J Am Geriatr Soc. 2025 Mar 18. doi: 10.1111/jgs.19440. Online ahead of print.

ABSTRACT

BACKGROUND: Recent evidence underscores the significant impact of social determinants of health (SDoH) on cardiovascular disease (CVD). However, available CVD risk assessment tools often neglect SDoH. This study aimed to integrate SDoH with traditional risk factors to predict CVD risk.

METHODS: The data was sourced from the ASPirin in Reducing Events in the Elderly (ASPREE) longitudinal study, and its sub-study, the ASPREE Longitudinal Study of Older Persons (ALSOP). The study included 12,896 people (5884 men and 7012 women) aged 70 or older who were initially free of CVD, dementia, and independence-limiting physical disability. The participants were followed for a median of eight years. CVD risk was predicted using state-of-the-art machine learning (ML) and deep learning (DL) models: Random Survival Forest (RSF), Deepsurv, and Neural Multi-Task Logistic Regression (NMTLR), incorporating both SDoH and traditional CVD risk factors as candidate predictors. The permutation-based feature importance method was further utilized to assess the predictive potential of the candidate predictors.

RESULTS: Among men, the RSF model achieved relatively good performance (C-index = 0.732, integrated brier score (IBS) = 0.071, 5-year and 10-year AUC = 0.657 and 0.676 respectively). For women, DeepSurv was the best-performing model (C-index = 0.670, IBS = 0.042, 5-year and 10-year AUC = 0.676 and 0.677 respectively). Regarding the contribution of the candidate predictors, for men, age, urine albumin-to-creatinine ratio, and smoking, along with SDoH variables, were identified as the most significant predictors of CVD. For women, SDoH variables, such as social network, living arrangement, and education, predicted CVD risk better than the traditional risk factors, with age being the exception.

CONCLUSION: SDoH can improve the accuracy of CVD risk prediction and emerge among the main predictors for CVD. The influence of SDoH was greater for women than for men, reflecting gender-specific impacts of SDoH.

PMID:40099367 | DOI:10.1111/jgs.19440

Front Endocrinol (Lausanne). 2025 Mar 3;16:1495306. doi: 10.3389/fendo.2025.1495306. eCollection 2025.

ABSTRACT

BACKGROUND: Machine learning (ML) models are being increasingly employed to predict the risk of developing and progressing diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus (T2DM). However, the performance of these models still varies, which limits their widespread adoption and practical application. Therefore, we conducted a systematic review and meta-analysis to summarize and evaluate the performance and clinical applicability of these risk predictive models and to identify key research gaps.

METHODS: We conducted a systematic review and meta-analysis to compare the performance of ML predictive models. We searched PubMed, Embase, the Cochrane Library, and Web of Science for English-language studies using ML algorithms to predict the risk of DKD in patients with T2DM, covering the period from database inception to April 18, 2024. The primary performance metric for the models was the area under the receiver operating characteristic curve (AUC) with a 95% confidence interval (CI). The risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST) checklist.

RESULTS: 26 studies that met the eligibility criteria were included into the meta-analysis. 25 studies performed internal validation, but only 8 studies conducted external validation. A total of 94 ML models were developed, with 81 models evaluated in the internal validation sets and 13 in the external validation sets. The pooled AUC was 0.839 (95% CI 0.787-0.890) in the internal validation and 0.830 (95% CI 0.784-0.877) in the external validation sets. Subgroup analysis based on the type of ML showed that the pooled AUC for traditional regression ML was 0.797 (95% CI 0.777-0.816), for ML was 0.811 (95% CI 0.785-0.836), and for deep learning was 0.863 (95% CI 0.825-0.900). A total of 26 ML models were included, and the AUCs of models that were used three or more times were pooled. Among them, the random forest (RF) models demonstrated the best performance with a pooled AUC of 0.848 (95% CI 0.785-0.911).

CONCLUSION: This meta-analysis demonstrates that ML exhibit high performance in predicting DKD risk in T2DM patients. However, challenges related to data bias during model development and validation still need to be addressed. Future research should focus on enhancing data transparency and standardization, as well as validating these models' generalizability through multicenter studies.

SYSTEMATIC REVIEW REGISTRATION: https://inplasy.com/inplasy-2024-9-0038/, identifier INPLASY202490038.

PMID:40099258 | PMC:PMC11911190 | DOI:10.3389/fendo.2025.1495306

Clin Ophthalmol. 2025 Mar 13;19:907-914. doi: 10.2147/OPTH.S506519. eCollection 2025.

ABSTRACT

PURPOSE: This study introduces a fully automated approach using deep learning-based segmentation to select the conjunctiva as the region of interest (ROI) for large-scale, multi-site clinical trials. By integrating a precise, objective grading system, we aim to minimize inter- and intra-grader variability due to perceptual biases. We evaluate the impact of adding a "horizontality" parameter to the grading system and assess this method's potential to enhance grading precision, reduce sample size, and improve clinical trial efficiency.

METHODS: We analyzed 29,640 images from 450 subjects in a multi-visit, multi-site clinical trial to assess the performance of an automated grading model compared to expert graders. Images were graded on a 0-4 scale, in 0.5 increments. The model utilizes the DeepLabV3 architecture for image segmentation, extracting two key features-horizontality and redness. The algorithm then uses these features to predict eye redness, validated by comparison with expert grader scores.

RESULTS: The bivariate model using both redness and horizontality performed best, with a Mean Absolute Error (MAE) of 0.450 points (SD=0.334) on the redness scale relative to expert scores. Expert graded scores were within one unit of the mean grade in over 85% cases, ensuring consistency and optimal training set for the predictive model. Models incorporating both features outperformed those using only redness, reducing MAE by 5-6%. The optimal generalized model improved predictive accuracy with horizontality such that 93.0% of images were predicted with an absolute error less than one unit difference in grading.

CONCLUSION: This study demonstrates that fully automating image analysis allows thousands of images to be graded efficiently. The addition of the horizontality parameter enhances model performance, reduces error, and supports its relevance to specific Dry Eye manifestations. This automated method provides a continuous scale and greater sensitivity to treatment effects than standard clinical scales.

PMID:40099234 | PMC:PMC11912931 | DOI:10.2147/OPTH.S506519

J Pharm Anal. 2025 Mar;15(3):101144. doi: 10.1016/j.jpha.2024.101144. Epub 2024 Nov 14.

ABSTRACT

Drug development remains a critical issue in the field of biomedicine. With the rapid advancement of information technologies such as artificial intelligence (AI) and the advent of the big data era, AI-assisted drug development has become a new trend, particularly in predicting drug-target associations. To address the challenge of drug-target prediction, AI-driven models have emerged as powerful tools, offering innovative solutions by effectively extracting features from complex biological data, accurately modeling molecular interactions, and precisely predicting potential drug-target outcomes. Traditional machine learning (ML), network-based, and advanced deep learning architectures such as convolutional neural networks (CNNs), graph convolutional networks (GCNs), and transformers play a pivotal role. This review systematically compiles and evaluates AI algorithms for drug- and drug combination-target predictions, highlighting their theoretical frameworks, strengths, and limitations. CNNs effectively identify spatial patterns and molecular features critical for drug-target interactions. GCNs provide deep insights into molecular interactions via relational data, whereas transformers increase prediction accuracy by capturing complex dependencies within biological sequences. Network-based models offer a systematic perspective by integrating diverse data sources, and traditional ML efficiently handles large datasets to improve overall predictive accuracy. Collectively, these AI-driven methods are transforming drug-target predictions and advancing the development of personalized therapy. This review summarizes the application of AI in drug development, particularly in drug-target prediction, and offers recommendations on models and algorithms for researchers engaged in biomedical research. It also provides typical cases to better illustrate how AI can further accelerate development in the fields of biomedicine and drug discovery.

PMID:40099205 | PMC:PMC11910364 | DOI:10.1016/j.jpha.2024.101144

Front Psychiatry. 2025 Feb 20;16:1485286. doi: 10.3389/fpsyt.2025.1485286. eCollection 2025.

ABSTRACT

INTRODUCTION: Autism Spectrum Disorder (ASD) identification poses significant challenges due to its multifaceted and diverse nature, necessitating early discovery for operative involvement. In a recent study, there has been a lot of talk about how deep learning algorithms might improve the diagnosis of ASD by analyzing neuroimaging data.

METHOD: To overrule the negatives of current techniques, this research proposed a revolutionary strategic model called the Unified Transformer Block for Multi-View Graph Attention Networks (MVUT_GAT). For the purpose of extracting delicate outlines from physical and efficient functional MRI data, MVUT_GAT combines the advantages of multi-view learning with attention processes.

RESULT: With the use of the ABIDE dataset, a thorough analysis shows that MVUT_GAT performs better than Mutli-view Site Graph Convolution Network (MVS_GCN), outperforming it in accuracy by +3.40%. This enhancement reinforces our suggested model's effectiveness in identifying ASD. The result has implications over higher accuracy metrics. Through improving the accuracy and consistency of ASD diagnosis, MVUT_GAT will help with early interference and assistance for ASD patients.

DISCUSSION: Moreover, the proposed MVUT_GAT's which patches the distance between the models of deep learning and medical visions by helping to identify biomarkers linked to ASD. In the end, this effort advances the knowledge of recognizing autism spectrum disorder along with the powerful ability to enhance results and the value of people who are undergone.

PMID:40099145 | PMC:PMC11913004 | DOI:10.3389/fpsyt.2025.1485286

Front Genet. 2025 Mar 3;16:1471037. doi: 10.3389/fgene.2025.1471037. eCollection 2025.

ABSTRACT

INTRODUCTION: Melanoma, a highly aggressive malignancy characterized by rapid metastasis and elevated mortality rates, predominantly originates in cutaneous tissues. While surgical interventions, immunotherapy, and targeted therapies have advanced, the prognosis for advanced-stage melanoma remains dismal. Globally, melanoma incidence continues to rise, with the United States alone reporting over 100,000 new cases and 7,000 deaths annually. Despite the exponential growth of tumor data facilitated by next-generation sequencing (NGS), current analytical approaches predominantly emphasize single-gene analyses, neglecting critical insights into complex gene interaction networks. This study aims to address this gap by systematically exploring immune gene regulatory dynamics in melanoma progression.

METHODS: We developed a bidirectional, weighted, signed, and directed topological immune gene regulatory network to compare transcriptional landscapes between benign melanocytic nevi and cutaneous melanoma. Advanced network analysis tools were employed to identify structural disparities and functional module shifts. Key driver genes were validated through topological centrality metrics. Additionally, deep learning models were implemented to predict drug-target interactions, leveraging molecular features derived from network analyses.

RESULTS: Significant topological divergences emerged between nevi and melanoma networks, with dominant functional modules transitioning from cell cycle regulation in benign lesions to DNA repair and cell migration pathways in malignant tumors. A group of genes, including AURKA, CCNE1, APEX2, and EXOC8, were identified as potential orchestrators of immune microenvironment remodeling during malignant transformation. The deep learning framework successfully predicted 23 clinically actionable drug candidates targeting these molecular drivers.

DISCUSSION: The observed module shift from cell cycle to invasion-related pathways provides mechanistic insights into melanoma progression, suggesting early therapeutic targeting of DNA repair machinery might mitigate metastatic potential. The identified hub genes, particularly AURKA and DDX19B, represent novel candidates for immunomodulatory interventions. Our computational drug prediction strategy bridges molecular network analysis with clinical translation, offering a paradigm for precision oncology in melanoma. Future studies should validate these targets in preclinical models and explore network-based biomarkers for early detection.

PMID:40098976 | PMC:PMC11911340 | DOI:10.3389/fgene.2025.1471037

Front Oncol. 2025 Mar 3;15:1480645. doi: 10.3389/fonc.2025.1480645. eCollection 2025.

ABSTRACT

INTRODUCTION: Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin lymphoma (NHL) in humans, and it is a highly heterogeneous malignancy with a 40% to 50% risk of relapsed or refractory (R/R), leading to a poor prognosis. So early prediction of R/R risk is of great significance for adjusting treatments and improving the prognosis of patients.

METHODS: We collected clinical information and H&E images of 227 patients diagnosed with DLBCL in Xuzhou Medical University Affiliated Hospital from 2015 to 2018. Patients were then divided into R/R group and non-relapsed & non-refractory group based on clinical diagnosis, and the two groups were randomly assigned to the training set, validation set and test set in a ratio of 7:1:2. We developed a model to predict the R/R risk of patients based on clinical features utilizing the random forest algorithm. Additionally, a prediction model based on histopathological images was constructed using CLAM, a weakly supervised learning method after extracting image features with convolutional networks. To improve the prediction performance, we further integrated image features and clinical information for fusion modeling.

RESULTS: The average area under the ROC curve value of the fusion model was 0.71±0.07 in the validation dataset and 0.70±0.04 in the test dataset. This study proposed a novel method for predicting the R/R risk of DLBCL based on H&E images and clinical features.

DISCUSSION: For patients predicted to have high risk, follow-up monitoring can be intensified, and treatment plans can be adjusted promptly.

PMID:40098696 | PMC:PMC11911189 | DOI:10.3389/fonc.2025.1480645

Discov Imaging. 2025;2(1):2. doi: 10.1007/s44352-025-00006-1. Epub 2025 Mar 10.

ABSTRACT

BACKGROUND: Colorectal cancer (CRC) is a globally prevalent cancer. Emerging research implicates the gut microbiome in CRC pathogenesis. Bacteria such as Clostridium scindens can produce the carcinogenic bile acid deoxycholic acid (DCA). It is unknown whether imaging methods can differentiate DCA-producing and DCA-non-producing C. scindens cells.

METHODS: Light microscopy images of anaerobically cultured C. scindens in four conditions were acquired at 100× magnification using the Tissue FAX system: C. scindens in media alone (DCA-non-producing state), C. scindens in media with cholic acid (DCA-producing state), or C. scindens in co-culture with one of two Bacteroides species (intermediate DCA production states). We evaluated three approaches: whole-image classification, per-cell classification, and image segmentation-based classification. For whole-image classification, we used a custom Convolutional Neural Network (CNN), pre-trained DenseNet, pre-trained ResNet, and ResNet enhanced by integrating the Digital Images of Bacterial Species (DIBaS) dataset. For cell detection and classification, we applied thresholding (OTSU or adaptive thresholding) followed by a ResNet model. Finally, image segmentation-based classification was performed using nnU-Net.

RESULTS: For whole-image analysis, DIBaS-enhanced ResNet models achieved the best performance in distinguishing C. scindens states in monoculture (accuracy 0.89 ± 0.006) and in co-cultures (accuracy 0.86 ± 0.004). Per-cell analysis was optimal at a C constant value of 3, with the ResNet model achieving 62-74% accuracy for C. scindens states in monoculture. Segmentation-based analysis using nnU-Net resulted in Dice coefficients of 87% for C. scindens and 74-76% for the Bacteroides species.

CONCLUSIONS: This study demonstrates feasibility of image-based deep learning models in identifying health-relevant gut bacterial metabolic states.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s44352-025-00006-1.

PMID:40098681 | PMC:PMC11912549 | DOI:10.1007/s44352-025-00006-1

Front Oncol. 2025 Mar 3;14:1461542. doi: 10.3389/fonc.2024.1461542. eCollection 2024.

ABSTRACT

BACKGROUND: Breast cancer ranks as one of the leading malignant tumors among women worldwide in terms of incidence and mortality. Ultrasound examination is a critical method for breast cancer screening and diagnosis in China. However, conventional breast ultrasound examinations are time-consuming and labor-intensive, necessitating the development of automated and efficient detection models.

METHODS: We developed a novel approach based on an improved deep learning model for the intelligent auxiliary diagnosis of breast tumors. Combining an optimized U2NET-Lite model with the efficient DeepCardinal-50 model, this method demonstrates superior accuracy and efficiency in the precise segmentation and classification of breast ultrasound images compared to traditional deep learning models such as ResNet and AlexNet.

RESULTS: Our proposed model demonstrated exceptional performance in experimental test sets. For segmentation, the U2NET-Lite model processed breast cancer images with an accuracy of 0.9702, a recall of 0.7961, and an IoU of 0.7063. In classification, the DeepCardinal-50 model excelled, achieving higher accuracy and AUC values compared to other models. Specifically, ResNet-50 achieved accuracies of 0.78 for benign, 0.67 for malignant, and 0.73 for normal cases, while DeepCardinal-50 achieved 0.76, 0.63, and 0.90 respectively. These results highlight our model's superior capability in breast tumor identification and classification.

CONCLUSION: The automatic detection of benign and malignant breast tumors using deep learning can rapidly and accurately identify breast tumor types at an early stage, which is crucial for the early diagnosis and treatment of malignant breast tumors.

PMID:40098633 | PMC:PMC11911202 | DOI:10.3389/fonc.2024.1461542

Therap Adv Gastroenterol. 2025 Mar 17;18:17562848251325525. doi: 10.1177/17562848251325525. eCollection 2025.

ABSTRACT

Integrating artificial intelligence (AI) into histologic disease assessment is transforming the management of inflammatory bowel disease (IBD). AI-aided histology enables precise, objective evaluations of disease activity by analysing whole-slide images, facilitating accurate predictions of histologic remission (HR) in ulcerative colitis and Crohn's disease. Additionally, AI shows promise in predicting adverse outcomes and therapeutic responses, making it a promising tool for clinical practice and clinical trials. By leveraging advanced algorithms, AI enhances diagnostic accuracy, reduces assessment variability and streamlines histological workflows in clinical settings. In clinical trials, AI aids in assessing histological endpoints, enabling real-time analysis, standardising evaluations and supporting adaptive trial designs. Recent advancements are further refining AI-aided digital pathology in IBD. New developments in multimodal AI models integrating clinical, endoscopic, histologic and molecular data pave the way for a comprehensive approach to precision medicine in IBD. Automated assessment of intestinal barrier healing - a deeper level of healing beyond endoscopic and HR - shows promise for improved outcome prediction and patient management. Preliminary evidence also suggests that AI applied to colitis-associated neoplasia can aid in the detection, characterisation and molecular profiling of lesions, holding potential for enhanced dysplasia management and organ-sparing approaches. Although challenges remain in standardisation, validation through randomised controlled trials and ethical considerations. AI is poised to revolutionise IBD management by advancing towards a more personalised and efficient care model, while the path to full clinical implementation may be lengthy. However, the transformative impact of AI on IBD care is already shining through.

PMID:40098604 | PMC:PMC11912177 | DOI:10.1177/17562848251325525

Cureus. 2025 Feb 15;17(2):e79036. doi: 10.7759/cureus.79036. eCollection 2025 Feb.

ABSTRACT

Erasmus syndrome (ES) is a rare condition characterized by the link between crystalline silica exposure, with or without silicosis, and systemic sclerosis (SSc). Although first noted over a century ago, its underlying mechanisms remain unclear. However, it is indistinguishable from idiopathic SSc in the general population. Its clinical presentation is heterogeneous, depending on the affected systems, with notable features, including skin fibrosis, microstomia, telangiectasia, Raynaud's phenomenon, arthralgia, and interstitial lung disease. Currently, there is no unified consensus on its treatment; however, organ-specific therapy is a reasonable approach. We report the case of a 43-year-old miner diagnosed with diffuse cutaneous SSc, where ES was diagnosed after an exhaustive history was taken, occupational exposure was characterized, differential diagnoses were excluded, and radiological and histopathological evidence of pulmonary silicosis was presented.

PMID:40099047 | PMC:PMC11912300 | DOI:10.7759/cureus.79036

BMC Med Genomics. 2025 Mar 17;18(1):54. doi: 10.1186/s12920-025-02110-x.

ABSTRACT

BACKGROUND: Post-Covid Pulmonary Fibrosis (PCPF) has emerged as a significant global issue associated with a poor quality of life and significant morbidity. Currently, our understanding of the molecular pathways of PCPF is limited. Hence, in this study, we performed whole transcriptome sequencing of the RNA isolated from the bronchoalveolar lavage (BAL) samples of PCPF and compared it with idiopathic pulmonary fibrosis (IPF) and non-ILD (Interstitial Lung Disease) control to understand the gene expression profile and associated pathways.

METHODS: BAL samples from PCPF (n = 3), IPF (n = 3), and non-ILD Control (n = 3) (individuals with apparent healthy lung without interstitial lung disease) groups were obtained and RNA were isolated for whole transcriptomic sequencing. Differentially Expressed Genes (DEGs) were determined followed by functional enrichment analysis and qPCR validation.

RESULTS: A panel of differentially expressed genes were identified in bronchoalveolar lavage fluid cells (BALF) of PCPF as compare to control and IPF. Our analysis revealed dysregulated pathways associated with cell cycle regulation, immune responses, and neuroinflammatory processes. Real-time validation further supported these findings. The PPI network and module analysis shed light on potential biomarkers and underscore the complex interplay of molecular mechanisms in PCPF. The comparison of PCPF and IPF identified a significant downregulation of pathways that were more prominent in IPF.

CONCLUSION: This investigation provides crucial insights into the molecular mechanism of PCPF and also outlines avenues for prospective research and the development of therapeutic approaches.

PMID:40098116 | DOI:10.1186/s12920-025-02110-x

Front Immunol. 2025 Mar 3;16:1511902. doi: 10.3389/fimmu.2025.1511902. eCollection 2025.

ABSTRACT

INTRODUCTION: Alterations of the adaptive immune system have been shown to impact bone healing and may result in impaired healing in some patients. Apart from T cells, B cells are the key drivers of adaptive immunity. Therefore, their role in age-associated impairments of bone healing might be essential to understand delays during the healing process. B cells are essential for bone formation, and their dysfunction has been associated with aging or autoimmune diseases. But whether age-associated changes in B cell phenotypes are involved in bone regeneration is unknown.

METHODS: Here, we aimed to characterize the role of immune aging in B cell phenotypes during the early inflammatory phase of bone healing. By comparing non-immune experienced with young and immune experienced mice we aimed to analyze the effect of gained immune experience on B cells. Our single cell proteo-genomics analysis quantified thousands of transcriptomes of cells that were isolated from post osteotomy hematoma and the proximal and distal bone marrow cavities, and enabled us to evaluate cell proportion, differential gene expression and cell trajectories.

RESULTS: While the B cell proportion in young and non-immune experienced animals did not significantly change from 2 to 5 days post osteotomy in the hematoma, we found a significant decrease of the B cell proportion in the immune experienced mice, which was accompanied by the decreased expression of B cell specific genes, suggesting a specific response in immune experienced animals. Furthermore, we detected the most extensive B cell differentiation block in immune-experienced mice compared to non-immune experienced and young animals, predominantly in the transition from immature to mature B cells.

DISCUSSION: Our results suggest that the pronounced impairment of B cell production found in immune experienced animals plays an important role in the initial phase leading to delayed bone healing. Therefore, novel therapeutic approaches may be able target the B cell differentiation defect to retain B cell functionality even in the immune experienced setting, which is prone to delayed healing.

PMID:40098964 | PMC:PMC11911212 | DOI:10.3389/fimmu.2025.1511902

Expert Opin Drug Discov. 2025 Mar 17. doi: 10.1080/17460441.2025.2481259. Online ahead of print.

ABSTRACT

INTRODUCTION: The landscape of early drug discovery is rapidly evolving, fueled by significant advancements in artificial intelligence (AI) and machine learning (ML), which are transforming the way drugs are discovered. As traditional drug discovery faces growing challenges in terms of time, cost, and efficacy, there is a pressing need to integrate these emerging technologies to enhance the discovery process.

AREAS COVERED: In this perspective, the authors explore the role of AI and ML in modern early drug discovery and discuss their application in drug target identification, compound screening, and biomarker discovery. This article is based on a thorough literature search using the PubMed database to identify relevant studies that highlight the use of AI/ML models in computational chemistry, systems biology, and data-driven approaches to drug development. Emphasis is placed on how these technologies address key challenges such as data integration, predictive performance, and cost-efficiency in the drug discovery pipeline.

EXPERT OPINION: AI and ML have the potential to revolutionize early drug discovery by improving the accuracy and speed of identifying viable drug candidates. However, successful integration of these technologies requires overcoming challenges related to data quality, model interpretability, and the need for interdisciplinary collaboration.

PMID:40098331 | DOI:10.1080/17460441.2025.2481259

BMC Plant Biol. 2025 Mar 18;25(1):345. doi: 10.1186/s12870-025-06322-2.

ABSTRACT

BACKGROUND: The quality of groundnut produce is adversely impacted due to aflatoxin contamination by the fungus Aspergillus flavus. Although the transcriptomic control is not fully understood, the interaction between long non-coding RNAs and microRNAs in regulating A. flavus and aflatoxin contamination remains unclear. This study was carried out to identify microRNAs (miRNAs) to enhance the understanding of in vitro seed colonization (IVSC) resistance mechanism in groundnut.

RESULT: In this study, resistant (J 11) and susceptible (JL 24) varieties of groundnut were treated with toxigenic A. flavus (strain AF-11-4), and total RNA was extracted at 1 day after inoculation (1 DAI), 2 DAI, 3 DAI and 7 DAI. Seeds of JL 24 showed higher mycelial growth than J 11 at successive days after inoculation. A total of 208 known miRNAs belonging to 36 miRNA families, with length varying from 20-24 nucleotides, were identified, along with 27 novel miRNAs, with length varying from 20-22 nucleotides. Using psRNATarget server, 952 targets were identified for all the miRNAs. The targeted genes function as disease resistant proteins encoding, auxin responsive proteins, squamosa promoter binding like proteins, transcription factors, pentatricopeptide repeat-containing proteins and growth regulating factors. Through differential expression analysis, seven miRNAs (aly-miR156d-3p, csi-miR1515a, gma-miR396e, mtr-miR2118, novo-miR-n27, ptc-miR482d-3p and ppe-miR396a) were found common among 1 DAI, 2 DAI, 3 DAI and 7 DAI in J 11, whereas ten miRNAs (csi-miR159a-5p, csi-miR164a-3p, novo-miR-n17, novo-miR-n2, osa-miR162b, mtr-miR2118, ptc-miR482d-3p, ptc-miR167f-3p, stu-miR319-3p and zma-miR396b-3p) were found common among 1 DAI, 2 DAI, 3 DAI and 7 DAI in JL 24. Two miRNAs, ptc-miR482d-3p and mtr-miR2118, showed contrasting expression at different time intervals between J 11 and JL 24. These two miRNAs were found to target those genes with NBS-LRR function, making them potential candidates for marker development in groundnut breeding programs aimed at enhancing resistance against A. flavus infection.

CONCLUSION: This study enhances our understanding of the involvement of two miRNAs namely, ptc-miR482d-3p and mtr-miR2118, along with their NBS-LRR targets, in conferring resistance against A. flavus-induced aflatoxin contamination in groundnut under in vitro conditions.

PMID:40098099 | DOI:10.1186/s12870-025-06322-2

Nat Genet. 2025 Mar 17. doi: 10.1038/s41588-025-02121-5. Online ahead of print.

ABSTRACT

Genetic mutation and drift, coupled with natural and human-mediated selection and migration, have produced a wide variety of genotypes and phenotypes in farmed animals. We here introduce the Farm Animal Genotype-Tissue Expression (FarmGTEx) Project, which aims to elucidate the genetic determinants of gene expression across 16 terrestrial and aquatic domestic species under diverse biological and environmental contexts. For each species, we aim to collect multiomics data, particularly genomics and transcriptomics, from 50 tissues of 1,000 healthy adults and 200 additional animals representing a specific context. This Perspective provides an overview of the priorities of FarmGTEx and advocates for coordinated strategies of data analysis and resource-sharing initiatives. FarmGTEx aims to serve as a platform for investigating context-specific regulatory effects, which will deepen our understanding of molecular mechanisms underlying complex phenotypes. The knowledge and insights provided by FarmGTEx will contribute to improving sustainable agriculture-based food systems, comparative biology and eventual human biomedicine.

PMID:40097783 | DOI:10.1038/s41588-025-02121-5

Breast Cancer Res Treat. 2025 Mar 17. doi: 10.1007/s10549-025-07666-x. Online ahead of print.

ABSTRACT

PURPOSE: Endocrine therapy resistance is the major challenge of managing patients with estrogen receptor positive (ER+) breast cancer. We previously reported frequent overexpression of FGFR4 in endocrine-resistant cell lines and breast cancers that recurred and metastasized following endocrine therapy, suggesting FGFR4 as a potential driver of endocrine resistance. In this study, we investigated the role of FGFR4 in mediating endocrine resistance and explored the therapeutic potential of targeting FGFR4 in advanced breast cancer.

METHODS: A gene expression signature of FGFR4 activity was examined in ER+breast cancer pre- and post-neoadjuvant endocrine therapy and the association between FGFR4 expression and patient survival was examined. A correlation analysis was used to uncover potential regulators of FGFR4 overexpression. To investigate if FGFR4 is necessary to drive endocrine resistance, we tested response to FGFR4 inhibition in long-term estrogen-deprived (LTED) cells and their paired parental cells. Doxycycline inducible FGFR4 overexpression and knockdown cell models were generated to examine if FGFR4 was sufficient to confer endocrine resistance. Finally, we examined response to FGFR4 monotherapy or combination therapy with fulvestrant in breast cancer cell lines to explore the potential of FGFR4 targeted therapy for advanced breast cancer and assessed the importance of PAM50 subtype in response to FGFR4 inhibition.

RESULTS: A FGFR4 activity gene signature was significantly upregulated post-neoadjuvant aromatase inhibitor treatment, and high FGFR4 expression predicted poorer survival in patients with ER+breast cancer. Gene expression association analysis using TCGA, METABRIC, and SCAN-B datasets uncovered ER as the most significant gene negatively correlated with FGFR4 expression. ER negatively regulates FGFR4 expression at both the mRNA and protein level across multiple ER+breast cancer cell lines. Despite robust overexpression of FGFR4, LTED cells did not show enhanced responses to FGFR4 inhibition compared to parental cells. Similarly, FGFR4 overexpression and knockdown did not substantially alter response to endocrine treatment in ER+cell lines, nor did FGFR4 and fulvestrant combination treatment show synergistic effects. The HER2-like subtype of breast cancer showed elevated expression of FGFR4 and an increased response to FGFR4 inhibition relative to other breast cancer subtypes.

CONCLUSIONS: Despite ER-mediated upregulation of FGFR4 post-endocrine therapy, our study does not support a general role of FGFR4 in mediating endocrine resistance in ER+breast cancer. The significant upregulation of FGFR4 expression in treatment-resistant clinical samples and models following endocrine therapy does not necessarily establish a causal link between the gene and treatment response. Our data suggest that specific genomic backgrounds such as HER2 expression may be required for FGFR4 function in breast cancer and should be further explored.

PMID:40097769 | DOI:10.1007/s10549-025-07666-x

Nat Struct Mol Biol. 2025 Mar 17. doi: 10.1038/s41594-025-01517-5. Online ahead of print.

ABSTRACT

Deubiquitylases (DUBs) are crucial in cell signaling and are often regulated by interactions within protein complexes. The BRCC36 isopeptidase complex (BRISC) regulates inflammatory signaling by cleaving K63-linked polyubiquitin chains on type I interferon receptors (IFNAR1). As a Zn2+-dependent JAMM/MPN (JAB1, MOV34, MPR1, Pad1 N-terminal) DUB, BRCC36 is challenging to target with selective inhibitors. Here, we discover first-in-class inhibitors, termed BRISC molecular glues (BLUEs), which stabilize a 16-subunit human BRISC dimer in an autoinhibited conformation, blocking active sites and interactions with the targeting subunit, serine hydroxymethyltransferase 2. This unique mode of action results in selective inhibition of BRISC over related complexes with the same catalytic subunit, splice variants and other JAMM/MPN DUBs. BLUE treatment reduced interferon-stimulated gene expression in cells containing wild-type BRISC and this effect was abolished when using structure-guided, inhibitor-resistant BRISC mutants. Additionally, BLUEs increase IFNAR1 ubiquitylation and decrease IFNAR1 surface levels, offering a potential strategy to mitigate type I interferon-mediated diseases. Our approach also provides a template for designing selective inhibitors of large protein complexes by promoting rather than blocking protein-protein interactions.

PMID:40097626 | DOI:10.1038/s41594-025-01517-5

NPJ Syst Biol Appl. 2025 Mar 17;11(1):27. doi: 10.1038/s41540-025-00501-5.

ABSTRACT

A molecular systems architecture is presented for the neuromuscular junction (NMJ) in order to provide a framework for organizing complexity of biomolecular interactions in amyotrophic lateral sclerosis (ALS) using a systematic literature review process. ALS is a fatal motor neuron disease characterized by progressive degeneration of the upper and lower motor neurons that supply voluntary muscles. The neuromuscular junction contains cells such as upper and lower motor neurons, skeletal muscle cells, astrocytes, microglia, Schwann cells, and endothelial cells, which are implicated in pathogenesis of ALS. This molecular systems architecture provides a multi-layered understanding of the intra- and inter-cellular interactions in the ALS neuromuscular junction microenvironment, and may be utilized for target identification, discovery of single and combination therapeutics, and clinical strategies to treat ALS.

PMID:40097438 | DOI:10.1038/s41540-025-00501-5

BMC Vet Res. 2025 Mar 17;21(1):178. doi: 10.1186/s12917-025-04616-z.

ABSTRACT

Cystic echinococcosis (CE) is a neglected zoonotic disease that causes significant economic losses in livestock and poses health risks to humans, necessitating improved diagnostic and therapeutic strategies. This study investigates CE in donkeys using a multifaceted approach that includes molecular identification, gene expression analysis, serum biochemical profiling, histopathological and immunohistochemical examination, and in vitro drug efficacy evaluation. Molecular analysis of hydatid cyst protoscolices (HC-PSCs) from infected donkey livers and lungs revealed a high similarity to Echinococcus equinus (GenBank accession: PP407081). Additionally, gene expression analysis indicated significant increases (P < 0.0001) in interleukin 1β (IL-1β) and interferon γ (IFN-γ) levels in lung and liver homogenates. Serum biochemical analysis showed elevated aspartate transaminase (AST), alkaline phosphatase (ALP), and globulin levels, alongside decreased albumin compared to non-infected controls. Histopathological examination revealed notable alterations in pulmonary and hepatic tissues associated with hydatid cyst infection. Immunohistochemical analysis showed increased expression of nuclear factor kappa B (NF-κB), tumor necrosis factor-α (TNF-α), and toll-like receptor-4 (TLR-4), indicating a robust inflammatory response. In vitro drug evaluations revealed that Paroxetine (at concentrations of 2.5, and 5 mg/mL) demonstrated the highest efficacy among repurposed drugs against HC-PSCs, resulting in the greatest cell mortality. Colmediten followed closely in effectiveness, whereas both Brufen and Ator exhibited minimal effects. This study identifies Paroxetine as a promising alternative treatment for hydatidosis and provides a framework for investigating other parasitic infections and novel therapies.

PMID:40098107 | DOI:10.1186/s12917-025-04616-z