Combinatorial chemistry & high throughput screening最新文献

{"title":"Development of An Explainable Machine Learning Model for Predicting Fatigue in Rheumatoid Arthritis.","authors":"Yucao Ma, Yiyan Zhang, Yunxi He, Luming Zhao, Chunping Liu, Hailong Wang, Liu Lv","doi":"10.2174/0113862073462843260622175147","DOIUrl":"https://doi.org/10.2174/0113862073462843260622175147","url":null,"abstract":"<p><strong>Introduction: </strong>Fatigue has been identified as one of the common symptoms among people who have rheumatoid arthritis (RA), which has a significant impact on their quality of life (QoL). Although fatigue has been identified as a symptom, few predictive models using clinical indicators have been developed for RA-related fatigue. This paper aims to create predictive models using machine learning (ML) algorithms.</p><p><strong>Methods: </strong>A retrospective analysis of clinical data was conducted on 271 patients with RA from two hospitals. The patients were randomly split into a training set and an external validation set. Feature selection on the training set was performed using three different algorithms: Least Absolute Shrinkage and Selection Operator (Lasso), Boruta, and Recursive Feature Elimination using a Random Forest (RF-RFE). Subsequently, multiple models using machine learning ML) techniques such as Support Vector Machine (SVM), XGBoost, LightGBM, Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), and Random Forest (RF) were developed. Model calibration, Receiver Operating Curve (ROC) analyses, Decision Curve Analysis (DCA), and SHapley Additive exPlanations (SHAP) analyses were used to evaluate the models. Moreover, an overall evaluation using ten-fold cross-validation and an external evaluation using an independent dataset were undertaken.</p><p><strong>Results: </strong>The final dataset comprised 190 patients in the training group and 81 in the external validation set, with no significant differences in demographic features such as age and sex (P > 0.05). The six important predictors identified using a variety of feature selectors were CCP, ESR, lymphocyte count, MDGA, VAS for pain, and TG. In external validation, ML model classifiers such as SVM, XGBoost, and Random Forest performed better at prediction than logistic regression.</p><p><strong>Discussion: </strong>This study demonstrates the potential of ML models to capture nonlinear relationships between clinical variables when predicting fatigue in RA patients. However, the modest performance metrics suggest that these findings are preliminary.</p><p><strong>Conclusion: </strong>A fatigue-prediction model using routine clinical parameters was developed using ML techniques. The SVM model showed potential in identifying RA patients at risk of fatigue. However, the modest performance metrics indicate the preliminary nature of these results, necessitating validation through larger, multicenter studies.</p>","PeriodicalId":10491,"journal":{"name":"Combinatorial chemistry & high throughput screening","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148519970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Therapeutic Effects of the Traditional Chinese Formula Qifuyin on Cognition, Lipid Metabolism, and Gut Microbiota in ApoE4 Mice.","authors":"Ying Yu, Hongwei Zhao, Yun He, Junqi Zhao, Xiuzhao Yang, Xijian Liu, Xiaorui Cheng","doi":"10.2174/0113862073453341260621182306","DOIUrl":"https://doi.org/10.2174/0113862073453341260621182306","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Introduction: &lt;/strong&gt;Apolipoprotein E4 (ApoE4) is the strongest genetic risk factor for sporadic Alzheimer's disease (AD). Qifuyin is a promising herbal formula used clinically for cognitive decline, but its effects on ApoE4-associated cognitive and systemic phenotypes remain unclear. This study aimed to evaluate the effects of Qifuyin on cognitive performance in ApoE4 transgenic mice and to preliminarily explore its associations with lipid metabolism and gut microbiota alterations.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Ten-month-old ApoE4 transgenic mice were treated with Qifuyin by gavage for 321 days, once daily for the first 123 days and once every two days thereafter. Cognitive function was assessed using the step-down test, novel object recognition test (NORT), and Morris water maze test (MWM). Aging- and frailty-related phenotypes were evaluated using senescence grading scores. Serum triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), and apolipoprotein B (ApoB) levels were measured to assess lipid metabolism. Gut microbiota composition and functional profiles were analyzed by 16S rRNA and metagenomic sequencing.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Qifuyin treatment significantly reduced error counts and prolonged latency in the stepdown test, increased the 24h preference index in the NORT, shortened escape latency, and increased platform crossings in the MWM in ApoE4 transgenic mice. High-dose Qifuyin reduced aging scores in males and in all doses in females and in the pooled dataset. Qifuyin decreased serum TG and ApoB levels, and increased serum HDL-C levels. 16S rRNA sequencing indicated that Qifuyin increased alpha diversity and shifted beta diversity toward the control profile. At the phylum level, Qifuyin altered the relative abundances of Firmicutes, Bacteroidota, Cyanobacteria, and Synergistota. At the family and genus levels, Qifuyin treatment was associated with increased abundances of Helicobacteraceae, Bacteroidaceae, Helicobacter, and Bacteroides, and a reduced abundance of Ruminococcaceae. Metagenomic annotation analysis showed altered abundances of K02003, K06147, COG1961, CBM37, and GH35-related features.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Discussion: &lt;/strong&gt;These findings suggest that Qifuyin may benefit ApoE4-associated cognitive and systemic dysfunction through its integrated effects on lipid metabolism and gut microbiota alterations. The microbiota-related changes observed in this study may provide a potential link between peripheral metabolic regulation and cognitive improvement, although their mechanistic significance requires further validation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Qifuyin improved cognitive performance and lipid metabolism, and was associated with alterations in gut microbiota composition in ApoE4 transgenic mice. These findings suggest that Qifuyin may exert beneficial effects on cognitive and systemic phenotypes in this model, while the biological significance of specific microbial changes warrants f","PeriodicalId":10491,"journal":{"name":"Combinatorial chemistry & high throughput screening","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148519980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SPATC1L as a Prognostic Marker in Hepatocellular Carcinoma: An Integrative Bioinformatic Analysis of Immune Infiltration and Homologous Recombination.","authors":"Bo Wu, Yubing Lu, Wei Mao, Xi Su, Ting Wang, Junhui Chen, Erbao Chen","doi":"10.2174/0113862073469787260609104636","DOIUrl":"https://doi.org/10.2174/0113862073469787260609104636","url":null,"abstract":"<p><strong>Introduction: </strong>To investigate SPATC1L expression in Hepatocellular Carcinoma (HCC), its association with patient prognosis, and its potential clinical significance and associated biological processes.</p><p><strong>Methods: </strong>SPATC1L expression profiles and clinical data were obtained from The Cancer Genome Atlas database. Co-expression and differential expression analyses were performed using multiple public resources. Associations between SPATC1L and immune cell infiltration, immune checkpoint signatures, and Homologous Recombination (HR)-related genes were evaluated using correlation analyses. Diagnostic performance was evaluated using receiver operating characteristic analysis and logistic regression. Prognostic value was assessed using Kaplan-Meier analysis with log-rank tests and Cox regression models. A nomogram was constructed, and its performance was validated using calibration curves.</p><p><strong>Results: </strong>SPATC1L was significantly upregulated in HCC tissues compared with normal controls. Higher expression was associated with advanced tumor stage, higher histologic grade, and vascular invasion. In multivariate Cox regression analyses adjusted for T stage, pathological stage, histologic grade, tumor status, race, AFP level, and residual tumor status, SPATC1L expression remained independently associated with poorer overall survival and disease-specific survival. SPATC1L expression was also correlated with immune infiltration levels and HRrelated genes.</p><p><strong>Discussion: </strong>These findings suggest a potential link to the tumor immune microenvironment and DNA repair pathways, which could partly explain its association with aggressive tumor features and poor survival. Although derived from bioinformatics analyses, the results provide a rationale for mechanistic and experimental validation of SPATC1L as a candidate biomarker and therapeutic target.</p><p><strong>Conclusions: </strong>Elevated SPATC1L expression is closely associated with unfavorable clinicopathological characteristics and poor outcomes, supporting its potential value as a diagnostic and prognostic biomarker in HCC.</p>","PeriodicalId":10491,"journal":{"name":"Combinatorial chemistry & high throughput screening","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148535470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the Potential Mechanism of Isoliquiritigenin in the Treatment of Osteoarthritis through Integrated Network Pharmacology, Molecular Docking, and Experimental Validation.","authors":"Wei Wang, Qiwang He, Zhiwen Zhang, Tubei Zhang, Ziren Xiong, Tianle Zhou, Ruibing Feng, Ximing Liu","doi":"10.2174/0113862073486376260621183559","DOIUrl":"https://doi.org/10.2174/0113862073486376260621183559","url":null,"abstract":"<p><strong>Introduction: </strong>Osteoarthritis (OA) is a prevalent and chronic joint disease characterized by progressive cartilage degeneration and chronic inflammation. Isoliquiritigenin (ISL), a bioactive flavonoid derived from licorice, has demonstrated significant anti-inflammatory potential in various diseases. However, its specific molecular targets and systemic mechanisms in the treatment of OA remain to be fully elucidated. This study aimed to investigate the potential targets and molecular mechanisms of ISL in OA treatment using an integrated pharmacological and experimental approach.</p><p><strong>Methods: </strong>Overlapping targets between ISL and OA were identified using multiple public databases. A PPI network was constructed to identify hub genes, followed by GO and KEGG enrichment analyses to predict key signaling pathways. A ceRNA regulatory network was also established. Furthermore, molecular docking was employed to assess the binding stability between ISL and core targets, and the findings were validated through histopathological evaluation (Mankin score) and RT-qPCR analysis in a rabbit ACLT-induced OA model.</p><p><strong>Results: </strong>A total of 79 shared targets were identified, from which 11 core targets were selected for further investigation. Enrichment analysis revealed that the therapeutic effects of ISL are primarily associated with the MAPK, PI3K-Akt, and mTOR signaling pathways, which are essential for maintaining chondrocyte homeostasis. Molecular docking indicated that ISL exhibits strong binding affinities (all binding energies < -5.0 kcal/mol) for the core targets. In vivo experiments confirmed typical cartilage degradation in the OA group, accompanied by significantly elevated Mankin scores. RT-qPCR results verified the significant differential expression of six core targets (IGF1R, PLAU, EGFR, PTGS2, PPARG, and GSK3B) in the OA cartilage, validating their involvement in OA pathogenesis.</p><p><strong>Discussion: </strong>In this study, we preliminarily determined the potential therapeutic effects of ISL on OA through its modulation of multiple targets and complex signaling pathways. The integration of network pharmacology and in vivo validation suggests that ISL may exert its anti-OA effects by targeting key inflammatory and metabolic mediators identified in our PPI and ceRNA networks.</p><p><strong>Conclusion: </strong>ISL is predicted to have substantial potential for clinical application in OA treatment. These findings provide a novel theoretical foundation and specific candidate targets for future research into targeted therapeutic strategies for OA.</p>","PeriodicalId":10491,"journal":{"name":"Combinatorial chemistry & high throughput screening","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148548109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the Mechanism of Action of Isorhynchophylline in Stroke Based on Network Pharmacology and Molecular Docking Technology.","authors":"Bifei Li, Xujie Wang, Min Qiu, Ziyang Zhang, Xiaolei Liu, Kaiwen Zhang, Xiaoe Jia, Wei Xie, Yabin Xie, Bade Ren-Gui, Rui Shi, Mulan Bao, You Liu, Guo Shao, Shuyuan Jiang","doi":"10.2174/0113862073466718260622174600","DOIUrl":"https://doi.org/10.2174/0113862073466718260622174600","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Introduction: &lt;/strong&gt;This study aimed to elucidate the neuroprotective mechanism of Isorhynchophylline (IRN) in stroke.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;Using network pharmacology, the action targets of IRN were predicted using the SwissTargetPrediction, HERB, and TCMIP 2.0 databases. We identified sixty-five targets common to IRN and stroke. We conducted GO and KEGG enrichment analyses using R software and constructed a compound- target- pathway- disease network. Additionally, molecular docking experiments were performed to investigate the binding interactions between IRN and its main targets. Subsequently, an oxygen-glucose deprivation/reoxygenation (OGD/R) model was established using the human neuroblastoma cell line SH-SY5Y under hypoxic conditions (1% O2). The effective working concentration of IRN was determined by measuring cell viability using the CCK-8 assay. Then, an ELISA experiment was conducted to detect the expression of inflammatory factors IL-1β and IL-10. The activity of reactive oxygen species (ROS) was measured using the dual fluorescence detection method. TUNEL staining was employed to assess the apoptosis of cells. Finally, a Western blot was used to examine the effect of IRN on EGFR and AKT expression in cells.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The results of network pharmacology analysis indicated that the main targets of IRN in treating stroke are EGFR, mTOR, JAK2, AKT, and JUN. Enrichment analysis revealed that IRN is associated with neurotransmitter receptor function and participates in the EGFR signaling pathway. Molecular docking results showed that it has a particularly strong binding affinity for AKT1 (binding energy ≈ -8.7 kcal/mol). The in vitro experiments showed that IRN treatment significantly alleviated the damage induced by OGD/R. Specifically, the levels of IL-1β and ROS decreased, the level of IL-10 increased, and the apoptosis rate decreased. Western blot results showed that, compared with the OGD/R group, IRN significantly upregulated the expression of p-EGFR and p-AKT. We demonstrated that IRN employs a multi-target approach to counteract stroke pathology, as revealed by computational predictions and experimental validation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Discussion: &lt;/strong&gt;The convergence of network pharmacology, molecular docking, and in vitro assays enabled the identification of key targets, including EGFR, AKT1, and JAK2. These methods also indicate that IRN can simultaneously regulate the inflammatory, oxidative stress, and apoptotic processes. Compared to singletarget agents, IRN exhibits superior multi-target therapeutic potential. These findings, derived from database analyses and cell-based assays, provide a comprehensive overview of IRN's pharmacological effects, which are consistent with its known neuroprotective benefits. Although these results are obtained from cell-based models and predictive databases, they provide a cohesive mechanistic rationale that is consistent with the neuroprotective prop","PeriodicalId":10491,"journal":{"name":"Combinatorial chemistry & high throughput screening","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148560971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the Molecular Mechanisms of Xuefu Zhuyu Pills in Treating Antipsychotic-Induced Hyperprolactinemia: An Integrated Approach Based on Plasma Component Analysis, Network Pharmacology, Molecular Docking, and Molecular Dynamics Simulations.","authors":"Mingyue Liu, Zihuan Zhang, Guanli Su, Yimeng Liu, Shuocheng Qiu, Yifei Wang, Qinchao Wang, Linliu Du, Jianxin Wang","doi":"10.2174/0113862073457198260622185516","DOIUrl":"https://doi.org/10.2174/0113862073457198260622185516","url":null,"abstract":"<p><strong>Introduction: </strong>Antipsychotic-induced hyperprolactinemia (HPRL) is a prevalent and troubling side effect that impairs treatment adherence. Xuefu Zhuyu Pills (XFZY), a traditional Chinese medicine formula, have shown efficacy in clinical practice; however, their active components and mechanisms of action remain unknown.</p><p><strong>Methods: </strong>We identified the components of XFZY in human plasma by UPLC-Q-TOF-MS/MS and performed network pharmacology analysis to screen key targets and core active components through topological analysis. Intersecting targets were subjected to GO and KEGG enrichment analyses on Metascape, followed by molecular docking and molecular dynamics simulations to assess binding stability between core active components and key targets.</p><p><strong>Results: </strong>A total of 67 absorbed components were found in human plasma. Based on the network pharmacology results, we identified seven core targets (AKT1, ESR1, IL6, TNF, ERBB2, CTNNB1, and MAOA) and three potential active ingredients (Makisterone B, 3-O-beta-Dglucopyranosylplatycodigenin, and 4',5,6,7-Tetramethoxyflavone). GO and KEGG suggested that hormone level regulation, the neuroactive ligand-receptor interaction, the apelin signaling pathway, and the cGMP-PKG signaling pathway may play key roles in treating HPRL caused by antipsychotic drugs.</p><p><strong>Discussion: </strong>Molecular docking results showed that the core active ingredient binds well with the key targets, and molecular dynamics simulations further verified their stability.</p><p><strong>Conclusion: </strong>This study preliminarily revealed the components in human plasma of XFZY in HPRL treatment and their potential pharmacological mechanism. These findings provide a scientific basis for clinical studies.</p>","PeriodicalId":10491,"journal":{"name":"Combinatorial chemistry & high throughput screening","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148561096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of Chemical-induced Autonomic Neurotoxicity with Machine Learning Approaches.","authors":"Zheng-Kun Kuang, Qing Huang, Xiaoling Duan","doi":"10.2174/0113862073491170260616113137","DOIUrl":"https://doi.org/10.2174/0113862073491170260616113137","url":null,"abstract":"<p><strong>Introduction: </strong>Autonomic neurotoxicity associated with chemical exposure represents a significant clinical and safety concern. Traditional assessment relies on in vivo methods that are costly and time-consuming, and few computational tools specifically address this endpoint. This study aimed to develop reliable computational models for the prediction of chemical-induced autonomic neurotoxicity, while elucidating the molecular determinants governing this adverse effect.</p><p><strong>Materials and methods: </strong>The autonomic neurotoxicity data were retrieved from the ADReCS and SIDER databases. Multiple machine learning and deep learning algorithms were utilized to develop Quantitative Structure-Activity Relationship (QSAR) models via the ensemble modeling method. The chemical structures were characterized using diverse molecular descriptor packages. Furthermore, molecular properties and structural alerts responsible for autonomic neurotoxicity were analyzed.</p><p><strong>Results: </strong>The consensus model was built based on four best-performing individual models (Py- Descriptor_RFR, QNPR_RFR, RDKIT_ASNN, and CNF), which achieved good results on both five-fold cross-validation (ACC value of 0.80, AUC value of 0.90, and MCC value of 0.60) and independent test validation (ACC value of 0.84, AUC value of 0.92, and MCC value of 0.68). Additionally, the analysis identified seven key molecular properties (MW, MPSA, AlogP, nHBA, nHBD, nRB, and nAR) and 19 structural alerts that are significantly associated with autonomic neurotoxicity.</p><p><strong>Discussion: </strong>The superior performance of the consensus model compared to individual algorithms highlights its ability to enhance the overall stability of the results. The analysis of molecular properties demonstrated that autonomic neurotoxicants tend to exhibit higher lipophilicity, smaller molecular size, reduced hydrogen-bonding capacity, lower molecular flexibility, and increased aromaticity. Furthermore, the 19 structural alerts offer interpretable insights into the specific chemical motifs associated with toxicity. These alerts serve as practical tools for medicinal chemists to facilitate the early identification of toxicophores.</p><p><strong>Conclusion: </strong>This study comprehensively elucidates the autonomic neurotoxicity and the associated structural hallmarks, offering a validated computational framework to guide highthroughput virtual screening and the safe design of therapeutic agents.</p>","PeriodicalId":10491,"journal":{"name":"Combinatorial chemistry & high throughput screening","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148561036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine Learning Classification of Serum miR-210 as a Biomarker of Exercise Training Response.","authors":"Manaf AlMatar, Fatma Hassan Abd Elbasset Mourgan, Ali Al Shamli, Nana El Dawy Ahmed Hefny, Eman Abdelhalim Mohamed Ali, Wafaa Mohmoud Abdellatif Bekir","doi":"10.2174/0113862073454504260622175952","DOIUrl":"https://doi.org/10.2174/0113862073454504260622175952","url":null,"abstract":"<p><strong>Introduction: </strong>MicroRNAs (miRNAs) in circulating blood are increasingly recognized as biomarkers of exercise-induced physiological adaptation. Of these, miR-210, a hypoxiainducible miRNA associated with angiogenesis, mitochondrial control, and cellular stress response, has shown mixed exercise-associated modifications. Its classifier status for individuallevel training response has yet to be established.</p><p><strong>Methods: </strong>Ten young adults (n=10) completed standardized exercise training. Demographic, anthropometric, and training characteristics were recorded, and serum miR-210 expression was quantified using qRT-PCR. Upregulation of miR-210 was the primary outcome, defined as a median divide of expression levels (≥ median = high; < median = low). Predictors were training frequency (sessions/week) and age. Three Machine learning (ML) models-ridge-penalized logistic regression (LR), random forest classifier (RFC), and Support Vector Machine (SVM) were evaluated with Leave-One-Out Cross-Validation (LOOCV). Performance metrics were accuracy, area under the curve (AUC), Brier score, sensitivity, specificity, precision, and calibration.</p><p><strong>Results: </strong>The variability for serum miR-210 was 0.677 to 1.220 (median 0.782) in five subjects per outcome group. Ordinary LR was 80% accurate, while the ML classifiers performed significantly better: ridge-penalized logistic regression, 90% accuracy (AUC 0.88, sensitivity 1.00, specificity 0.80); random forest, 90% accuracy (AUC 0.92, specificity 1.00, sensitivity 0.80); and support vector machine, 90% accuracy (AUC 0.88, sensitivity 1.00, specificity 0.80). Calibration analysis verified the accuracy of predicted probabilities, and RFC had the most consistent agreement, making it the best-performing model. There were negative, non-significant correlations between miR-210 and frequency of training (r = -0.256) and age (r = -0.117).</p><p><strong>Discussion: </strong>ML models identify upregulation of serum miR-210 with ≥90% accuracy, and its utility as a discriminative biomarker of inter-individual exercise responses is hence suggested Conclusion: In this small exploratory cohort (n = 10), machine learning methods detected a distinctive sign in the expression level of miR-210 following exercise training. These findings are preliminary and hypothesis-generating, and larger, adequately powered investigations are needed to ascertain whether miR-210 has clinically evocative predictive value as an exercise biomarker.</p>","PeriodicalId":10491,"journal":{"name":"Combinatorial chemistry & high throughput screening","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148561117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An herbal formula alleviates hyperuricemia: Evidence from network pharmacology and in vivo experiments.","authors":"Luyin Yang, Zhihao Wu, Hai Du, Gang Luo, Raoqiong Wang, Wei Ren, Houping Xu","doi":"10.2174/0113862073490788260627105302","DOIUrl":"https://doi.org/10.2174/0113862073490788260627105302","url":null,"abstract":"<p><p>Qianhua Tongfeng Capsule (QHTFC), an herbal formula, has demonstrated favorable therapeutic efficacy in the clinical management of hyperuricemia (HUA) and gout. Phytochemical analysis revealed that QHTFC is primarily composed of coumarins, flavonoids, and alkaloids. Nevertheless, the anti-hyperuricemic mechanisms of QHTFC remain to be further elucidated. This study aimed to elucidate the molecular targets and potential mechanisms of QHTFC in the treatment of hyperuricemia (HUA) by combining network pharmacology with in vivo experimental validation. Network pharmacology was employed to predict the potential targets and signaling pathways of QHTFC against HUA. Subsequently, in vivo experiments were conducted to evaluate the effects of QHTFC on serum biochemical markers, oxidative stress indices, and renal histopathological changes in HUA rats. Moreover, the expression levels of uric acid transporter-related proteins in the kidney were analyzed using Western blotting to further validate the underlying mechanisms. Based on network pharmacology analysis, 57 intersecting targets between QHTFC and HUA were identified. Among them, IL-6, TNF, PPARγ, CXCL8, PTGS2, and ABCG2 were determined as key targets. KEGG enrichment analysis further indicated that the primary therapeutic pathway of QHTFC against HUA involved the IL-17 signaling pathway. In vivo results revealed that QHTFC exerted potent uric acid-lowering efficacy in hyperuricemic models by bidirectional modulation of uric acid transporters. Mechanistically, QHTFC suppressed liver XOD activity, thereby reducing uric acid production. Additionally, it promoted uric acid excretion both by upregulating OAT1 and ABCG2 transporters and suppressing reabsorption through the downregulation of GLUT9 and URAT1 expression. Our findings suggest that QHTFC can significantly reduce serum UA levels and ameliorate renal injury by regulating oxidative stress, inflammation, and kidney fibrosis. This research provides new evidence supporting the application of QHTFC in the treatment of hyperuricemia.</p>","PeriodicalId":10491,"journal":{"name":"Combinatorial chemistry & high throughput screening","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148454892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to: Jeduxiaoliu Formula can Induce Apoptosis of Lymphoma Cells <i>In Vitro</i> and <i>In Vivo</i>.","authors":"XiaoWei Feng, Ming Hu, Cong Ming Wei, Hong Yu Zhang, Wen Wen Jiang, Qi Hu","doi":"10.2174/1386207329999260708103700","DOIUrl":"https://doi.org/10.2174/1386207329999260708103700","url":null,"abstract":"<p><p>It has been identified that Figure 7 in the published version of this article [1] was published incorrectly. The corrected version of the figure is provided below. The publisher regrets this error and apologizes for any inconvenience caused to the authors and readers. The original article can be found online at https://www.benthamscience.com/article/137138.</p>","PeriodicalId":10491,"journal":{"name":"Combinatorial chemistry & high throughput screening","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2026-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148454911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}