvent was vortexed for 6 min, <span class="html-italic">n</span> = 3).</p>
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<p>Excitation and emission spectra: (<b>a</b>) excitation and emission spectra of carbaryl desorbed; (<b>b</b>) excitation and emission spectra of carbaryl before extraction.</p>
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