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Search Results (612)

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27 pages, 2559 KiB  
Article
Multiple Learning Strategies and a Modified Dynamic Multiswarm Particle Swarm Optimization Algorithm with a Master Slave Structure
by Ligang Cheng, Jie Cao, Wenxian Wang and Linna Cheng
Appl. Sci. 2024, 14(16), 7035; https://doi.org/10.3390/app14167035 (registering DOI) - 11 Aug 2024
Viewed by 256
Abstract
It is a challenge for the particle swarm optimization algorithm to effectively control population diversity and select and design efficient learning models. To aid in this process, in this paper, we propose multiple learning strategies and a modified dynamic multiswarm particle swarm optimization [...] Read more.
It is a challenge for the particle swarm optimization algorithm to effectively control population diversity and select and design efficient learning models. To aid in this process, in this paper, we propose multiple learning strategies and a modified dynamic multiswarm particle swarm optimization with a master slave structure (MLDMS-PSO). First, a dynamic multiswarm strategy with a master–slave structure and a swarm reduction strategy was introduced to dynamically update the subswarm so that the population could maintain better diversity and more exploration abilities in the early stage and achieve better exploitation abilities in the later stage of the evolution. Second, three different particle updating strategies including a modified comprehensive learning (MCL) strategy, a united learning (UL) strategy, and a local dimension learning (LDL) strategy were introduced. The different learning strategies captured different swarm information and the three learning strategies cooperated with each other to obtain more abundant population information to help the particles effectively evolve. Finally, a multiple learning model selection mechanism with reward and punishment factors was designed to manage the three learning strategies so that the particles could select more advantageous evolutionary strategies for different fitness landscapes and improve their evolutionary efficiency. In addition, the results of the comparison between MLDMS-PSO and the other nine excellent PSOs on the CEC2017 test suite showed that MLDMS-PSO achieved an excellent performance on different types of functions, contributing to a higher accuracy and a better performance. Full article
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Figure 1
<p>MLDMS-PSO flowchart.</p>
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<p>The multiswarm segmentation scheme with a master–slave structure. The red stars represent master particles and the black dots represent slave particles.</p>
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<p>The schematic diagram of the strategy selection functions.</p>
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<p>The result of the strategy selection function with the reward and punishment factors on benchmark function f1.</p>
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<p>Parameter investigation result in MLDMS-PSO.</p>
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<p>Convergence progress on the unimodal functions (f1 and f3).</p>
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<p>Convergence progress on the simple multimodal functions (f4–f10).</p>
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<p>Convergence progress on the hybrid functions (f11–f20).</p>
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<p>Convergence progress on the hybrid functions (f21–f30).</p>
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15 pages, 4798 KiB  
Article
Solanine Inhibits Proliferation and Angiogenesis and Induces Apoptosis through Modulation of EGFR Signaling in KB-ChR-8-5 Multidrug-Resistant Oral Cancer Cells
by Prathibha Prasad, Mohamed Jaber, Tahani Awad Alahmadi, Hesham S. Almoallim and Arun Kumar Ramu
J. Clin. Med. 2024, 13(15), 4493; https://doi.org/10.3390/jcm13154493 - 31 Jul 2024
Viewed by 461
Abstract
Background: The most important factors contributing to multi-drug resistance in oral cancer include overexpression of the EGFR protein and the downstream malignancy regulators that are associated with it. This study investigates the impact of solanine on inflammation, proliferation, and angiogenesis inhibition in multidrug-resistant [...] Read more.
Background: The most important factors contributing to multi-drug resistance in oral cancer include overexpression of the EGFR protein and the downstream malignancy regulators that are associated with it. This study investigates the impact of solanine on inflammation, proliferation, and angiogenesis inhibition in multidrug-resistant oral cancer KB-Chr-8-5 cells through inhibition of the EGFR/PI3K/Akt/NF-κB signaling pathway. Methods: Cell viability was assessed using an MTT assay to evaluate cytotoxic effects. Production of reactive oxygen species (ROS), mitochondrial membrane potential (ΔΨM), and AO/EtBr staining were analyzed to assess apoptosis and mitochondrial dysfunction. Western blotting was employed to examine protein expression related to angiogenesis, apoptosis, and signaling pathways. Experiments were conducted in triplicate. Results: Solanine treatment at concentrations of 10, 20, and 30 μM significantly increased ROS production, which is indicative of its antioxidant properties. This increase was associated with decreased mitochondrial membrane potential (ΔΨM) with p < 0.05, suggesting mitochondrial dysfunction. Inhibition of EGFR led to reduced activity of PI3K, Akt, and NF-κB, resulting in decreased expression of iNOS, IL-6, Cyclin D1, PCNA, VEGF, Mcl-1, and HIF-1α and increased levels of the apoptotic proteins Bax, caspase-9, and caspase-3. These changes collectively inhibited the growth of multidrug-resistant (MDR) cancer cells. Conclusions: Solanine acts as a potent disruptor of cellular processes by inhibiting the EGFR-mediated PI3K/Akt/NF-κB signaling pathway. These results suggest that solanine holds promise as a potential preventive or therapeutic agent against multidrug-resistant cancers. Full article
(This article belongs to the Section Pharmacology)
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Figure 1
<p>Evaluation of cytotoxic activity of solanine in human (<b>A</b>) multidrug-resistant oral cancer KB-ChR-8-5 cells and (<b>B</b>) normal fibroblast L929 cells. The MTT test was used to assess the viability of the cells after they had been exposed to various concentrations of solanine for 24 h. All data are presented as mean values ± standard deviations and are representative of at least three independent experiments.</p>
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<p>The effect of solanine on the production of intracellular ROS in KB-ChR-8-5 cells was investigated via the DCFH-DA-staining technique. (<b>A</b>) On the basis of the micrographs, it was discovered that untreated KB-ChR-8-5 cells had weak DCF fluorescence and that different doses of solanine (10, 20, and 30 µM) resulted in enhanced generation of ROS, suggesting a significant amount of DCF fluorescence intensity. The FLoid Cell Imaging Station captured this picture of cells (40× magnifications, scale bar = 100 µm). (<b>B</b>) A spectrofluorometer may be used to determine the amount of ROS production. Except as otherwise stated, all experiments were conducted in triplicate, and the results are represented as the mean standard deviation (mean ± SD). The statistical significance of the results was determined using an analysis of variance. The data are presented as the mean ± SD (<span class="html-italic">n</span> = 3). * <span class="html-italic">p</span> &lt; 0.05 compared with the control.</p>
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<p>To investigate the effects of solanine on mitochondrial membrane potential (MMP) in KB-ChR-8-5 cells, rhodamine 123 staining was used. (<b>A</b>) In the KB-ChR-8-5 untreated cells, strong fluorescence was seen, indicating polarization of the mitochondrial membrane. Different doses of solanine (10, 20, and 30 μM) administered over a 24 h period suggest the collapse of the mitochondrial matrix. A FLoid Cell Imaging Station captured this picture of cells (40× magnification, scale bar = 100 µm). (<b>B</b>) The spectrofluorometer was used to determine the intensity of the fluorescence. All experiments were conducted in triplicate, and the results are represented as the mean ± the standard deviation (mean ± SD). The statistical significance of the results was determined using an analysis of variance. The data are presented as the mean ± SD (<span class="html-italic">n</span> = 3). * <span class="html-italic">p</span> &lt; 0.05 compared with the control.</p>
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<p>Apoptosis was seen in the presence of solanine, as determined by dual staining (AO/EtBr) in the presence of solanine. (<b>A</b>) KB-ChR-8-5 cells were treated with solanine at varied doses (10, 20, and 30 μM), and the percentage of apoptotic cells in each treatment group significantly increased as compared to untreated cells (40× magnifications, scale bar = 100 µm). (<b>B</b>) To show that the proportion of apoptotic cells has been calculated, the data are given as the mean ± the standard deviation of three separate experiments (SD). All experiments were conducted in triplicate, and the results are represented as the mean ± the standard deviation (mean ± SD). The statistical significance of the results was determined using an analysis of variance. The data are presented as the mean ± SD (<span class="html-italic">n</span> = 3). * <span class="html-italic">p</span> &lt; 0.05 compared with the control.</p>
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<p>Effects of solanine on the expression of proteins associated with inflammation and proliferation. (<b>A</b>) The expression of iNOS, IL-6, cyclin-D1, and PCNA in KB-ChR-8-5 cells after 24 h of treatment with and without solanine is shown in the representative immunoblot study. GAPDH served as a loading-control protein. (<b>B</b>) Densitometric analysis. Protein expression in the control lysates was assessed in triplicate and is represented as 100% in the graph. The statistical significance of the results was determined using an analysis of variance. The data are presented as the mean ± SD (<span class="html-italic">n</span> = 3). * <span class="html-italic">p</span> &lt; 0.05 compared with the control.</p>
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<p>Effects of solanine on the expression of invasion-related proteins. (<b>A</b>) The expression of VEGF and HIF-1α in KB-ChR-8-5 cells after 24 h of treatment with and without solanine is shown in the representative immunoblot study. GAPDH served as a loading-control protein. (<b>B</b>) Densitometric analysis. Protein expression in the control lysates was assessed in triplicate and is represented as 100% in the graph. The statistical significance of the results was determined using an analysis of variance. The data are presented as the mean ± SD (<span class="html-italic">n</span> = 3). * <span class="html-italic">p</span> &lt; 0.05 compared with the control.</p>
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<p>The effect of solanine on the expression of apoptotic proteins. (<b>a</b>) The expression of Mcl-1, Bax, Caspase-9, and Caspase-3 in KB-ChR-8-5 cells after 24 h of treatment with and without solanine is shown in the representative immunoblot study. GAPDH served as a loading control protein. (<b>b</b>) Densitometric analysis. Protein expression in the control lysates was assessed in triplicate and is represented as 100% in the graph. The statistical significance of the results was determined using an analysis of variance. The data are presented as the mean ± SD (<span class="html-italic">n</span> = 3). * <span class="html-italic">p</span> &lt; 0.05 compared with the control.</p>
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<p>Effects of solanine on EGFR/PI3K/AKT/NF-κB protein expression. (<b>A</b>) The expression of EGFR, PI3K, Akt, and NF-κB in KB-ChR-8-5 cells after 24 h of treatment with and without solanine is shown in the representative immunoblot study. GAPDH served as a loading control protein. (<b>B</b>) Densitometric analysis. Protein expression in the control lysates was assessed in triplicate and is represented as 100% in the graph. The statistical significance of the results was determined using an analysis of variance. The data are presented as the mean ± SD (<span class="html-italic">n</span> = 3). * <span class="html-italic">p</span> &lt; 0.05 compared with the control.</p>
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18 pages, 4871 KiB  
Article
Microbial Biosynthesis of Medium-Chain-Length Polyhydroxyalkanoate (mcl-PHA) from Waste Cooking Oil
by Ahmed M. Elazzazy, Khawater Ali Abd, Noor M. Bataweel, Maged M. Mahmoud and Afra M. Baghdadi
Polymers 2024, 16(15), 2150; https://doi.org/10.3390/polym16152150 - 29 Jul 2024
Viewed by 456
Abstract
Waste cooking oil is a common byproduct in the culinary industry, often posing disposal challenges. This study explores its conversion into the valuable bioplastic material, medium-chain-length polyhydroxyalkanoate (mcl-PHA), through microbial biosynthesis in controlled bioreactor conditions. Twenty-four bacterial isolates were obtained from oil-contaminated soil [...] Read more.
Waste cooking oil is a common byproduct in the culinary industry, often posing disposal challenges. This study explores its conversion into the valuable bioplastic material, medium-chain-length polyhydroxyalkanoate (mcl-PHA), through microbial biosynthesis in controlled bioreactor conditions. Twenty-four bacterial isolates were obtained from oil-contaminated soil and waste materials in Mahd Ad-Dahab, Saudi Arabia. The best PHA-producing isolates were identified via 16S rDNA analysis as Neobacillus niacini and Metabacillus niabensis, with the sequences deposited in GenBank (accession numbers: PP346270 and PP346271). This study evaluated the effects of various carbon and nitrogen sources, as well as environmental factors, such as pH, temperature, and shaking speed, on the PHA production titer. Neobacillus niacini favored waste cooking oil and yeast extract, achieving a PHA production titer of 1.13 g/L, while Metabacillus niabensis preferred waste olive oil and urea, with a PHA production titer of 0.85 g/L. Both strains exhibited optimal growth at a neutral pH of 7, under optimal shaking -flask conditions. The bioreactor performance showed improved PHA production under controlled pH conditions, with a final titer of 9.75 g/L for Neobacillus niacini and 4.78 g/L for Metabacillus niabensis. Fourier transform infrared (FT-IR) spectroscopy and gas chromatography–mass spectrometry (GC-MS) confirmed the biosynthesized polymer as mcl-PHA. This research not only offers a sustainable method for transforming waste into valuable materials, but also provides insights into the optimal conditions for microbial PHA production, advancing environmental science and materials engineering. Full article
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Figure 1
<p>PHA-producing bacterial strains under a light microscope after staining with Sudan Black B dye; arrows indicate the PHA granules accumulated inside the bacteria.</p>
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<p>Phylogenetic tree showing the positions of the PHA-producing bacterial strains <span class="html-italic">Neobacillus niacini</span> (M2.1) and <span class="html-italic">Metabacillus niabensis</span> (O2.1), along with their closest related strains based on 16S rRNA gene sequences.</p>
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<p>Effect of different carbon sources on the production of PHA by PHA-producing bacterial strains; where the vertical axis represents the mass of produced PHA (g) from 1 g of dried cells.</p>
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<p>Effect of different nitrogen sources on the production of PHA by PHA-producing bacterial strains; where the vertical axis represents the weight of produced PHA (g) from 1 g of dried cells.</p>
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<p>Effect of different pH values on the production of PHA by PHA-producing bacterial strains; where the vertical axis represents the weight of produced PHA (g) from 1 g of dried cells.</p>
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<p>Effect of different temperatures on the production of PHA by PHA-producing bacterial strains; where the vertical axis represents the weight of produced PHA (g) from 1 g of dried cells.</p>
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<p>Effect of different agitation speeds on the production of PHA by PHA-producing bacterial strains; where the vertical axis represents the weight of produced PHA (g) from 1 g of dried cells.</p>
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<p>(<b>A</b>,<b>B</b>) Bioreactor performance analysis for <span class="html-italic">Metabacillus niabensis</span>: (<b>A</b>) uncontrolled pH conditions and (<b>B</b>) controlled pH conditions. The analysis tracks key parameters, such as pH, dissolved oxygen (D.O.), PHA productivity, substrate consumption, and dry weight, over different time phases.</p>
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<p>(<b>A</b>,<b>B</b>) Bioreactor performance analysis for <span class="html-italic">Neobacillus niacini</span>: (<b>A</b>) uncontrolled pH conditions and (<b>B</b>) controlled pH conditions. The analysis tracks key parameters, such as pH, dissolved oxygen (D.O.), PHA productivity, substrate consumption, and dry weight, over different time phases.</p>
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<p>(<b>a</b>,<b>b</b>). Obtained FT-IR spectrum of plastic samples extracted from isolates <span class="html-italic">Neobacillus niacin</span> and <span class="html-italic">Metabacillus niabensis</span>.</p>
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<p>Gas chromatography–mass spectrometry (GC-MS) of extracted PHA from <span class="html-italic">Neobacillus niacini.</span></p>
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<p>Gas chromatography–mass spectrometry (GC-MS) of extracted PHA from <span class="html-italic">Metabacillus niabensis</span>.</p>
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18 pages, 3364 KiB  
Article
Superior Anticancer and Antifungal Activities of New Sulfanyl-Substituted Niclosamide Derivatives
by Jingyi Ma, Dileepkumar Veeragoni, Hindole Ghosh, Nicole Mutter, Gisele Barbosa, Lauren Webster, Rainer Schobert, Wendy van de Sande, Prasad Dandawate and Bernhard Biersack
Biomedicines 2024, 12(7), 1621; https://doi.org/10.3390/biomedicines12071621 - 21 Jul 2024
Viewed by 568
Abstract
The approved anthelmintic salicylanilide drug niclosamide has shown promising anticancer and antimicrobial activities. In this study, new niclosamide derivatives with trifluoromethyl, trifluoromethylsulfanyl, and pentafluorosulfanyl substituents replacing the nitro group of niclosamide were prepared (including the ethanolamine salts of two promising salicylanilides) and tested [...] Read more.
The approved anthelmintic salicylanilide drug niclosamide has shown promising anticancer and antimicrobial activities. In this study, new niclosamide derivatives with trifluoromethyl, trifluoromethylsulfanyl, and pentafluorosulfanyl substituents replacing the nitro group of niclosamide were prepared (including the ethanolamine salts of two promising salicylanilides) and tested for their anticancer activities against esophageal adenocarcinoma (EAC) cells. In addition, antifungal activity against a panel of Madurella mycetomatis strains, the most abundant causative agent of the neglected tropical disease eumycetoma, was evaluated. The new compounds revealed higher activities against EAC and fungal cells than the parent compound niclosamide. The ethanolamine salt 3a was the most active compound against EAC cells (IC50 = 0.8–1.0 µM), and its anticancer effects were mediated by the downregulation of anti-apoptotic proteins (BCL2 and MCL1) and by decreasing levels of β-catenin and the phosphorylation of STAT3. The plausibility of binding to the latter factors was confirmed by molecular docking. The compounds 2a and 2b showed high in vitro antifungal activity against M. mycetomatis (IC50 = 0.2–0.3 µM) and were not toxic to Galleria mellonella larvae. Slight improvements in the survival rate of G. mellonella larvae infected with M. mycetomatis were observed. Thus, salicylanilides such as 2a and 3a can become new anticancer and antifungal drugs. Full article
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Figure 1
<p>The structures of the antifungal salicylanilides niclosamide and MMV665807.</p>
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<p>Time- and dose-dependent activity of niclosamide (upper row) and <b>3a</b> (bottom row) against EAC cell lines (SK-GT-4 and FLO-1) and THP-1 monocytes. * <span class="html-italic">p</span> &lt; 0.05 and ** <span class="html-italic">p</span> &lt; 0.01 as analyzed by one-way ANOVA test.</p>
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<p>(<b>A</b>) Niclosamide and <b>3a</b> (1.5 and 0.8 µM for SK-GT-4 and FLO-1, respectively) inhibit colony formation by EAC cells at 48 h time point. (<b>B</b>) Niclosamide and <b>3a</b> (1.5 µM) inhibit spheroid formation by SK-GT-4 cells.</p>
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<p>Effects of niclosamide and <b>3a</b> (1.5 and 0.8 µM for SK-GT-4 and FLO-1, respectively) on PARP cleavage, and expression of pro-apoptotic BAX and anti-apoptotic BCL2 and MCL1 in EAC cell lines. GAPDH (glyceraldehyde 3-phosphate dehydrogenase) served as control.</p>
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<p>Molecular docking of niclosamide and <b>2a</b> (<b>A</b>) in the protein cavity of STAT3 and (<b>B</b>) in the protein cavity of β-catenin at the TCF4 binding site using the Autodock vina software program. Surface view of the binding mode. White: protein; pink: interacting amino acid; green: compound; black dotted line: H bond.</p>
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<p>Effects of niclosamide and <b>3a</b> (1.5 and 0.8 µM for SK-GT-4 and FLO-1, respectively) on levels of p-STAT3<sup>Tyr705</sup>, STAT3, and (active) non-phospho-β-catenin in EAC cell lines. GAPDH (glyceraldehyde 3-phosphate dehydrogenase) served as control.</p>
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<p>Evaluation of compounds <b>2a–c</b> and <b>2e</b> (20 µM) in <span class="html-italic">Galleria mellonella</span> larvae. (<b>A</b>) Toxicity evaluation of test compounds for 10 days indicated by larval survival (in %). (<b>B</b>) Activity of test compounds in <span class="html-italic">M. mycetomatis</span> MM55-infected <span class="html-italic">G. mellonella</span> larvae indicated by larval survival (in %).</p>
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<p>Reagents and conditions: (i) subst. aniline, EDCI, CH<sub>2</sub>Cl<sub>2</sub>, r.t., 24 h, 33–50%; (ii) ethanolamine, EtOH, r.t., 1 h, 66–93%.</p>
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18 pages, 1922 KiB  
Article
Trans-[Pt(amine)Cl2(PPh3)] Complexes Target Mitochondria and Endoplasmic Reticulum in Gastric Cancer Cells
by Jorge Melones-Herrero, Patricia Delgado-Aliseda, Sofía Figueiras, Javier Velázquez-Gutiérrez, Adoración Gomez Quiroga, Carmela Calés and Isabel Sánchez-Pérez
Int. J. Mol. Sci. 2024, 25(14), 7739; https://doi.org/10.3390/ijms25147739 - 15 Jul 2024
Viewed by 661
Abstract
Gastric cancer prognosis is still notably poor despite efforts made to improve diagnosis and treatment of the disease. Chemotherapy based on platinum agents is generally used, regardless of the fact that drug toxicity leads to limited clinical efficacy. In order to overcome these [...] Read more.
Gastric cancer prognosis is still notably poor despite efforts made to improve diagnosis and treatment of the disease. Chemotherapy based on platinum agents is generally used, regardless of the fact that drug toxicity leads to limited clinical efficacy. In order to overcome these problems, our group has been working on the synthesis and study of trans platinum (II) complexes. Here, we explore the potential use of two phosphine-based agents with the general formula trans-[Pt(amine)Cl2(PPh3)], called P1 and P2 (with dimethylamine or isopropylamine, respectively). A cytotoxicity analysis showed that P1 and especially P2 decrease cell viability. Specifically, P2 exhibits higher activity than cisplatin in gastric cancer cells while its toxicity in healthy cells is slightly lower. Both complexes generate Reactive Oxygen Species, produce DNA damage and mitochondrial membrane depolarization, and finally lead to induced apoptosis. Thus, an intrinsic apoptotic pathway emerges as the main type of cell death through the activation of BAX/BAK and BIM and the degradation of MCL1. Additionally, we demonstrate here that P2 produces endoplasmic reticulum stress and activates the Unfolded Protein Response, which also relates to the impairment observed in autophagy markers such as p62 and LC3. Although further studies in other biological models are needed, these results report the biomolecular mechanism of action of these Pt(II)-phosphine prototypes, thus highlighting their potential as novel and effective therapies. Full article
(This article belongs to the Section Molecular Biology)
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Figure 1
<p>Phosphine agents decrease cell proliferation. (<b>a</b>) Cell viability studies with P1 (red), P2 (purple), and CDDP (grey) used as a positive control in GC AGS cells (left side) and in healthy HPDE cells (right side) after 48 h of treatment. The cells were treated with increasing concentrations of either agent (0–25 µM). The percentage of viable cells was quantified by an MTS assay. The data represent the mean values obtained in three experiments performed in quadruplicate. (<b>b</b>) A Colony Forming Unit (CFU) assay was used to determine cell clonogenicity. The AGS cells were treated with CDDP (20 µM), P1 (10 and 20 µM), or P2 (5 and 10 µM), and the colonies that formed were stained with crystal violet and quantified 10 days later. (<b>c</b>) The AGS cells were pre-treated with CDDP (20 µM), P1 (20 µM), and P2 (10 µM) for 3 h. Then, the cells were counted and seeded (see the <a href="#sec4-ijms-25-07739" class="html-sec">Section 4</a>), and the colonies were then stained with crystal violet and quantified 10 days later. In both CFU assays, statistical significance was evaluated by one-way ANOVA with a Dunnett post-test (ns = not significant, * <span class="html-italic">p</span> &lt; 0.05) compared to the untreated cells (C: control). N = 3. (<b>d</b>) The AGS cells were treated with increasing concentrations of P1 and P2 (10, 20, and 30 µM). Twenty-four hours after the treatments, the cells were fixed and stained with propidium iodide. The graphs show the cell cycle profile after the treatments (left panel) and the percentage of cells in each phase (right panel). Apoptosis was quantified as the percentage of cells with DNA content &lt; 2N. Statistical significance was evaluated by one-way ANOVA followed by a Dunnett post-test (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, **** <span class="html-italic">p</span> &lt; 0.0001) compared to the untreated cells (C: control). N = 3.</p>
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<p>P1 and P2 generate ROS and cause DNA damage. (<b>a</b>) The detection of ROS in AGS cells after treatment with H<sub>2</sub>O<sub>2</sub> (positive control, 200 µM), CDDP (10 µM), P1 (20 µM), and P2 (10 µM) by confocal microscopy using DHE and MitoSox as O<sub>2</sub><sup>•−</sup> Red fluorescence indicator in cytosol and mitochondria, respectively. The cells were treated with the compounds for 1 h followed by 30 min of incubation with the probes. Representative images of each condition were taken (H<sub>2</sub>O<sub>2</sub> and CDDP are included in <a href="#app1-ijms-25-07739" class="html-app">Figure S3b</a>) in fluorescence (upper panel) and brightfield (lower panel) conditions. The scale bar represents 20 μm. Fluorescence intensity (per cell) was quantified and depicted in the graph. Statistical significance was evaluated by one-way ANOVA followed by a Dunnett post-test (ns = not significant, **** <span class="html-italic">p</span> &lt; 0.0001) compared to the untreated cells (C: control). N = 3. (<b>b</b>) The ratio of GSH/GSSG determined with a commercial colorimetric kit (see the <a href="#sec4-ijms-25-07739" class="html-sec">Section 4</a>). The AGS cells were treated with the IC<sub>50</sub> concentration of the compounds and were collected after 24 h to perform the assay. N = 3. Statistical significance was evaluated with Student’s 2-tailed <span class="html-italic">t</span>-test (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01) compared to the untreated cells (control). (<b>c</b>) The AGS cells were treated with CDDP (20 µM), P1 (20 µM), and P2 (10 µM) for 3 h. γ-H<sub>2</sub>AX foci (green fluorescence) were detected by immunofluorescence using DAPI to stain nuclear DNA (blue fluorescence). Representative images of each condition were taken (Scale bar: 20 µm). The graph represents the number of foci per nuclei for each condition. Statistical significance was evaluated by one-way ANOVA followed by a Dunnett post-test, **** <span class="html-italic">p</span> &lt; 0.0001) compared to the untreated cells (C: control). N = 3.</p>
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<p>P1 and P2 target mitochondria and induce intrinsic apoptosis. (<b>a</b>) Phosphine agents produce mitochondrial dysfunction in AGS GC cells. Mean fold-change ± SD of the ratio of the ΔΨm probe CMX-ROS (Red) versus the Mitochondrial Mass probe MitoGreen (Green) as a measurement to evaluate mitochondrial functionality in AGS cells treated for 2 h with CDDP (10 µM), P1 (20 µM), and P2 (10 µM). Representative images for each condition were taken (CDDP was used as a positive control). The scale bar represents 20 μm. Fluorescence intensity (per cell) was quantified and depicted in the graph. * <span class="html-italic">p</span> &lt; 0.05, *** <span class="html-italic">p</span> &lt; 0.001, as determined by one-way ANOVA followed by a Dunnett post-test, compared to the control, set as 1.0. N = 3. (<b>b</b>) Western blot analysis of mitochondrial apoptosis: MCL1 and BAK (left panel), and BIM (right panel). Representative Western blots in AGS cells after treatment with IC<sub>50</sub> concentration of CDDP, P1, or P2 at different times (3, 6, and 24 h). GAPDH was used as an endogenous loading control. The graphs show the mean ± SD densitometric analyses of each protein normalized with GAPDH from three independent experiments by using ImageJ (area under the peak method), control cells (C, white bars), CDDP (grey), P1 (red), and P2 (purple). Statistical significance was evaluated with Student’s 2-tailed <span class="html-italic">t</span>-test (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001) compared to the untreated cells (C: control), set as 1.0.</p>
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<p>P2 produces ER stress. (<b>a</b>) RNA was isolated from the AGS cells stimulated with a 24 h treatment of the complexes. HSPA5, ERN1, XBP1, EIF2AK3, ATF4, DDIT3, and ATF6 were quantified by RT-qPCR. Target gene expression was normalized to GAPDH. All experiments were performed three times per triplicate with IC<sub>50</sub> concentrations of each compound used in all the assays. Statistical significance was evaluated by Student’s 2-tailed <span class="html-italic">t</span>-test (ns: not significant * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001) compared to untreated cells (C), set as 1.0. (<b>b</b>) Western blot analysis of reticulum stress proteins: GRP78, p-eIF2α, CHOP, XBP1 * inespecific bands), and ATF6. GAPDH or α-Tubulin was used as endogenous loading controls. The graphs show the mean ± SD densitometric analyses of each protein normalized with GAPDH from three independent experiments by using ImageJ (area under the peak method), control cells (C, white bars), CDDP (grey), P1 (red), and P2 (purple). Statistical significance was evaluated by Student’s 2-tailed <span class="html-italic">t</span>-test (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001) compared to the untreated cells (C: control), set as 1.0.</p>
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<p>(<b>a</b>,<b>b</b>) P1 and P2 impair autophagy. Western blot analysis of autophagy: p62 and LC3, and Nrf2. Representative Western blots in AGS cells after the treatment with IC<sub>50</sub> concentration of CDDP, P1, or P2 at different time points (3, 6, and 24 h). GAPDH was used as an endogenous loading control. The graphs show the mean ± SD densitometric analyses of each protein normalized with GAPDH from three independent experiments by using ImageJ (area under the peak method), control cells (C, white bars), CDDP (grey), P1 (red), and P2 (purple). Statistical significance was evaluated with Student’s 2-tailed <span class="html-italic">t</span>-test (* <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01) compared to the untreated cells (C: control), set as 1.0.</p>
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<p>Hypothetical model of P1 and P2 mechanism of action. Created using biorender.com <a href="https://www.biorender.com/" target="_blank">https://www.biorender.com/</a> (accessed on 28 June 2024).</p>
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15 pages, 2725 KiB  
Article
Chemical Compositions and Fumigation Effects of Essential Oils Derived from Cardamom, Elettaria cardamomum (L.) Maton, and Galangal, Alpinia galanga (L.) Willd, against Red Flour Beetle, Tribolium castaneum (Herbst) (Coleoptera: Tenebrionidae)
by Ruchuon Wanna, Parinda Khaengkhan and Hakan Bozdoğan
Plants 2024, 13(13), 1845; https://doi.org/10.3390/plants13131845 - 4 Jul 2024
Viewed by 440
Abstract
This study explores the use of essential oils from cardamom (Elettaria cardamomum (L.) Maton) and galangal (Alpinia galanga (L.) Willd) as alternatives to synthetic insecticides for controlling the red flour beetle, Tribolium castaneum (Herbst). The chemical compositions of these oils were [...] Read more.
This study explores the use of essential oils from cardamom (Elettaria cardamomum (L.) Maton) and galangal (Alpinia galanga (L.) Willd) as alternatives to synthetic insecticides for controlling the red flour beetle, Tribolium castaneum (Herbst). The chemical compositions of these oils were analyzed using GC-MS, and their fumigation effects were tested in a vapor-phase bioassay. The experiment followed a factorial design with four types of essential oils, namely, those manually extracted from cardamom leaves (MCL) and galangal leaves (MGL) and those commercially produced from cardamom seeds (CCS) and galangal rhizomes (CGR), at seven concentrations (0, 50, 100, 150, 200, 250, and 300 µL/L air). The manually extracted oils yielded 0.6% from cardamom leaves and 0.25% from galangal leaves. MCL contained 28 components, with eucalyptol (25.2%) being the most abundant, while CCS had 34 components, primarily α-terpinyl acetate (46.1%) and eucalyptol (31.2%). MGL included 25 components, mainly caryophyllene (28.7%) and aciphyllene (18.3%), whereas CGR comprised 27 components, with methyl cis-cinnamate (47.3%) and safrole (19.8%) as the major constituents. The fumigation bioassay results revealed that CGR was the most effective, demonstrating the highest mortality rates of T. castaneum across all the tested periods and concentrations, achieving up to 96% mortality at 168 h with a concentration of 300 µL/L air. Statistical analyses showed significant differences in mortality based on the type and concentration of essential oil, particularly after 96 h. These findings highlight the potential of CGR, with its advantages and differences in chemical composition, as an effective biopesticide against T. castaneum, with increasing efficacy over time and at higher concentrations. Full article
(This article belongs to the Special Issue Emerging Topics in Botanical Biopesticides—2nd Edition)
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<p>Effect of the essential oil types on mortality of adult <span class="html-italic">T. castaneum</span>. Insecticidal activity of MCL (the manually extracted essential oil from cardamom leaves), CCS (the commercially produced essential oil from cardamom seeds), MGL (the manually extracted essential oil from galangal leaves), and CGR (the commercially produced essential oil from galangal rhizomes) against adult <span class="html-italic">T. castaneum</span> after exposure within 168 h was found to be significantly different (<span class="html-italic">p</span> &lt; 0.01). Means for the same period followed by the same letter were not significantly different (LSD: <span class="html-italic">p</span> &gt; 0.05).</p>
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<p>Effect of the concentrations on mortality of adult <span class="html-italic">T. castaneum</span>. Insecticidal activity was assessed at seven different concentrations, i.e., 0 µL/L air (control), 50 µL/L air, 100 µL/L air, 150 µL/L air, 200 µL/L air, 250 µL/L air, and 300 µL/L air, against adult <span class="html-italic">T. castaneum</span> after exposure within 168 h. Significant difference was found (<span class="html-italic">p</span> &lt; 0.05). Means of the same period followed by the same letter were not significantly different (LSD: <span class="html-italic">p</span> &gt; 0.05).</p>
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<p>Effect of the interaction between types of essential oils and concentrations on mortality of adult <span class="html-italic">T. castaneum</span>. Insecticidal activity was assessed using four types of essential oils at seven different concentrations (with 0 µL/L air as the control, not presented) against adult <span class="html-italic">T. castaneum</span> over 24–72 h. Insecticidal activity of MCL (the manually extracted essential oil from cardamom leaves), CCS (the commercially produced essential oil from cardamom seeds), MGL (the manually extracted essential oil from galangal leaves), and CGR (the commercially produced essential oil from galangal rhizomes) against adult <span class="html-italic">T. castaneum</span>. Mortality ranged between 2–26%, with no significant differences observed (<span class="html-italic">p</span> &gt; 0.05). Significant differences were found within 96–168 h (<span class="html-italic">p</span> &lt; 0.05). Means of the same period followed by the same letter were not significantly different (LSD: <span class="html-italic">p</span> &gt; 0.05).</p>
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<p>Mortality of adult <span class="html-italic">T. castaneum</span> exposed to different essential oils at 300 µL/L air over various time intervals. There were no significant differences observed in the mortality within 24–72 h (<span class="html-italic">p</span> &gt; 0.05). Significant differences were found within 96–168 h (<span class="html-italic">p</span> &lt; 0.05). Means of the same period followed by the same letter were not significantly different (LSD: <span class="html-italic">p</span> &gt; 0.05).</p>
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<p>Mortality of adult <span class="html-italic">T. castaneum</span> exposed to varying concentrations of CGR (the commercially produced essential oil from galangal rhizomes) over various time intervals. There were no significant differences observed in the mortality (4–26%) within 24–72 h (<span class="html-italic">p</span> &gt; 0.05). Significant differences were found within 96–168 h (<span class="html-italic">p</span> &lt; 0.05). Means of the same period followed by the same letter were not significantly different (LSD: <span class="html-italic">p</span> &gt; 0.05).</p>
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25 pages, 4377 KiB  
Article
Insight into Mantle Cell Lymphoma Pathobiology, Diagnosis, and Treatment Using Network-Based and Drug-Repurposing Approaches
by Georgia Orfanoudaki, Konstantina Psatha and Michalis Aivaliotis
Int. J. Mol. Sci. 2024, 25(13), 7298; https://doi.org/10.3390/ijms25137298 - 2 Jul 2024
Cited by 1 | Viewed by 776
Abstract
Mantle cell lymphoma (MCL) is a rare, incurable, and aggressive B-cell non-Hodgkin lymphoma (NHL). Early MCL diagnosis and treatment is critical and puzzling due to inter/intra-tumoral heterogeneity and limited understanding of the underlying molecular mechanisms. We developed and applied a multifaceted analysis of [...] Read more.
Mantle cell lymphoma (MCL) is a rare, incurable, and aggressive B-cell non-Hodgkin lymphoma (NHL). Early MCL diagnosis and treatment is critical and puzzling due to inter/intra-tumoral heterogeneity and limited understanding of the underlying molecular mechanisms. We developed and applied a multifaceted analysis of selected publicly available transcriptomic data of well-defined MCL stages, integrating network-based methods for pathway enrichment analysis, co-expression module alignment, drug repurposing, and prediction of effective drug combinations. We demonstrate the “butterfly effect” emerging from a small set of initially differentially expressed genes, rapidly expanding into numerous deregulated cellular processes, signaling pathways, and core machineries as MCL becomes aggressive. We explore pathogenicity-related signaling circuits by detecting common co-expression modules in MCL stages, pointing out, among others, the role of VEGFA and SPARC proteins in MCL progression and recommend further study of precise drug combinations. Our findings highlight the benefit that can be leveraged by such an approach for better understanding pathobiology and identifying high-priority novel diagnostic and prognostic biomarkers, drug targets, and efficacious combination therapies against MCL that should be further validated for their clinical impact. Full article
(This article belongs to the Section Molecular Pathology, Diagnostics, and Therapeutics)
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<p>Computational analysis pipeline. Transcriptional data obtained measuring RNA levels of tissue or blood samples of MCL patients and normal cases following a modular pipeline of network-based methods. Five microarray studies were revisited. Kimura et al. (GSE30189) isolated tissue samples of 17 CCND1-positive MCL patients at four stepwise distinct stages (in situ, classical, intermediate and aggressive) and four normal mantle zone B lymphocytes samples. Espinet et al. (GSE45717) collected PB B cells of five typical MCL patients and eight healthy individuals and obtained transcriptional data using Affymetrix Human Exon 1.0 ST arrays. Three Affymetrix Human U133 Plus 2 datasets were also analyzed. Leshchenko et al. (GSE19243) purified CD19+ fractions from peripheral blood of newly diagnosed MCL patients (five selected). Hartmann et al. extracted RNA from lymph node specimens (GSE21452) of 64 previously untreated MCL patients. Finally, Newman et al. (GSE65135) compared different human hematopoietic cell phenotypes, including B cells of five healthy tonsils. First, transcriptional data were preprocessed, then comparative analysis was performed between pairs of different sample groups. SVM models were also trained using the most variable genes of each sample group, and MCL and samples were recategorized accordingly. The Limma R package was employed and differentially expressed genes (DEGs) were selected based on an adjusted <span class="html-italic">p</span>-value threshold of 0.05. Next, DEGs were used as an input to four independent subsequent analysis: pathway enrichment (performed by two bioinformatic tools pathfindR [<a href="#B18-ijms-25-07298" class="html-bibr">18</a>] and PaintOmics [<a href="#B19-ijms-25-07298" class="html-bibr">19</a>]), drug repurposing, co-expression network spectral clustering, and compound drug combination prediction analyses.</p>
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<p>Differentially expressed genes and enriched biological pathways at different MCL stages. (<b>A</b>) Bar chart summarizing the number of identified DEGs at each stepwise MCL stage comparison, including normal (N) to any other MCL stage transitions. Results of the comparative analysis between healthy and typical MCL patients (HtoMCLx), normal B cells and newly diagnosed MCL patients (BtoMCL(e)), normal B cells and progressed-MCL-stage patients (BtoMCL), and newly diagnosed and progressed-MCL-stage patients (MCL(e)toMCL) are also depicted. Differentially expressed transcripts are also colored based on their type (blue: protein coding, gray: pseudogenes, green: asRNAs, red: lncRNAs, yellow: ncRNAs and orange: snoRNA). The overlap between the different sets of DEGs is represented in the form of Venn diagrams at the top of the bar chart. (<b>B</b>) Heatmap of 168 enriched KEGG pathways based on the <span class="html-italic">FE</span> calculated by the PathfindR [<a href="#B18-ijms-25-07298" class="html-bibr">18</a>] bioinformatic tool for the different sample group comparisons (<a href="#ijms-25-07298-t002" class="html-table">Table 2</a>). Enriched pathways (rows) and sample comparisons (columns) were hierarchically clustered. For limited space reasons, disease-specific pathways are not depicted (e.g., “bladder cancer” or “thyroid cancer”).</p>
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<p>Co-expression network spectral clustering. Gene co-expression networks at different MCL stages were constructed using the MRNETB algorithm [<a href="#B25-ijms-25-07298" class="html-bibr">25</a>]. Modules of co-expressed genes were identified and aligned between networks using a spectral clustering approximation approach [<a href="#B23-ijms-25-07298" class="html-bibr">23</a>,<a href="#B24-ijms-25-07298" class="html-bibr">24</a>]. The union of the aligned modules between (<b>A</b>) normal and in situ, (<b>B</b>) in situ and intermediate, (<b>C</b>) intermediate and aggressive are depicted. Up- and down-regulated genes are depicted as regular or flipped triangles, respectively. The log<sub>2</sub> fold change of the co-expression between MCL stages is used as a weight of the edges. Loss of co-expression is depicted with green (negative log<sub>2</sub> fold change), whereas gain of co-expression with red (positive log<sub>2</sub> fold change). As a result, “green communities” are genes where co-expression relationships are weakened as the MCL progresses to the next stage. The node border line color depicts the Wiki pathway in which a gene participates. A gene can participate in more than one pathway, but here we show only selected pathways (orange = apoptosis, cyan = cytoplasmic ribosomal proteins, magenta = MAPK signaling pathway, blue = PI3K–Akt signaling pathway, yellow = VEGFA–VEGFR2 signaling pathway, brown = cell cycle, purple = TGF-beta signaling pathway).</p>
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<p>Proposed drugs for the distinct stages of MCL. Bipartite drug–gene graph of the 388 FDA-approved drugs proposed in the current analysis for the treatment of MCL. The potential drugs were obtained using DrugBank’s [<a href="#B26-ijms-25-07298" class="html-bibr">26</a>] catalogue of FDA-approved drugs and their gene/protein targets. Drug–gene pairs were selected based on the top DEGs identified in nine sample group comparisons (<a href="#ijms-25-07298-t001" class="html-table">Table 1</a>) and filtered given the type of action. Genes and drugs are depicted as circles and diamonds/triangles, respectively. Two types of drugs exist in the DrugBank database [<a href="#B26-ijms-25-07298" class="html-bibr">26</a>], the small molecules (diamonds) and the biotech molecular entities (arrowheads). Drugs are colored based on their original indication or clinical trial: MCL drugs (cyan), T-cell lymphomas (orange), other lymphoma/leukemia subtypes (green), drugs at clinical trial for the treatment of MCL [<a href="#B28-ijms-25-07298" class="html-bibr">28</a>] (yellow, drugs indicated in other types of cancer (brown) and nutraceutical substances (white). Each gene circle is a donut chart, where each donut slice represents one of the nine sample group comparisons where the gene was found to be significantly deregulated. Donut slices (i.e., sample comparisons) have the following color code: comparisons of the three MCL stages to the normal case (NtoIS, NtoI, NtoA) are gray-shaded with darker shades corresponding to more progressed MCL stage; light and dark teal represent IStoI and ItoA transitions; light and dark purple correspond to BtoMCL and BtoMCL(e) comparisons, whereas magenta corresponds to MCL(e)toMCL and blue to HtoMCLx. Finally, drug–gene relationships are characterized by the type of drug action (inhibition, agonist, antagonist etc.), and are represented either with different target arrow shapes or line types.</p>
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<p>Synopsis of stage-specific pathways and the proposed MCL treatments, summarizing biological pathways that participate in MCL progression along with proposed treatments, from benign lymphadenitis to in situ, intermediate, and finally aggressive stage. Proposed drug treatments are colored or grouped based on their previous indication(s) or their clinical trial state regarding MCL or lymphoma treatment ([C]: at clinical trial). As proof of our approach, a considerable number of compounds are indicated in MCL (cyan), T-cell lymphoma (orange), other lymphomas (green), leukemia (gray), R/R lymphoma, or other cancer treatments. Some of them are at clinical trials for the treatment of MCL (magenta) or other subtypes of lymphoma (blue). Our approach also identifies groups of compounds originally prescribed in other diseases such as schizophrenia or bipolar disease (DD: depressive disorder; RA: rheumatoid arthritis or osteoarthritis; PD: Parkinson’s disease).</p>
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<p>Compound and target combinations in MCL stages. Network-based stratification of drug combinations for the treatment of MCL stages. Networks of effective drug–drug networks and the corresponding target–target combination for the pairwise MCL stage comparisons, NtoIS, IStoI, and ItoA. Drugs are depicted as circles and targets as rectangles. Drugs are colored based on the original disease indication; targets are also colored based on the indications of the respective drugs targeting them (purple: MCL at clinical trials; magenta: MCL; orange: T-cell lymphoma; green: other lymphoma; lilac: psychosis/depressive disorder/bipolar/anxiety/ADHD; gray: osteoarthritis/rheumatoid arthritis; yellow: Alzheimer’s disease; blue: allergic rhinitis; white: dietary supplement).</p>
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<p>Prediction of effective compound combinations in early and progressed MCL patients. Dual heatmaps present both the network proximity <math display="inline"><semantics> <mrow> <mi>s</mi> </mrow> </semantics></math> of drug–drug pairs (lower half) and the network principle of the drug pair given a disease module (upper half). Three drug combination analyses are summarized here, each differing in the selected disease module and the list of proposed drugs assessed: (<b>A</b>) BtoMCL (<b>B</b>) BtoMCL(e) and (<b>C</b>) MCL(e)toMCL. In each analysis, the disease module is defined as the set of top 100 DEGs identified in the respective sample comparison, while only the pairs of the respective proposed drugs are assessed (51, 23, and 66 drugs respectively). Hierarchical clustering of drugs is based on the separation (<math display="inline"><semantics> <mrow> <mi>s</mi> </mrow> </semantics></math>) values and clusters are colored in gray shades (rightmost hierarchical tree). Drugs names are colored based on the DEG(s) targets (top color bars) and drug pairs with indications for combined treatment of MCL or other lymphoma subtypes are marked with black or empty circles (see C). (<b>D</b>) Target–target network summarizing the effective gene pairs to be treated in early MCL (analysis of BtoMCL(e) transition). The results are based on 190 out of the 388 proposed drugs that can act in combination based on the in-silico screening. Targets are colored based on their original indication(s) and gene names are separated with pipe symbol (|) if they are target of the same drug (magenta: MCL; purple: MCL at clinical trial; dark green: other lymphoma; light green: leukemia; teal: nHL; orange: T-cell lymphoma; light purple: mental illnesses; brown: other cancer; gray: rheumatoid arthritis or osteoarthritis; cyan: Crohn’s disease; red: antibiotic; light yellow: anesthetic; purple: anti-inflammatory; white: dietary supplement).</p>
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12 pages, 2981 KiB  
Systematic Review
Does Combined Reconstruction of the Medial Collateral and Anterior Cruciate Ligaments Provide Better Knee Function? A Systematic Review and Meta-Analysis
by Károly Csete, Bálint Baráth, Lilla Sándor, Helga Holovic, Péter Mátrai, László Török and Petra Hartmann
J. Clin. Med. 2024, 13(13), 3882; https://doi.org/10.3390/jcm13133882 - 1 Jul 2024
Viewed by 538
Abstract
Objective: This study aimed to determine if medial collateral ligament reconstruction (MCLR) alongside anterior cruciate ligament reconstruction (ACLR) preserves knee functionality better than isolated ACLR in combined ACL and MCL tears. Methods: MEDLINE, EMBASE, Scopus, CENTRAL, and Web of Science were searched systematically [...] Read more.
Objective: This study aimed to determine if medial collateral ligament reconstruction (MCLR) alongside anterior cruciate ligament reconstruction (ACLR) preserves knee functionality better than isolated ACLR in combined ACL and MCL tears. Methods: MEDLINE, EMBASE, Scopus, CENTRAL, and Web of Science were searched systematically on 31 March 2023. Studies reporting post-operative function after ACLR and ACLR + MCLR in combined injuries were included. Outcomes included International Knee Documentation Committee (IKDC) score, side-to-side difference (SSD), Lysholm, and Tegner scale values. Results: Out of 2362 papers, 8 studies met the criteria. The analysis found no significant difference in outcomes (MD = 3.63, 95% CI: [−5.05, 12.3] for IKDC; MD = −0.64, 95% CI: [−3.24, 1.96] for SSD at 0° extension; MD = −1.79, 95% CI: [−4.61, 1.04] for SSD at 30° extension; MD = −1.48, 95% CI: [−16.35, 13.39] for Lysholm scale; MD = −0.21, 95% CI: [−4.29, 3.87] for Tegner scale) between treatments. Conclusions: This meta-analysis found no significant difference in outcomes between ACLR and ACLR + MCLR, suggesting that adding MCLR does not provide additional benefits. Due to the heterogeneity and quality of the included studies, further high-quality randomized controlled trials are needed to determine the optimal treatment for combined severe MCL–ACL injuries. Full article
(This article belongs to the Special Issue Clinical Treatment and Management of Orthopedic Trauma)
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<p>PRISMA flowchart of a search strategy with inclusions and exclusions.</p>
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<p>Risk Of Bias (ROB2) assessment of included RCT studies [<a href="#B20-jcm-13-03882" class="html-bibr">20</a>,<a href="#B29-jcm-13-03882" class="html-bibr">29</a>].</p>
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<p>Risk of Bias in Non-Randomised Studies of Interventions (ROBINS-I) assessment of included non-RCT studies [<a href="#B5-jcm-13-03882" class="html-bibr">5</a>,<a href="#B6-jcm-13-03882" class="html-bibr">6</a>,<a href="#B12-jcm-13-03882" class="html-bibr">12</a>,<a href="#B19-jcm-13-03882" class="html-bibr">19</a>,<a href="#B30-jcm-13-03882" class="html-bibr">30</a>,<a href="#B31-jcm-13-03882" class="html-bibr">31</a>].</p>
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<p><b>Forest plot comparing IKDC scores following ACLR + MCL vs. ACLR.</b> Squares represent individual study effects, with the size of the square indicating the weight of the study in the meta-analysis. The diamond represents the summary effect from meta-analysis. Horizontal bars denote the 95% CIs. There is no evidence of small study effects in the test or the formal plot. MD: mean difference; SD: standard deviation; CI: confidence interval [<a href="#B6-jcm-13-03882" class="html-bibr">6</a>,<a href="#B29-jcm-13-03882" class="html-bibr">29</a>,<a href="#B30-jcm-13-03882" class="html-bibr">30</a>,<a href="#B31-jcm-13-03882" class="html-bibr">31</a>].</p>
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<p><b>Forest plot comparing SSD scores at 0° extension following ACLR + MCL vs. ACLR.</b> Squares represent individual study effects, with the size of the square indicating the weight of the study in the meta-analysis. The diamond represents the summary effect from the meta-analysis. Horizontal bars denote the 95% CIs. There is no evidence of small study effects in the test or the formal plot. MD: mean difference; SD: standard deviation; CI: confidence interval [<a href="#B5-jcm-13-03882" class="html-bibr">5</a>,<a href="#B6-jcm-13-03882" class="html-bibr">6</a>,<a href="#B12-jcm-13-03882" class="html-bibr">12</a>,<a href="#B29-jcm-13-03882" class="html-bibr">29</a>,<a href="#B30-jcm-13-03882" class="html-bibr">30</a>].</p>
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<p><b>Forest plot comparing SSD scores at 30° extension following ACLR + MCL vs. ACLR.</b> Squares represent individual study effects, with the size of the square indicating the weight of the study in the meta-analysis. The diamond represents the summary effect from the meta-analysis. Horizontal bars denote the 95% CIs. There is no evidence of small study effects in the test or the formal plot. MD: mean difference; SD: standard deviation; CI: confidence interval [<a href="#B5-jcm-13-03882" class="html-bibr">5</a>,<a href="#B6-jcm-13-03882" class="html-bibr">6</a>,<a href="#B12-jcm-13-03882" class="html-bibr">12</a>,<a href="#B30-jcm-13-03882" class="html-bibr">30</a>].</p>
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<p><b>Forest plot comparing Lysholm scale following ACLR + MCL vs. ACLR.</b> Squares represent individual study effects, with the size of the square indicating the weight of the study in the meta-analysis. The diamond represents the summary effect from the meta-analysis. Horizontal bars denote the 95% CIs. There is no evidence of small study effects in the test or the formal plot. MD: mean difference; SD: standard deviation; CI: confidence interval [<a href="#B6-jcm-13-03882" class="html-bibr">6</a>,<a href="#B20-jcm-13-03882" class="html-bibr">20</a>,<a href="#B29-jcm-13-03882" class="html-bibr">29</a>,<a href="#B30-jcm-13-03882" class="html-bibr">30</a>].</p>
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<p><b>Forest plot comparing Tegner scale following ACLR + MCL vs. ACLR.</b> Squares represent individual study effects, with the size of the square indicating the weight of the study in the meta-analysis. The diamond represents the summary effect from the meta-analysis. Horizontal bars denote the 95% CIs. There is no evidence of small study effects in the test or the formal plot. MD: mean difference; SD: standard deviation; CI: confidence interval [<a href="#B19-jcm-13-03882" class="html-bibr">19</a>,<a href="#B20-jcm-13-03882" class="html-bibr">20</a>,<a href="#B30-jcm-13-03882" class="html-bibr">30</a>].</p>
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16 pages, 10821 KiB  
Article
Synergistic Solutions: Exploring Clotrimazole’s Potential in Prostate and Bladder Cancer Cell Lines
by Mariana Pereira and Nuno Vale
Drugs Drug Candidates 2024, 3(3), 455-470; https://doi.org/10.3390/ddc3030027 - 28 Jun 2024
Viewed by 533
Abstract
Clotrimazole (CLZ), traditionally an antifungal agent, unveils promising avenues in cancer therapy, particularly in addressing bladder and prostate cancers. In vitro assessments underscore its remarkable efficacy as a standalone treatment, significantly diminishing cancer cell viability. Mechanistically, CLZ operates through multifaceted pathways, including the [...] Read more.
Clotrimazole (CLZ), traditionally an antifungal agent, unveils promising avenues in cancer therapy, particularly in addressing bladder and prostate cancers. In vitro assessments underscore its remarkable efficacy as a standalone treatment, significantly diminishing cancer cell viability. Mechanistically, CLZ operates through multifaceted pathways, including the inhibition of Ca2+-dependent K+ channels, suppression of glycolysis-related enzymes, and modulation of the ERK-p65 signaling cascade, thus underscoring its potential as a versatile therapeutic agent. Our investigation sheds light on intriguing observations regarding the resilience of UM-UC-5 bladder cancer cells against high doses of paclitaxel (PTX), potentially attributed to heightened levels of the apoptosis-regulating protein Mcl-1. However, synergistic studies demonstrate that the combination of Doxorubicin (DOXO) and CLZ emerges as particularly potent, especially in prostate cancer contexts. This effectiveness could be associated with the inhibition of drug efflux mediated by multidrug resistance-associated protein 1 (MRP1), underscoring the importance of exploring combination therapies in cancer treatment paradigms. In essence, our findings shed light on the anticancer potential of CLZ, emphasizing the significance of tailored approaches considering specific cancer types and molecular pathways in drug repurposing endeavors. While further validation and clinical exploration are warranted, the insights gleaned from this study offer promising prospects for enhancing cancer therapy efficacy. Full article
(This article belongs to the Section Marketed Drugs)
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<p>Chemical structures of clotrimazole (<b>a</b>), Doxorubicin (<b>b</b>), and paclitaxel (<b>c</b>). All structures were obtained using ChemDraw software (version 12.0, PerkinElmer, Inc., Waltham, MA, USA). Molecule with blue color is a repurposing drug. Red, are antineoplastic agents or anticancer drugs.</p>
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<p>Results of PC-3 cell cytotoxicity following exposure to a single drug and a combination of DOXO and CLZ for 48 h. Both drugs were added at the same time and at concentrations of 0.25×, 0.5×, 1×, 2×, and 4× the IC<sub>50</sub> of the drugs. To the controls cells, 0.1% DMSO (vehicle) was added. The MTT test was used to determine cell viability, and the findings are shown as mean ± SEM (n = 3). **** Indicate <span class="html-italic">p</span> &lt; 0.0001 when compared to the control. **** Statistically significant vs. drug alone at <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Results of PC-3 cell cytotoxicity following exposure to a single drug and a combination of PTX and CLZ for 48 h. Both drugs were added at the same time and at concentrations of 0.25×, 0.5×, 1×, 2×, and 4× the IC<sub>50</sub> of the drugs. To the controls cells, 0.1% DMSO (vehicle) was added. The MTT test was used to determine cell viability, and the findings are shown as mean ± SEM (n = 3). * Statistically significant vs. drug alone at <span class="html-italic">p</span> &lt; 0.05; *** statistically significant vs. drug alone at <span class="html-italic">p</span> &lt; 0.001; **** statistically significant vs. drug alone at <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Morphological evaluation of PC-3 cells after exposure to a single drug and a combination of DOXO and CLZ for 48 h. Both drugs were added at the same time and at concentrations of 0.25×, 0.5×, 1×, 2×, and 4× the IC<sub>50</sub> of the drugs. To the controls cells, 0.1% DMSO (vehicle) was added. Three separate experiments are represented by these pictures. The scale bar is 200 μm.</p>
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<p>Results of UM-UC-5 cell viability following exposure to DOXO (<b>a</b>), PTX (<b>b</b>), and CLZ (<b>c</b>) at escalating concentrations for 48 h. To the control cells, 0.01% DMSO was applied (vehicle). The MTT test was used to determine cell viability, and the findings are shown as mean ± SEM (n = 3). **** Indicate <span class="html-italic">p</span> &lt; 0.0001 when compared to the control. **** Statistically significant vs. control (vehicle) at <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>UM-UC-5 morphology after exposure to DOXO for 48 h at concentrations of 0.01, 0.1, 1, 10, 25, and 50 µM (n = 3). Control cells received the vehicle treatment (0.01% DMSO). The scale bar is 200 µm.</p>
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<p>UM-UC-5 morphology after exposure to PTX for 48 h at concentrations of 0.01, 0.1, 1, 10, and 25 µM (n = 3). Control cells received the vehicle treatment (0.01% DMSO). The scale bar is 200 µm.</p>
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<p>UM-UC-5 morphology after exposure to CLZ for 48 h at concentrations of 0.01, 0.1, 1, 10, 25, 50, and 100 µM (n = 3). Control cells received the vehicle treatment (0.01% DMSO). The scale bar is 200 µm.</p>
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<p>Results of UM-UC-5 cell cytotoxicity following exposure to a single drug and combinations of DOXO and CLZ (<b>a</b>) and PTX and CLZ (<b>b</b>) for 48 h. Both drugs were added at the same time and at concentrations of 0.25×, 0.5×, 1×, 2×, and 4× the IC<sub>50</sub> of the drugs. To the controls cells, 0.1% DMSO (vehicle) was added. The MTT test was used to determine cell viability, and the findings are shown as mean ± SEM (n = 3). *, *** and **** indicate <span class="html-italic">p</span> &lt; 0.05, <span class="html-italic">p</span> &lt; 0.001 and <span class="html-italic">p</span> &lt; 0.0001, respectively, when compared to the control. **** Statistically significant vs. drug alone at <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Morphological evaluation of UM-UC-5 cells after exposure to a single drug and a combination of DOXO and CLZ for 48 h. Both drugs were added at the same time and at concentrations of 0.25×, 0.5×, 1×, 2×, and 4× the IC<sub>50</sub> of the drugs. To the controls cells, 0.1% DMSO (vehicle) was added. Three separate experiments are represented by these pictures. The scale bar is 200 μm.</p>
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<p>Morphological evaluation of UM-UC-5 cells after exposure to a single drug and combination of PTX and CLZ for 48 h. Both drugs were added at the same time and at concentrations of 0.25×, 0.5×, 1×, 2×, and 4× the IC<sub>50</sub> of the drugs. To the controls cells, 0.1% DMSO (vehicle) was added. Three separate experiments are represented by these pictures. The scale bar is 200 μm.</p>
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<p>Combination model used in this study of concentrations around the IC<sub>50</sub> and its benefits.</p>
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30 pages, 12824 KiB  
Article
Quantification and Profiling of Early and Late Differentiation Stage T Cells in Mantle Cell Lymphoma Reveals Immunotherapeutic Targets in Subsets of Patients
by Lavanya Lokhande, Daniel Nilsson, Joana de Matos Rodrigues, May Hassan, Lina M. Olsson, Paul-Theodor Pyl, Louella Vasquez, Anna Porwit, Anna Sandström Gerdtsson, Mats Jerkeman and Sara Ek
Cancers 2024, 16(13), 2289; https://doi.org/10.3390/cancers16132289 - 21 Jun 2024
Viewed by 739
Abstract
With the aim to advance the understanding of immune regulation in MCL and to identify targetable T-cell subsets, we set out to combine image analysis and spatial omic technology focused on both early and late differentiation stages of T cells. MCL patient tissue [...] Read more.
With the aim to advance the understanding of immune regulation in MCL and to identify targetable T-cell subsets, we set out to combine image analysis and spatial omic technology focused on both early and late differentiation stages of T cells. MCL patient tissue (n = 102) was explored using image analysis and GeoMx spatial omics profiling of 69 proteins and 1812 mRNAs. Tumor cells, T helper (TH) cells and cytotoxic (TC) cells of early (CD57−) and late (CD57+) differentiation stage were analyzed. An image analysis workflow was developed based on fine-tuned Cellpose models for cell segmentation and classification. TC and CD57+ subsets of T cells were enriched in tumor-rich compared to tumor-sparse regions. Tumor-sparse regions had a higher expression of several key immune suppressive proteins, tentatively controlling T-cell expansion in regions close to the tumor. We revealed that T cells in late differentiation stages (CD57+) are enriched among MCL infiltrating T cells and are predictive of an increased expression of immune suppressive markers. CD47, IDO1 and CTLA-4 were identified as potential targets for patients with T-cell-rich MCL TIME, while GITR might be a feasible target for MCL patients with sparse T-cell infiltration. In subgroups of patients with a high degree of CD57+ TC-cell infiltration, several immune checkpoint inhibitors, including TIGIT, PD-L1 and LAG3 were increased, emphasizing the immune-suppressive features of this highly differentiated T-cell subset not previously described in MCL. Full article
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<p>Overview of the study: (<b>A</b>) Three biological questions are explored in the study. These include (1) comparison between the differences in T-cell abundance and molecular profiles in tumor-rich and tumor-sparse regions, (2) the identification of unique mRNA and proteins on early- and late-stage T-cell differentiation stages and (3) the association between cell frequencies and molecular profiles of tumor and T cells. (<b>B</b>) Proteomic and transcriptomic data were collected from phenotypically identified cell-specific AOIs in MCL tissue from replicate TMA sections. Data collection focused on T-cell subsets using phenotypic staining of CD3, CD8 and CD57, allowing T<sub>C,57−</sub> (CD57− CD3+ CD8+ T cytotoxic cells), T<sub>H,57−</sub> (CD57− CD3+ CD8− T helper cells), T<sub>C,57+</sub> (CD57+ CD3+ CD8+ T cytotoxic cells) and T<sub>H,57+</sub> (CD57+ CD3+ CD8− T helper cells) to be enriched and collected in separate AOIs. Proteomic data collection focused on CD20+ MCL cells was achieved by staining for CD20 and CD3 to allow the identification of the separate tumor-rich and tumor-sparse compartments, where the latter is often rich in T cells. Transcriptional profiling included CD20+ MCL cells, T<sub>H</sub> and T<sub>C</sub> cells. (<b>C</b>) Image analysis was performed on mIF images stained for Syto13, CD3, CD8 and CD57. Cellpose models for nuclei and cell segmentation were finetuned using Syto13 staining and cell membrane markers (CD3, CD8 and CD57), respectively, which generated four cell masks. Cells were classified into four cell types by overlapping the generated cell segmentation masks with the centroid of the nuclei mask. Image-derived cell metrics were extracted and used in conjunction with expression data. <a href="https://Biorender.com" target="_blank">https://Biorender.com</a> was used to create the illustrations.</p>
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<p>A retrained image segmentation/classification pipeline was used to classify cells. (<b>A</b>) Composite mIF image and the four channels separated with predicted segmentation masks. The segmentation masks generated from the fine-tuned Cellpose models were overlapped and projected to classify individual cells based on appearance of an individual marker. (<b>B</b>) Pre-trained vs. fine-tuned Cellpose nuclei model showing the output of the nuclei segmentation performance when the model weights are updated based on project-specific images. (<b>C</b>) Depiction of the improved performance in cell segmentation and classification of fine-tuned Cellpose cyto models (Panel 4) in comparison to pre-trained Cellpose models (Panel 3). A secondary model was developed using the masks from fine-tuned Cellpose nuclei model which was expanded to the cell boundary and a random forest classifier was trained to distribute the cells into the T-cell subtypes (Panel 2). (<b>D</b>) Quantitative comparison of F1 score, recall (sensitivity), precision and accuracy of the three models applied to a subset (<span class="html-italic">n</span> = 11) of cropped images. (<b>E</b>) Example of final segmentation and classification of cells into the four T-cell subtypes based on the defined workflow. Scalebar is 10 µm for all sub-figures but (<b>E</b>) that is 20 µm.</p>
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<p>Tumor-infiltrating CD57+ T cells of both T<sub>C</sub> and T<sub>H</sub> subsets are present in MCL. (<b>A</b>) Histograms of tumor-infiltrating T-cell frequencies showing that tumor infiltration (%) is mainly composed of T<sub>H,57−</sub> cells followed by T<sub>C,57−</sub> cells. CD57+ cells were found in both the T<sub>H</sub> and T<sub>C</sub> compartment but were more common among T<sub>C</sub>. In the heatmap (right panel), cell frequencies were ranked (1–186) and sorted based on CD3+ cell frequency, to highlight relative differences. The columns represent the core-IDs. The ranked heatmap emphasizes the dependencies between total CD3 frequency and the CD57− T-cell subsets. (<b>B</b>) Paired analysis (<span class="html-italic">n</span> = 39) investigating the differences of T-cell frequencies in tumor-rich and tumor-sparse regions showing high variation of the CD57− T-cell subsets, with more such T cells in the tumor-sparse region. CD57+ T cells were equally abundant in the two regions. Analysis of the relative proportion compared to T57− subtypes shows that both T<sub>C,57+</sub> and T<sub>H,57+</sub> had a higher proportion in the tumor-rich compared to tumor-sparse area. (<b>C</b>) Shannon Diversity Index (SDI) is plotted (upper panel) in relation to the distribution of the four investigated T-cell subsets (middle panel) in tumor-rich regions. No correlation between SDI and total CD3 frequency (lower panel) is observed (also see (<b>D</b>)). The higher SDI scores are associated with presence of CD57+ subsets, particularly the T<sub>C,57+</sub> (also see (<b>E</b>)), and larger variation in relative abundance of the four T-cell subsets. Lower scores were associated with dominance of mostly T<sub>H,57−</sub> cells. (<b>D</b>) Spearman correlation for SDI vs. CD3 frequency and (<b>E</b>) SDI vs. T<sub>C,57+</sub> cell frequency, showing that the score is positively (R = 0.73) associated with increasing CD57+ T<sub>C</sub> cells. The other subtypes exhibited less pronounced correlation: T<sub>C,57−</sub> (R = 0.21, <span class="html-italic">p</span> = 0.0036), T<sub>H,57+</sub> (R = 0.23, <span class="html-italic">p</span> = 0.0015) and T<sub>H,57−</sub> (R = −0.45, <span class="html-italic">p</span> &lt; 0.00001).</p>
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<p>Deconvolution analysis investigating the predicted cell subtypes from tumor and T-cell AOIs. Boxplots show pairwise Wilcoxon analysis of cell types identified by deconvolution on paired (<span class="html-italic">n</span> = 25) transcriptome data. Data from tumor cells, and T<sub>C</sub> and T<sub>H</sub> in both tumor-rich and tumor-sparse regions were included in the analysis. Pink boxes indicate data sampled in tumor-rich regions, and blue boxes indicate data sampled in tumor-sparse regions. (<b>A</b>) Endothelial cells and fibroblasts, (<b>B</b>) macrophages, mDCs, monocytes (NCI, non-classical), neutrophils, NK and pDCs, (<b>C</b>) CD4+ memory T cells, CD4+ naïve T cells, CD8+ memory T cells, CD8+ naïve T cells, regulatory T cells, (<b>D</b>) naïve B cells, memory B cells and plasma B cells. ns, non-statistical significance, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Functional differences in MCL-infiltrating T-cytotoxic and T-helper cell populations compared to adjacent T-cell-rich regions. Transcriptional data from <span class="html-italic">n</span> = 25 patients and proteomic data from <span class="html-italic">n</span> = 39 patients were used as these patients had data collected in both tumor-rich and tumor-sparse regions. (<b>A</b>) Gene set enrichment analysis of 1482 mRNA transcripts, performed for T<sub>H</sub> and T<sub>C</sub> cells separately. The plot has been adjusted to show pathways of interest in immuno-oncology. Paired linear mixed model (LMM) analysis (Patient ID as a random effect) comparing the differential expression of (<b>B</b>) mRNA transcripts (m) in T<sub>H</sub>, (<b>C</b>) mRNA transcripts (m) in T<sub>C</sub> and (<b>D</b>) proteins (p) inT<sub>H,57−</sub>, and (<b>E</b>) proteins (p) in T<sub>C,57−</sub> cells in tumor-sparse and tumor-rich regions, are visualized. (<b>F</b>) Tile plot summarizing the overlap of differentially expressed proteins in T<sub>H,57−</sub> and T<sub>C,57−</sub> cells in relation to spatial localization, as identified by paired LMM analysis (panel <b>D</b>,<b>E</b>). The values represent the direction of enrichment in relation to the spatial compartment. Proteins with higher abundance in the tumor-rich area are shown in pink while proteins with lower abundance are shown in blue.</p>
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<p>Comparison of the functional and phenotypic variation in infiltrating T-cell subtypes. (<b>A</b>) Spearman correlation plot exploring co-regulation between differentially expressed transcripts, as identified by differential gene expression using LMM analysis comparing the infiltrating T<sub>H</sub> and T<sub>C</sub> cells (<span class="html-italic">n</span> = 63). Color legend indicates positive (red) or negative (blue) correlation value. (<b>B</b>) Tile plot highlighting the differentially expressed proteins between the infiltrating four T-cell subsets (<span class="html-italic">n</span> = 102), as identified by ANOVA followed by Tukey-HSD test (q-value cutoff: 0.05). The values show the difference in the mean value between groups. The reference and comparison group are given in the tables below. Color code indicates relative higher (red) or lower (blue) abundance (difference in group means). (<b>C</b>) PCA biplot using the analytes identified in (<b>B</b>) showing the group segregation of the four T-cells subsets based on the magnitude and direction of differential protein expression between the two components. (<b>D</b>) Boxplot and Wilcoxon <span class="html-italic">p</span>-value analysis of PD-L1, PD-L2 and PD-1 expression among the four infiltrating T-cell subsets. Tumor-rich associated mean value expressions were aggregated by patient ID.</p>
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<p>Multi-omics investigation of TIME with respect to infiltrating CD3 T-cell frequency using DIABLO. DIABLO prefers complete datasets, and to include relevant number of patients only five out of eight collected omics datasets were used resulting in data from <span class="html-italic">n</span> = 62 patients. Proteomic datasets included CD20 (pCD20), T<sub>H</sub> (pT<sub>H</sub>) and T<sub>C</sub> (pT<sub>C</sub>). Transcriptomic data included CD20 (mCD20) and T<sub>C</sub> (mT<sub>C</sub>). (<b>A</b>) Circos plot highlighting the identified analytes in each omics dataset for the optimally selected first component. The outer blue and red lines indicate the direction of the association between the individual parameter (gene or transcript) and the CD3 frequency group (high or low, cut-of 8.4%). The inner lines connect parameters with positive (red) or negative (blue) association-based correlation analogues to Pearson (R &gt; ±0.6). The groups were determined based on optimal cut-off for high/low CD3 T-cell infiltration based on survival analysis in <a href="#app1-cancers-16-02289" class="html-app">Supplementary Materials Figure S4</a>. (<b>B</b>) Boxplot analysis with <span class="html-italic">t</span>-test significance per omics dataset of the analytes identified in (<b>A</b>). * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001. (<b>C</b>) Bar plot distributions of the high (<span class="html-italic">n</span> = 35) and low (<span class="html-italic">n</span> = 27) infiltration groups used for this analysis.</p>
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<p>Multi-omics investigation of TIME with respect to infiltrating T<sub>C,57+</sub> frequency using DIABLO. DIABLO prefers complete datasets, and to include a relevant number of patients only five out of eight collected omics datasets were used, resulting in data from <span class="html-italic">n</span> = 62 patients. Proteomic datasets included CD20 (pCd20), T<sub>H</sub> (pT<sub>H</sub>) and T<sub>C</sub> (pT<sub>C</sub>). Transcriptomic data included CD20 (mCD20) and T<sub>C</sub> (mT<sub>C</sub>). (<b>A</b>) Circos plot highlighting the identified analytes in each type of omics data for the optimally selected first component. The outer blue and red lines indicate the direction of the association between the individual parameter (gene or transcript) and the T<sub>C,57+</sub> frequency group (high or low, using median = 0.686% as cut-off). The inner lines connect parameters with positive (red) or negative (blue) association-based correlation analogues to Pearson (R &gt; ±0.6). (<b>B</b>) Boxplot analysis with <span class="html-italic">t</span>-test significance per type of omics data of the analytes identified in (<b>A</b>). (<b>C</b>) Bar plot distribution of the high (<span class="html-italic">n</span> = 34) and low (<span class="html-italic">n</span> = 28) infiltration groups used for this analysis. ns, non-statistical significance, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001, **** <span class="html-italic">p</span> &lt; 0.0001.</p>
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<p>Summary of results. (<b>A</b>) Proteins (green box) and transcripts (purple box) upregulated in tumor-sparse regions (data shown over pink background) compared to tumor-rich regions. (<b>B</b>) Key proteins displayed on surface of the individual cells and mRNAs (purple boxes) on each T-cell subset. The left box refers to upregulated transcripts in T<sub>H</sub> subsets and the right box refers to upregulated transcripts in T<sub>C</sub> subsets. (<b>C</b>) Main proteins and mRNAs predictive of high or low CD3+ T-cell infiltration. Purple boxes indicate upregulated transcripts in each indicated cell type. (<b>D</b>) Main proteins and mRNAs predictive of high or low T<sub>C,57+</sub> infiltration. Purple boxes indicate differentially upregulated mRNAs in each indicated cell type. Biorender.com was used to create the illustrations.</p>
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15 pages, 4010 KiB  
Article
Modeling the Binding of Anticancer Peptides and Mcl-1
by Shamsa Husain Ahmed Alhammadi, Bincy Baby, Priya Antony, Amie Jobe, Raghad Salman Mohammed Humaid, Fatema Jumaa Ahmed Alhammadi and Ranjit Vijayan
Int. J. Mol. Sci. 2024, 25(12), 6529; https://doi.org/10.3390/ijms25126529 - 13 Jun 2024
Viewed by 586
Abstract
Mcl-1 (myeloid cell leukemia 1), a member of the Bcl-2 family, is upregulated in various types of cancer. Peptides representing the BH3 (Bcl-2 homology 3) region of pro-apoptotic proteins have been demonstrated to bind the hydrophobic groove of anti-apoptotic Mcl-1, and this interaction [...] Read more.
Mcl-1 (myeloid cell leukemia 1), a member of the Bcl-2 family, is upregulated in various types of cancer. Peptides representing the BH3 (Bcl-2 homology 3) region of pro-apoptotic proteins have been demonstrated to bind the hydrophobic groove of anti-apoptotic Mcl-1, and this interaction is responsible for regulating apoptosis. Structural studies have shown that, while there is high overall structural conservation among the anti-apoptotic Bcl-2 (B-cell lymphoma 2) proteins, differences in the surface groove of these proteins facilitates binding specificity. This binding specificity is crucial for the mechanism of action of the Bcl-2 family in regulating apoptosis. Bim-based peptides bind specifically to the hydrophobic groove of Mcl-1, emphasizing the importance of these interactions in the regulation of cell death. Molecular docking was performed with BH3-like peptides derived from Bim to identify high affinity peptides that bind to Mcl-1 and to understand the molecular mechanism of their interactions. The interactions of three identified peptides, E2gY, E2gI, and XXA1_F3dI, were further evaluated using 250 ns molecular dynamics simulations. Conserved hydrophobic residues of the peptides play an important role in their binding and the structural stability of the complexes. Understanding the molecular basis of interaction of these peptides will assist in the development of more effective Mcl-1 specific inhibitors. Full article
(This article belongs to the Special Issue New Insights into Anti-cancer Drug Discovery and Development)
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<p>Structure of Mcl-1 (grey) with a modified Bim BH3 peptide, SAH-MS1-18 (cyan) (PDB: 5W89) [<a href="#B35-ijms-25-06529" class="html-bibr">35</a>].</p>
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<p>Sequence of the top scoring Bim-based peptides. The heptad convention used to refer to positions in the BH3 peptide is shown. Numbering uses the convention (abcdefg)n. Complete heptad repeats 2 to 4 are indicated above the sequences.</p>
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<p>(<b>A</b>) The crystal structure of Mcl-1 (grey), complexed with a modified Bim BH3 peptide SAH-MS1-18 (cyan) (PDB ID:5W89) [<a href="#B35-ijms-25-06529" class="html-bibr">35</a>] as well as docked E2gI (orange), E2gY (green), and XXA1 F3dI (pink). (<b>B</b>) Docked pose of E2gY in Mcl-1. (<b>C</b>) Docked pose of E2gI in Mcl-1. (<b>D</b>) Docked pose of XXA1 F3dI in Mcl-1. Mcl-1 residues are colored in blue.</p>
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<p>(<b>A</b>) Root mean square deviation (RMSD) of protein Cα atoms obtained from 250 ns simulations of the crystal structure (PDB ID: 5W89) with bound SAH-MS1-18 (cyan) and docked E2gI (orange), E2gY (green), and XXA1 F3dI (pink). (<b>B</b>) Root mean square fluctuation (RMSF) of protein residues obtained from 250 ns simulations. Crystal structure (PDB ID: 5W89) with bound SAH-MS1-18 (cyan) and docked E2gI (orange), E2gY(green), and XXA1 F3dI (pink).</p>
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<p>Percentage of simulation time during which intermolecular polar and hydrophobic contacts were retained between Mcl-1 and peptides in the 250 ns systems. (<b>A</b>) Mcl-1/SAH-MS1-18 inhibitor, and (<b>B</b>) Mcl-1/E2gI.</p>
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<p>Percentage of simulation time during which intermolecular polar and hydrophobic contacts were retained between Mcl-1 and peptides in the 250 ns systems. (<b>A</b>) Mcl-1/E2gY, and (<b>B</b>) Mcl-1/XXA1 F3dI.</p>
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23 pages, 3078 KiB  
Article
Non-Targeted PFAS Suspect Screening and Quantification of Drinking Water Samples Collected through Community Engaged Research in North Carolina’s Cape Fear River Basin
by Rebecca A. Weed, Grace Campbell, Lacey Brown, Katlyn May, Dana Sargent, Emily Sutton, Kemp Burdette, Wayne Rider, Erin S. Baker and Jeffrey R. Enders
Toxics 2024, 12(6), 403; https://doi.org/10.3390/toxics12060403 - 31 May 2024
Viewed by 1148
Abstract
A community engaged research (CER) approach was used to provide an exposure assessment of poly- and perfluorinated (PFAS) compounds in North Carolina residential drinking water. Working in concert with community partners, who acted as liaisons to local residents, samples were collected by North [...] Read more.
A community engaged research (CER) approach was used to provide an exposure assessment of poly- and perfluorinated (PFAS) compounds in North Carolina residential drinking water. Working in concert with community partners, who acted as liaisons to local residents, samples were collected by North Carolina residents from three different locations along the Cape Fear River basin: upper, middle, and lower areas of the river. Residents collected either drinking water samples from their homes or recreational water samples from near their residence that were then submitted by the community partners for PFAS analysis. All samples were processed using weak anion exchange (WAX) solid phase extraction and analyzed using a non-targeted suspect screening approach as well as a quantitative approach that included a panel of 45 PFAS analytes, several of which are specific to chemical industries near the collection site locations. The non-targeted approach, which utilized a suspect screening list (obtained from EPA CompTox database) identified several PFAS compounds at a level two confidence rating (Schymanski scale); compounds identified included a fluorinated insecticide, a fluorinated herbicide, a PFAS used in polymer chemistry, and another that is used in battery production. Notably, at several locations, PFOA (39.8 ng/L) and PFOS (205.3 ng/L) were at levels that exceeded the mandatory EPA maximum contaminant level (MCL) of 4 ng/L. Additionally, several sites had detectable levels of PFAS that are unique to a local chemical manufacturer. These findings were communicated back to the community partners who then disseminated this information to the local residents to help empower and aid in making decisions for reducing their PFAS exposure. Full article
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Graphical abstract
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<p>Quantitative results for the PFAS panel reported in ng/L and organized by compound class: PFCA (<b>A</b>), PFSA (<b>B</b>), PFECA and PFESA (<b>C</b>), and FTS, PFSAm, and Zwitterions (<b>D</b>). Sample names are denoted by their collection region and site number: Pittsboro (P), Fayetteville (F), and Wilmington (W). Note, several sites had high levels of a specific PFAS compound, so the x-axis was split to account for the higher concentrations.</p>
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<p>Concentrations per site of PFOA (<b>A</b>) and PFOS (<b>B</b>) that exceed the EPA MCL of 4 ng/L are denoted by the red dashed line.</p>
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<p>NTA results for Fipronil sulfone (<b>A</b>) structure, (<b>B</b>) Isotopic pattern, (<b>C</b>) fragmentation spectra with subclass structures for ions matching to either the Fluoro-match or NIST compound fragmentation databases, (<b>D</b>) mzCloud mirror plot, where green fragments match to experimental database hits and red fragments are missing, and (<b>E</b>) box and whisker plots of the peak abundances for each geographical region (Pittsboro: dark blue circles, Fayetteville (tan triangles), and Wilming-ton (grey squares).</p>
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<p>NTA results for bistriflimide (<b>A</b>) structure, (<b>B</b>) Isotopic pattern, (<b>C</b>) fragmentation spectra with subclass structures for ions matching to either the Fluoromatch or NIST compound fragmentation databases (green) or Barola et. al. [<a href="#B63-toxics-12-00403" class="html-bibr">63</a>] (blue), and (<b>D</b>) box and whisker plots of the peak abundances for each geographical region (Pittsboro: dark blue circles, Fayetteville (tan triangles), and Wilmington (grey squares) and notable sites are marked with identifier and if the water was drinking (<b>D</b>) or recreational water (<b>R</b>).</p>
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<p>NTA results for 3-(Tridecafluoroundecyl)catechol (<b>A</b>) structure, (<b>B</b>) isotopic pattern, (<b>C</b>) fragmentation spectra with subclass structures for ions matching to either the Fluoromatch or NIST compound in-silico fragmentation databases (green) or METFrag (blue), and (<b>D</b>) box and whisker plots of the peak abundances for each geographical region (Pittsboro: dark blue circles, Fayetteville (tan triangles), and Wilmington (grey squares) and notable sites are marked with identifier and if the water was drinking (<b>D</b>) or recreational water (<b>R</b>).</p>
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<p>NTA results for 2-Ethyl-4-nitro-6-(trifluoromethyl)-1H-benzimidazol-1-ol (<b>A</b>) structure, (<b>B</b>) Isotopic pattern, (<b>C</b>) fragmentation spectra with subclass structures for ions matching to either the Fluoromatch or NIST compound fragmentation databases (green) or METFrag (blue), (<b>D</b>) Mirror plot of an experimental scan compared to a reference scan for a related metabolite from the mzCloud database, where green fragments match to database hits and red fragments are missing, and (<b>E</b>) box and whisker plots of the peak abundances for each geographical region (Pittsboro: dark blue circles, Fayetteville (tan triangles), and Wilmington (grey squares) and notable sites are marked with identifier and if the water was drinking (<b>D</b>) or recreational water (<b>R</b>).</p>
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11 pages, 2624 KiB  
Article
Effectiveness of Erythrocyte Morphology Observation as an Indicator for the Selection and Qualification of Blood in a Mechanically Induced Hemolysis Test
by Jeonghwa Kim, Taeho Kim, Sekyung Kim, Joonho Eom and Taewon Kim
Appl. Sci. 2024, 14(11), 4695; https://doi.org/10.3390/app14114695 - 29 May 2024
Viewed by 680
Abstract
Background: This study was conducted to confirm the reliability of an in vitro mechanically induced hemolysis test (ISO 10993-4:2017), which is essential for ensuring the safety of blood pumps. Methods: For appropriate anticoagulant selection, porcine blood was prepared in anticoagulant citrate dextrose solution [...] Read more.
Background: This study was conducted to confirm the reliability of an in vitro mechanically induced hemolysis test (ISO 10993-4:2017), which is essential for ensuring the safety of blood pumps. Methods: For appropriate anticoagulant selection, porcine blood was prepared in anticoagulant citrate dextrose solution A (ACD-A), heparin, and citrate phosphate dextrose adenine (CPDA-1), respectively, according to the ASTM F1830 standard. Anticoagulant-treated porcine and bovine blood were circulated in a mock circulatory loop (MCL) for 6 h to observe the rate of plasma-free hemoglobin (pfHb) and RBCs with morphological integrity. Results: A morphological loss of red blood cells (RBCs) was observed over time. While there were differences in morphological loss depending on the anticoagulant, no consistent trend could be identified. The pfHb concentration was significantly higher in bovine than in porcine blood. Conversely, the number of RBCs with morphological integrity decreased over time in both, but the ratio of RBCs with morphological integrity was similar across all timepoints. Conclusions: The percentage of RBCs with morphological integrity can be used as a reliable indicator for the interpretation of mechanically induced hemolysis results in different blood types. Furthermore, the reliability of the in vitro mechanically induced hemolysis test (ISO 10993-4:2017) was assessed. Full article
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<p>Mock circulatory loop (MCL). (<b>A</b>) Schematic and (<b>B</b>) image of the in vitro MCL system model.</p>
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<p>Red blood cell observation. (<b>A</b>) Red blood cell counts of Case 1 porcine with various anticoagulants (%). (<b>B</b>) Red blood cell counts of Case 2 porcine with various anticoagulants (%).</p>
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<p>Test conditions. (<b>A</b>) Changes in pump flow rate over time. (<b>B</b>) Changes in temperature over time. (<b>C</b>) Changes in pressure over time.</p>
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<p>Hemolysis test. (<b>A</b>) Plasma-free hemoglobin (Δ<span class="html-italic">pfHb</span>) over time. (<b>B</b>) The percentages of morphologically intact red blood cells over time. (<b>C</b>) Light microscopy observations of red blood cells over time (20×).</p>
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<p>Hemolysis test. (<b>A</b>) Plasma-free hemoglobin (Δ<span class="html-italic">pfHb</span>) over time. (<b>B</b>) The percentages of morphologically intact red blood cells over time. (<b>C</b>) Light microscopy observations of red blood cells over time (20×).</p>
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<p>Hemolysis analysis. (<b>A</b>) Delta plasma-free hemoglobin (ΔpfHb) and (<b>B</b>) normalized hemolytic index (NIH) over time. Roller pumps are depicted in dark gray and centrifugal pumps are depicted in light gray. (<b>C</b>) Light microscopy observations of untreated red blood cells (200×). (<b>D</b>) The percentages of morphologically intact red blood cells over time.</p>
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36 pages, 15367 KiB  
Article
Marine Cytotoxin Santacruzamate A Derivatives as Potent HDAC1-3 Inhibitors and Their Synergistic Anti-Leukemia Effects with Venetoclax
by Wanting Hao, Leyan Wang, Tongqiang Xu, Geng Jia, Yuqi Jiang, Chong Qin and Xiaoyang Li
Mar. Drugs 2024, 22(6), 250; https://doi.org/10.3390/md22060250 - 28 May 2024
Viewed by 910
Abstract
Acute myeloid leukemia (AML) is a hematologic malignancy characterized by infiltration of the blood and bone marrow, exhibiting a low remission rate and high recurrence rate. Current research has demonstrated that class I HDAC inhibitors can downregulate anti-apoptotic proteins, leading to apoptosis of [...] Read more.
Acute myeloid leukemia (AML) is a hematologic malignancy characterized by infiltration of the blood and bone marrow, exhibiting a low remission rate and high recurrence rate. Current research has demonstrated that class I HDAC inhibitors can downregulate anti-apoptotic proteins, leading to apoptosis of AML cells. In the present investigation, we conducted structural modifications of marine cytotoxin Santacruzamate A (SCA), a compound known for its inhibitory activity towards HDACs, resulting in the development of a novel series of potent class I HDACs hydrazide inhibitors. Representative hydrazide-based compound 25c exhibited concentration-dependent induction of apoptosis in AML cells as a single agent. Moreover, 25c exhibited a synergistic anti-AML effect when combined with Venetoclax, a clinical Bcl-2 inhibitor employed in AML therapy. This combination resulted in a more pronounced downregulation of anti-apoptotic proteins Mcl-1 and Bcl-xL, along with a significant upregulation of the pro-apoptotic protein cleaved-caspase3 and the DNA double-strand break biomarker γ-H2AX compared to monotherapy. These results highlighted the potential of 25c as a promising lead compound for AML treatment, particularly when used in combination with Venetoclax. Full article
(This article belongs to the Section Synthesis and Medicinal Chemistry of Marine Natural Products)
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<p>Basis of compound design. (<b>a</b>) Docking pose of SCA (green) in the binding site of HDAC3 (PDB: 4A69); (<b>b</b>) chemical structures of the designed analogs of SCA.</p>
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<p>Western blot analysis of the enzyme substrates that interact with <b>25c</b> in MV4-11 cells. (<b>a</b>) Western blot analysis of AcHH3(Lys27), AcHH4, Ac-tubulin, HDAC6 and α-tubulin in MV4-11 cells after treatment of compound <b>25c</b> and SCA at the concentrations of 0.2, 0.4 and 200 μM for 24 h; (<b>b</b>) The expression levels of AcHH3(Lys27), AcHH4, Ac-tubulin and α-tubulin were quantified. Data are shown as mean ± SEM from three independent experiments.</p>
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<p>Combination index (CI) values for <b>25c</b> and Venetoclax after treatment for 72 h. (<b>a</b>) Inhibition rate and CI for the combination of <b>25c</b> and Venetoclax after treatment for 72 h. (<b>b</b>) Fraction affected and CI for <b>25c</b> and Venetoclax after treatment for 72 h. Data were analyzed using CompuSyn Software. CIs of &lt;1, =1 and &gt;1 indicate synergism, additive effect and antagonism, respectively.</p>
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<p>Apoptosis analysis of <b>25c</b> and Venetoclax in MV4-11 for 24 h.</p>
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<p>Cell cycle analysis of <b>25c</b> and Venetoclax in MV4-11 cells for 24 h.</p>
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<p>Changes in Bcl-2 family apoptotic regulatory proteins and apoptotic pathway proteins detected by Western blot.</p>
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<p>Stability of compound <b>25c</b> in rat plasma, artificial gastroenteric fluid and artificial intestinal fluid within 24 h.</p>
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<p>Preparation of hydrazide substituent compound <b>4</b>. Reagents and conditions: (a) tert-butyl hydrazinecarboxylate, toluene, 83% yield; (b) 1-bromopropane, TBAB and K<sub>2</sub>CO<sub>3</sub>, acetonitrile, 90% yield; (c) monomethylhydrazine, ethanol, 85% yield.</p>
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<p>Synthesis of compounds <b>9a</b>–<b>9m</b>. Reagents and conditions: (d) 2-phenylethan-1-amine, HATU, DIPEA, DMF, 55% yield; (e) 1.5 N NaOH aqueous, MeOH; 1 N HCl aqueous, pH = 5, 85% yield; (f) <b>4</b>, HATU and DIPEA, DMF, 67% yield. (g) TFA, DCM; saturated 1 N NaOH aqueous, pH = 9, 76% yield.</p>
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<p>Synthesis of the compounds <b>14a</b>–<b>14f</b>. Reagents and conditions: (h) 2-phenylacetyl chloride, DIPEA, THF, 75% yield; (i) 1.5 N NaOH aqueous, MeOH; 1 N HCl aqueous, pH = 5, 85% yield; (j) <b>4</b>, HATU and DIPEA, DMF, 67% yield; (k) TFA, DCM; saturated 1 N NaOH aqueous, pH = 9, 76% yield.</p>
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<p>Synthesis of compounds <b>19a</b>–<b>19d</b>. Reagents and conditions: (l) 2-(4-(3-methoxy-3-oxoprop-1-en-1-yl)phenyl)acetic acid, HATU, DIPEA, DMF, 65% yield; (m) 1.5 N NaOH aqueous, MeOH; 1 N HCl aqueous, pH = 5, 85% yield; (n) <b>4</b>, HATU and DIPEA, DMF, 67% yield; (o) TFA, DCM; saturated 1 N NaOH aqueous, pH = 9, 76% yield.</p>
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<p>Synthesis of compounds <b>25a</b>–<b>25g</b>. Reagents and conditions: (p) methyl 3-(4-(bromomethyl)phenyl)acrylate, K<sub>2</sub>CO<sub>3</sub>, DMF, 45% yield; (q) (Boc)<sub>2</sub>O, TEA, DCM, 70% yield; (r) 1.5 N NaOH aqueous, MeOH; 1 N HCl aqueous, pH = 5, 85% yield; (s) <b>4</b>, HATU and DIPEA, DMF, 67% yield; (t) TFA, DCM; saturated 1 N NaOH aqueous, pH = 9, 76% yield.</p>
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<p>Synthesis of compound <b>30</b>. Reagents and conditions: (u) methyl 3-(4-(bromomethyl)phenyl)acrylate, K<sub>2</sub>CO<sub>3</sub>, DMF, 70% yield; (v) 1.5 N NaOH aqueous, MeOH; 1 N HCl aqueous, pH = 5, 85% yield; (w) <b>4</b>, HATU and DIPEA, DMF, 55% yield; (x) TFA, DCM; saturated 1 N NaOH aqueous, pH = 9, 80% yield.</p>
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17 pages, 6301 KiB  
Article
Mechanical, Fire, and Electrical Insulation Properties of Polyurethane Fly Ash Composites
by Kunigal N. Shivakumar, Bharath Kenchappa and Kazi A. Imran
Polymers 2024, 16(11), 1507; https://doi.org/10.3390/polym16111507 - 27 May 2024
Viewed by 650
Abstract
This paper demonstrates that ash composites, comprising fly ash and polyurethane, can be used to develop value-added products that exhibit an effective decrease in the leaching of coal ash inorganics to less than one-third of the Environmental Protection Agency (EPA)’s maximum contaminant level [...] Read more.
This paper demonstrates that ash composites, comprising fly ash and polyurethane, can be used to develop value-added products that exhibit an effective decrease in the leaching of coal ash inorganics to less than one-third of the Environmental Protection Agency (EPA)’s maximum contaminant level (MCL) when soaked in a water circulation system for 14 months. Furthermore, the composite blocks remain safe even with ruptured surfaces. The concept of encapsulating fly ash within ash composites by using a polar polymer to bind the fine inorganic particles, mimicking how nature does it in the original unburned coal, ensures the safety of the composite. The ash composites can be formulated to have designed mechanical, fire, and electrical properties by controlling the formulation and the density. The properties of typical density composites were produced, measured, and compared with commercial materials. This paper also demonstrates that ash composite technology can be extended to coal ash stored in ponds. Finally, a typical electric utility box cover was designed, fabricated, and test validated. The box cover has less than one-half the weight of the original box cover for the same design limits. Finally, the benefits of this ash-composite technology for product manufacturers, society, and ash producers are summarized. Full article
(This article belongs to the Special Issue Mechanical Behaviors and Properties of Polymer Materials)
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<p>Coal and coal combustion residuals [<a href="#B3-polymers-16-01507" class="html-bibr">3</a>].</p>
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<p>Ash composite samples fabricated (panel, decorative mold, and block) [<a href="#B3-polymers-16-01507" class="html-bibr">3</a>].</p>
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<p>EPA M1313 tumbling test for ash particles [<a href="#B3-polymers-16-01507" class="html-bibr">3</a>].</p>
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<p>Circulating water system for leach testing of ash-composite blocks [<a href="#B3-polymers-16-01507" class="html-bibr">3</a>].</p>
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<p>Unground and ground ash composite blocks.</p>
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<p>Test specimen configuration and loading.</p>
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<p>(<b>a</b>) Test setup (Picture taken from ASTM D635); (<b>b</b>) Test setup used for fire test.</p>
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<p>Specimen extraction for 0.5 in and 3 in samples.</p>
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<p>Compression strength versus ash composite density.</p>
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<p>Flexure strength versus ash composite density.</p>
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<p>Utility box cover, loading conditions, and designed panel.</p>
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<p>Simulated test conducted at NC A&amp;T State University.</p>
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