Int J Gen Med. 2025 Mar 1;18:1173-1180. doi: 10.2147/IJGM.S507100. eCollection 2025.

ABSTRACT

PURPOSE: This study aims to evaluate the performance of a deep learning-based artificial intelligence (AI) diagnostic system in the analysis of retinal diseases, assessing its consistency with expert diagnoses and its overall utility in screening applications.

METHODS: A total of 3076 patients attending our hospital underwent comprehensive ophthalmic examinations. Initial assessments were performed using the AI, the Comprehensive AI Retinal Expert (CARE) system, followed by thorough manual reviews to establish final diagnoses. A comparative analysis was conducted between the AI-generated results and the evaluations by senior ophthalmologists to assess the diagnostic reliability and feasibility of the AI system in the context of ophthalmic screening.

RESULTS: : The AI diagnostic system demonstrated a sensitivity of 94.12% and specificity of 98.60% for diabetic retinopathy (DR); 89.50% sensitivity and 98.33% specificity for age-related macular degeneration (AMD); 91.55% sensitivity and 97.40% specificity for suspected glaucoma; 90.77% sensitivity and 99.10% specificity for pathological myopia; 81.58% sensitivity and 99.49% specificity for retinal vein occlusion (RVO); 88.64% sensitivity and 99.18% specificity for retinal detachment; 83.33% sensitivity and 99.80% specificity for macular hole; 82.26% sensitivity and 99.23% specificity for epiretinal membrane; 94.55% sensitivity and 97.82% specificity for hypertensive retinopathy; 83.33% sensitivity and 99.74% specificity for myelinated fibers; and 75.00% sensitivity and 99.95% specificity for retinitis pigmentosa. Additionally, the system exhibited notable performance in screening for other prevalent conditions, including DR, suspected glaucoma, suspected glaucoma, pathological myopia, and hypertensive retinopathy.

CONCLUSIONS: : The AI-assisted screening system exhibits high sensitivity and specificity for a majority of retinal diseases, suggesting its potential as a valuable tool for screening practices. Its implementation is particularly beneficial for grassroots and community healthcare settings, facilitating initial diagnostic efforts and enhancing the efficacy of tiered ophthalmic care, with important implications for broader clinical adoption.

PMID:40051895 | PMC:PMC11882464 | DOI:10.2147/IJGM.S507100

Eur Heart J Imaging Methods Pract. 2025 Mar 6;2(4):qyaf011. doi: 10.1093/ehjimp/qyaf011. eCollection 2024 Oct.

ABSTRACT

AIMS: Assessment of cardiac function is essential for diagnosis and treatment planning in cardiovascular disease. Volume of cardiac regions and the derived measures of stroke volume (SV) and ejection fraction (EF) are most accurately calculated from imaging. This study aims to develop a fully automatic deep learning approach for calculation of cardiac function from computed tomography (CT).

METHODS AND RESULTS: Time-resolved CT data sets from 39 patients were used to train segmentation models for the left side of the heart including the left ventricle (LV), left atrium (LA), and left atrial appendage (LAA). We compared nnU-Net, 3D TransUNet, and UNETR. Dice Similarity Scores (DSS) were similar between nnU-Net (average DSS = 0.91) and 3D TransUNet (DSS = 0.89) while UNETR performed less well (DSS = 0.69). Intra-class correlation analysis showed nnU-Net and 3D TransUNet both accurately estimated LVSV (ICCnnU-Net = 0.95; ICC3DTransUNet = 0.94), LVEF (ICCnnU-Net = 1.00; ICC3DTransUNet = 1.00), LASV (ICCnnU-Net = 0.91; ICC3DTransUNet = 0.80), LAEF (ICCnnU-Net = 0.95; ICC3DTransUNet = 0.81), and LAASV (ICCnnU-Net = 0.79; ICC3DTransUNet = 0.81). Only nnU-Net significantly predicted LAAEF (ICCnnU-Net = 0.68). UNETR was not able to accurately estimate cardiac function. Time to convergence during training and time needed for inference were both faster for 3D TransUNet than for nnU-Net.

CONCLUSION: nnU-Net outperformed two different vision transformer architectures for the segmentation and calculation of function parameters for the LV, LA, and LAA. Fully automatic calculation of cardiac function parameters from CT using deep learning is fast and reliable.

PMID:40051867 | PMC:PMC11883084 | DOI:10.1093/ehjimp/qyaf011

Chronic Dis Transl Med. 2024 Jun 9;11(1):1-21. doi: 10.1002/cdt3.137. eCollection 2025 Mar.

ABSTRACT

Chronic diseases such as heart disease, cancer, and diabetes are leading drivers of mortality worldwide, underscoring the need for improved efforts around early detection and prediction. The pathophysiology and management of chronic diseases have benefitted from emerging fields in molecular biology like genomics, transcriptomics, proteomics, glycomics, and lipidomics. The complex biomarker and mechanistic data from these "omics" studies present analytical and interpretive challenges, especially for traditional statistical methods. Machine learning (ML) techniques offer considerable promise in unlocking new pathways for data-driven chronic disease risk assessment and prognosis. This review provides a comprehensive overview of state-of-the-art applications of ML algorithms for chronic disease detection and prediction across datasets, including medical imaging, genomics, wearables, and electronic health records. Specifically, we review and synthesize key studies leveraging major ML approaches ranging from traditional techniques such as logistic regression and random forests to modern deep learning neural network architectures. We consolidate existing literature to date around ML for chronic disease prediction to synthesize major trends and trajectories that may inform both future research and clinical translation efforts in this growing field. While highlighting the critical innovations and successes emerging in this space, we identify the key challenges and limitations that remain to be addressed. Finally, we discuss pathways forward toward scalable, equitable, and clinically implementable ML solutions for transforming chronic disease screening and prevention.

PMID:40051825 | PMC:PMC11880127 | DOI:10.1002/cdt3.137

Front Psychiatry. 2025 Feb 20;16:1532256. doi: 10.3389/fpsyt.2025.1532256. eCollection 2025.

ABSTRACT

INTRODUCTION: Long-time mobile phone use (LTMPU) has been linked to emotional issues such as anxiety and depression while the enlarged perivascular spaces (EPVS), as marker of neuroinflammation, is closely related with mental disorders. In the current study, we aim to develop a predictive model utilizing MRI-quantified EPVS metrics and machine learning algorithms to assess the severity of anxiety and depression symptoms in patients with LTMPU.

METHODS: Eighty-two participants with LTMPU were included, with 37 suffering from anxiety and 44 suffering from depression. Deep learning algorithms were used to segment EPVS lesions and extract quantitative metrics. Comparison and correlation analyses were performed to investigate the relationship between EPVS and self-reported mood states. Training and testing datasets were randomly assigned in the ratio of 8:2 to perform radiomics analysis, where EPVS metrics combined with sex and age were used to select the most valuable features to construct machine learning models for predicting the severity of anxiety and depression.

RESULTS: Several EPVS features were significantly different between the two comparisons. For classifying anxiety status, eight features were selected to construct a logistic regression model, with an AUC of 0.819 (95%CI 0.573-1.000) in the testing dataset. For classifying depression status, eight features were selected to construct a K nearest neighbors model with an AUC value of 0.931 (95%CI 0.814-1.000) in the testing dataset.

DISCUSSION: The utilization of MRI-quantified EPVS metrics combined with machine-learning algorithms presents a promising method for evaluating severity of anxiety and depression symptoms in patients with LTMPU, which might introduce a non-invasive, objective, and quantitative approach to enhance diagnostic efficiency and guide personalized treatment strategies.

PMID:40051766 | PMC:PMC11882520 | DOI:10.3389/fpsyt.2025.1532256

Front Med (Lausanne). 2025 Feb 20;12:1497651. doi: 10.3389/fmed.2025.1497651. eCollection 2025.

ABSTRACT

BACKGROUND: The prognostic prediction of patients with hypercapnic respiratory failure holds significant clinical value. The objective of this study was to develop and validate a predictive model for predicting survival in patients with hypercapnic respiratory failure.

METHODS: The study enrolled a total of 697 patients with hypercapnic respiratory failure, including 565 patients from the First People's Hospital of Yancheng in the modeling group and 132 patients from the People's Hospital of Jiangsu Province in the external validation group. The three selected models were random survival forest (RSF), DeepSurv, a deep learning-based survival prediction algorithm, and Cox Proportional Risk (CoxPH). The model's predictive performance was evaluated using the C-index and Brier score. Receiver operating characteristic curve (ROC), area under ROC curve (AUC), and decision curve analysis (DCA) were employed to assess the accuracy of predicting the prognosis for survival at 6, 12, 18, and 24 months.

RESULTS: The RSF model (c-index: 0.792) demonstrated superior predictive ability for the prognosis of patients with hypercapnic respiratory failure compared to both the traditional CoxPH model (c-index: 0.699) and DeepSurv model (c-index: 0.618), which was further validated on external datasets. The Brier Score of the RSF model demonstrated superior performance, consistently measuring below 0.25 at the 6-month, 12-month, 18-month, and 24-month intervals. The ROC curve confirmed the superior discrimination of the RSF model, while DCA demonstrated its optimal clinical net benefit in both the modeling group and the external validation group.

CONCLUSION: The RSF model offered distinct advantages over the CoxPH and DeepSurv models in terms of clinical evaluation and monitoring of patients with hypercapnic respiratory failure.

PMID:40051730 | PMC:PMC11882423 | DOI:10.3389/fmed.2025.1497651

Heliyon. 2025 Feb 14;11(4):e42667. doi: 10.1016/j.heliyon.2025.e42667. eCollection 2025 Feb 28.

ABSTRACT

Little is known about differences in interstitial lung disease (ILD) diagnosis by geographic location. The aim of this study is to evaluate differences in cross-sectional ILD diagnosis between patients in urban and rural areas.

METHODS: This is a retrospective analysis of participants (n = 1992) in the Pulmonary Fibrosis Foundation (PFF) Patient Registry. Diagnoses were grouped as follows: idiopathic pulmonary fibrosis (IPF); idiopathic interstitial pneumonia other than IPF (IIP, non-IPF); connective tissue disease-associated ILD (CTD-ILD); fibrotic hypersensitivity pneumonitis (fibrotic HP); exposure-related ILD; and other ILDs. Patient-reported zip codes were mapped to county Federal Information Processing Series (FIPS) codes using data from U.S. Department of Housing and Urban Development (HUD). Frequencies of ILD diagnoses were compared between urban and rural groups using two-sample Z-test with 0.05 significance level. County-level variables including occupation and fuel use were then compared by ILD diagnosis using analysis of variance (ANOVA) with 0.05 significance level.

RESULTS: Median age at consent was 69 years, 63 % were male, and 89.5 % were white. By county classification, 12 % resided in a rural area. Rates of IPF, IIP (non-IPF), and CTD-ILD diagnosis were similar between urban and rural residents, however rates of fibrotic HP and exposure-related ILD were higher among rural residents. Residence in a county with coal fuel use or wood fuel use was higher among those with exposure-related ILD (p < 0.0001 and p = 0.0001, respectively).

CONCLUSION: ILD diagnoses differ in urban versus rural ILD patients, with fibrotic HP and exposure-related ILD being significantly more prevalent among residents in rural areas. Type of fuel use also was associated with fibrotic HP and exposure-related ILD.

PMID:40051848 | PMC:PMC11883350 | DOI:10.1016/j.heliyon.2025.e42667

Clin J Pain. 2025 Mar 7. doi: 10.1097/AJP.0000000000001279. Online ahead of print.

ABSTRACT

OBJECTIVES: This study aimed to characterize pain intensity (average, worst) and disease severity in youth with inflammatory bowel disease in the 12-months post-diagnosis, and to examine the relation between pain and risk (disease severity) and resilience (optimism, pain self-efficacy) factors over time.

METHODS: Data collection ran from February 2019 to March 2022. Newly diagnosed youth aged 8-17 with IBD completed numerical rating scales for average and worst pain intensity, Youth Life Orientation Test for optimism, and Pain Self-Efficacy Scale for pain self-efficacy via REDCap; weighted Pediatric Crohn's Disease Activity Index and the Pediatric Ulcerative Colitis Activity Index were used as indicators of disease severity. Descriptive statistics characterized pain and disease severity. Multilevel modeling explored relations between variables over time, including moderation effects of optimism and pain self-efficacy.

RESULTS: At baseline, 83 youth (Mage=13.9, SD=2.6; 60.2% Crohn's disease; 39.8% female) were included. Attrition rates at 4 and 12 months were 6.0% and 9.6%, respectively. Across time, at least 52% of participants reported pain. Participants in disease remission increased from 4% to 70% over 12-months. Higher disease severity predicted higher worst pain, regardless of time since diagnosis. Higher pain self-efficacy: (a) predicted lower average and worst pain, especially at later time points; and (b) attenuated the association between disease severity and worst pain when included as a moderator. Higher optimism predicted lower worst pain.

DISCUSSION: Pain is prevalent in pediatric inflammatory bowel disease and impacted by disease severity, pain self-efficacy, and optimism. Findings highlight modifiable intervention targets.

PMID:40052200 | DOI:10.1097/AJP.0000000000001279

NPJ Metab Health Dis. 2025;3(1):7. doi: 10.1038/s44324-024-00047-w. Epub 2025 Mar 4.

ABSTRACT

Organisms have to adapt to changes in their environment. Cellular adaptation requires sensing, signalling and ultimately the activation of cellular programs. Metabolites are environmental signals that are sensed by proteins, such as metabolic enzymes, protein kinases and nuclear receptors. Recent studies have discovered novel metabolite sensors that function as gene regulatory proteins such as chromatin associated factors or RNA binding proteins. Due to their function in regulating gene expression, metabolite-induced allosteric control of these proteins facilitates a crosstalk between metabolism and gene expression. Here we discuss the direct control of gene regulatory processes by metabolites and recent progresses that expand our abilities to systematically characterize metabolite-protein interaction networks. Obtaining a profound map of such networks is of great interest for aiding metabolic disease treatment and drug target identification.

PMID:40052108 | PMC:PMC11879850 | DOI:10.1038/s44324-024-00047-w

Front Genet. 2025 Feb 20;16:1572285. doi: 10.3389/fgene.2025.1572285. eCollection 2025.

ABSTRACT

[This corrects the article DOI: 10.3389/fgene.2021.753839.].

PMID:40051703 | PMC:PMC11882524 | DOI:10.3389/fgene.2025.1572285

Front Mol Biosci. 2025 Feb 20;12:1493891. doi: 10.3389/fmolb.2025.1493891. eCollection 2025.

ABSTRACT

Pseudomonas aeruginosa (PA) is a ubiquitous, Gram-negative, bacteria that can attribute its survivability to numerous sensing and signaling pathways; conferring fitness due to speed of response. Post-transcriptional regulation is an energy efficient approach to quickly shift gene expression in response to the environment. The conserved post-transcriptional regulator RsmA is involved in regulating translation of genes involved in pathways that contribute to virulence, metabolism, and antibiotic resistance. Prior high-throughput approaches to map the full regulatory landscape of RsmA have estimated a target pool of approximately 500 genes; however, these approaches have been limited to a narrow range of growth phase, strain, and media conditions. Computational modeling presents a condition-independent approach to generating predictions for binding between the RsmA protein and highest affinity mRNAs. In this study, we improve upon a two-state thermodynamic model to predict the likelihood of RsmA binding to the 5' UTR sequence of genes present in the PA genome. Our modeling approach predicts 1043 direct RsmA-mRNA binding interactions, including 457 novel mRNA targets. We then perform GO term enrichment tests on our predictions that reveal significant enrichment for DNA binding transcriptional regulators. In addition, quorum sensing, biofilm formation, and two-component signaling pathways were represented in KEGG enrichment analysis. We confirm binding predictions using in vitro binding assays, and regulatory effects using in vivo translational reporters. These reveal RsmA binding and regulation of a broader number of genes not previously reported. An important new observation of this work is the direct regulation of several novel mRNA targets encoding for factors involved in Quorum Sensing and the Type IV Secretion system, such as rsaL and mvaT. Our study demonstrates the utility of thermodynamic modeling for predicting interactions independent of complex and environmentally-sensitive systems, specifically for profiling the post-transcriptional regulator RsmA. Our experimental validation of RsmA binding to novel targets both supports our model and expands upon the pool of characterized target genes in PA. Overall, our findings demonstrate that a modeling approach can differentiate direct from indirect binding interactions and predict specific sites of binding for this global regulatory protein, thus broadening our understanding of the role of RsmA regulation in this relevant pathogen.

PMID:40051501 | PMC:PMC11882435 | DOI:10.3389/fmolb.2025.1493891

J Basic Microbiol. 2025 Mar 6:e70013. doi: 10.1002/jobm.70013. Online ahead of print.

ABSTRACT

Speciation in prokaryotes is often driven by complex genetic exchanges such as horizontal gene transfer (HGT), which facilitates genomic divergence and adaptation. In this study, we inferred the evolutionary transitions of the mobilome (plasmids, transposons, and phages) between Methanosarcina and bacteria in driving speciation within the Methanosarcina genus. By conducting evolutionary and phylogenetic analyses of Methanosarcina acetivorans, M. barkeri, M. mazei, and M. siciliae, we identified key mobilome elements acquired through HGT from distantly related bacterial species. These mobile genetic elements have shaped genomic plasticity, enabling Methanosarcina to adapt to diverse environmental niches and potentially facilitating lineage divergence. The acquisition of mobilome-associated genes involved in antibiotic resistance, DNA repair, and stress responses suggests their significant role in the ecological speciation of Methanosarcina. Overall, we hypothesized that their mobile genetic element might have been acquired from distantly related bacteria by HGT and subsequently established as new functional homologs in the present lineage. This study provides insight into how mobilome-mediated gene flow contributes to genomic divergence and speciation within microbial populations, highlighting the broader significance of mobilome in microbial evolution and speciation processes.

PMID:40051073 | DOI:10.1002/jobm.70013

J Basic Microbiol. 2025 Mar 6:e70015. doi: 10.1002/jobm.70015. Online ahead of print.

ABSTRACT

The convergent evolution of coenzyme metabolism in methanogens provides critical insights into primitive life and metabolic adaptations. This study investigated the molecular evolution and functional dynamics of eight coenzymes and cofactors in Methanosarcina mazei, a model methanogen essential for methane production and energy conservation in anaerobic environments. Phylogenetic and genetic diversity analyses of the 706 protein sequences revealed conserved evolutionary trajectories interspersed with lineage-specific adaptations driven by gene duplication, horizontal gene transfer, and selective pressures. Key findings included the purifying selection of methanofuran (Tajima's D = -2.9589) and coenzyme A (Tajima's D = -2.8555), indicating the conservation of critical metabolic functions. The coenzyme B biosynthesis pathway showed balanced selection (Tajima's D = 2.38602), reflecting its evolutionary plasticity. Phylogenetic analyses linked coenzyme F420 biosynthetic enzymes closely to Methanosarcina horonobensis, while coenzyme F430 enzymes highlighted prokaryotic specialization distinct from their eukaryotes. Coenzyme M biosynthetic genes have demonstrated unique evolutionary connections with species across domains, such as Methanothermobacter thermautotrophicus and Gekko japonicus, emphasizing their broad adaptive significance. These evolutionary trajectories reveal how M. mazei optimized its metabolic pathways to thrive in extreme anaerobic environments, bridging ancient metabolic systems from the Last Universal Common Ancestor with contemporary ecological adaptations.

PMID:40051064 | DOI:10.1002/jobm.70015

Cancer Cell Int. 2025 Mar 6;25(1):79. doi: 10.1186/s12935-025-03712-2.

ABSTRACT

BACKGROUND: Colorectal cancer (CRC) poses a significant clinical challenge because of drug resistance, which can adversely impact patient outcomes. Recent research has shown that abnormalities within the tumor microenvironment, especially hyperglycemia, play a crucial role in promoting metastasis and chemoresistance, and thereby determine the overall prognosis of patients with advanced CRC.

METHODS: This study employs data mining and consensus molecular subtype (CMS) techniques to identify pitavastatin and atorvastatin as potential agents for targeting high glucose-induced drug resistance in advanced CRC cells. CRC cells maintained under either low or high glucose conditions were established and utilized to assess the cytotoxic effects of pitavastatin and atorvastatin, both with and without 5-fluorouracil (5-FU). CRC 3D spheroids cultured were also included to demonstrate the anti-drug resistance of pitavastatin and atorvastatin.

RESULTS: A bioinformatics analysis identified pitavastatin and atorvastatin as promising drug candidates. The CMS4 CRC cell line SW480 (SW480-HG) was established and cultured under high glucose conditions to simulate hyperglycemia-induced drug resistance and metastasis in CRC patients. Pitavastatin and atorvastatin could inhibit cell proliferation and 3D spheroid formation of CMS4 CRC cells under high glucose conditions. In addition, both pitavastatin and atorvastatin can synergistically promote the 5-FU-mediated cytotoxic effect and inhibit the growth of 5-FU-resistant CRC cells. Mechanistically, pitavastatin and atorvastatin can induce apoptosis and synergistically promote the 5-FU-mediated cytotoxic effect by activating autophagy, as well as the PERK/ATF4/CHOP signaling pathway while decreasing YAP expression.

CONCLUSION: This study highlights the biomarker-guided precision medicine strategy for drug repurposing. Pitavastatin and atorvastatin could be used to assist in the treatment of advanced CRC, particularly with CMS4 subtype CRC patients who also suffer from hyperglycemia. Pitavastatin, with an achievable dosage used for clinical interventions, is highly recommended for a novel CRC therapeutic strategy.

PMID:40050889 | DOI:10.1186/s12935-025-03712-2

BMC Cancer. 2025 Mar 6;25(1):412. doi: 10.1186/s12885-025-13733-9.

ABSTRACT

BACKGROUND: Targeting mitochondrial dynamics offers promising strategies for treating glioblastoma multiforme. Celastrol has demonstrated therapeutic effects on various cancers, but its impact on mitochondrial dynamics in glioblastoma multiforme remains largely unknown. We studied the effects of Celastrol on mitochondrial dynamics, redox homeostasis, and the proliferation.

METHODS: Mito-Tracker Green staining was conducted on U251, LN229, and U87-MG cells to evaluate the effects of Celastrol on mitochondrial dynamics. The Western blot analysis quantified the expression levels of mitochondrial dynamin, antioxidant enzymes, and cell cycle-related proteins. JC-1 staining was performed to discern mitochondrial membrane potential. Mitochondrial reactive oxygen species were identified using MitoSOX. The proliferative capacity of cells was assessed using Cell Counting Kit-8 analysis, and colony formation assays. Survival analysis was employed to evaluate the therapeutic efficacy of Celastrol in C57BL/6J mice with glioblastoma.

RESULTS: Our findings suggest that Celastrol (1 and 1.5 µM) promotes mitochondrial fission by downregulating the expression of mitofusin-1. A decrease in mitochondrial membrane potential at 1 and 1.5 µM indicates that Celastrol impaired mitochondrial function. Concurrently, an increase in mitochondrial reactive oxygen species and impaired upregulation of antioxidant enzymes were noted at 1.5 µM, indicating that Celastrol led to an imbalance in mitochondrial redox homeostasis. At both 1 and 1.5 µM, cell proliferation was inhibited, which may be related to the decreased expression levels of Cyclin-dependent kinase 1 and Cyclin B1. Celastrol extended the survival of GBM-afflicted mice.

CONCLUSION: Celastrol promotes mitochondrial fission in glioblastoma multiforme cells by reducing mitofusin-1 expression, accompanying mitochondrial dysfunction, lower mitochondrial membrane potential, heightened oxidative stress, and decreased Cyclin-dependent kinase 1 and Cyclin B1 levels. This indicates that Celastrol possesses potential for repurposing as an agent targeting mitochondrial dynamics in glioblastoma multiforme, warranting further investigation.

PMID:40050778 | DOI:10.1186/s12885-025-13733-9

Sci Rep. 2025 Mar 6;15(1):7840. doi: 10.1038/s41598-025-91757-8.

ABSTRACT

Drug repositioning is a transformative approach in drug discovery, offering a pathway to repurpose existing drugs for new therapeutic uses. In this study, we introduce the IDDNMTF model designed to predict drug repositioning opportunities with greater precision. The IDDNMTF model integrates multiple datasets, allowing for a more comprehensive analysis of drug-disease associations. We evaluated the IDDNMTF model using various combinations of datasets and found that its performance, as measured by AUC, AUPR, and F1 scores, improved with the inclusion of more data. This trend underscores the importance of data diversity in strengthening predictive capabilities. Comparatively, the IDDNMTF model demonstrated superior performance against the NMF model, solidifying its potential in drug repositioning. In summary, the IDDNMTF model offers a promising tool for identifying new therapeutic uses for existing drugs. Its predictive accuracy and interpretability are poised to accelerate the transition from bench to bedside, contributing to personalized medicine and the development of targeted treatments.

PMID:40050702 | DOI:10.1038/s41598-025-91757-8

Eur J Pharmacol. 2025 Mar 4:177461. doi: 10.1016/j.ejphar.2025.177461. Online ahead of print.

ABSTRACT

INTRODUCTION: & Aim: Novel treatments for idiopathic pulmonary fibrosis (IPF) are needed urgently. A better understanding of the molecular pathways activated by TGFβ1 in human lung tissue may facilitate the development of more effective anti-fibrotic medications. This study utilized proteomic analysis to test the hypothesis that TGFβ1 induces pro-fibrotic effects on human lung parenchyma proteome, and to evaluate the viability of this model for testing novel therapeutic targets.

METHODS: Non-fibrotic human lung parenchymal tissue from 11 patients was cultured for 7 days in serum-free (SF) media supplemented with TGFβ1 (10 ng/mL) or vehicle control, and the putative antifibrotic KCa3.1 ion channel blocker senicapoc or vehicle control. The tissue was homogenized, digested for bottom-up proteomics, and analysed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Principal component analysis, differential expression analysis, pathway analysis, and drug repurposing analysis were performed.

RESULTS: TGFβ1 stimulation for 7 days induced a strong fibrotic protein response relevant to IPF pathology. A total of 2,391 proteins were quantified, 306 upregulated and 285 downregulated (FDR-adjusted p-value<0.05). Of these, 118 were upregulated and 28 downregulated at log2(FC)>0.58. These changes were attenuated by senicapoc (100 nM). Drug repurposing analysis identified 265 drugs predicted to inhibit the effects of TGFβ1 in this model. These included clotrimazole, a KCa3.1 blocker, and nintedanib, a drug licenced for the treatment of IPF, providing validation of this approach.

CONCLUSION: A pro-fibrotic proteome is induced in human lung parenchyma exposed to TGFβ1, sensitive to pharmacological intervention. This approach has the potential to enhance therapeutic drug screening for IPF treatment.

PMID:40049575 | DOI:10.1016/j.ejphar.2025.177461

EBioMedicine. 2025 Mar 5;114:105629. doi: 10.1016/j.ebiom.2025.105629. Online ahead of print.

ABSTRACT

BACKGROUND: First described in 1972, idiopathic subglottic stenosis (iSGS) is a serious chronic orphan disease characterised by recurrent scarring of the subglottis. Although the cause is unknown, iSGS is almost exclusively restricted to Caucasian females typically in their fourth to sixth decade. However, given its rare incidence (1:400,000), understanding the clinical trajectory and molecular factors associated with iSGS disease development and prognosis has been difficult. In the current study we sought to unravel the pathogenesis of iSGS at the clinical, transcriptional, and genetic level in a prospective cohort.

METHODS: We prospectively enrolled 126 patients with iSGS, 104 controls, and 13 patients with traumatic SGS. Within this cohort, we profiled 114 human epiglottis and 121 human subglottis biopsies across three different conditions: control, iSGS, and intubation-related traumatic stenosis using bulk and single nucleus RNA-sequencing. Whole exome sequencing for germline variants was performed for 70 controls and 75 patients with iSGS.

FINDINGS: Patients with iSGS received a median number of five (range 0-18) surgical dilations at a rate of 1.031 dilations (range: 0.12-6.2) per year. Older age at diagnosis and higher Cotton-Myers grade were associated with increased number of surgical dilations over time. Cohort-level bulk transcriptomics found that iSGS pathology was restricted within the subglottis and did not affect anatomically adjacent epiglottis, opposite to previous hypotheses. We further identified cellular subsets associated with iSGS prognosis and severity. Finally, patients with iSGS exhibit lower testosterone predicted using a polygenic score.

INTERPRETATION: Together, our data refines our understanding of laryngeal biology and provides insights into the clinical trajectory of subglottic stenoses. Future research should explore the role of testosterone in the development of iSGS.

FUNDING: This study was funded by a grant from the American Laryngology Association (#1082), an Academic Medical Organization of Southwestern Ontario innovation fund grant (INN21-016), grant support from the Departments of Otolaryngology-Head and Neck Surgery at University of Toronto and Western University. ACN was supported by the Wolfe Surgical Research Professorship in the Biology of Head and Neck Cancers Fund. PYFZ was supported by a Vanier Canada Graduate Scholarship and PSI foundation fellowship.

PMID:40048847 | DOI:10.1016/j.ebiom.2025.105629

Biochem Genet. 2025 Mar 6. doi: 10.1007/s10528-025-11058-7. Online ahead of print.

ABSTRACT

Methotrexate (MTX) pharmacogenetics has been extensively investigated due to the high inter-individual variability in response to treatment. This wide variability can lead to treatment discontinuation or even death. Several genes involved in the pharmacodynamics and pharmacokinetics of MTX have been studied. However, there are still no guidelines for pharmacogenetics-guided MTX dosing. The FPGS rs1544105 and GGH rs3758149 gene polymorphisms were genotyped and their allele frequencies were determined. Their associations with MTX treatment response and toxicity in Uruguayan adults with haematological malignancies receiving high-dose MTX were analyzed. A worldwide systematic review of the association of these gene polymorphisms with response and toxicity to high-dose MTX treatment was also conducted. The allele frequencies of FPGS rs1544105 were 0.54 and 0.46 (C and T, respectively), and of GGH rs3758149 were 0.77 and 0.23 (C and T, respectively). Several associations were found between toxicity (gastrointestinal, hepatic and hematological) and the FPGS rs1544105 T allele (p = 0.01, p < 0.001 and p = 0.04, respectively) and between mucositis and the FPGS TT genotype (p < 0.001). The GGH rs375814 TT genotype was associated with gastrointestinal and hepatic toxicity (p = 0.01 and p < 0.001, respectively). Both the FPGS rs1544105 C allele and the GGH rs3758149 TT genotype were associated with remission (p < 0.001 and p = 0.04, respectively). The systematic review identified 247 publications and finally included 17 research articles. Few consistent data were found due to the lack of homogeneity between study groups.

PMID:40050537 | DOI:10.1007/s10528-025-11058-7

Eur J Pharmacol. 2025 Mar 4:177475. doi: 10.1016/j.ejphar.2025.177475. Online ahead of print.

ABSTRACT

Senescence, defined by the cessation of cell proliferation, plays a critical and multifaceted role in breast cancer progression and treatment. Senescent cells produce senescence-associated secretory phenotypes (SASP) comprising inflammatory cytokines, chemokines, and small molecules, which actively shape the tumor microenvironment, influencing cancer development, progression, and metastasis. This review provides a comprehensive analysis of the types and origins of senescent cells in breast cancer, alongside their markers and detection methods. Special focus is placed on pharmacological strategies targeting senescence, including drugs that induce or inhibit senescence, their molecular mechanisms, and their roles in therapeutic outcomes when combined with chemotherapy and radiotherapy. By exploring these pharmacological interventions and their impact on breast cancer treatment, this review underscores the potential of senescence-targeting therapies to revolutionize breast cancer management.

PMID:40049574 | DOI:10.1016/j.ejphar.2025.177475

Fungal Genet Biol. 2025 Mar 4:103975. doi: 10.1016/j.fgb.2025.103975. Online ahead of print.

ABSTRACT

Environmental conditions have a huge impact on the development of all living things but are especially important in the case of single-celled organisms such as Saccharomyces cerevisiae that must respond quickly and appropriately to any change. Many molecular mechanisms of response to stress have been identified in yeast, but only a few reports address physiological and morphological changes. To investigate S. cerevisiae recovery from ten mild stress conditions and to describe the viability and fitness, we performed a series of growth analysis experiments. Moreover, label-free live cell imaging of yeast subjected to ten environmental stresses has been achieved using holotomography - a leading-edge high resolution 3D quantitative phase imaging. We determined that recovery times of yeast cultures subjected to hyperosmotic and sugar starvation stresses were the shortest, as were the doubling times. Substantially lower proliferation capacity was recorded in yeast after applying sugar- and AA starvation, and high pH stresses, compared to control. Furthermore, the stationary growth was much shorter after subjecting yeast to hypoosmotic and heat stresses, and much longer after anaerobic and UV stresses. Further, we determined changes in shape, colony formation, cell wall damage, volume, sphericity, protein and lipid contents in yeast cells under stress conditions. The most prominent changes were observed for UV and hyperosmotic stresses. Condluding, stress conditions applied to yest cultures affected them differently, causing detrimental effects to their growth, metabolism, fitness and morphology. Moreover, we have proven that holotomography is excellent for precisely determining morphological changes of single cells.

PMID:40049444 | DOI:10.1016/j.fgb.2025.103975