Front Pharmacol. 2025 Apr 16;16:1552251. doi: 10.3389/fphar.2025.1552251. eCollection 2025.

ABSTRACT

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) has a higher morbidity and poor prognosis. Gui-Zhi-Fu-Ling-Wan (GFW) is a traditional Chinese herbal formula which exerts anti-inflammatory and anti-oxidative effects. The goal was to determine the protective effect of GFW on bleomycin (BLM)-induced pulmonary fibrosis.

METHODS: One hundred and twenty-four mice were randomly divided into eight groups, and orally supplemented with GFW (1 g/kg) in 1 week ago and continuing to 1 week later of single BLM intratracheal injection (5.0 mg/kg). Lung tissues were collected in 7 days and 21 days after BLM injection. BEAS-2B cells were pretreated with GFW (100 μg/mL) for three consecutive days before BLM (10 μg/mL) exposure. Cells were harvested in 12 or 24 h after BLM co-culture.

RESULTS: GFW supplementation alleviated BLM-induced alveolar structure destruction and inflammatory cell infiltration in mice lungs. BLM-incurred collagen deposition was attenuated by GFW. In addition, GFW pretreatment repressed BLM-evoked downregulation of E-cadherin, and elevation of N-cadherin and Vimentin in mouse lungs. Besides, BLM-excited GPX4 reduction, ferritin increases, lipid peroxidation, and free iron overload were significantly relieved by GFW pretreatment in mouse lungs and BEAS-2B cells. Notably, BLM-provoked mitochondrial reactive oxygen species (mtROS) excessive production, elevation of mitochondrial stress markers, such as HSP70 and CLPP, and mitochondrial injury, were all abolished in mouse lungs and BEAS-2B cells by GFW pretreatment.

CONCLUSION: GFW supplementation attenuated BLM-evoked lung injury and pulmonary fibrosis partially through repressing EMT and mtROS-mediated ferroptosis in pulmonary epithelial cells.

PMID:40308766 | PMC:PMC12041222 | DOI:10.3389/fphar.2025.1552251

R Soc Open Sci. 2025 Apr 30;12(4):241025. doi: 10.1098/rsos.241025. eCollection 2025 Apr.

ABSTRACT

Different isogenic cells exhibit different responses to the same extracellular signals. Several authors assumed that this variation arose from stochastic signalling noise with the implication that single eukaryotic cells could not detect their surroundings accurately, but work by us and others has shown that the variation is dominated instead by persistent cell-to-cell differences. Here, we analysed previously published data to quantify the sources of variation in pheromone-induced gene expression in Saccharomyces cerevisiae. We found that 91% of response variation was due to stable cell-to-cell differences, 8% from experimental measurement error, and 1% from signalling noise and expression noise. Low noise enabled precise signalling; individual cells could transmit over 3 bits of information through the pheromone response system and so respond differently to eight different pheromone concentrations. Additionally, if individual cells could reference their responses against constitutively expressed proteins, then cells could determine absolute pheromone concentrations with 2 bits of accuracy. These results help explain how individual yeast cells can accurately sense and respond to different extracellular pheromone concentrations.

PMID:40309186 | PMC:PMC12040454 | DOI:10.1098/rsos.241025

R Soc Open Sci. 2025 Apr 30;12(4):241791. doi: 10.1098/rsos.241791. eCollection 2025 Apr.

ABSTRACT

The bond graph approach has been recognized as a useful conceptual basis for understanding the behaviour of living entities modelled as a system with hierarchical interacting parts exchanging energy. One such behaviour is oscillation, which underpins many essential biological functions. In this paper, energy-based modelling of biochemical systems using the bond graph approach is combined with classical feedback control theory to give a novel approach to the analysis, and potentially synthesis, of biochemical oscillators. It is shown that oscillation is dependent on the interplay between active and passive feedback and this interplay is formalized using classical frequency-response analysis of feedback systems. In particular, the phase margin is suggested as a simple scalar indicator of the presence or absence of oscillations; it is shown how this indicator can be used to investigate the effect of both the structure and parameters of biochemical system on oscillation. It follows that the combination of classical feedback control theory and the bond graph approach to systems biology gives a novel analysis and design methodology for biochemical oscillators. The approach is illustrated using an introductory example similar to the Goodwin oscillator, the Sel'kov model of glycolytic oscillations and the repressilator.

PMID:40309185 | PMC:PMC12040473 | DOI:10.1098/rsos.241791

Biophys Rep. 2025 Apr 30;11(2):129-142. doi: 10.52601/bpr.2024.240045.

ABSTRACT

In this study, we conducted a comprehensive proteomic analysis of B cells from the spleen, mesenteric lymph nodes (mLN), and peripheral blood mononuclear cells (PBMC) in a time-course model of systemic lupus erythematosus (SLE) using female MRL/lpr mice. By combining fluorescence-activated cell sorting (FACS) and 4D-Data-Independent Acquisition (4D-DIA) mass spectrometry, we quantified nearly 8000 proteins, identifying significant temporal and tissue-specific proteomic changes during SLE progression. PBMC-derived B cells exhibited early proteomic alterations by Week 9, while spleen-derived B cells showed similar changes by Week 12. We identified key regulatory proteins, including BAFF, BAFFR, and NFKB2, involved in B cell survival and activation, as well as novel markers such as CD11c and CD117, which have previously been associated with other immune cells. The study highlights the dynamic reprogramming of B cell proteomes across different tissues, with distinct contributions to SLE pathogenesis, providing valuable insights into the molecular mechanisms underlying B cell dysregulation in lupus. These findings offer potential therapeutic targets and biomarkers for SLE.

PMID:40308935 | PMC:PMC12035744 | DOI:10.52601/bpr.2024.240045

Virus Evol. 2025 Mar 11;11(1):veaf015. doi: 10.1093/ve/veaf015. eCollection 2025.

ABSTRACT

Mutations within the N-terminal domain (NTD) of the spike (S) protein are critical for the emergence of successful severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral lineages. The NTD has been repeatedly impacted by deletions, often exhibiting complex and dynamic patterns, such as the recurrent emergence and disappearance of deletions in dominant variants. This study investigates the influence of repair of NTD lineage-defining deletions found in the BA.1 lineage (Omicron variant) on viral success. We performed comparative genomic analyses of >10 million SARS-CoV-2 genomes from the Global Initiative on Sharing All Influenza Data (GISAID) EpiCov database to evaluate the detection of viruses lacking S:ΔH69/V70, S:ΔV143/Y145, or both. These findings were contrasted against a screening of publicly available raw sequencing data, revealing substantial discrepancies between data repositories, suggesting that spurious deletion repair observations in GISAID may result from systematic artifacts. Specifically, deletion repair events were approximately an order of magnitude less frequent in the read-run survey. Our results suggest that deletion repair events are rare, isolated events with limited direct influence on SARS-CoV-2 evolution or transmission. Nevertheless, such events could facilitate the emergence of fitness-enhancing mutations. To explore potential drivers of NTD deletion repair patterns, we characterized the viral phenotype of such markers in a surrogate in vitro system. Repair of the S:ΔH69/V70 deletion reduced viral infectivity, while simultaneous repair with S:ΔV143/Y145 led to lower fusogenicity. In contrast, individual S:ΔV143/Y145 repair enhanced both fusogenicity and susceptibility to neutralization by sera from vaccinated individuals. This work underscores the complex genotype-phenotype landscape of the spike NTD in SARS-CoV-2, which impacts viral biology, transmission efficiency, and immune escape potential, offering insights with direct relevance to public health, viral surveillance, and the adaptive mechanisms driving emerging variants.

PMID:40308784 | PMC:PMC12041916 | DOI:10.1093/ve/veaf015

DEN Open. 2025 Apr 29;6(1):e70124. doi: 10.1002/deo2.70124. eCollection 2026 Apr.

ABSTRACT

A 74-year-old man with decreased appetite, weight, and heartburn was referred to our hospital. His medications included olmesartan. Esophagogastroduodenoscopy (EGD) revealed antral-dominant erosive gastritis and nodular mucosa. A gastric biopsy revealed inflammatory cell infiltration. The serum anti-Helicobacter pylori immunoglobulin G antibody test result was negative. Famotidine was ineffective in relieving his symptoms, and esomeprazole failed to prevent overt gastric bleeding, which required endoscopic hemostasis. The working diagnosis was drug-induced gastritis, particularly olmesartan-induced gastritis. His appetite loss started to improve within a week of olmesartan withdrawal. The erosions healed on EGD 2 months later. Over the next 10 months, he remained in his usual state until olmesartan was inadvertently administered. Subsequent EGD revealed a mild gastritis relapse. We diagnosed olmesartan-induced gastritis and discontinued olmesartan treatment. Mucosal healing was confirmed by EGD 1 year later. Olmesartan is known to cause angiotensin II receptor blocker-induced enteropathy. Although angiotensin II receptor blocker-induced enteropathy affects the stomach, angiotensin II receptor blocker-induced gastritis without lower gastrointestinal symptoms is rare. The characteristic endoscopic appearance may provide a clue to the correct diagnosis.

PMID:40309044 | PMC:PMC12038180 | DOI:10.1002/deo2.70124

Cureus. 2025 Mar 31;17(3):e81513. doi: 10.7759/cureus.81513. eCollection 2025 Mar.

ABSTRACT

Capivasertib, a protein kinase B (AKT) inhibitor manufactured by AstraZeneca pharmaceutical and used in the treatment of various malignancies, has been implicated in cases of drug-induced diabetic ketoacidosis (DKA). We present a case of capivasertib-induced DKA in a patient with no prior history of diabetes, highlighting the metabolic complications associated with this targeted therapy. The proposed mechanism involves AKT inhibition leading to impaired insulin signaling, reduced glucose uptake, and increased lipolysis, ultimately resulting in ketogenesis. This case underscores the need for vigilant glucose monitoring in patients receiving capivasertib, especially those with predisposing risk factors for insulin resistance or pancreatic dysfunction.

PMID:40308416 | PMC:PMC12043024 | DOI:10.7759/cureus.81513

Am J Hum Genet. 2025 Jun 5;112(6):1376-1387. doi: 10.1016/j.ajhg.2025.04.003. Epub 2025 Apr 29.

ABSTRACT

The diagnosis of mitochondrial DNA (mtDNA) diseases remains challenging with next-generation sequencing, where bioinformatic analysis is usually more focused on the nuclear genome. We developed a workflow for the evaluation of mtDNA diseases and applied it in a large European rare disease cohort (Solve-RD). A semi-automated bioinformatic pipeline with MToolBox was used to filter the unsolved Solve-RD cohort for rare mtDNA variants after validating this pipeline on exome datasets of 42 individuals previously diagnosed with mtDNA variants. Variants were filtered based on blood heteroplasmy levels (≥1%) and reported association with disease. Overall, 10,157 exome and genome datasets from 9,923 affected individuals from 9,483 families within Solve-RD met the quality inclusion criteria. 136 mtDNA variants in 135 undiagnosed individuals were prioritized using the filtering approach. A focused MitoPhen-based phenotype similarity scoring method was tested in a separate genetically diagnosed "phenotype test cohort" consisting of nuclear gene and mtDNA diseases using a receiving operator characteristic evaluation. We applied the MitoPhen-based phenotype similarity score of >0.3, which was highly sensitive for detecting mtDNA diseases in the phenotype test cohort, to the filtered cohort of 135 undiagnosed individuals. This aided the prioritization of 34 out of 37 (92%) individuals who received confirmed and likely causative mtDNA disease diagnoses. The phenotypic evaluation was limited by the quality of input data in some individuals. The overall pipeline led to an additional diagnostic yield of 0.4% in a cohort where mitochondrial disease was not initially suspected. This highlights the value of our mtDNA analysis pipeline in diverse datasets.

PMID:40306282 | DOI:10.1016/j.ajhg.2025.04.003

Sci Rep. 2025 Apr 30;15(1):15160. doi: 10.1038/s41598-025-99646-w.

ABSTRACT

Laser cleaning is widely used industrially to remove surface contaminants with high precision. Conventional methods, however, lack real-time monitoring and feedback loops, often necessitating over-machining to ensure complete contaminant removal, which leads to inefficient energy use and potential substrate damage. In this work, we demonstrate a concept of selective laser cleaning via the application of femtosecond laser pulses and polystyrene microbeads with a diameter of 15 μm. These microbeads model challenging scenarios in high-precision optical work and delicate surface treatments across laboratory and production settings. To enable adaptive, real-time cleaning, we integrated a neural network that predicts the sample's appearance after each laser pulse into a feedback loop, tailoring the cleaning process to a bespoke target pattern. This method ensures precise contaminant removal with minimal energy use, making it highly promising for applications demanding strict material control, such as wafer cleaning, sensitive surface treatments, and heritage restoration. By combining machine learning with ultrafast laser technology, our approach significantly enhances the efficiency and precision of cleaning processes.

PMID:40307358 | DOI:10.1038/s41598-025-99646-w

Sci Rep. 2025 Apr 30;15(1):15166. doi: 10.1038/s41598-025-99309-w.

ABSTRACT

Recent advancements in deep learning have significantly impacted medical image processing domain, enabling sophisticated and accurate diagnostic tools. This paper presents a novel hybrid deep learning framework that combines convolutional neural networks (CNNs) and recurrent neural networks (RNNs) for diabetic retinopathy (DR) early detection and progression monitoring using retinal fundus images. Utilizing the sequential nature of disease progression, the proposed method integrates temporal information across multiple retinal scans to enhance detection accuracy. The proposed model utilizes publicly available DRIVE and Kaggle diabetic retinopathy datasets to evaluate the performance. The benchmark datasets provide a diverse set of annotated retinal images and the proposed hybrid model employs a CNN to extract spatial features from retinal images. The spatial feature extraction is enhanced by multi-scale feature extraction to capture fine details and broader patterns. These enriched spatial features are then fed into an RNN with attention mechanism to capture temporal dependencies so that most relevant data aspects can be considered for analysis. This combined approach enables the model to consider both current and previous states of the retina, improving its ability to detect subtle changes indicative of early-stage DR. Proposed model experimental evaluation demonstrate the superior performance over traditional deep learning models like CNN, RNN, InceptionV3, VGG19 and LSTM in terms of both sensitivity and specificity, achieving 97.5% accuracy on the DRIVE dataset, 94.04% on the Kaggle dataset, 96.9% on the Eyepacs Dataset. This research work not only advances the field of automated DR detection but also provides a framework for utilizing temporal information in medical image analysis.

PMID:40307328 | DOI:10.1038/s41598-025-99309-w

Sci Data. 2025 Apr 30;12(1):719. doi: 10.1038/s41597-025-05030-8.

ABSTRACT

Virological plaque assay is the major method of detecting and quantifying infectious viruses in research and diagnostic samples. Furthermore, viral plaque phenotypes contain information about the life cycle and spreading mechanism of the virus forming them. While some modernisations have been proposed, the conventional assay typically involves manual quantification of plaque phenotypes, which is both laborious and time-consuming. Here, we present an annotated dataset of digital photographs of plaque assay plates of Vaccinia virus - a prototypic propoxvirus. We demonstrate how analysis of these plates can be performed using deep learning by training models based on the leading architecture for biomedical instance segmentation - StarDist. Finally, we show that the entire analysis can be achieved in a single step by HydraStarDist - the modified architecture we propose.

PMID:40307255 | DOI:10.1038/s41597-025-05030-8

Korean J Radiol. 2025 May;26(5):446-445. doi: 10.3348/kjr.2024.1059.

ABSTRACT

OBJECTIVE: To compare the quality of deep learning-reconstructed turbo spin-echo (DL-TSE) and conventionally interpolated turbo spin-echo (Conv-TSE) techniques in contrast-enhanced MRI of the neck.

MATERIALS AND METHODS: Contrast-enhanced T1-weighted DL-TSE and Conv-TSE images were acquired using 3T scanners from 106 patients. DL-TSE employed a closed-source, 'work-in-progress' (WIP No. 1062, iTSE, version 10; Siemens Healthineers) algorithm for interpolation and denoising to achieve the same in-plane resolution (axial: 0.26 × 0.26 mm²; coronal: 0.29 × 0.29 mm²) while reducing scan times by 15.9% and 52.6% for axial and coronal scans, respectively. The full width at half maximum (FWHM) and percent signal ghosting were measured using stationary and flow phantom scans, respectively. In patient images, non-uniformity (NU), contrast-to-noise ratio (CNR), and regional mucosal FWHM were evaluated. Two neuroradiologists visually rated the patient images for overall quality, sharpness, regional mucosal conspicuity, artifacts, and lesions using a 5-point Likert scale.

RESULTS: FWHM in the stationary phantom scan was consistently sharper in DL-TSE. The percent signal ghosting outside the flow phantom was lower in DL-TSE (0.06% vs. 0.14%) but higher within the phantom (8.92% vs. 1.75%) compared to Conv-TSE. In patient scans, DL-TSE showed non-inferior NU and higher CNR. Regional mucosal FWHM was significantly better in DL-TSE, particularly in the oropharynx (coronal: 1.08 ± 0.31 vs. 1.52 ± 0.46 mm) and hypopharynx (coronal: 1.26 ± 0.35 vs. 1.91 ± 0.56 mm) (both P < 0.001). DL-TSE demonstrated higher overall image quality (axial: 4.61 ± 0.49 vs. 3.32 ± 0.54) and sharpness (axial: 4.40 ± 0.56 vs. 3.11 ± 0.53) (both P < 0.001). In addition, mucosal conspicuity was improved, especially in the oropharynx (axial: 4.41 ± 0.67 vs. 3.40 ± 0.69) and hypopharynx (axial: 4.45 ± 0.58 vs. 3.58 ± 0.63) (both P < 0.001). Extracorporeal ghost artifacts were reduced in DL-TSE (axial: 4.32 ± 0.60 vs. 3.90 ± 0.71, P < 0.001) but artifacts overlapping anatomical structures were slightly more pronounced (axial: 3.78 ± 0.74 vs. 3.95 ± 0.72, P < 0.001). Lesions were detected with higher confidence in DL-TSE.

CONCLUSION: DL-based reconstruction applied to accelerated neck MRI improves overall image quality, sharpness, mucosal conspicuity in motion-prone regions, and lesion detection confidence. Despite more pronounced ghost artifacts overlapping anatomical structures, DL-TSE enables substantial scan time reduction while enhancing diagnostic performance.

PMID:40307199 | DOI:10.3348/kjr.2024.1059

BMC Bioinformatics. 2025 Apr 30;26(1):117. doi: 10.1186/s12859-025-06132-1.

ABSTRACT

Accelerating drug discovery for glucocorticoid receptor (GR)-related disorders, including innovative machine learning (ML)-based approaches, holds promise in advancing therapeutic development, optimizing treatment efficacy, and mitigating adverse effects. While experimental methods can accurately identify GR antagonists, they are often not cost-effective for large-scale drug discovery. Thus, computational approaches leveraging SMILES information for precise in silico identification of GR antagonists are crucial, enabling efficient and scalable drug discovery. Here, we develop a new ensemble learning approach using a multi-step stacking strategy (M3S), termed M3S-GRPred, aimed at rapidly and accurately discovering novel GR antagonists. To the best of our knowledge, M3S-GRPred is the first SMILES-based predictor designed to identify GR antagonists without the use of 3D structural information. In M3S-GRPred, we first constructed different balanced subsets using an under-sampling approach. Using these balanced subsets, we explored and evaluated heterogeneous base-classifiers trained with a variety of SMILES-based feature descriptors coupled with popular ML algorithms. Finally, M3S-GRPred was constructed by integrating probabilistic feature from the selected base-classifiers derived from a two-step feature selection technique. Our comparative experiments demonstrate that M3S-GRPred can precisely identify GR antagonists and effectively address the imbalanced dataset. Compared to traditional ML classifiers, M3S-GRPred attained superior performance in terms of both the training and independent test datasets. Additionally, M3S-GRPred was applied to identify potential GR antagonists among FDA-approved drugs confirmed through molecular docking, followed by detailed MD simulation studies for drug repurposing in Cushing's syndrome. We anticipate that M3S-GRPred will serve as an efficient screening tool for discovering novel GR antagonists from vast libraries of unknown compounds in a cost-effective manner.

PMID:40307679 | DOI:10.1186/s12859-025-06132-1

J Glob Antimicrob Resist. 2025 Apr 28:S2213-7165(25)00082-7. doi: 10.1016/j.jgar.2025.04.010. Online ahead of print.

ABSTRACT

OBJECTIVES: Chronic Pseudomonas aeruginosa infections are a leading cause of acute pulmonary exacerbations in people with cystic fibrosis (pwCF). Intrinsic antibiotic resistance and biofilm formation complicate treatment. This study investigates the genomic diversity and cefiderocol efficacy against planktonic and biofilm-associated forms of P. aeruginosa isolates from pwCF.

METHODS: Eight P. aeruginosa clinical isolates and three laboratory strains underwent whole genome sequencing (WGS). Biofilm formation was assessed through biomass, cell count, metabolic activity, and extracellular DNA (eDNA). The minimum bactericidal concentration (MBC90) and biofilm eradication concentration (MBEC90) were also determined.

RESULTS: WGS revealed significant genomic diversity, identifying ten distinct sequence types (STs). Antibiotic susceptibility testing (AST) showed that 10/11 strains were susceptible to cefiderocol, with one isolate (MPA9) displaying resistance linked to the blaOXA486 gene. Adding the β-lactamase inhibitor avibactam (AVI) restored susceptibility in this resistant strain. Although iron metabolism genes were highly conserved across isolates, MPA9 lacked the fpvA iron receptor, potentially contributing to cefiderocol resistance. Biofilm formation significantly increased tolerance to cefiderocol, with an 8-fold rise in MBEC90 compared to MBC90.

CONCLUSION: These findings highlight the genomic diversity and adaptive potential of P. aeruginosa in pwCF. Cefiderocol shows promise against planktonic and biofilm-associated P. aeruginosa, and combining it with AVI may counteract β-lactamase-mediated resistance.

PMID:40306463 | DOI:10.1016/j.jgar.2025.04.010

Endocr Pract. 2025 Apr 28:S1530-891X(25)00129-6. doi: 10.1016/j.eprac.2025.04.011. Online ahead of print.

ABSTRACT

Cystic fibrosis (CF) is a multisystem autosomal recessive disease arising from mutations in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. Dysfunction of the CFTR protein leads to progressive pulmonary disease, pancreatic exocrine insufficiency, and nutritional deficiencies. Survival has significantly increased over the last several decades due to improved pulmonary and nutritional management, including CFTR modulator therapy. The adult CF population now faces new challenges of aging, such as menopause-related symptoms and age-related osteoporosis superimposed on underlying CF-related bone disease. The menopausal transition and early post-menopause are characterized by rapid bone loss and represent a window of opportunity to preserve bone mass. Menopausal hormone therapy may alleviate vasomotor symptoms and improve bone density in appropriately selected people. This review will discuss the current knowledge of menopause and bone health in females with CF, address CF-specific considerations on osteoporosis and menopause treatment options, and explore opportunities for future areas of research.

PMID:40306365 | DOI:10.1016/j.eprac.2025.04.011