Curr Opin Oncol. 2025 May 1;37(3):250-258. doi: 10.1097/CCO.0000000000001141. Epub 2025 Mar 19.

ABSTRACT

PURPOSE OF REVIEW: Rare cancers of the genitourinary (GU) tract are often clinically aggressive yet have few or no standard-of-care treatments. Multiple antibody-drug conjugates (ADCs) have been approved in solid malignancies. This review explores the use of ADCs in rare GU tumors in the context of biological pathways and ongoing research in solid tumors.

RECENT FINDINGS: Few clinical trials of ADCs focus on recruiting participants with rare tumors of the GU tract, including trials testing enfortumab vedotin as monotherapy or combined with pembrolizumab, and sacituzumab govitecan as monotherapy or combined with atezolizumab. We highlight many ongoing trials of novel ADCs for advanced/metastatic solid tumors and emphasize the potential eligibility of patients with rare GU tumors for tumor-agnostic trials.

SUMMARY: ADCs are being tested in multiple solid tumors, including rare GU tumors. Ongoing preclinical research supports the use of some ADCs in several rare GU tumors and improves our understanding of their pathophysiology.

PMID:40110990 | DOI:10.1097/CCO.0000000000001141

Med Image Anal. 2025 Mar 13;102:103539. doi: 10.1016/j.media.2025.103539. Online ahead of print.

ABSTRACT

Pancreatic cancer is a lethal invasive tumor with one of the worst prognosis. Accurate and reliable segmentation for pancreas and pancreatic cancer on computerized tomography (CT) images is vital in clinical diagnosis and treatment. Although certain deep learning-based techniques have been tentatively applied to this task, current performance of pancreatic cancer segmentation is far from meeting the clinical needs due to the tiny size, irregular shape and extremely uncertain boundary of the cancer. Besides, most of the existing studies are established on the black-box models which only learn the annotation distribution instead of the logical thinking and diagnostic experience of high-level medical experts, the latter is more credible and interpretable. To alleviate the above issues, we propose a novel Segment-Like-A-Doctor (SLAD) framework to learn the reliable clinical thinking and experience for pancreas and pancreatic cancer segmentation on CT images. Specifically, SLAD aims to simulate the essential logical thinking and experience of doctors in the progressive diagnostic stages of pancreatic cancer: organ, lesion and boundary stage. Firstly, in the organ stage, an Anatomy-aware Masked AutoEncoder (AMAE) is introduced to model the doctors' overall cognition for the anatomical distribution of abdominal organs on CT images by self-supervised pretraining. Secondly, in the lesion stage, a Causality-driven Graph Reasoning Module (CGRM) is designed to learn the global judgment of doctors for lesion detection by exploring topological feature difference between the causal lesion and the non-causal organ. Finally, in the boundary stage, a Diffusion-based Discrepancy Calibration Module (DDCM) is developed to fit the refined understanding of doctors for uncertain boundary of pancreatic cancer by inferring the ambiguous segmentation discrepancy based on the trustworthy lesion core. Experimental results on three independent datasets demonstrate that our approach boosts pancreatic cancer segmentation accuracy by 4%-9% compared with the state-of-the-art methods. Additionally, the tumor-vascular involvement analysis is also conducted to verify the superiority of our method in clinical applications. Our source codes will be publicly available at https://github.com/ZouLiwen-1999/SLAD.

PMID:40112510 | DOI:10.1016/j.media.2025.103539

Gene. 2025 Mar 18:149421. doi: 10.1016/j.gene.2025.149421. Online ahead of print.

ABSTRACT

BACKGROUND: Pharmacogenomics is used to identify genetic factors that influence drug responses, thereby optimizing therapeutic outcomes and reducing adverse effects. The objective of this study is to identify pharmacogenomic variations and their clinical relevance to drug metabolism and toxicity within the Pumi population.

METHODS: Eighty-two genetic variants in 43 genes were genotyped in 200 unrelated Pumi individuals using the Agena MassARRAY Assay. Chi-square tests, adjusted for multiple comparisons with Bonferroni correction, were used to compare genotype frequency divergences between the Pumi population and 26 other populations. Population genetic structure diversity and pairwise F-statistics (Fst) were assessed across 27 populations using Structure v2.3.1 and Arlequin v3.5 software.

RESULTS: After Bonferroni correction, a number of single nucleotide variations (SNVs) exhibited significant differences in frequency between the Pumi population and other populations. The allele frequencies of ADH1A rs975833, ADH1B rs1229984, TPMT rs1142345, and CYP2A6 rs8192726 in the Pumi population were notably different from the East Asian population or the other 26 populations. PharmGKB data indicate that rs1229984, rs1142345, and rs8192726 are associated with the metabolic efficiency of acetaldehyde, mercaptopurine, and efavirenz, respectively. Additionally, the genetic structure analysis (K = 5) and pairwise Fst calculations revealed that the Pumi population shared a similar genetic background with CHB (Fst = 0.031), JPT (Fst = 0.033), KHV (Fst = 0.035), CHS (Fst = 0.036), and CDX (Fst = 0.037) populations.

CONCLUSION: Our findings reveal unique genetic variations and biomarkers within the Pumi population, which contributes pharmacogenomic insights and theoretical foundations for personalized medicine tailored to the Pumi population.

PMID:40113188 | DOI:10.1016/j.gene.2025.149421

Arch Bronconeumol. 2025 Mar 1:S0300-2896(25)00071-7. doi: 10.1016/j.arbres.2025.02.010. Online ahead of print.

ABSTRACT

BACKGROUND: Cystic fibrosis causes exercise limitation due to impaired lung function and other complications, which in turn increases the chance of mortality. CFTR modulators, particularly the elexacaftor/tezacaftor/ivacaftor (ETI) combination, improve lung function in children older than 6 years in real-life studies.

OBJECTIVE: This study aimed to assess the impact of ETI on aerobic capacity in children with CF aged 6-11 years under real-life conditions and to evaluate whether prior CFTR modulator treatment affects these outcomes.

METHODS: A multicenter, prospective cohort study was conducted with pediatric CF patients. Participants underwent evaluations 6-8 months before ETI (T1), at the start of ETI (T2), and 6-8 months post-treatment (T3). Primary outcomes included cardiorespiratory fitness assessed via peak oxygen consumption (VO2peak) during a cardiopulmonary exercise test (CPET), and secondary outcomes encompassed lung function, quality of life, physical activity, and functional mobility.

RESULTS: Of the 28 patients (mean age 9.02±1.59 years), 19 were ETI-naive, and 9 had prior CFTR modulator treatment. Significant improvements were observed in FEV1 (p<0.001), and several functional mobility tests (30CST, Stair Climb Test, 10MWT). However, VO2peak showed no significant changes between T1 and T3. Quality of life scores improved notably in emotional, eating, and respiratory domains, and a slight improvement was noted in physical activity levels (p=0.037).

CONCLUSIONS: ETI treatment significantly enhances lung function and certain aspects of quality of life and physical fitness in pediatric CF patients. However, it does not significantly alter aerobic capacity (VO2peak) within the observed period.

PMID:40113488 | DOI:10.1016/j.arbres.2025.02.010

Diagn Interv Imaging. 2025 Mar 19:S2211-5684(25)00048-8. doi: 10.1016/j.diii.2025.03.004. Online ahead of print.

ABSTRACT

PURPOSE: The purpose of this study was to validate a deep learning algorithm that generates T2*-weighted images from diffusion-weighted (DW) images and to compare its performance with that of true T2*-weighted images for hemorrhage detection on MRI in patients with acute stroke.

MATERIALS AND METHODS: This single-center, retrospective study included DW- and T2*-weighted images obtained less than 48 hours after symptom onset in consecutive patients admitted for acute stroke. Datasets were divided into training (60 %), validation (20 %), and test (20 %) sets, with stratification by stroke type (hemorrhagic/ischemic). A generative adversarial network was trained to produce generative T2*-weighted images using DW images. Concordance between true T2*-weighted images and generative T2*-weighted images for hemorrhage detection was independently graded by two readers into three categories (parenchymal hematoma, hemorrhagic infarct or no hemorrhage), and discordances were resolved by consensus reading. Sensitivity, specificity and accuracy of generative T2*-weighted images were estimated using true T2*-weighted images as the standard of reference.

RESULTS: A total of 1491 MRI sets from 939 patients (487 women, 452 men) with a median age of 71 years (first quartile, 57; third quartile, 81; range: 21-101) were included. In the test set (n = 300), there were no differences between true T2*-weighted images and generative T2*-weighted images for intraobserver reproducibility (κ = 0.97 [95 % CI: 0.95-0.99] vs. 0.95 [95 % CI: 0.92-0.97]; P = 0.27) and interobserver reproducibility (κ = 0.93 [95 % CI: 0.90-0.97] vs. 0.92 [95 % CI: 0.88-0.96]; P = 0.64). After consensus reading, concordance between true T2*-weighted images and generative T2*-weighted images was excellent (κ = 0.92; 95 % CI: 0.91-0.96). Generative T2*-weighted images achieved 90 % sensitivity (73/81; 95 % CI: 81-96), 97 % specificity (213/219; 95 % CI: 94-99) and 95 % accuracy (286/300; 95 % CI: 92-97) for the diagnosis of any cerebral hemorrhage (hemorrhagic infarct or parenchymal hemorrhage).

CONCLUSION: Generative T2*-weighted images and true T2*-weighted images have non-different diagnostic performances for hemorrhage detection in patients with acute stroke and may be used to shorten MRI protocols.

PMID:40113490 | DOI:10.1016/j.diii.2025.03.004

J Orthop Res. 2025 Mar 20. doi: 10.1002/jor.26071. Online ahead of print.

ABSTRACT

Articular cartilage, essential for smooth joint movement, can sustain micrometer-scale microcracks in its collagen network from low-energy impacts previously considered non-injurious. These microcracks may propagate under cyclic loading, impairing cartilage function and potentially initiating osteoarthritis (OA). Detecting and analyzing microcracks is crucial for understanding early cartilage damage but traditionally relies on manual analyses of second harmonic generation (SHG) images, which are labor-intensive, limit scalability, and delay insights. To address these challenges, we established and validated a YOLOv8-based deep learning model to automate the detection, segmentation, and quantification of cartilage microcracks from SHG images. Data augmentation during training improved model robustness, while evaluation metrics, including precision, recall, and F1-score, confirmed high accuracy and reliability, achieving a true positive rate of 95%. Our model consistently outperformed human annotators, demonstrating superior accuracy, repeatability, all while reducing labor demands. Error analyses indicated precise predictions for microcrack length and width, with moderate variability in estimations of orientation. Our results demonstrate the transformative potential of deep learning in cartilage research, enabling large-scale studies, accelerating analyses, and providing insights into soft tissue damage and engineered material mechanics. Expanding our data set to include diverse anatomical regions and disease stages will further enhance performance and generalization of our YOLOv8-based model. By automating microcrack detection, this study advances understanding of microdamage in cartilage and potential mechanisms of progression of OA. Our publicly available model and data set empower researchers to develop personalized therapies and preventive strategies, ultimately advancing joint health and preserving quality of life.

PMID:40113341 | DOI:10.1002/jor.26071

Environ Pollut. 2025 Mar 18:126083. doi: 10.1016/j.envpol.2025.126083. Online ahead of print.

ABSTRACT

The rapid and accurate identification of antibiotics in environmental samples is critical for addressing the growing concern of antibiotic pollution, particularly in water sources. Antibiotic contamination poses a significant risk to ecosystems and human health by contributing to the spread of antibiotic resistance. SERS, known for its high sensitivity and specificity, is a powerful tool for antibiotic identification. However, its broader application is constrained by the lack of a large-scale antibiotic spectral database crucial for environmental and clinical use. To address this need, we systematically collected 12,800 SERS spectra for 200 environmentally relevant antibiotics and developed an open-access, web-based database at http://sers.test.bniu.net/. We compared six machine learning algorithms with a CNN model, which achieved the highest accuracy at 98.94%, making it the preferred database model. For external validation, CNN demonstrated an accuracy of 82.8%, underscoring its reliability and practicality for real-world applications. The SERS database and CNN prediction model represent a novel resource for environmental monitoring, offering significant advantages in terms of accessibility, speed, and scalability. This study establishes the large-scale, public SERS spectral databases for antibiotics, facilitating the integration of SERS into environmental programs, with the potential to improve antibiotic detection, pollution management, and resistance mitigation.

PMID:40113206 | DOI:10.1016/j.envpol.2025.126083

Cell Syst. 2025 Mar 19;16(3):101229. doi: 10.1016/j.cels.2025.101229.

ABSTRACT

The three-dimensional (3D) morphology of cells emerges from complex cellular and environmental interactions, serving as an indicator of cell state and function. In this study, we used deep learning to discover morphology representations and understand cell states. This study introduced MorphoMIL, a computational pipeline combining geometric deep learning and attention-based multiple-instance learning to profile 3D cell and nuclear shapes. We used 3D point-cloud input and captured morphological signatures at single-cell and population levels, accounting for phenotypic heterogeneity. We applied these methods to over 95,000 melanoma cells treated with clinically relevant and cytoskeleton-modulating chemical and genetic perturbations. The pipeline accurately predicted drug perturbations and cell states. Our framework revealed subtle morphological changes associated with perturbations, key shapes correlating with signaling activity, and interpretable insights into cell-state heterogeneity. MorphoMIL demonstrated superior performance and generalized across diverse datasets, paving the way for scalable, high-throughput morphological profiling in drug discovery. A record of this paper's transparent peer review process is included in the supplemental information.

PMID:40112779 | DOI:10.1016/j.cels.2025.101229

Cell Syst. 2025 Mar 19;16(3):101242. doi: 10.1016/j.cels.2025.101242.

ABSTRACT

One snapshot of the peer review process for "Geometric deep learning and multiple instance learning for 3D cell shape profiling" (De Vries et al., 2025).1.

PMID:40112776 | DOI:10.1016/j.cels.2025.101242

EBioMedicine. 2025 Mar 19;114:105657. doi: 10.1016/j.ebiom.2025.105657. Online ahead of print.

ABSTRACT

BACKGROUND: Heart failure (HF) is a complex syndrome with varied presentations and progression patterns. Traditional classification systems based on left ventricular ejection fraction (LVEF) have limitations in capturing the heterogeneity of HF. We aimed to explore the application of deep learning, specifically a Transformer-based approach, to analyse electronic health records (EHR) for a refined subtyping of patients with HF.

METHODS: We utilised linked EHR from primary and secondary care, sourced from the Clinical Practice Research Datalink (CPRD) Aurum, which encompassed health data of over 30 million individuals in the UK. Individuals aged 35 and above with incident reports of HF between January 1, 2005, and January 1, 2018, were included. We proposed a Transformer-based approach to cluster patients based on all clinical diagnoses, procedures, and medication records in EHR. Statistical machine learning (ML) methods were used for comparative benchmarking. The models were trained on a derivation cohort and assessed for their ability to delineate distinct clusters and prognostic value by comparing one-year all-cause mortality and HF hospitalisation rates among the identified subgroups in a separate validation cohort. Association analyses were conducted to elucidate the clinical characteristics of the derived clusters.

FINDINGS: A total of 379,108 patients were included in the HF subtyping analysis. The Transformer-based approach outperformed alternative methods, delineating more distinct and prognostically valuable clusters. This approach identified seven unique HF patient clusters characterised by differing patterns of mortality, hospitalisation, and comorbidities. These clusters were labelled based on the dominant clinical features present at the initial diagnosis of HF: early-onset, hypertension, ischaemic heart disease, metabolic problems, chronic obstructive pulmonary disease (COPD), thyroid dysfunction, and late-onset clusters. The Transformer-based subtyping approach successfully captured the multifaceted nature of HF.

INTERPRETATION: This study identified seven distinct subtypes, including COPD-related and thyroid dysfunction-related subgroups, which are two high-risk subgroups not recognised in previous subtyping analyses. These insights lay the groundwork for further investigations into tailored and effective management strategies for HF.

FUNDING: British Heart Foundation, European Union - Horizon Europe, and Novo Nordisk Research Centre Oxford.

PMID:40112740 | DOI:10.1016/j.ebiom.2025.105657

Food Chem. 2025 Mar 15;480:143886. doi: 10.1016/j.foodchem.2025.143886. Online ahead of print.

ABSTRACT

In this study, an innovative intelligent system for supervising the quality of fresh produce was proposed, which combined 3D printing technology and deep convolutional neural networks (DCNN). Through 3D printing technology, sensitive, lightweight, and customizable dual-color CO2 monitoring labels were fabricated using bromothymol blue and methyl red as indicators. These labels were applied to sensitively monitor changes in CO2 levels during the storage of vegetables such as green vegetables, cucumbers, okras, plums, and jujubes. The ΔE of the labels was found to have a significant positive correlation with CO2 levels and weight loss rate, while showing a strong inverse relationship with hardness, indirectly reflecting the freshness of the produce. In addition, four lightweight DCNN models (GhostNet, MobileNetv2, ShuffleNet, and Xception) were applied to recognize label images from different storage days, with MobileNetv2 achieving the best performance. The classification accuracy for three freshness levels of okra was 96.06 %, 91.12 %, and 93.86 %, respectively. A mobile application was developed based on this model, which demonstrated excellent performance in recognizing labels at different storage stages, making it suitable for practical applications and effectively distinguishing freshness levels. By combining the novel labels with advanced DCNN models, the accuracy and real-time capabilities of food monitoring can be significantly improved.

PMID:40112721 | DOI:10.1016/j.foodchem.2025.143886

Comput Methods Programs Biomed. 2025 Mar 15;264:108726. doi: 10.1016/j.cmpb.2025.108726. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: Single-cell imaging plays a key role in various fields, including drug development, disease diagnosis, and personalized medicine. To obtain multi-modal information from a single-cell image, especially for label-free cells, this study develops modal expansion cytometry for label-free single-cell analysis.

METHODS: The study utilizes a deep learning-based architecture to expand single-mode light scattering images into multi-modality images, including bright-field (non-fluorescent) and fluorescence images, for label-free single-cell analysis. By combining adversarial loss, L1 distance loss, and VGG perceptual loss, a new network optimization method is proposed. The effectiveness of this method is verified by experiments on simulated images, standard spheres of different sizes, and multiple cell types (such as cervical cancer and leukemia cells). Additionally, the capability of this method in single-cell analysis is assessed through multi-modal cell classification experiments, such as cervical cancer subtypes.

RESULTS: This is demonstrated by using both cervical cancer cells and leukemia cells. The expanded bright-field and fluorescence images derived from the light scattering images align closely with those obtained through conventional microscopy, showing a contour ratio near 1 for both the whole cell and its nucleus. Using machine learning, the subtyping of cervical cancer cells achieved 92.85 % accuracy with the modal expansion images, which represents an improvement of nearly 20 % over single-mode light scattering images.

CONCLUSIONS: This study demonstrates the light scattering imaging modal expansion cytometry with deep learning has the capability to expand the single-mode light scattering image into the artificial multimodal images of label-free single cells, which not only provides the visualization of cells but also helps for the cell classification, showing great potential in the field of single-cell analysis such as cancer cell diagnosis.

PMID:40112688 | DOI:10.1016/j.cmpb.2025.108726

Comput Methods Programs Biomed. 2025 Mar 15;264:108722. doi: 10.1016/j.cmpb.2025.108722. Online ahead of print.

ABSTRACT

BACKGROUND: Atrial fibrillation (AF) is the most common cardiac arrhythmia, affecting up to 2 % of the population. Catheter ablation is a promising treatment for AF, particularly for paroxysmal AF patients, but it often has high recurrence rates. Developing in silico models of patients' atria during the ablation procedure using cardiac MRI data may help reduce these rates.

OBJECTIVE: This study aims to develop an effective automated deep learning-based segmentation pipeline by compiling a specialized dataset and employing standardized labeling protocols to improve segmentation accuracy and efficiency. In doing so, we aim to achieve the highest possible accuracy and generalization ability while minimizing the burden on clinicians involved in manual data segmentation.

METHODS: We collected LGE-MRI data from VMRC and the cDEMRIS database. Two specialists manually labeled the data using standardized protocols to reduce subjective errors. Neural network (nnU-Net and smpU-Net++) performance was evaluated using statistical tests, including sensitivity and specificity analysis. A new database of LGE-MRI images, based on manual segmentation, was created (VMRC).

RESULTS: Our approach with consistent labeling protocols achieved a Dice coefficient of 92.4 % ± 0.8 % for the cavity and 64.5 % ± 1.9 % for LA walls. Using the pre-trained RIFE model, we attained a Dice score of approximately 89.1 % ± 1.6 % for atrial LGE-MRI imputation, outperforming classical methods. Sensitivity and specificity values demonstrated substantial enhancement in the performance of neural networks trained with the new protocol.

CONCLUSION: Standardized labeling and RIFE applications significantly improved machine learning tool efficiency for constructing 3D LA models. This novel approach supports integrating state-of-the-art machine learning methods into broader in silico pipelines for predicting ablation outcomes in AF patients.

PMID:40112687 | DOI:10.1016/j.cmpb.2025.108722

Neural Netw. 2025 Mar 13;187:107368. doi: 10.1016/j.neunet.2025.107368. Online ahead of print.

ABSTRACT

In this paper, we propose a novel neural architecture search (NAS) method called MAPOCNN, which leverages an enhanced version of the Artificial Protozoa Optimizer (APO) to optimize the architecture of Convolutional Neural Networks (CNNs). The APO is known for its rapid convergence, high stability, and minimal parameter involvement. To further improve its performance, we introduce MAPO (Modified Artificial Protozoa Optimizer), which incorporates the phototaxis behavior of protozoa. This addition helps mitigate the risk of premature convergence, allowing the algorithm to explore a broader range of possible CNN architectures and ultimately identify more optimal solutions. Through rigorous experimentation on benchmark datasets, including Rectangle and Mnist-random, we demonstrate that MAPOCNN not only achieves faster convergence times but also performs competitively when compared to other state-of-the-art NAS algorithms. The results highlight the effectiveness of MAPOCNN in efficiently discovering CNN architectures that outperform existing methods in terms of both speed and accuracy. This work presents a promising direction for optimizing deep learning architectures using biologically inspired optimization techniques.

PMID:40112636 | DOI:10.1016/j.neunet.2025.107368

Thorax. 2025 Mar 20:thorax-2023-221063. doi: 10.1136/thorax-2023-221063. Online ahead of print.

ABSTRACT

BACKGROUND: Long-term oxygen therapy (LTOT) improves survival in patients with chronic severe resting hypoxaemia, but effects on hospitalisation are unknown. This study evaluated the potential impact of starting LTOT on acute exacerbation and hospital burden in patients with chronic obstructive pulmonary disease (COPD), interstitial lung disease (ILD) and pulmonary hypertension (PH).

METHODS: Longitudinal analysis of consecutive patients in the population-based Swedish DISCOVERY cohort who started LTOT between 2000 and 2018 with a follow-up duration≥3 months. Total and hospitalised acute exacerbations of the underlying disease, all-cause hospitalisations, and all-cause outpatient visits were annualised and compared between the year before and after LTOT initiation for each disease cohort, and by hypercapnic status in patients with COPD.

RESULTS: Patients with COPD (n=10 134) had significant reduction in annualised rates of total and hospitalised acute exacerbations, as well as all-cause hospitalisations, following LTOT initiation, with increment in those with ILD (n=2507) and PH (n=850). All-cause outpatient visits increased across all cohorts following LTOT initiation. Similar findings were observed in patients with hypercapnic and non-hypercapnic COPD. Sensitivity analyses of patients with 12 months of follow-up showed reduced acute exacerbations and all-cause hospitalisations in the ILD and PH cohorts.

CONCLUSION: LTOT is associated with reduced rates of both total and hospitalised acute exacerbations and all-cause hospitalisations in patients with COPD, as well as patients with ILD and PH with 12 months of follow-up. There is increased all-cause outpatient visits in all disease groups following LTOT initiation.

PMID:40113248 | DOI:10.1136/thorax-2023-221063

Bioorg Chem. 2025 Mar 11;159:108360. doi: 10.1016/j.bioorg.2025.108360. Online ahead of print.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a progressive, irreversible, and fatal pulmonary disease. Owing to its complex pathogenesis and lack of effective treatment, patients have a short survival time after diagnosis. Although pirfenidone and nintedanib can mitigate declines in lung function, neither has stopped the progression of IPF nor significantly improved long-term survival in patients. HDAC6 inhibitors have been reported to inhibit TGF-β1-induced collagen expression to protect mice from pulmonary fibrosis, and this pharmacological mechanism has been supported by immunohistochemical studies of HDAC6 overexpression in IPF lung tissue. In this study, a series of novel derivatives were obtained based on the reported active compounds through the ring closure strategy in scaffold hopping theory. Compound W28 was selected from in vitro screening for better HDAC6 selectivity, and it was used for in-depth pharmacokinetic and pharmacodynamic studies. Detailed molecular docking studies, molecular dynamics (MD) simulations and the structure-activity relationship (SAR) discussion will contribute to guiding the design of new molecules. In further studies, the ability of W28 to inhibit the IPF phenotype was confirmed, and the corresponding pharmacological mechanism was also demonstrated. Moreover, the pharmacokinetic characteristics of W28 were also tested to guide pharmacodynamic studies in vivo, and the therapeutic effect of W28 on bleomycin-induced pulmonary fibrosis in mice was found to be satisfactory. The results reported in this paper may provide a reference for promoting the discovery of new selective HDAC6 inhibitors as drug molecules for the treatment of IPF.

PMID:40112668 | DOI:10.1016/j.bioorg.2025.108360

FEMS Microbiol Ecol. 2025 Mar 20:fiaf028. doi: 10.1093/femsec/fiaf028. Online ahead of print.

ABSTRACT

Environmental factors like diet and antibiotics modulate the gut microbiota in early life. During weaning, gut microbiota progressively diversifies through exposure to non-digestible carbohydrates (NDCs) from diet, while antibiotic perturbations might disrupt this process. Supplementing an infant's diet with prebiotic NDCs may mitigate the adverse effects of antibiotics on gut microbiota development. This study evaluated the influence of supplementation with 2-fucosyllactose (2'-FL), galacto-oligosaccharides (GOS), or isomalto/malto-polysaccharides containing 87% of α(1→6) linkages (IMMP-87), on the recovery of antibiotic-perturbed microbiota. The TIM-2 in vitro colon model inoculated with fecal microbiota of nine-month-old infants was used to simulate the colon of weaning infants exposed to the antibiotics amoxicillin/clavulanate or azithromycin. Both antibiotics induced changes in microbiota composition, with no signs of recovery in azithromycin-treated microbiota within 72 h. Moreover, antibiotic exposure affected microbiota activity, indicated by a low valerate production, and azithromycin treatment was associated with increased succinate production. The IMMP-87 supplementation promoted the compositional recovery of amoxicillin/clavulanate-perturbed microbiota, associated with the recovery of Ruminococcus, Ruminococcus gauvreauii group, and Holdemanella. NDC supplementation did not influence compositional recovery of azithromycin-treated microbiota. Irrespective of antibiotic exposure, supplementation with 2'-FL, GOS, or IMMP-87 enhanced microbiota activity by increasing short-chain fatty acids production (acetate, propionate, and butyrate).

PMID:40113239 | DOI:10.1093/femsec/fiaf028

Lett Appl Microbiol. 2025 Mar 20:ovaf043. doi: 10.1093/lambio/ovaf043. Online ahead of print.

ABSTRACT

Sleep is an essential homeostatic process that undergoes dynamic changes throughout the lifespan, with distinct life stages predisposed to specific sleep pathologies. Similarly, the gut microbiome also varies with age, with different signatures associated with health and disease in the latest decades of life. Emerging research has shown significant cross-talk between the gut microbiota and the brain through several pathways, suggesting the microbiota may influence sleep, though the specific mechanisms remain to be elucidated. Here, we critically examine the existing literature on the potential impacts of the gut microbiome on sleep and how this relationship varies across adulthood. We suggest that age-related shifts in gut microbiome composition and immune function may, in part, drive age-related changes in sleep. We conclude with an outlook on the therapeutic potential of microbiome-targeted interventions aimed at improving sleep across adulthood, particularly for individuals experiencing high stress or with sleep complaints.

PMID:40113228 | DOI:10.1093/lambio/ovaf043

Plant Physiol. 2025 Mar 21:kiaf112. doi: 10.1093/plphys/kiaf112. Online ahead of print.

ABSTRACT

Late blight caused by Phytophthora infestans is a major threat to global potato and tomato production. Sustainable management of late blight requires the development of resistant crop cultivars. This process can be facilitated by high-throughput identification of functional genes involved in late blight pathogenesis. In this study, we generated a high-quality transcriptomic time-course dataset focusing on the initial twenty-four hours of contact between P. infestans and three solanaceous plant species, tobacco(Nicotiana benthamiana), tomato (Solanum lycopersicum), and potato (Solanum tuberosum). Our results demonstrate species-specific transcriptional regulation in early stages of the infection. Transient silencing of putative RIBOSE-5-PHOSPHATE ISOMERASE and HMG-CoA REDUCTASE genes in N. benthamiana lowered plant resistance against P. infestans. Furthermore, heterologous expression of a putative tomato Golgi-localized nucleosugar transporter-encoding gene exacerbated P. infestans infection of N. benthamiana. In comparison, bioassays using transgenic tomato lines showed that the quantitative disease resistance genes were required but insufficient for late blight resistance; genetic knock-out of the susceptibility gene enhanced resistance. The same RNA-seq dataset was exploited to examine the transcriptional landscape of P. infestans and revealed host-specific gene expression patterns in the pathogen. This temporal transcriptomic diversity, in combination with genomic distribution features, identified the P. infestans IPI-B family GLYCINE-RICH PROTEINs as putative virulence factors that promoted disease severity or induced plant tissue necrosis when transiently expressed in N. benthamiana. These functional genes underline the effectiveness of functional gene-mining through a time-course dual RNA-seq approach and provide insight into the molecular interactions between solanaceous plants and P. infestans.

PMID:40112880 | DOI:10.1093/plphys/kiaf112

Cell. 2025 Mar 18:S0092-8674(25)00202-8. doi: 10.1016/j.cell.2025.02.017. Online ahead of print.

ABSTRACT

The relationships of human diversity with biomedical phenotypes are pervasive yet remain understudied, particularly in a single-cell genomics context. Here, we present the Asian Immune Diversity Atlas (AIDA), a multi-national single-cell RNA sequencing (scRNA-seq) healthy reference atlas of human immune cells. AIDA comprises 1,265,624 circulating immune cells from 619 donors, spanning 7 population groups across 5 Asian countries, and 6 controls. Though population groups are frequently compared at the continental level, we found that sub-continental diversity, age, and sex pervasively impacted cellular and molecular properties of immune cells. These included differential abundance of cell neighborhoods as well as cell populations and genes relevant to disease risk, pathogenesis, and diagnostics. We discovered functional genetic variants influencing cell-type-specific gene expression, which were under-represented in non-Asian populations, and helped contextualize disease-associated variants. AIDA enables analyses of multi-ancestry disease datasets and facilitates the development of precision medicine efforts in Asia and beyond.

PMID:40112801 | DOI:10.1016/j.cell.2025.02.017