Computational Toxicology最新文献

{"title":"Hybrid QSAR modeling of LD50 in organophosphorus nerve agents: a mechanistic approach using DFT and molecular docking","authors":"Youngchan Jang ,&nbsp;Jeongyun Kim ,&nbsp;Doo-Hee Lee ,&nbsp;Jin Yoo ,&nbsp;Jeongwan Park ,&nbsp;Ku Kang","doi":"10.1016/j.comtox.2025.100363","DOIUrl":"10.1016/j.comtox.2025.100363","url":null,"abstract":"<div><div>Chemical warfare agents (CWAs), particularly organophosphorus (OP) nerve agents, are among the most toxic and persistent compounds known, posing significant threats to human health and security. Experimental determination of their median lethal dose (LD₅₀) values is limited by ethical, biosafety, and accessibility constraints. While conventional QSAR models provide useful approximations, they often lack mechanistic interpretability, especially for novel agents.</div><div>In this study, we present a hybrid QSAR framework that integrates mechanistically relevant descriptors derived from density functional theory (DFT) and molecular docking simulations with conventional physicochemical features to predict LD₅₀ of OP nerve agents. The key mechanistic descriptors include acetylcholinesterase (AChE) binding affinity and serine phosphorylation interaction energy, capturing distinct toxicodynamic phases of nerve agent action.</div><div>We evaluate both linear regression and random forest models to assess predictive performance and interpretability. Cross-validation confirms that incorporating mechanistic features modestly improves accuracy and generalizability. Feature importance analysis identifies interaction energy as the most influential predictor, aligning with the irreversible inhibition mechanism of AChE.</div><div>Importantly, the model is capable of predicting LD₅₀ values for structurally untested agents, including GF and Novichok compounds, thereby extending its utility to substances lacking experimental data. This study highlights the potential of mechanistically grounded in silico methods as an ethically sound and scalable alternative to animal testing for acute toxicity assessment. By aligning with regulatory needs for interpretable and reproducible predictions, the proposed approach contributes to integrated testing strategies, and new approach methodologies in computational toxicology.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"35 ","pages":"Article 100363"},"PeriodicalIF":3.1,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144298564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revisiting the Role of Liver X Receptors (LXRs) in Disease: In-silico Discovery of Novel Modulators Through Molecular Docking and Chemico-Pharmacokinetic Profiling","authors":"Sarder Arifuzzaman MS ,&nbsp;Md. Harun-Or-Rashid PhD ,&nbsp;Farhina Rahman Laboni M. Pharm. ,&nbsp;Mst Reshma Khatun MS ,&nbsp;Nargis Sultana Chowdhury PhD","doi":"10.1016/j.comtox.2025.100361","DOIUrl":"10.1016/j.comtox.2025.100361","url":null,"abstract":"<div><div>Liver X Receptors (LXRs) play a critical role in regulating lipid metabolism and inflammation, with their altered activity linked to several metabolic diseases. Although several LXR agonists have been identified, their clinical use has been limited due to adverse effects. In this study, we first leveraged multiple biological data repositories (including RNA-seq, Human Protein Atlas, DisGeNET, and WebGestalt) to examine the expression of LXRs at both the mRNA and protein levels across various tissues. We performed network and pathway analyses to redefine the physiological roles and disease associations of LXRs. Our findings emphasize the diverse functions of LXRs and highlight the potential for small molecules to pharmacologically modulate LXR activity for therapeutic purposes. In the second phase, we conducted an in-silico search for novel LXR modulators, beginning with molecular docking studies of eleven ligands that have been previously tested in preclinical or clinical settings. Based on docking scores and chemico-pharmacokinetic properties, we identified T0901317 and AZ876 as leading candidates, showing the highest binding affinity for LXR-α and LXR-β, respectively. In the final step, we extended our screening to discover new LXR ligands guided by the chemical structures of T0901317 and AZ876. Our docking and molecular dynamics (MD) simulations revealed that ZINC000095464663 and ZINC000021912925 exhibited the strongest binding affinities, alongside favorable pharmacokinetic profiles for both LXR subtypes. In conclusion, our in-silico approach, combining network analysis, virtual screening, molecular docking, MD simulations, and chemico-pharmacokinetic assessments, has uncovered two promising ligands for oral administration, offering potential for future therapeutic interventions targeting LXRs.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100361"},"PeriodicalIF":3.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144212197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards a quantitative adverse outcome pathway for liver carcinogenesis: From proliferation to prediction","authors":"Christina H.J. Veltman ,&nbsp;Hiba Khalidi ,&nbsp;Elias Zgheib ,&nbsp;Bob van de Water ,&nbsp;Mirjam Luijten ,&nbsp;Jeroen L.A. Pennings","doi":"10.1016/j.comtox.2025.100359","DOIUrl":"10.1016/j.comtox.2025.100359","url":null,"abstract":"<div><div>Hazard assessment of non-genotoxic carcinogens could greatly benefit from next generation risk assessment approaches, driven by the multitude of mechanisms through which non-genotoxic carcinogens operate. One method for structuring new approach methodology-derived data is the adverse outcome pathway (AOP) concept. Currently, mostly qualitative AOPs are described, limiting their application for regulatory decision making. In contrast, quantitative AOPs use mathematical terms to describe the relationships between key events (KEs), allowing for the derivation of a Point of Departure (PoD). Here, we report quantification of the key event relationship (KER) between sustained hepatocyte proliferation and liver tumour formation, two KEs of AOP#220 relating to CYP2E1 activation leading to liver cancer. We use incidence of histopathological lesions indicative of proliferation, as well as BrdU labelling obtained from existing sub-chronic toxicity studies in rats, to quantify proliferation. For liver cancer, incidences of hepatocellular adenoma and carcinoma from 2-year rodent carcinogenicity studies were collected. Data for both KEs were combined to calibrate a response-response model, and Bayesian logistic regression analysis was applied to obtain predictions and credible intervals for carcinogenicity. Proliferative lesion incidence was observed to be a highly specific, yet insensitive predictor, and combining this with BrdU labelling yields more accurate predictions of carcinogenicity. Importantly, we demonstrate that for most of the chemicals tested, inclusion of BrdU labelling returns more precise predicted benchmark dose intervals for PoD derivation. To further explore this quantitative KER and its regulatory application, we propose to include and standardize BrdU labelling for sub-chronic toxicity studies performed for regulatory purposes.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100359"},"PeriodicalIF":3.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144253717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Alarming Consequences of Workforce Reductions at the FDA, EPA, NIH and CDC in the United States","authors":"Martin van den Berg PhD (Editor-in-Chief, Regulatory Toxicology Pharmacology Current Opinion in Toxicology) ,&nbsp;Daniel R. Dietrich PhD (Editor-in-Chief, Chemico-Biological Interactions, Computational Toxicology, Journal of Toxicology and Regulatory Policy) ,&nbsp;Sonja von Aulock PhD (Editor-in-Chief, ALTEX – Alternatives to Animal Experimentation) ,&nbsp;Anna Bal-Price PhD (Editor-in-Chief, Reproductive Toxicology) ,&nbsp;Michael D. Coleman PhD (Editor-in-Chief, Environmental Toxicology and Pharmacology) ,&nbsp;Mark T.D. Cronin PhD (Editor-in-Chief, Computational Toxicology) ,&nbsp;Paul Jennings PhD (Editor-in-Chief, Toxicology in Vitro) ,&nbsp;Angela Mally PhD (Editor-in-Chief, Toxicology Letters) ,&nbsp;Mathieu Vinken PhD (Editor-in-Chief, Toxicology NAM Journal) ,&nbsp;Matthew C. Wright PhD (Editor-in-Chief, Food and Chemical Toxicology)","doi":"10.1016/j.comtox.2025.100352","DOIUrl":"10.1016/j.comtox.2025.100352","url":null,"abstract":"","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100352"},"PeriodicalIF":3.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144279466","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From blood to body tissues: a dynamic framework for estimating volatile organic compound exposure using Kalman filtering and physiological models","authors":"Laurent Simon","doi":"10.1016/j.comtox.2025.100362","DOIUrl":"10.1016/j.comtox.2025.100362","url":null,"abstract":"<div><div>Accurate quantification of volatile organic compound (VOC) concentrations in target tissues is critical for robust exposure assessment and toxicological risk analysis. Conventional methods that rely on blood measurements and partition behaviors often fail to consider the transient nature of real-time exposure. Physiologically-based pharmacokinetic (PBPK) models advance predictive capabilities by simulating absorption, distribution, metabolism, and excretion (ADME) processes. However, their accuracy is limited by measurement errors and parameter uncertainties. This study combines the Kalman Filter (KF) with a linear PBPK model (KF-PBPK) to dynamically refine VOC tissue concentration estimates and support real-time exposure assessment using blood measurements. The Kalman Filter is an algorithm that continuously updates model predictions based on new measurements. It filters out noise and improves the accuracy of estimates. The application of the KF-Expectation Maximization (KF-EM) approach to human m-xylene exposure data improved the signal-to-noise ratio (SNR) from 13.9 dB to 17.4 dB. The KF-PBPK scheme effectively captured the multi-compartment kinetics of VOC distribution across several compartments. Filtered estimates closely matched the experimental data, demonstrating the framework’s effectiveness in modeling and predicting human VOC exposure. This research suggests that the KF-PBPK is a reliable tool for improving VOC exposure assessments, with potential implications for environmental pollution monitoring, risk assessment and regulatory decision-making.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100362"},"PeriodicalIF":3.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144272478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chronic and acute eco-toxicity modeling of carcinogenic and hazardous air pollutants toward humans for critical risk assessment and regulatory decision-making","authors":"Ankur Kumar ,&nbsp;Probir Kumar Ojha ,&nbsp;Kunal Roy","doi":"10.1016/j.comtox.2025.100358","DOIUrl":"10.1016/j.comtox.2025.100358","url":null,"abstract":"<div><div>Rapid and regular exposure to carcinogenic, toxic, and hazardous chemicals in humans and other living organisms can cause serious chronic (long-term) and acute (short-term) health issues. Since <em>in-vitro</em> and <em>in-vivo</em> toxicity testing requires a long time, a large number of animal experiments, and a high cost, in-silico toxicity testing is the best alternative supported by various regulatory organizations. In our current work, multiple regression-based Quantitative structure–activity relationship models (two chronic toxicity models, a QAAR (quantiative activity-activity relationship) model (chronic studies), and seven acute toxicity models) have been developed to assess the chronic and acute toxicities of carcinogenic chemicals toward humans rigorously following the OECD principles. Statistical validation metrics (R² = 0.604–0.990, Q²_LOO = 0.558––0.988, Q²_F1 = 0.580–0.990, Q²_F2 = 0.503–0.988, MAE_test = 0.103–0.766) demonstrated the robustness, reliability, reproducibility, and predictivity of the developed models. The developed models were utilized to screen the PPDB database, and their predictions were validated against real-world data to confirm their predictive accuracy and reliability. Thus, the present work will significantly aid in bridging the chronic and acute toxicity data gap, identifying carcinogenic chemicals, screening various chemical databases, and developing safer (from observed bio-marker), non-carcinogenic, and greener chemicals strictly obeying the reduction, refinement, and replacement (3Rs) guidelines.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100358"},"PeriodicalIF":3.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144196203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparative study of biostatistical pipelines for benchmark concentration modeling of in vitro screening assays","authors":"Kelly E. Carstens ,&nbsp;Arif Dönmez ,&nbsp;Jui-Hua Hsieh ,&nbsp;Kristina Bartmann ,&nbsp;Katie Paul Friedman ,&nbsp;Katharina Koch ,&nbsp;Martin Scholze ,&nbsp;Ellen Fritsche","doi":"10.1016/j.comtox.2025.100360","DOIUrl":"10.1016/j.comtox.2025.100360","url":null,"abstract":"<div><div>New approach methods (NAMs) have been prioritized to reduce the use of animals for chemical safety assessment while continuing to protect human health and the environment. A key challenge of generating toxicity data is the implementation of a standardized analysis approach for transparent and reproducible benchmark concentration (BMC) estimation and uncertainty quantification for assay developers, regulators, and other stakeholders. In this study, we compared the bioactivity results of 321 chemical samples from four established BMC analysis pipelines used for evaluation of developmental neurotoxicity (DNT) NAMs data: the ToxCast pipeline (tcpl), CRStats, DNT DIVER (Curvep and Hill pipelines). We found an overall activity hit call concordance of 77.2 % and highly correlated BMC estimations (r = 0.92 ± 0.02 SD), demonstrating generally good agreement across pipelines. Discordance appeared to be explained predominantly by noise within the data and borderline activity (activity occuring near the benchmark response level). Evaluation of the BMC confidence intervals indicated that pipeline selection may impact the estimation of the BMC lower bound. Consideration of biphasic models appeared important for capturing biologically-relevant changes in activity in the DNT battery. Lastly, different approaches to compute ‘selective’ bioactivity (activity below the threshold of cytotoxicity) were compared, identifying the CRstats classification model as more stringent for classifying selective activity. Overall, these findings indicated greater confidence in NAMs bioactivity results and emphasize the importance of understanding strengths and uncertainties of concentration–response modeling pipelines for informing biological interpretation and application decision making.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100360"},"PeriodicalIF":3.1,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144240741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The first report on chronic toxicity assessment of metals towards Ceriodaphnia dubia using QSTR technique: A step towards healthier and safer human health and eco-system","authors":"Ankur Kumar ,&nbsp;Joyita Roy ,&nbsp;Probir Kumar Ojha","doi":"10.1016/j.comtox.2025.100357","DOIUrl":"10.1016/j.comtox.2025.100357","url":null,"abstract":"<div><div>Exposure of humans and other living organisms to metals (including heavy metals) can lead to serious chronic and acute health effects, which may sometimes be life-threatening. As a result, assessing the toxicity of heavy metals is essential. However, experimental toxicity data for heavy metals is limited, and their toxicity estimation can be highly costly, lengthy analysis durations, and may require animal testing. Therefore, <em>in-silico</em> approaches such as quantitative structure–activity relationship (QSAR) are a suitable alternative. In this work, we have developed multi-endpoints MLR-QSAR models to assess the chronic toxicity of heavy metal towards <em>Ceriodaphnia dubia</em> using 48 data points and obeying the Organization for Economic Cooperation and Development (OECD) guidelines. Intra-endpoint uni-variate models were developed to fill the toxicity data gaps between the endpoints (acute to chronic). The statistical results of the developed models (individual models M1-M4; R² = 0.691–0.738, Q²_LOO = 0.542–0.578, Q²_F1 = 0.673–0.732, Q²_F2 = 0.552–0.580, MAE_95%data = 0.437–0.753; intra-endpoints models IEM1-IEM9; R² = 0.952–0.988, Q²_LOO = 0.907–0.987, Q²_F1 = 0.885–0.991, Q²_F2 = 0.979–0.991, MAE_95%data = 0.120–0.436) infer that the models are robust, reliable, reproducible, and predictive. The descriptors contributing to the development of the model imply that the release of electrons, formation of cations, higher electronegativity, and the presence of neutrons in the heavy metals significantly influence the toxicity caused by the metals. Thus, this study presents <em>in silico</em> models aimed at controlling the exposure of living organisms to toxic heavy metals. It assesses both acute and chronic toxicity, addresses gaps in toxicity data, and strives to create healthier and safer ecosystems by strictly following the principles of reduction, replacement, and refinement (the RRR framework).</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100357"},"PeriodicalIF":3.1,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144168106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of progesterone receptor binding potency, agonism and antagonism using machine learning models","authors":"Nemanja Milošević ,&nbsp;Nataša Sukur Milošević ,&nbsp;Svetlana Fa Nedeljkovic ,&nbsp;Bojana Stanic ,&nbsp;Nebojsa Andric","doi":"10.1016/j.comtox.2025.100351","DOIUrl":"10.1016/j.comtox.2025.100351","url":null,"abstract":"<div><div>The use of Machine Learning (ML) models to predict the binding potency of chemicals to estrogen and androgen receptors has become well-established, helping in the prioritization of chemicals for endocrine disruption testing. However, the potential of ML models for other endocrine targets, such as the progesterone receptor (PR), remains underexplored. In this study, we developed an ML model to predict PR binding affinity and assess the agonistic/antagonistic properties of chemicals. The model achieved a training accuracy of 99.72% and a validation accuracy of 74.46%. External validation was conducted on a dataset of approximately 10,000 chemicals, including 5720 compounds from the training set for which there is a known outcome. External predictions aligned closely with experimental <em>in vitro</em> data, achieving an accuracy of 96.85%. Additionally, the model successfully predicted PR binding affinity and agonistic/antagonistic properties for chemicals without available experimental data. In summary, this study highlights the potential of ML as an effective tool for prioritizing chemicals for future <em>in vitro</em> and <em>in vivo</em> testing of PR binding potency and agonistic/antagonistic properties of chemicals.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100351"},"PeriodicalIF":3.1,"publicationDate":"2025-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An R-based predictive model for skin-sensitizing potential of substances with known structures","authors":"Yuri Hatakeyama ,&nbsp;Kosuke Imai ,&nbsp;Hayato Nishida ,&nbsp;Shiho Oeda ,&nbsp;Tomomi Atobe ,&nbsp;Morihiko Hirota","doi":"10.1016/j.comtox.2025.100350","DOIUrl":"10.1016/j.comtox.2025.100350","url":null,"abstract":"<div><div>Evaluation of skin-sensitizing potential is important to confirm the safety of cosmetics. As animal testing is no longer permitted, several alternative methods based on the adverse outcome pathway (AOP) approach have been reported. In addition, integrated approaches to testing and assessment (IATA), which combine the results of multiple alternative methods to assess skin sensitization potential, have been developed. We have reported an artificial neural network (ANN) model for sensitization risk assessment using commercial software, QwikNet. In the present study, we constructed a new sensitization prediction model for substances with known structures using the free and open-source software R for statistical analysis, and compared the results with those of the QwikNet model. The R model was confirmed to show similar predictive performance for estimated concentration three (EC3) which is the concentration of a test substance needed to produce a stimulation index of 3 to the QwikNet model on the same training set of 134 compounds. The accuracy, overpredicted rate, and underpredicted rate of the R model were 81.3%, 10.4%, and 8.2%, respectively, versus 79.9%, 10.4%, and 9.7% for the QwikNet model. In case studies of compounds not included in the training set, the R model showed generally good predictive ability. For less-well-predicted substances, additional <em>in silico</em> and read-across evaluations complemented the ANN model and improved the predictive accuracy. This study demonstrates that the ANN model is portable to the R software system. Furthermore, the combination of ANN prediction with <em>in silico</em> predictions and read-across taking account of substructures improves the prediction of skin-sensitizing potential in a weight-of-evidence approach.</div></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"34 ","pages":"Article 100350"},"PeriodicalIF":3.1,"publicationDate":"2025-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144089788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}