BMC Genomics. 2025 May 26;26(1):527. doi: 10.1186/s12864-025-11703-w.

ABSTRACT

BACKGROUND: House dust mites (HDMs) such as Dermatophagoides pteronyssinus are major allergy elicitors worldwide, yet their gene expression across developmental stages remains underexplored. Herein, we report a comprehensive RNAseq analysis of larvae, nymphs, and adult males and females, mapped to a recently published high-quality genome with extended functional annotations.

RESULTS: Analysis of differentially expressed genes (DEG) revealed that female-biased expression was the most prevalent profile (16% of genes), while males exhibited the highest fold-change differences. DEG data, combined with network clustering and functional enrichment analysis, highlighted distinct genes and biological processes for each stage and sex: females showed upregulation of genes related to cell division and oogenesis, with vitellogenins among the most abundant transcripts; males exhibited increased expression of genes encoding putative seminal fluid proteins (e.g. endopeptidases, serpins, antimicrobial peptides), and those involved in reproductive regulation (e.g. testis-specific serine kinases); while juveniles displayed enhanced expression of genes related to energy metabolism and growth. Further analysis of endocrine pathways revealed non-canonic mechanisms compared to insect models, particularly in ecdysteroid and sesquiterpenoid biosynthesis and regulation. Expression patterns in genes involved in cuticle formation were also identified, reflecting their role in developmental transitions and sexual differentiation. Allergen and allergen-related gene expression showed an overall increase in feeding juveniles, as well as sex-biased expression, with Der p 27 upregulated in females. These findings provide insight into the physiological roles of allergens in digestion, immunity, and muscle formation, among other functions. Additionally, seven new horizontally transferred genes, including a DNA-repair photolyase linked to females, and novel multigene families (e.g. 119 male-specific beta-propeller proteins, 70 hypothetical cuticular proteins, 23 tetraspanin-like proteins, 5 female-associated putative odorant-binding proteins) were identified.

CONCLUSIONS: This study provides the first genome-wide transcriptomic analysis of a HDM across life stages and sexes, expanding our understanding of the molecular mechanisms underlying mite development, sexual reproduction, and allergen expression. The generated data, fully available via supplementary spreadsheet and the ORCAE online platform, provide a valuable foundation for future allergy research and the development of new mite control strategies.

PMID:40419976 | DOI:10.1186/s12864-025-11703-w

BMC Bioinformatics. 2025 May 26;26(1):137. doi: 10.1186/s12859-025-06162-9.

ABSTRACT

Understanding the molecular mechanisms underlying diseases is crucial for more precise, personalized medicine. Pathway-level differential co-expression analysis, a powerful approach for transcriptomics, identifies condition-specific changes in gene-gene interaction networks, offering targeted insights. However, a key challenge is the lack of robust methods and benchmarks specifically for evaluating algorithms' ability to identify disrupted gene-gene associations across conditions. We introduce DRaCOoN (Differential Regulatory and Co-expression Networks), a Python package and web tool for pathway-level differential co-expression analysis. DRaCOoN uniquely integrates multiple association and differential metrics, with a novel, computationally efficient permutation test for significance assessment. Crucially, DRaCOoN also provides a benchmarking framework for comprehensive method evaluation. Extensive benchmarking on simulated data and three real-world datasets (bone healing, colorectal cancer, and head/neck carcinoma) showed that DRaCOoN, particularly with an entropy-based association measure and the s differential metric, consistently outperforms eight other methods. It remains highly accurate in balanced datasets, robust to varying gene perturbation levels, and identifies biologically relevant regulatory changes. Furthermore, DRaCOoN serves as both a powerful tool and a benchmarking framework for elucidating disease mechanisms from transcriptomics data, advancing precision medicine by uncovering critical gene regulatory alterations.

PMID:40419963 | DOI:10.1186/s12859-025-06162-9

J Headache Pain. 2025 May 26;26(1):130. doi: 10.1186/s10194-025-02058-4.

ABSTRACT

BACKGROUND: Migraine, characterized by recurrent episodes of severe headache, remains mechanistically enigmatic. While traditional theories emphasize trigeminovascular activation, the role of meningeal stromal-immune crosstalk in disease chronicity is poorly understood.

METHODS: A migraine-related chronic hypersensitivity model was utilized via intermittent intraperitoneal nitroglycerin (NTG, 10 mg/kg, every other day for 9 days) and peripheral mechanical hypersensitivity was assessed using von Frey filaments. Single-cell RNA sequencing (scRNA-seq) was performed on dura tissues to construct a cellular atlas of NTG-induced remodeling. These data were then integrated with migraine genome-wide association study (GWAS) risk genes, cell-cell interaction networks, and transcriptional regulation analysis to dissect NTG-driven meningeal remodeling.

RESULTS: The NTG-induced migraine-related chronic hypersensitivity model demonstrated sustained mechanical allodynia, as evidenced by significantly decreased paw withdrawal thresholds (p < 0.0001). Single-cell profiling of the dura mater revealed a 2.4-fold expansion of a pro-inflammatory fibroblast subpopulation (Fibro_c5: 1.9% in Vehicle vs. 4.6% in NTG group), which exhibited marked activation of TNF-α/NF-κB signaling pathways (normalized enrichment score [NES] = 1.83). Concomitantly, we observed an 82% increase in meningeal monocytes (5.7-10.4%) that showed preferential interaction with Fibro_c5 fibroblasts through Angptl1-mediated stromal-immune crosstalk (log2 fold change = 1.41). Regulatory network analysis identified Mafk as the upstream transcriptional regulator orchestrating Angptl1 expression in this pathological communication axis.

CONCLUSION: Our study reveals that NTG reprograms meningeal fibroblasts to expand a pro-inflammatory fibroblast subtype, which drives migraine-related chronic hypersensitivity through TNF-α/NF-κB signaling and Angptl1-mediated monocyte crosstalk. The identified Mafk-Angptl1 axis presents a potential therapeutic target, though human validation remains essential.

PMID:40419944 | DOI:10.1186/s10194-025-02058-4

Sci Rep. 2025 May 26;15(1):18388. doi: 10.1038/s41598-025-94530-z.

ABSTRACT

Breast cancer recurrence presents a significant global health challenge, and accurate prediction is crucial for effective patient management and improved outcomes. Reliable predictive tools can help tailor therapeutic approaches, provide personalized care, and enhance patient outcomes. In light of the current lack of such tools in clinical practice, our study aimed to develop predictive models for breast cancer recurrence within three years of treatment. We analyzed data from 408 breast cancer patients at the King Fahd Specialist Hospital in Dammam, Saudi Arabia and divided them into training (n = 285) and test (n = 123) cohorts. Using multivariable penalized logistic regression combined with a nested cross-validation framework and multivariate Cox regression analysis to determine time-dependent risk factors for breast cancer recurrence, we developed prognostic models that incorporated age, stage, tumor size, and treatment type. We evaluated the performance of the models using both the area under the receiver operating characteristic curve for multivariate logistic regression and C-index for multivariate Cox regression. The multivariate logistic regression model achieved an area under the curve (AUC) of 76% (95% confidence interval [CI]: 72-81%) for the training set and 76% (95% CI: 66-87%) for the test set. The Cox regression analysis yielded a C-index of 0.81 for the training set (95% CI: 0.73-0.84) and 0.84 for the test set (95% CI: 0.76-0.89). Chemotherapy was found to decrease recurrence odds by 86% (adjusted odds ratio [AOR]: 0.143, 95% CI: 0.089-0.218, p < 0.0001), and surgery resulted in a 99% reduction in recurrence probability (AOR: 0.009, 95% CI: 0.005-0.014, p < 0.0001). Increased tumor size improved the recurrence odds by 48.5% (AOR: 1.485, 95% CI: 1.128-1.918, p = 0.0043), while age did not significantly predict recurrence (AOR: 0.841, 95% CI: 0.657-1.061, p = 0.1398). The newly developed, routinely collected baseline clinical features to predict breast cancer recurrence may be a valuable tool for clinical decision-making and is freely available online. The tool can be accessed through the following link: https://iv3p9h-nurudeen-adegoke.shinyapps.io/breast_cancer .

PMID:40419677 | DOI:10.1038/s41598-025-94530-z

Nat Methods. 2025 May 26. doi: 10.1038/s41592-025-02700-8. Online ahead of print.

ABSTRACT

Enhancers and transcription factors (TFs) are crucial in regulating cellular processes. Current multiomic technologies to study these elements in gene regulatory mechanisms lack multiplexing capability and scalability. Here we present single-cell ultra-high-throughput multiplexed sequencing (SUM-seq) for co-assaying chromatin accessibility and gene expression in single nuclei. SUM-seq enables profiling hundreds of samples at the million cell scale and outperforms current high-throughput single-cell methods. We demonstrate the capability of SUM-seq to (1) resolve temporal gene regulation of macrophage M1 and M2 polarization to bridge TF regulatory networks and immune disease genetic variants, (2) define the regulatory landscape of primary T helper cell subsets and (3) dissect the effect of perturbing lineage TFs via arrayed CRISPR screens in spontaneously differentiating human induced pluripotent stem cells. SUM-seq offers a cost-effective, scalable solution for ultra-high-throughput single-cell multiomic sequencing, accelerating the unraveling of complex gene regulatory networks in cell differentiation, responses to perturbations and disease studies.

PMID:40419657 | DOI:10.1038/s41592-025-02700-8

Nat Methods. 2025 May 26. doi: 10.1038/s41592-025-02657-8. Online ahead of print.

NO ABSTRACT

PMID:40419656 | DOI:10.1038/s41592-025-02657-8

Nat Commun. 2025 May 26;16(1):4878. doi: 10.1038/s41467-025-60004-z.

ABSTRACT

Bacterial colonies growing on solid surfaces can exhibit robust expansion kinetics, with constant radial growth and saturating vertical expansion, suggesting a common developmental program. Here, we study this process for Escherichia coli cells using a combination of modeling and experiments. We show that linear radial colony expansion is set by the verticalization of interior cells due to mechanical constraints rather than radial nutrient gradients as commonly assumed. In contrast, vertical expansion slows down from an initial linear regime even while radial expansion continues linearly. This vertical slowdown is due to limitation of cell growth caused by vertical nutrient gradients, exacerbated by concurrent oxygen depletion. Starvation in the colony interior results in a distinct death zone which sets in as vertical expansion slows down, with the death zone increasing in size along with the expanding colony. Thus, our study reveals complex heterogeneity within simple monoclonal bacterial colonies, especially along the vertical dimension. The intricate dynamics of such emergent behavior can be understood quantitatively from an interplay of mechanical constraints and nutrient gradients arising from obligatory metabolic processes.

PMID:40419492 | DOI:10.1038/s41467-025-60004-z

Toxicol In Vitro. 2025 May 24:106086. doi: 10.1016/j.tiv.2025.106086. Online ahead of print.

ABSTRACT

Irradiation-induced immune impairment has been linked to human immune diseases, such as myelodysplastic syndromes (MDS) and leukemia. Global molecular responses to genome instability in immune cells can be identified by using transcriptomics. However, it is hard to link the molecular mechanism to the disease outcomes in the previous mechanistic studies. Here, transcriptomic dose-responses in human CD4+ T lymphocytes exposed to ultraviolet and heavy ion radiation were revealed by identification of the gene expression patterns of differential expression genes (DEGs) and calculating the point of departure (POD) of each DEG and molecular pathway, which provided an opportunity for quantitively illustrating the biological process of irradiation-induced immune impairments. Two potential adverse outcome pathways (AOPs) to irradiation-related leukemia were identified by mapping the molecular pathways into the biological event cascades, which provided phenotypic anchoring for the toxicological mechanisms. In addition, this study also revealed that NOP14/ NOP14-AS1 could be potential biomarkers of irradiation-induced immune impairment. Our works strengthen the use of AOP network in the next-generation risk assessment of irradiation-related diseases.

PMID:40419229 | DOI:10.1016/j.tiv.2025.106086

Biosystems. 2025 May 24:105501. doi: 10.1016/j.biosystems.2025.105501. Online ahead of print.

ABSTRACT

We explore here the fundamental and striking paradigmatic shifts between 'Domain of Laws' and 'Domain of No Laws', where the former is an apt encapsulation of our remarkably successful but orthodox science world view (including classical physics and quantum mechanics) with well- defined and stable configuration spaces having deterministic or stochastic evolution. The latter is a radically new Domain of No Law with evolving configuration spaces, non-deducible information creation, genuine novelties, and an unprestatable Adjacent Possible. We explore the features of these two distinct domains asking what can be defined with respect to work, energy, entropy, and agency. We offer a reconstruction of quantum mechanics to reframe traditional assumptions and address lingering questions concerning the nature of living, complex adaptive systems. We propose that a genuine responsible free will and a central role of agency are essential features of an evolving Biosphere. Here we extend this theme to call for a radically new and comprehensive view of science itself.

PMID:40419105 | DOI:10.1016/j.biosystems.2025.105501

JMIR Infodemiology. 2025 May 26;5:e69040. doi: 10.2196/69040.

ABSTRACT

BACKGROUND: Eosinophilia and hypereosinophilic syndrome (HES) are rare disorders grouped under the term hypereosinophilic disorders. They are diagnosed based on an increased number of eosinophils. They can also cause serious symptoms, including skin, lung, and gastrointestinal problems. These disorders are very rarely recognized due to their rarity and misdiagnosis.

OBJECTIVE: This study analyzes public interest in hypereosinophilic disorders using data on internet search volume in Germany between 2020 and 2023. Objectives include identifying frequently searched terms, evaluating temporal trends, analyzing seasonal patterns, evaluating geographic differences in search behavior, and identifying unmet information needs and frequently searched risk factors.

METHODS: A retrospective analysis using Google Ads Keyword Planner gathered monthly search volume data for 12 German terms related to hypereosinophilic disorders. These terms were selected based on their medical relevance and common usage identified from medical literature. Data were analyzed descriptively, with trends, seasonal variations, and geographical distributions examined. Chi-square tests and correlation analysis assessed statistical significance.

RESULTS: A total of 178 keywords were identified, resulting in a search volume of 1,745,540 queries. The top keyword was "eosophile," a misspelling, followed by "eosinophilia" and "HES." The main categories included "Eosinophilia," "Eosinophils," and "Churg-Strauss syndrome." Temporal analysis showed seasonal growth in search volumes, peaking in January 2023, with higher interest during winter. Geographical analysis showed regional variations.

CONCLUSIONS: This research shows a growing public interest in eosinophilic diseases, reflected by a steadily increasing search volume over time. This is particularly evident in searches for basic definitions and diagnostic criteria, such as "eosinophils" or "symptoms of eosinophilic diseases." This increase in search volume, which peaked in January 2023, indicates an increased interest in accurate and readily available information for rare conditions.

PMID:40418815 | DOI:10.2196/69040

Health Technol Assess. 2025 May;29(19):1-111. doi: 10.3310/CPLD8546.

ABSTRACT

BACKGROUND: Cystic fibrosis is a life-limiting genetic condition that affects over 9000 people in England. Cystic fibrosis is usually diagnosed through newborn screening and causes symptoms throughout the body, including the lungs and digestive system. Around 90% of individuals with cystic fibrosis have at least one copy of the F508del mutation on the cystic fibrosis transmembrane conductance regulator gene.

OBJECTIVES: To appraise the clinical effectiveness and cost-effectiveness of elexacaftor-tezacaftor-ivacaftor, tezacaftor-ivacaftor and lumacaftor-ivacaftor within their expected marketing authorisations for treating people with cystic fibrosis and at least one F508del mutation, compared with each other and with established clinical management before these treatments.

METHODS: A de novo systematic literature review (search date February 2023) was conducted searching electronic databases (MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials), bibliographies of relevant systematic literature reviews, clinical trial registers, recent conferences and evidence provided by Vertex Pharmaceuticals (Boston, MA, USA). Data on the following outcomes were summarised: acute change in per cent predicted forced expiratory volume in 1 second (change in weight-for-age z-score; and change in pulmonary exacerbation frequency requiring intravenous antibiotics. Network meta-analyses were conducted where head-to-head data were not available. Data from clinical trials and real-world evidence were examined to assess long-term effectiveness. A patient-level simulation model was developed to assess the cost-effectiveness of the three modulator treatments. The model employed a lifetime horizon and was developed from the perspective of the National Health Service.

RESULTS: Data from 19 primary studies and 7 open-label extension studies were prioritised in the systematic literature review. Elexacaftor/tezacaftor/ivacaftor was associated with a statistically significant increase in predicted forced expiratory volume in 1 second and weight-for-age z-score and a reduction in pulmonary exacerbations compared with established clinical management, lumacaftor/ivacaftor and tezacaftor/ivacaftor, and also led to a reduction in the rate of predicted forced expiratory volume in 1 second decline relative to established clinical management, although the magnitude of this decrease was uncertain. Lumacaftor/ivacaftor and tezacaftor/ivacaftor were also associated with a statistically significant increase in predicted forced expiratory volume in 1 second and reduction in pulmonary exacerbations relative to established clinical management, but with a smaller effect size than elexacaftor/tezacaftor/ivacaftor. There was some evidence that tezacaftor/ivacaftor reduced the rate of predicted forced expiratory volume in 1 second decline relative to established clinical management, but little evidence that lumacaftor/ivacaftor reduced the rate of predicted forced expiratory volume in 1 second decline relative to established clinical management. The incremental cost-effectiveness ratios from the economic analysis were confidential. However, for all genotypes studied the incremental cost-effectiveness ratios were above what would be considered cost-effective based on the National Institute for Health and Care Excellence threshold of £20,000-30,000 per quality-adjusted life-year gained.

CONCLUSIONS: Despite the improved clinical benefits observed, none of the cystic fibrosis transmembrane conductance regulator gene modulators assessed would be considered cost-effective based on the National Institute for Health and Care Excellence threshold of £20,000-30,000 per quality-adjusted life-year gained. This is largely driven by the high acquisition costs of cystic fibrosis transmembrane conductance regulator gene modulator treatments.

STUDY REGISTRATION: This study is registered as PROSPERO CRD42023399583.

FUNDING: This award was funded by the National Institute for Health and Care Research (NIHR) Evidence Synthesis programme (NIHR award ref: NIHR135829) and is published in full in Health Technology Assessment; Vol. 29, No. 19. See the NIHR Funding and Awards website for further award information.

PMID:40418577 | DOI:10.3310/CPLD8546

Comput Biol Med. 2025 May 25;193:110334. doi: 10.1016/j.compbiomed.2025.110334. Online ahead of print.

ABSTRACT

Parkinson's disease (PD) is a neurodegenerative disorder that occurs as a result of a decrease in the chemical called dopamine in the brain. There is no definitive treatment for PD, but some medications used to control symptoms in the early stages have a critical effect on the progression of the disease. Approximately 90% of patients with PD have vocal problems, and although voice disorders seen in the early stages are not apparent in the patient's speech, they can be detected by acoustic analysis. In this study, a decision support system was proposed for the diagnosis of PD utilizing the feature extraction power of autoencoder (AE) & long short-term memory (LSTM) models by using speech signals as input data. Firstly, simple (SAE), convolutional (CAE), and recurrent (RAE) AE models were created for the ablation analysis. Then, the effect of hybridization and deepening of these models with LSTM layers on the classification performance was observed. Within the scope of the study, RAE achieved the highest accuracy among the base models while CAE & LSTM hybrid model provided the highest performance among all models with 95.79% accuracy for PD diagnosis based on audio signals. It was concluded that hybridization of the AE and LSTM models significantly improved the performance of simple and convolutional AE, and deepening the network to a certain extent improves the classification performance according to the type of AE.

PMID:40418858 | DOI:10.1016/j.compbiomed.2025.110334

Microsc Res Tech. 2025 May 26. doi: 10.1002/jemt.24879. Online ahead of print.

ABSTRACT

The texture coefficient (TC) is a critical parameter used to analyze the microstructural characteristics of rocks and predict their mechanical behavior. In recent years, various computational programs and software have been employed to estimate the TC values of rocks. However, existing methods remain insufficient and time-consuming for accurately determining rock TCs. In this study, thin-section images of 20 different igneous, metamorphic, and sedimentary rocks were acquired and segmented to calculate TC values using a novel approach. The computation process was implemented using Python-based software that integrates segmentation and image processing techniques to determine TC values. The thin-section images were segmented utilizing a deep learning-based image processing technique, and a Python-based algorithm was developed for TC calculations. The proposed method offers a unique approach to TC estimation in rocks, achieving a high segmentation accuracy (IoU = 0.97). Furthermore, with this method, the TC value of any given rock can be computed in approximately 1 min.

PMID:40418716 | DOI:10.1002/jemt.24879

IEEE Trans Med Imaging. 2025 May 26;PP. doi: 10.1109/TMI.2025.3573706. Online ahead of print.

ABSTRACT

Due to the association with higher incidence of left ventricular dysfunction and complications, segmentation of left ventricle and related pathological tissues: microvascular obstruction and myocardial infarction from late gadolinium enhancement cardiac magnetic resonance images is crucially important. However, lack of datasets, diverse shapes and locations, extreme imbalanced class, severe intensity distribution overlapping are the main challenges. We first release a late gadolinium enhancement cardiac magnetic resonance benchmark dataset LGE-LVP containing 140 patients with left ventricle myocardial infarction and concomitant microvascular obstruction. Then, a progressive deep learning model LVPSegNet is proposed to segment the left ventricle and its pathologies via adaptive region of interest extraction, sample augmentation, curriculum learning, and multiple receptive field fusion in dealing with the challenges. Comprehensive comparisons with state-of-the-art models on the internal and external datasets demonstrate that the proposed model performs the best on both geometric and clinical metrics and it most closely matched the clinician's performance. Overall, the released LGE-LVP dataset alongside the LVPSegNet we proposed offer a practical solution for automated left ventricular and its pathologies segmentation by providing data support and facilitating effective segmentation. The dataset and source codes will be released via https://github.com/DFLAG-NEU/LVPSegNet.

PMID:40418612 | DOI:10.1109/TMI.2025.3573706

IEEE Trans Biomed Eng. 2025 May 26;PP. doi: 10.1109/TBME.2025.3573581. Online ahead of print.

ABSTRACT

OBJECTIVE: In this paper we develop and evaluate ECG-SMART-NET for occlusion myocardial infarction (OMI) identification. OMI is a severe form of heart attack characterized by complete blockage of one or more coronary arteries requiring immediate referral for cardiac catheterization to restore blood flow to the heart. Two thirds of OMI cases are difficult to visually identify from a 12-lead electrocardiogram (ECG) and can be potentially fatal if not identified quickly. Previous works on this topic are scarce, and current state-of-the-art evidence suggests both feature-based random forests and convolutional neural networks (CNNs) are promising approaches to improve ECG detection of OMI.

METHODS: While the ResNet architecture has been adapted for use with ECG recordings, it is not ideally suited to capture informative temporal features within each lead and the spatial concordance or discordance across leads. We propose a clinically informed modification of the ResNet-18 architecture. The model first learns temporal features through temporal convolutional layers with 1xk kernels followed by a spatial convolutional layer, after the residual blocks, with 12x1 kernels to learn spatial features.

RESULTS: ECG-SMART-NET was benchmarked against the original ResNet-18 and other state-of-the-art models on a multisite real-word clinical dataset that consists of 10,393 ECGs from 7,397 unique patients (rate of OMI = 7.2%). ECG-SMART-NET outperformed other models in the classification of OMI with a test AUC of 0.953 [0.921, 0.978].

CONCLUSION AND SIGNIFICANCE: ECG-SMART-NET can outperform the state-of-the-art random forest for OMI prediction and is better suited for this task than the original ResNet-18 architecture.

PMID:40418608 | DOI:10.1109/TBME.2025.3573581

J Vis Exp. 2025 May 9;(219). doi: 10.3791/67953.

ABSTRACT

Interstitial lung disease (ILD) represents a broad spectrum of disorders characterized by the progressive and often irreversible scarring of the lung parenchyma, the most common being idiopathic pulmonary fibrosis (IPF). Several animal models of IPF have been developed, with the bleomycin murine model being the most widely used. Bleomycin is a chemotherapeutic known to induce DNA damage in the alveolar epithelium, resulting in acute lung injury and pulmonary fibrosis in humans. Rodent models of IPF use bleomycin administration via various methods, the most common being intratracheal (IT). Recently, the oropharyngeal aspiration (OA) technique has been shown to be equally efficacious as IT for multiple fibrosing agents, with considerably fewer side effects and an easier route of delivery. This protocol details the OA method of bleomycin delivery into the murine lung and highlights examples of potential downstream applications for data quantification. This methodology offers a simple, quick, and safe way to utilize this widely used animal model for studying the molecular mechanisms underlying IPF.

PMID:40418675 | DOI:10.3791/67953

ALTEX. 2025 May 26. doi: 10.14573/altex.2501291. Online ahead of print.

ABSTRACT

Chemical risk assessment is evolving from traditional deterministic approaches to embrace probabilistic methodologies, where risk of hazard manifestation is understood as a more or less probable event depending on exposure, individual factors, and stochastic processes. This is driven by advancements in human stem cells, complex tissue engineering, high-performance computing, and cheminformatics, and is more recently facilitated by large-scale artificial intelligence models. These innovations enable a more nuanced understanding of chemical hazards, capturing the complexity of biological responses and variability within populations. However, each technology comes with its own uncertainties impacting on the estimation of hazard probabilities. This shift addresses the limitations of point estimates and thresholds that oversimplify hazard assessment, allowing for the integration of kinetic variability and uncertainty metrics into risk models. By leveraging modern technologies and expansive toxicological data, probabilistic approaches offer a comprehensive evaluation of chemical safety. This paper summarizes a workshop held in 2023 and discusses the technological and data-driven enablers, and the challenges faced in their implementation, with particular focus on perturbation of biology as the basis of hazard estimates. The future of toxicological risk assessment lies in the successful integration of these probabilistic models, promising more accurate and holistic hazard evaluations.

PMID:40418784 | DOI:10.14573/altex.2501291

J Am Soc Nephrol. 2025 May 26. doi: 10.1681/ASN.0000000706. Online ahead of print.

ABSTRACT

BACKGROUND: Alterations in pre-mRNA splicing are crucial to the pathophysiology of various diseases. However, the effects of alternative splicing of mRNA on podocytes in hypertensive nephropathy are still unknown. The Sys_CARE project aimed to identify alternative splicing events involved in the development and progression of glomerular hypertension.

METHODS: Murine podocytes were exposed to mechanical stretch, after which proteins and mRNA were analyzed by proteomics, RNA sequencing and several bioinformatic alternative splicing tools.

RESULTS: Using transcriptomic and proteomic analysis, we identified significant changes in gene expression and protein abundance due to mechanical stretch. RNA-Seq identified over 3,000 alternative spliced genes after mechanical stretch, including all types of alternative splicing events. Among these, 17 genes exhibited an alternative splicing event across four different splicing analysis tools. From this group, we focused on Myl6, a component of the myosin protein complex, and Shroom3, an actin-binding protein essential for podocyte function. We identified two Shroom3 isoforms with significant expression changes under mechanical stretch, which was validated by qRT-PCR and in situ hybridization. Additionally, we observed an expression switch of two Myl6 isoforms after mechanical stretch, accompanied by an alteration in the C-terminal amino acid sequence.

CONCLUSIONS: A comprehensive RNA-Seq analysis of mechanically stretched podocytes identified novel potential podocyte-specific biomarkers and highlighted significant alternative splicing events, notably in the mRNA of Shroom3 and Myl6.

PMID:40418580 | DOI:10.1681/ASN.0000000706

Mol Divers. 2025 May 26. doi: 10.1007/s11030-025-11214-6. Online ahead of print.

ABSTRACT

Tumor cell survival depends on the presence of oxygen and nutrients provided by existing blood vessels, particularly when cancer is in its early stage. Along with tumor growth in the vicinity of blood vessels, malignant cells require more nutrients; hence, capillary sprouting occurs from parental vessels, a process known as angiogenesis. Although multiple cellular pathways have been identified, controlling them with one single biomolecule as a multi-target inhibitor could be an attractive strategy for reducing medication side effects. Three critical pathways in angiogenesis have been identified, which are activated by the vascular endothelial growth factor receptor (VEGFR), fibroblast growth factor receptor (FGFR), and epidermal growth factor receptor (EGFR). This study aimed to develop a methodology to discover multi-target inhibitors among over 2000 FDA-approved drugs. Hence, a novel ensemble approach was employed, comprising classification and regression models. First, three different deep autoencoder classifications were generated for each target individually. The top 100 trained models were selected for the high-throughput virtual screening step. After that, all identified molecules with a probability of more than 0.9 in more than 70% of the models were removed to ensure accurate consideration in the regression step. Since the ultimate aim of virtual screening is to discover molecules with the highest success rate in the pharmaceutical industry, various aspects of the molecules in different assays were considered by integrating ten different regression models. In conclusion, this paper contributes to pharmaceutical sciences by introducing eleven diverse scaffolds and eight approved drugs that can potentially be used as inhibitors of angiogenesis receptors, including VEGFR, FGFR, and EGFR. Considering three target receptors simultaneously is another central concept and contribution used. This concept could increase the chance of success, while reducing the possibility of resistance to these agents.

PMID:40418485 | DOI:10.1007/s11030-025-11214-6

AMIA Annu Symp Proc. 2025 May 22;2024:1275-1283. eCollection 2024.

ABSTRACT

Large language models (LLMs) have shown great promise in clinical medicine, but their adoption in real-world settings has been limited by their tendency to generate incorrect and sometimes even toxic statements. This study presents a reliable rare disease intelligent agent called RDguru, which incorporates authoritative and reliable knowledge sources and tools into the reasoning and response of LLMs. In addition to answering questions about rare diseases more accurately, RDguru can conduct medical consultations to provide differential diagnosis decision support for clinical users. The DQN-based multi-source fusion diagnostic model integrates three diagnostic recommendation strategies, GPT-4, PheLR, and phenotype matching. Testing on 238 real rare disease cases showed that RDguru's top 10 list of recommended diagnoses was able to recall 69.1% of real diagnoses, the top 5 recommended diagnoses were able to recall 63.6% of real diagnoses, and the top ranked diagnosis was able to achieve an accuracy rate of 41.9%.

PMID:40417483 | PMC:PMC12099370