Pelvic floor dysfunction (PFD) is a highly prevalent health problem, encompassing urinary incontinence, emptying disorders of the bladder, fecal incontinence, emptying disorders of the bowel, pelvic organ prolapse, sexual dysfunction, and chronic pelvic pain. Mobile health (mHealth) interventions delivered through apps can provide remote health services to improve patient compliance and enhance treatment effectiveness. Although apps for preventing and managing PFD have been developed and used, the features and quality of these apps in China have not been systematically examined. This study aimed to systematically summarize the functions and evaluate the quality of the existing mHealth apps for preventing and managing all kinds of PFD, such as urinary incontinence, fecal incontinence, and chronic pelvic pain. We systematically searched for potential PFD apps on the Apple App Store, Huawei AppGallery, and VIVO App Store. Apps were included if they were free, designed for preventing or managing PFD, in the Chinese language, could be downloaded and run on Android, Harmony, or iOS operating systems (OS), and incorporated elements of preventing and managing PFD. We excluded apps that were intended for use by health care providers and not relevant to PFD. Apps that met the inclusion criteria were downloaded and included for final analysis. The user version of the Mobile App Rating Scale (uMARS) was used to assess the apps' quality and summarize the apps' functionality according to guidelines. Of the 3897 apps screened, 46 apps that met the inclusion criteria were included in the final analysis. All apps were developed by corporations. More than half of the apps had download counts exceeding 10,000, and 24 (52.2%) apps scored 4 or higher in app stores. Furthermore, nearly half of the apps (n=21, 45.7%) had been updated within the past month at the time of retrieval. The overall uMARS scores ranged from 2.29 to 4.50, with a mean uMARS score of 3.46 (SD 0.50), which is considered acceptable quality. Based on uMARS scores, 15.2% (n=7) were rated as poor quality, 65.2% (n=30) as acceptable, and 19.6% (n=9) as good quality. More than half of the apps provided the functions of exercise (n=44, 95.7%), personal information recording (n=31, 67.4%), and health education (n=28, 60.9%). Only 5 apps provided 5 or more functions. The apps for PFD revealed acceptable quality, and the majority provided exercise, personal information recording, and health education functions. However, many apps lacked comprehensive functionalities and did not provide immediate feedback or high-quality educational information. Health care providers should follow international guidelines to create high-quality, evidence-based, multifunctional apps for PFD. Future studies should explore the effects of the apps and real-world user feedback data in clinical settings.
Introduction Residual apnoea hypopnoea index (AHI) derived from positive airway pressure (PAP) device downloads is frequently used to assess treatment effectiveness in obstructive sleep apnoea (OSA). However, the accuracy of device reported AHI in real‑world settings remains uncertain. This study evaluated the agreement between PAP device derived AHI and AHI measured using home based cardiorespiratory polygraphy. Methods This secondary analysis of the 3DPiPPIn randomised controlled trial included adults with OSA (AHI ≥15 events/hr) naïve to domiciliary PAP therapy. Participants underwent two‑night home cardiorespiratory polygraphy at three and six months. Residual AHI was obtained from Löwenstein PAP devices via data download. Agreement between methods was assessed using Bland-Altman analysis, intraclass correlation coefficients (ICC), and weighted Kappa. Sensitivity and specificity for detecting inadequate control (AHI ≥7 events/hr) were calculated. The association between average leak and AHI discrepancy was examined using linear regression. Results Ninety‑eight participants were included. PAP derived AHI values consistently underestimated polygraphy derived AHI across all time points. Agreement was poor, with ICCs of 0.02 at three months and 0.16 at six months, and low diagnostic agreement across severity categories at both three months (K0.10, 95%CI: 0 to 0.26) and six months (K0.09, 95%CI: 0 to 0.28). Bland-Altman plots demonstrated systematic underestimation with wide limits of agreement. Sensitivity for detecting inadequate control was very low at both time points (3 months 12% and 6 months 21%), despite high specificity (97% at 3 months and 95% at 6 months). Higher interface leak was significantly associated with greater underestimation of AHI (three months: slope = 0.45, R² = 0.18, p < 0.0001; six months: slope = 0.38, R² = 0.14, p < 0.0001). Conclusion PAP device downloads substantially underestimated residual AHI and demonstrated poor agreement with home cardiorespiratory polygraphy. These findings highlight important limitations of device derived AHI in real‑world practice, particularly in the presence of interface leak. Where treatment adequacy is uncertain, confirmatory assessment with polygraphy remains necessary.
Radiotherapy for lung cancer can lead to significant fatigue and a decrease in physical activity. An app that reminds the patients several times per day to perform a certain number of steps may be helpful in this respect. Such an app has just been developed. We aimed to investigated the functionality and practicability of the app in healthy volunteers before testing it in a prospective clinical trial. Thirty healthy volunteers participated in this prospective study (APPAREL-HV) and evaluated the app by affirming (=satisfaction) or negating ten statements in three sections related to functionality and practicability. If the satisfaction rate was <80%, the app required further optimization. If it was <60%, it appeared not useful. Subgroup analyses were performed for iPhone vs. Android users, German vs. Danish participants, and younger (<50 years) vs. older (≥50 years) patients. Overall satisfaction rates were 86.7% (two statements), 93.3-100% (two statements), and 90.0-100% (six statements) in the sections Download and installation, Navigation, and Content/functions, respectively. Android users were significantly less satisfied with Download and installation (50.0% vs. 95.8%, p=0.018). Otherwise, satisfaction rates were always ≥80%, and subgroup analyses showed no significant differences between the groups compared. According to the results of the APPAREL-HV, the current version of the app appeared not sufficiently useful for Android users and required significant improvement for this subgroup in the section Download and installation.
The Single-Cell Pediatric Cancer Atlas (ScPCA) Portal is a resource for uniformly processed single-cell and single-nucleus RNA sequencing (RNA-seq) data and de-identified metadata from pediatric tumor samples. Originally comprising data from 10 projects funded by Alex's Lemonade Stand Foundation (ALSF), the Portal currently contains summarized gene expression data for over 700 samples across 55 cancer types from ALSF-funded and community-contributed datasets. Downloads include expression data as SingleCellExperiment or AnnData objects containing raw and normalized counts, principal-component analysis (PCA) and uniform manifold approximation and projection (UMAP) coordinates, automated cell-type annotations, and copy-number variation estimates, along with summary reports. Some samples have additional data from bulk RNA-seq, spatial transcriptomics, and/or feature barcoding (e.g., CITE-seq) included in the download. All data on the Portal were uniformly processed using scpca-nf, an efficient and open-source Nextflow workflow that uses alevin-fry to quantify gene expression. Comprehensive documentation, including file descriptions and a getting-started guide, are available online.
Although large language models (LLMs) have undergone substantial development, their applicability to epidemiological research has not been sufficiently examined. This study aims to develop and evaluate an LLM-based framework for hypothesis generation and testing, demonstrating its application in childhood asthma in the National Health and Nutrition Examination Survey (NHANES). Pilot study was conducted to explore factors associated with childhood asthma in the 2001-2020 NHANES cycles. A modular agent system was developed, including Database Query, Statistic, Paper Search, and Paper Download tools, along with two LLM models (Key Generator and Hypothesis Tester). Multivariable logistic regression was used to test for the association between each variable and current asthma, generating a tentative affirmative claim. The Key Generator module produced keywords for literature search, the Paper Search and Paper Download tools queried PubMed and retrieved relevant studies, and the Hypothesis Tester module synthesized evidence and determined the support for claims for each variable. Keywords and conclusions were reviewed by researchers and validated using multiple LLMs (ChatGPT, DeepSeek, and Gemini) to ensure consistency and robustness. 25,839 children with (n = 2928) and without (n = 22,911) current asthma, and 10,359 variables were included in the multivariable analysis, which yielded 100 variables associated with asthma. Of these, 21 were directly related to asthma (supporting published studies), 43 were indirectly related to asthma (based on background knowledge, though not explicitly discussed in the available publications), and 34 were unrelated to asthma. Two variables were excluded due to a lack of discriminative keywords. This study demonstrates the effectiveness of LLM-based models for generating and testing hypotheses about childhood asthma.
Deciphering phenotype-specific regulatory mechanisms is key to understanding the molecular basis of complex diseases and traits. However, constructing multi-omic regulatory networks (MO-RNs) is challenging, as it requires integrating heterogeneous omics data, incorporating biological context, and detecting regulatory mechanisms that vary across conditions. The R package MORE (Multi-Omics REgulation) addresses these challenges by applying robust statistical models to infer phenotype-specific regulatory networks from multi-omics data. However, the use of MORE typically requires programming expertise, limiting its accessibility to non-specialist users. To democratize access to advanced multi-omics modeling tools, we present MOREshiny, an interactive web application built on Shiny that extends the module of pathway enrichment analysis and automatically guides the choice of statistical methods. MOREshiny enables users to upload multi-omic data, configure their models, and interpret results through a user-friendly interface-without the need for coding skills. MOREshiny also allows users to download MORE results for their later exploration and study. To demonstrate the utility of MOREshiny, we showcase its functionalities on a multi-omic ovarian cancer dataset to understand regulatory differences between patients who did or did not require chemotherapy. MOREshiny is freely available for download as a dockerized R Shiny package at https://github.com/BiostatOmics/MOREshiny.
The recount3 online resource provides tens of thousands of uniformly processed RNA-seq samples across human and mouse from major sequencing repositories like the Sequence Read Archive. While access to these datasets has traditionally been centered in the R/Bioconductor ecosystem, the growing prominence of Python in bioinformatics and machine learning necessitates native, efficient tooling for Python users. Therefore, we present the recount3 Python package with robust application programming interface (API) and command-line interface (CLI) for discovering, downloading, and materializing recount3 resources. The software orchestrates uniform resource locator (URL) resolution, persistent on-disk caching, and the automatic parsing of data into analysis-ready data structures, including Pandas DataFrames and BiocPy RangedSummarizedExperiment objects. The recount3 Python package drastically lowers the barrier to entry for large-scale utilization of RNA-seq data in Python-based computational pipelines, bridging the gap between massive public transcriptomic data and modern machine learning ecosystems.
Chemical toxicity assessment commonly includes in vivo rat exposure experiments, with transcriptomic measurements collected at various exposure times and chemical doses. The mechanisms underlying chemical-induced toxicity are then inferred by analyzing changes in gene expression. Recently, genome-scale metabolic models (GSMs), which represent the metabolic network of a cell/organism and contain metabolites, reactions, genes, and the relationship between the genes and reactions, have been used to provide a systems-level understanding of gene expression. However, most of the algorithms that integrate gene expression with GSMs require familiarity with MATLAB or Python programming, making them less accessible for users without computational experience. Here, we introduce ToxMet (https://toxmet.bhsai.org), an open-access, user-friendly web application that provides tabular and graph-based network views to visualize the latest rat GSM (iRno v4.2) and predicts chemical-induced metabolic perturbations in rat tissues by integrating toxicogenomic measurements with the rat GSM. ToxMet uses two well-validated computational algorithms, TIMBR and Pheflux, to predict metabolic perturbations and provides the prediction results as interactive and downloadable tables, scatter plots, and network visualizations. As such, the web tool can process a maximum of 10 conditions for a single job, and the results can be used for dose-response studies to monitor organ metabolism at the subsystem level. We evaluated ToxMet's ability to predict toxicity mechanisms by applying it to publicly available toxicogenomic data for two exemplar toxicants: gentamicin and thioacetamide, which are known to induce kidney and liver injury, respectively. ToxMet predicted known toxicity mechanisms for both chemicals, thus demonstrating its ability to provide novel insights into the metabolic mechanisms of chemical-induced toxicity and aid in the discovery of biomarkers and therapeutics using gene expression data.
While studies suggested that Huangqi Guizhi Wuwu Decoction (HGWD) can mitigate doxorubicin-induced cardiotoxicity (DIC), the specific mechanism of action remains unclear. GSE106297, GSE157282, and GSE206803 were downloaded to screen for differentially expressed genes (DEGs), followed by gene set enrichment analysis and immune infiltration analysis. DIC-related genes were obtained by the intersection of weighted gene co-expression network analysis and DEGs. The active ingredients and target genes of HGWD were obtained from the Traditional Chinese Medicine System Pharmacology Database and Analysis Platform database, and HGWD-DIC common targets were identified by intersecting them with DIC-related genes. A drug-active ingredient-target network was constructed to select the core components of HGWD. Mitophagy-related genes were obtained from GeneCards, PHARMGKB, and OMIM databases, and intersecting them with common targets yielded the core genes, which were then subjected to enrichment analyses. A protein-protein interaction network was constructed to identify key genes, further assessing their diagnostic value. The effect of HGWD on the expression of key genes was further validated using prepared medicated serum. The interactions between the core components and key genes were validated through molecular docking and molecular dynamics simulation. A total of 2344 DEGs were identified, with gene set enrichment analysis results primarily enriched in categories such as apoptosis, p53 signaling pathway, cell cycle, PLK1 pathway, mitochondrial translation, and metabolism of RNA. Immune infiltration analysis suggested that the immune response may also be involved in the pathogenesis of DIC. We identified 2969 key modular genes by weighted gene co-expression network analysis, and intersecting these with DEGs yielded 1569 DIC-related genes. Network pharmacology analysis revealed 74 active ingredients and 692 target genes of HGWD, resulting in 64 common targets when intersected with DIC-related genes. The core components of HGWD were identified as quercetin and kaempferol. By intersecting the obtained mitophagy-related genes with common targets, 13 core genes were identified, with enrichment analyses indicating significant associations with cellular response to mitophagy and autophagy. Further analysis showed that 5 key genes: AKT1, TP53, BCL2L1, FASN, and HRAS, all demonstrated good diagnostic value, and their DOX-induced expression alterations were reversed by HGWD. Molecular docking and molecular dynamics simulation showed a strong binding affinity between the core components and key genes. HGWD may alleviate DIC by regulating mitophagy.
Protein structure prediction models released in recent years have presented tectonic changes in the field of structural biology. However, their potential has not yet been harnessed to its fullest due to their demands on hardware and technical expertise required for their usage. In this paper, we present Foldify, which makes prediction models accessible, integrating AlphaFold 3, AlphaFold 2, ColabFold, OmegaFold, and ESMFold into a single user-friendly, easy-to-use graphical interface, and ensures their stable operation within a scalable high-performance computing environment. Foldify accepts protein sequences, submitted through a web-based graphical interface as input, and allows executing multiple prediction models on the same protein sequence. The predicted protein structures can be directly visualized online through Mol* Viewer or can be downloaded from the website. Furthermore, the multiresult comparison mode allows visualization of multiple predicted structures in a single Mol* window, accompanied by qualitative metrics of the models' prediction similarity. The Foldify application is freely available at https://foldify-open.cloud.e-infra.cz/ with no login required.
Veterinary students often find it difficult to select antimicrobial drugs for patients, likely because it requires them to consider multiple factors and there are frequently several possible options with a lack of a defined "correct or incorrect" choice. Our goal was to develop a teaching tool to engage the students through the decision-making processes associated with antimicrobial drug selection in cats and dogs, designed to align with the CBVE competency of explaining a justification. We developed a series of small animal case vignettes with a set of antimicrobial choices. The students use a visual analogue scale (VAS) to indicate the relative safety and/or efficacy of the drug in question; they also provide a written justification for their selection. Student responses are anonymized and downloaded for instructor review. The instructor categorizes the frequency of selections according to the labeled quartiles and displays the results as a bar graph during the classroom session and selects some written justifications for the class to view as a group. Practical considerations for tool implementation include considerations of the curriculum, time spent reviewing individual answers, and the tool's utility as a supplementary aid to foster more student discourse about topics in pharmacology. Overall, the VAS tool has the potential to aid in generating discussion in clinical contexts where there is not a single best answer.
Cholangiocarcinoma (CCA) is a highly heterogeneous biliary malignancy with a poor prognosis. Finding early diagnosis and therapeutic targets for CCA is of great importance. The aim of this study was to screen for key genes involved in CCA using bioinformatics analysis, identify establishment of sister chromatid cohesion N-acetyltransferase 2 (ESCO2) as a core candidate, and validate its role experimentally. The CCA data were downloaded using the Gene Expression Comprehensive Database and the Cancer Genome Atlas, the core gene ESCO2 with strong correlation with CCA was screened by raw letter analysis, and the prognostic value of key CCA genes was analyzed by Kaplan-Meier. The effects of ESCO2 on CCA cells and its oncogenic effects were investigated by cell and animal experiments. A total of 1,372 differential genes were screened in this study, with 742 up-regulated genes including ESCO2 and 630 down-regulated genes. Patients with high ESCO2 expression had a poorer prognosis and were significantly associated with N stage. Immune infiltration analysis revealed that ESCO2 expression was negatively correlated with CD8A and FGFBP2. Cellular experiments showed that ESCO2 was significantly up-regulated in CCA cells. ESCO2 overexpression promotes CCA cell proliferation and inhibits apoptosis. In vivo experiments confirmed that ESCO2 promotes tumor growth and shortens survival in mice. ESCO2 is a key regulatory gene affecting the development of CCA and plays an important role in CCA cell proliferation, which could be a new target for CCA diagnosis and treatment.
Using ovarian cancer datasets from public databases, identify copper death-related genes in ovarian cancer tissues and construct a clinical prognostic risk scoring model for ovarian cancer patients based on these genes. We downloaded the OC data of TCGA, the GSE26193, GSE63885 dataset from GEO and retrieved 10 cuproptosis related genes (CRGs) and analyzed their chromosomal localization, expression correlation, and mutation patterns based on the datasets. Using these genes, we clustered the OC samples to identify different molecular subtypes of copper induced death. We analyzed the differential genes and functional enrichment between different subtypes and obtained feature genes with predictive ability for prognosis through survival regression analyses. Based on these feature genes, we constructed a risk scoring model and incorporated the clinical characteristics ofpatients to jointly predict their survival rate. In ovarian cancer samples, 10 copper death-related genes can stably divide the samples into two molecular subtypes, and there are significant differences in clinical and immune characteristics and drug sensitivity between them. After further screening, seven prognostic genes (RARRES1、CXCL10、PI3、CXCL11、THEMIS2、GBP2、RPL39L) were obtained, and the risk model based on them combined with age predicted that the AUC of patients' 1-, 3-, and 5-year survival rates were all greater than 0.7, showing good clinical application prospects. The mechanism of cuproptosis and its key genes might become therapeutic targets for ovarian cancer. The subtypes of cuproptosis provide a theoretical basis for personalized clinical treatment. The predictive model constructed by key prognostic genes has promising clinical application effects. 利用公共数据库中的卵巢癌数据集，确定卵巢癌组织中的铜死亡相关基因，并基于这些基因构建卵巢癌患者临床预后风险评分模型。 从 TCGA OC数据库和GEO数据库中的GSE26193、GSE63885数据集，筛选出10个铜死亡相关基因，并基于数据集分析其染色体定位、表达相关性和突变模式。基于这些基因，我们对卵巢癌样本进行聚类，以识别铜诱导死亡的不同分子亚型。分析不同亚型之间的差异表达基因和功能富集，并通过生存回归分析获得了具有预后预测能力的特征基因。基于这些特征基因，我们构建了一个风险评分模型，并结合患者的临床特征，共同预测其生存率。 在卵巢癌样本中，10个铜死亡相关基因可将样本稳定区分为两种分子亚型，二者临床及免疫特征、药物敏感性等方面差异明显。进一步筛选获得7个预后基因（RARRES1、CXCL10、PI3、CXCL11、THEMIS2、GBP2、RPL39L），基于其建立的风险模型结合年龄后对患者1、3、5年生存率预测的曲线下面积均大于0.7，显示良好临床应用前景。 铜死亡的机制及其关键基因可能成为卵巢癌的治疗靶点。该分型为临床个体化治疗提供了理论依据。由关键预后基因构建的预测模型具有良好的临床应用效果。
The use of virtual crossmatch for HLA antigen compatibility assessment before transplantation has become common practice in transplantation medicine. The accuracy of virtual crossmatch relies on accurate and complete donor HLA antigen typing and up-to-date patient HLA antigen antibody characterization. Here, we report a case in which anti-HLA-DP antibodies were detected in the patient, and the donor HLA-DPB1*29:01 was not included in the bead panel of Luminex-based single antigen bead assay (LSA). The deceased donor HLA antigen typing results were downloaded from the United Network for Organ Sharing. Serum samples were tested for HLA antibodies using LSA. Epitope analysis was performed manually based on alignment of HLA-DP using the Sequence Alignment Tool from the IPD-IMGT/HLA database (https://www.ebi.ac.uk/ipd/imgt/hla/). The LSA showed that anti-HLA-DP3, DP6, DP9, DP11, DP14, DP15, DP17, and DP20 were positive. HLA antigen typing with real-time polymerase chain reaction showed that the donor carried HLA-DPB1*29:01. Epitope analysis showed that the anti-HLA-DPB1*29:01 donor-specific antibody was present in this patient. The LSA can miss antibodies against HLA antigens not represented by the beads, leading to false-negative results for donor-specific antibodies. Failure to consider the possibility of unrepresented HLA proteins may potentially lead to incorrect clinical decision. Epitope analysis may help predict reactivity to HLA antigens not present on LSA beads.
Institutional research teams and core facilities routinely manage pre-publication omics datasets that span heterogeneous file types, nested project structures, and multiple downstream uses. Public repositories mainly support post-publication dissemination, while workflow systems and enterprise data platforms do not directly provide a lightweight governance and delivery layer for internal research assets. We present MetaServe, an open-source governance and delivery layer for pre-publication research assets in institutional multi-omics settings. MetaServe registers and delivers heterogeneous assets, including sequencing files, processed matrices, imaging data, analysis-ready objects, tabular files, and documents, without requiring repository-grade standardization. Its metadata-aware design combines file-type recognition, partial automatic extraction for selected formats, manually supplied project and biological annotations, and indexed faceted retrieval. MetaServe supports authenticated web download, viewer-oriented handoff for compatible services such as cellxgene, and path-manifest export for downstream workflows under shared-storage assumptions. The current implementation combines role-based controls, explicit file-level sharing, path-constrained delivery, and operational traceability to support controlled institutional access. MetaServe has been deployed at the Chinese Institutes for Medical Research (CIMR) as part of an institutional multi-omics data-management system. MetaServe provides a practical layer between institutional storage and downstream analytical platforms for pre-publication research data. Its contribution is the integration of lightweight metadata-aware registration, permission-aware retrieval, and controlled delivery for heterogeneous institutional omics assets. Rather than replacing workflow engines, public repositories, or enterprise-scale research data platforms, MetaServe offers a deployable governance layer for core facilities and collaborative teams that need structured discovery and traceable delivery before public deposition or manuscript release.
Understanding the regulatory consequences of genetic variation in the aging human brain requires molecular maps that span brain regions, cell types and regulatory modalities. We present the Alzheimer's Disease Sequencing Project Functional Genomics (FunGen-AD) xQTL Atlas, a harmonized resource of molecular quantitative trait loci from four postmortem brain studies, ROSMAP, MSBB, Knight-ADRC and MiGA. The atlas integrates histone acetylation, DNA methylation, gene expression, splicing and protein abundance QTLs across 14 brain regions, 7 major cell types and 17,566 samples, with standardized association, significance-filtered and fine-mapping outputs. To expand discovery beyond conventional 1-Mb cis windows, we include variants within Topologically Associating Domains (TAD) and their boundaries where appropriate, identifying on average 21% more variant-molecular-trait associations per dataset. Statistical fine-mapping reduced broad association sets by 95% into credible sets of candidate regulatory variants. Distributed through the NIAGADS xQTL portal and bulk-download services, the atlas provides a comprehensive functional-genomic foundation for interpreting genetic risk variants in Alzheimer's disease and aging-brain research.
Organisms within ecological systems often engage in molecular interactions that mediate key biological processes, such as protein-protein interactions involved in host-pathogen recognition and symbiosis. Characterization of these interactions at a molecular level is essential for understanding the mechanistic, evolutionary, and functional basis of interspecies interactions, as well as for informing potential therapeutic interventions. However, progress in this field is significantly impeded by the lack of a comprehensive database of interacting species at molecular resolution and the limited availability of experimental data. We introduce the Interacting Species Database (ISDB), a comprehensive resource that catalogs interspecies interactions, annotated with NCBI taxonomic identifiers, interaction types and known molecular interactions. The ISDB encompasses 858,229 interacting species pairs and 171,713 interspecies protein-protein interactions within 261,287 organisms. ISDB is designed to support researchers in searching for, downloading, and depositing interspecies interaction data, which facilitates the study of ecological dynamics across diverse research domains. The ISDB is available via a web interface (https://www.elhabashylab.org/isdb), open-source code on GitHub (https://github.com/ElhabashyLab/ISDB) under the MIT license and is archived on Zenodo (Version v1.0.1, DOI: 10.5281/zenodo.20162385).
The accurate parameterization of drug-like molecules is a fundamental prerequisite for molecular dynamics simulations in structure-based drug design. While the AM1-BCC charge model has served as the de facto "standard" for GAFF2 parameterization for two decades, the recently introduced ABCG2 model offers substantially improved accuracy in predicting hydration free energies and other key physicochemical properties. However, accessing ABCG2 parameters traditionally requires downloading and installing the full AmberTools suite-a multi-gigabyte software package with complex dependencies-presenting a significant barrier for many practitioners, particularly experimental collaborators and researchers new to the field. Here we present a major upgrade to the PrimaDORAC web interface that makes ABCG2 parameterization readily accessible to the entire drug design community. Through a minimalistic integration of essential AmberTools components directly into the web application framework, users can now obtain GAFF2 parameters with ABCG2 charges for any drug-like molecule in seconds, without any software installation. The interface accepts a single SMILES string or structure file and returns a complete archive containing GROMACS-compatible topology files and a ready-to-use PDB structure. By removing all technical barriers to accessing state-of-the-art ABCG2 parameters, the upgraded PrimaDORAC interface is aimed at empowering medicinal chemists, pharmacologists, and computational researchers alike to conduct more accurate MD simulations with minimal effort. The service is freely available at www1.chim.unifi.it/orac.
Lung adenocarcinoma (LUAD) is a common malignant tumor with a poor prognosis and limited effective therapeutic targets. The underlying molecular regulatory mechanisms driving its progression remain largely unclear. The study objectives were to build a circRNA-miRNA-mRNA ceRNA regulation network of LUAD and to identify miRNAs and mRNAs significantly related to the prognosis . The gene expression data and GSE101684 were downloaded from the UCSC Xene and NCBI-GEO databases, respectively. The differentially expressed RNAs (DEcircRNAs, DEmiRNAs, and DEmRNAs; DERs) were obtained by the Limma package in R. Then, the differential LUAD-related genes were identified, and the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of the differential LUAD-related genes were analyzed. Moreover, the circRNA-miRNA-mRNA ceRNA network of LUAD was built. The Kaplan-Meier (K-M) survival curve analysis of ceRNA network nodes was performed. In addition, the proliferation-related ceRNA network was built. A total of 382 DEcircRNAs, 1907 DEmRNAs and 156 DEmiRNAs were acquired. A total of 245 differential LUAD-related genes were acquired, which were significantly associated with 189 GO biological processes (BP) and 17 KEGG pathways. Moreover, the ceRNA network of LUAD was built. The K-M survival curve analysis of ceRNA network nodes revealed that a total of 2 miRNAs (hsa-miR-96-5p and hsa-miR-125b-2-3p) and 22 mRNAs (CGNL1, CTHRC1, TK1, etc) were significantly related to the prognosis. mRNAs were significantly enriched in 92 GO BPs (such as cell division, cell adhesion) and 9 KEGG pathways (such as cell cycle, HTLV-1 infection). In addition, the proliferation-related ceRNA network was built. This research built a ceRNA regulation network of LUAD and is of great significance for identifying biomarkers related to the prognosis in LUAD.
Traumatic cataract is a common blinding eye disease after ocular trauma, and its pathogenesis is closely related to lens epithelial cell dysfunction, while the definite molecular regulatory mechanism between upstream transcription factor and downstream target gene remains poorly clarified. This study aimed to clarify the role and molecular mechanism of the krüppel-like factor 5 (KLF5)/ thrombosponin 1 (THBS1) axis in regulating proliferation, migration, epithelial-mesenchymal transition and autophagy of lens epithelial cells in traumatic cataract, and to explore its potential clinical therapeutic value. The GSE295383 dataset in the gene expression omnibus (GEO) database was downloaded, and the differentially expressed genes (DEGs) were screened by linear models for microarray data (limma) package of R language. Combined with Weighted gene co-expression network analysis (WGCNA), the gene co-expression network was constructed and the key modules were screened. Gene ontology (GO), kyoto encyclopedia of genes and genomes (KEGG) and gene set enrichment analysis (GSEA) combined with human transcription factor target (hTFtarget) and JASPAR databases were used to predict the upstream transcription factors of THBS1. Subsequently, SRA01/04 cells were induced with transforming growth factor-beta 2 (TGF-β2) to construct a cataract cell model. THBS1 and KLF5 were highly expressed in LECs exposed to TGF-β2. KLF5 could activate THBS1 transcription by binding to THBS1 promoter - 174 to -165 sites. Knockdown of THBS1 inhibited TGF-β2-induced viability, proliferation, migration, and altered the expression of epithelial-mesenchymal transition (EMT)- and autophagy-related markers in LECs. Knockdown of KLF5 downregulated THBS1 expression and produced a similar inhibitory effect, while overexpression of THBS1 reversed the effect of KLF5 knockdown. This study demonstrated that KLF5 promoted the proliferation, migration, and EMT‑associated molecular changes of LECs in traumatic cataract through transcriptional activation of THBS1, and regulated the expression of autophagy‑related markers in LECs, suggesting that KLF5/THBS1 axis might be a potential target for the treatment of traumatic cataract.