Nat Methods. 2025 May 29. doi: 10.1038/s41592-025-02707-1. Online ahead of print.

ABSTRACT

Artificial intelligence has revolutionized computational biology. Recent developments in omics technologies, including single-cell RNA sequencing and spatial transcriptomics, provide detailed genomic data alongside tissue histology. However, current computational models focus on either omics or image analysis, lacking their integration. To address this, we developed OmiCLIP, a visual-omics foundation model linking hematoxylin and eosin images and transcriptomics using tissue patches from Visium data. We transformed transcriptomic data into 'sentences' by concatenating top-expressed gene symbols from each patch. We curated a dataset of 2.2 million paired tissue images and transcriptomic data across 32 organs to train OmiCLIP integrating histology and transcriptomics. Building on OmiCLIP, our Loki platform offers five key functions: tissue alignment, annotation via bulk RNA sequencing or marker genes, cell-type decomposition, image-transcriptomics retrieval and spatial transcriptomics gene expression prediction from hematoxylin and eosin-stained images. Compared with 22 state-of-the-art models on 5 simulations, and 19 public and 4 in-house experimental datasets, Loki demonstrated consistent accuracy and robustness.

PMID:40442373 | DOI:10.1038/s41592-025-02707-1

Sci Bull (Beijing). 2025 May 12:S2095-9273(25)00519-5. doi: 10.1016/j.scib.2025.05.016. Online ahead of print.

NO ABSTRACT

PMID:40441974 | DOI:10.1016/j.scib.2025.05.016

Nature. 2025 May 29. doi: 10.1038/s41586-025-09183-9. Online ahead of print.

ABSTRACT

Modelling liver disease requires in vitro systems that replicate disease progression1,2. Current tissue-derived organoids fail to reproduce the complex cellular composition and tissue architecture observed in vivo3. Here, we describe a multicellular organoid system composed of adult hepatocytes, cholangiocytes and mesenchymal cells that recapitulates the architecture of the liver periportal region and, when manipulated, models aspects of cholestatic injury and biliary fibrosis. We first generate reproducible hepatocyte organoids with functional bile canaliculi network that retain morphological features of in vivo tissue. By combining these with cholangiocytes and portal fibroblasts, we generate assembloids that mimic the cellular interactions of the periportal region. Assembloids are functional, consistently draining bile from bile canaliculi into the bile duct. Strikingly, manipulating the relative number of portal mesenchymal cells is sufficient to induce a fibrotic-like state, independently of an immune compartment. By generating chimeric assembloids of mutant and wild-type cells, or after gene knockdown, we show proof-of-concept that our system is amenable to investigating gene function and cell-autonomous mechanisms. Taken together, we demonstrate that liver assembloids represent a suitable in vitro system to study bile canaliculi formation, bile drainage, and how different cell types contribute to cholestatic disease and biliary fibrosis, in an all-in-one model.

PMID:40441268 | DOI:10.1038/s41586-025-09183-9

Mol Cell. 2025 May 21:S1097-2765(25)00412-5. doi: 10.1016/j.molcel.2025.05.007. Online ahead of print.

ABSTRACT

Coordination of adaptive metabolism through signaling networks is essential for cellular bioenergetics and homeostasis. Phosphorylation of metabolic enzymes provides a rapid, efficient, and dynamic mechanism to regulate metabolic networks. Our structural analysis stratified phosphosites on metabolic enzymes based on proximity to functional and dimerization domains. Most phosphosites occur on oxidoreductases and are enriched near substrate, cofactor, active sites, or dimer interfaces. Despite low stoichiometry, phosphotyrosine (pY) is overrepresented in functional domains. Using high-fat diet (HFD)-induced obesity in C57BL/6J mice and multiomics, we measured HFD-induced sex-specific dysregulation of pY and metabolites, which was reversible with the antioxidant butylated hydroxyanisole (BHA). Computational modeling revealed predictive pY sites for HFD- or BHA-induced metabolite changes. We characterized functional roles for predictive pY sites on glutathione S-transferase pi 1 (GSTP1), isocitrate dehydrogenase 1 (IDH1), and uridine monophosphate synthase (UMPS) using CRISPR interference (CRISPRi) rescue and stable isotope tracing. Our findings reveal mechanisms whereby cellular signaling fine-tunes enzyme activity and metabolism.

PMID:40441152 | DOI:10.1016/j.molcel.2025.05.007

PLoS One. 2025 May 29;20(5):e0323043. doi: 10.1371/journal.pone.0323043. eCollection 2025.

ABSTRACT

OBJECTIVE: Isolated sulfite oxidase deficiency (ISOD; OMIM #272300) is a devastating rare neurometabolic disorder due to biallelic pathogenic variants in the SUOX gene, that typically results in neonatal refractory epilepsy and progressive severe encephalopathy. Knowledge on the quantitative natural history of ISOD is limited and clinical outcome parameters for future clinical trials remain to be defined.

MATERIAL AND METHODS: We performed a comprehensive analysis of published cases (N=74) with ISOD applying quantitative retrospective natural history modeling (QUARNAM). Main outcome parameters were age of disease onset, diagnostic delay and survival. Clinical characteristics and potential associations between biochemical parameters and clinical outcome (i.e. age of disease onset, survival) were explored.

RESULTS: The median survival period of the study cohort was 60 months. ISOD typically presented shortly after birth with a median age of onset of 3 days. Median age at diagnosis was 10 months, leading to a substantial median diagnostic delay of 5.7 months. Homocysteine concentrations in plasma correlated with age of disease onset. An association of biochemical parameters of cysteine metabolism and survival could not be identified.

CONCLUSION: The present analysis describes long-term outcome measures adding to the quantitative understanding of the natural history of ISOD, which might be helpful in the planning of prospective clinical trials and potentially stimulate development of targeted therapies in the future.

PMID:40440599 | DOI:10.1371/journal.pone.0323043

Res Vet Sci. 2025 May 26;192:105720. doi: 10.1016/j.rvsc.2025.105720. Online ahead of print.

ABSTRACT

Several bacteria associated with chronic lung pathology use quorum sensing (QS) signaling molecules to regulate their virulence in pure cultures and poly-microbial communities. Their excessive growth and biofilm formation in the respiratory tract increase the morbidity and mortality of inflammatory airway diseases in humans, such as chronic obstructive pulmonary disease (COPD), asthma and cystic fibrosis (CF). In horses, severe equine asthma (SEA) has many parallels to these human diseases. We hypothesized that QS molecules associated with the most common biofilm-forming lung pathogens in humans (Pseudomonas aeruginosa, Stenotrophomonas maltophilia) may also be present in the lungs of horses with SEA. Samples of bronchoalveolar lavage fluid (BALf) were taken from twenty horses with exacerbated SEA. Microbiological cultures of the BALf samples were performed. Liquid chromatography coupled with tandem mass spectrometry was used to identify C4-HSL, C6-HSL, 3-oxo-C12-HSL and 11-methyl-2-dodecenoic acid, which are associated with the QS mechanisms of Pseudomonas aeruginosa and Stenotrophomonas maltophilia. Stenotrophomonas maltophilia was identified in three horses. Pseudomonas aeruginosa was not identified in any sample. The quorum sensing molecules C4-HSL, C6-HSL, 3-oxo-C12-HSL associated with biofilm formation by P. aeruginosa and 11-methyl-2-dodecenoic acid associated with biofilm formation by S. maltophila were not detected. It is unlikely that biofilm-forming bacterial strains associated with chronic lung disease in humans express similar virulence in SEA.

PMID:40441075 | DOI:10.1016/j.rvsc.2025.105720

Med Image Anal. 2025 May 23;104:103633. doi: 10.1016/j.media.2025.103633. Online ahead of print.

ABSTRACT

Invasive breast cancer (IBC) is a prevalent malignant tumor in women, and precise grading plays a pivotal role in ensuring effective treatment and enhancing survival rates. However, accurately grading IBC presents a significant challenge due to its heterogeneous nature and the need to harness the complementary information from multiple nuclei sources in histopathology images. To tackle this critical problem, we introduce a novel multi-source hybrid feature fusion network named MSFusion. This network incorporates two types of hybrid features: deep learning features extracted by a novel Swin Transformer-based multi-branch network called MSwinT, and traditional handcrafted features that capture the morphological characteristics of multi-source nuclei. The primary branch of MSwinT captures the overall characteristics of the original images, while multiple auxiliary branches focus on identifying morphological features from diverse sources of nuclei, including tumor, mitotic, tubular, and epithelial nuclei. At each of the four stages for the branches in MSwinT, a functional KDC (key diagnostic components) fusion block with channel and spatial attentions is proposed to integrate the features extracted by all the branches. Ultimately, we synthesize the multi-source hybrid deep learning features and handcrafted features to improve the accuracy of IBC diagnosis and grading. Our multi-branch MSFusion network is rigorously evaluated on three distinct datasets, including two private clinical datasets (Qilu dataset and QDUH&SHSU dataset) as well as a publicly available Databiox dataset. The experimental results consistently demonstrate that our proposed MSFusion model outperforms the state-of-the-art methods. Specifically, the AUC for the Qilu dataset and QDUH&SHSU dataset are 81.3% and 90.2%, respectively, while the public Databiox dataset yields an AUC of 82.1%.

PMID:40441045 | DOI:10.1016/j.media.2025.103633

EBioMedicine. 2025 May 28;116:105750. doi: 10.1016/j.ebiom.2025.105750. Online ahead of print.

ABSTRACT

BACKGROUND: Ductal Carcinoma In Situ (DCIS) can progress to ipsilateral invasive breast cancer (IBC) but over 75% of DCIS lesions do not progress if untreated. Currently, DCIS that might progress to IBC cannot reliably be identified. Therefore, most patients with DCIS undergo treatment resembling IBC. To facilitate identification of low-risk DCIS, we developed deep learning models using histology whole-slide images (WSIs) and clinico-pathological data.

METHODS: We predicted invasive recurrence in patients with primary, pure DCIS treated with breast-conserving surgery using clinical Cox proportional hazards models and deep learning. Deep learning models were trained end-to-end with only WSIs or in combination with clinical data (integrative). We employed nested k-fold cross-validation (k = 5) on a Dutch multicentre dataset (n = 558). Models were also tested on the UK-based Sloane dataset (n = 94).

FINDINGS: Evaluated over 20 years on the Dutch dataset, deep learning models using only WSIs effectively stratified patients into low-risk (no recurrence) and high-risk (invasive recurrence) groups (negative predictive value (NPV) = 0.79 (95% CI: 0.74-0.83); hazard ratio (HR) = 4.48 (95% CI: 3.41-5.88, p < 0.0001); area under the receiver operating characteristic curve (AUC) = 0.75 (95% CI: 0.70-0.79)). Integrative models achieved similar results with slightly enhanced hazard ratios compared to the image-only models (NPV = 0.77 (95% CI 0.73-0.82); HR = 4.85 (95% CI 3.65-6.45, p < 0.0001); AUC = 0.75 (95% CI 0.7-0.79)). In contrast, clinical models were borderline significant (NPV = 0.64 (95% CI 0.59-0.69); HR = 1.37 (95% CI 1.03-1.81, p = 0.041); AUC = 0.57 (95% CI 0.52-0.62)). Furthermore, external validation of the models was unsuccessful, limited by the small size and low number of cases (22/94) in our external dataset, WSI quality, as well as the lack of well-annotated datasets that allow robust validation.

INTERPRETATION: Deep learning models using routinely processed WSIs hold promise for DCIS risk stratification, while the benefits of integrating clinical data merit further investigation. Obtaining a larger, high-quality external multicentre dataset would be highly valuable, as successful generalisation of these models could demonstrate their potential to reduce overtreatment in DCIS by enabling active surveillance for women at low risk.

FUNDING: Cancer Research UK, the Dutch Cancer Society (KWF), and the Dutch Ministry of Health, Welfare and Sport.

PMID:40440915 | DOI:10.1016/j.ebiom.2025.105750

Forensic Sci Int. 2025 May 21;372:112511. doi: 10.1016/j.forsciint.2025.112511. Online ahead of print.

ABSTRACT

Accurate adult age estimation (AAE) is critical for forensic and anthropological applications, yet traditional methods relying on bone mineral density (BMD) face significant challenges due to biological variability and methodological limitations. This study aims to develop an end-to-end Deep Learning (DL) based pipeline for automated AAE using BMD from proximal femoral CT scans. The main objectives are to construct a large-scale dataset of 5151 CT scans from real-world clinical and cadaver cohorts, fine-tune the Segment Anything Model (SAM) for accurate femoral bone segmentation, and evaluate multiple convolutional neural networks (CNNs) for precise age estimation based on segmented BMD data. Model performance was assessed through cross-validation, internal clinical testing, and external post-mortem validation. SAM achieved excellent segmentation performance with a Dice coefficient of 0.928 and an average intersection over union (mIoU) of 0.869. The CNN models achieved an average mean absolute error (MAE) of 5.20 years in cross-validation (male: 5.72; female: 4.51), which improved to 4.98 years in the independent clinical test set (male: 5.32; female: 4.56). External validation on the post-mortem dataset revealed an MAE of 6.91 years, with 6.97 for males and 6.69 for females. Ensemble learning further improved accuracy, reducing MAE to 4.78 years (male: 5.12; female: 4.35) in the internal test set, and 6.58 years (male: 6.64; female: 6.37) in the external validation set. These findings highlight the feasibility of dl-driven AAE and its potential for forensic applications, offering a fully automated framework for robust age estimation.

PMID:40440868 | DOI:10.1016/j.forsciint.2025.112511

Comput Methods Programs Biomed. 2025 May 27;269:108844. doi: 10.1016/j.cmpb.2025.108844. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: Despite the profound advancements that deep learning models have achieved across a multitude of domains, their propensity to learn spurious correlations significantly impedes their applicability to tasks necessitating causal and counterfactual reasoning.

METHODS: In this paper, we propose a Bidirectional Neural Network, which innovatively consolidates forward causal reasoning with inverse counterfactual reasoning into a cohesive framework. This integration is facilitated through the implementation of multi-stacked affine coupling layers, which ensure the network's invertibility, thereby enabling bidirectional reasoning capabilities within a singular architectural construct. To augment the network's trainability and to ensure the bidirectional differentiability of the parameters, we introduce an orthogonal weight normalization technique. Additionally, the counterfactual reasoning capacity of the Bidirectional Neural Network is embedded within the policy function of reinforcement learning, thereby effectively addressing the challenges associated with reward sparsity in the blood glucose control scenario.

RESULTS: We evaluate our framework on two pivotal tasks: causal-based blood glucose forecasting and counterfactual-based blood glucose control. The empirical results affirm that our model not only exemplifies enhanced generalization in causal reasoning but also significantly surpasses comparative models in handling out-of-distribution data. Furthermore, in blood glucose control tasks, the integration of counterfactual reasoning markedly improves decision efficacy, sample efficiency, and convergence velocity.

CONCLUSION: It is our expectation that the Bidirectional Neural Network will pave novel pathways in the exploration of causal and counterfactual reasoning, thus providing groundbreaking methods for complex decision-making processes. Code is available at https://github.com/HITshenrj/BNN.

PMID:40440769 | DOI:10.1016/j.cmpb.2025.108844

Percept Mot Skills. 2025 May 29:315125251347006. doi: 10.1177/00315125251347006. Online ahead of print.

ABSTRACT

Frequency-following responses (FFRs) are neural signals that reflect the brain's encoding of acoustic characteristics, such as speech intonation. While traditional machine learning models have been used to classify FFRs elicited under various conditions, the potential of deep learning models in FFR research remains underexplored. This study investigated the efficacy of a three-layer artificial neural network (ANN) in detecting the presence or absence of FFRs elicited by a rising intonation of the English vowel /i/. The ANN was trained and tested on FFR recordings, using F0 estimates derived from the spectral domain as input data. Model performance was evaluated by systematically varying the number of inputs, hidden neurons, and the number of sweeps included in the recordings. The prediction accuracy of the ANN was significantly influenced by the number of inputs, hidden neurons, and sweeps. Optimal configurations included 6-8 inputs and 4-6 hidden neurons, achieving a prediction accuracy of approximately 84% when the signal-to-noise ratio was enhanced by including 100 or more sweeps. These results provide a foundation for future applications in auditory processing assessments and clinical diagnostics.

PMID:40440687 | DOI:10.1177/00315125251347006

Int J Surg. 2025 May 29. doi: 10.1097/JS9.0000000000002526. Online ahead of print.

ABSTRACT

BACKGROUND: Currently, there remains a critical need for reliable tools to accurately predict post-surgical outcomes in non-small cell lung cancer (NSCLC) patients in clinical practice. We aimed to develop and validate a deep learning-based model utilizing histopathological slides to accurately predict post-surgical outcomes in NSCLC patients.

METHODS: In this study, we analyzed histopathological slides and comprehensive clinical data from 337 Local-NSCLC patients for model development, and further validated the model using an independent cohort of 554 NSCLC patients from The Cancer Genome Atlas (TCGA) database. Utilizing the advanced Res2Net deep learning architecture, we developed and optimized a novel Surgical Prognosis Prediction Score (Sr-PPS) system.

RESULTS: The Sr-PPS model demonstrated significantly enhanced predictive accuracy for both disease-free survival (DFS) and overall survival (OS) in NSCLC patients. Multivariate Cox regression analysis validated Sr-PPS as a robust independent predictor of post-surgical outcomes in NSCLC patients. Patients with low Sr-PPS scores exhibited enhanced anti-tumor immune microenvironment characteristics, characterized by significant upregulation of immune activation pathways (particularly T-cell migration and B-cell receptor signaling), coupled with marked downregulation of oncogenic pathways, including insulin-like growth factor receptor signaling and STAT protein phosphorylation. Further genomic analyses revealed significant associations between Sr-PPS scores and mutations in key oncogenic driver genes, including CTNND2, PRRX1, and ALK.

CONCLUSIONS: Our deep learning-based Sr-PPS model not only demonstrates robust predictive capability for post-surgical outcomes in NSCLC patients but also elucidates underlying molecular mechanisms, thereby providing a valuable framework for personalized treatment stratification.

PMID:40440686 | DOI:10.1097/JS9.0000000000002526

JMIR Med Inform. 2025 May 29;13:e67859. doi: 10.2196/67859.

ABSTRACT

BACKGROUND: Leptospirosis, a zoonotic disease caused by Leptospira bacteria, continues to pose significant public health risks, particularly in tropical and subtropical regions.

OBJECTIVE: This systematic review aimed to evaluate the application of machine learning (ML) and deep learning (DL) techniques in predicting and diagnosing leptospirosis, focusing on the most used algorithms, validation methods, data types, and performance metrics.

METHODS: Using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS), and Prediction model Risk of Bias Assessment Tool (PROBAST) tools, we conducted a comprehensive review of studies applying ML and DL models for leptospirosis detection and prediction, examining algorithm performance, data sources, and validation approaches.

RESULTS: Out of a total of 374 articles screened, 17 studies were included in the qualitative synthesis, representing approximately 4.5% of the initial pool. The review identified frequent use of algorithms such as support vector machines, artificial neural networks, decision trees, and convolutional neural networks (CNNs). Among the included studies, 88% (15/17) used traditional ML methods, and 24% (4/17) used DL techniques. Several models demonstrated high predictive performance, with reported accuracy rates ranging from 80% to 98%, notably with the U-Net CNN achieving 98.02% accuracy. However, public datasets were underused, with only 35% (6/17) of studies incorporating publicly available data sources; the majority (65%, 11/17) relied primarily on private datasets from hospitals, clinical records, or regional surveillance systems.

CONCLUSIONS: ML and DL techniques demonstrate potential for improving leptospirosis prediction and diagnosis, but future research should focus on using larger, more diverse datasets, adopting transfer learning strategies, and integrating advanced ensemble and validation techniques to strengthen model accuracy and generalization.

PMID:40440642 | DOI:10.2196/67859

J Chem Inf Model. 2025 May 29. doi: 10.1021/acs.jcim.5c00489. Online ahead of print.

ABSTRACT

AlphaFold2 and other deep learning tools represent the state of the art for protein structure prediction; however, they are still limited in their ability to model multiple protein conformations. Since the function of many proteins depends on their ability to assume different stable conformational states, different approaches are required to access these alternative conformations. G-protein-coupled receptors regulate intracellular signaling by assuming two main conformational states: an active state able to bind G-protein and an inactive state. Receptor activation is characterized by large conformational changes at the intracellular region, where the G-protein interacts, accompanied by more subtle structural rearrangements at the extracellular ligand-binding site. Retrospective studies have demonstrated that, for many receptors, the inactive state is the favored conformation generated by AlphaFold2 when the receptor is modeled alone, while active-state structures can only be modeled by introducing a conformational bias in the template information used for the prediction or by explicitly incorporating the binding of a ligand into the modeled system. This benchmarking study extends previous analyses, confirming the opportunities of deep learning tools for modeling G-protein complexed to the active state of receptor, while also revealing limitations in the modeling of allosteric effects, particularly the reduced accuracy of predictions at the receptor extracellular site, which may impact their applicability in structure-based drug design.

PMID:40440630 | DOI:10.1021/acs.jcim.5c00489

JMIR Form Res. 2025 May 29;9:e67311. doi: 10.2196/67311.

ABSTRACT

BACKGROUND: Deaths related to physical trauma impose a heavy burden on society, and the Abbreviated Injury Scale (AIS) is an important tool for injury research. AIS covers injuries to various parts of the human body and scores them based on the severity of the injury. In practical applications, the complex AIS coding rules require experts to encode by consulting patient medical records, which inevitably increases the difficulty, time, and cost of evaluation of patient and also puts higher demands on the workload of information collection and processing. In some cases, the sheer number of patients or the inability to access detailed medical records necessary for coding further complicates independent AIS codes.

OBJECTIVE: This study aims to use advanced deep learning techniques to predict AIS codes based on easily accessible diagnostic information of patients to improve the accuracy of trauma assessment.

METHODS: We used a dataset of patients with trauma (n=26,810) collected by the Chongqing Daping Hospital between October 2013 and June 2024. We mainly selected the patient's diagnostic information, injury description, cause of injury, injury region, injury types, and present illness history as the key feature inputs. We used a robust optimization Bidirectional Encoder Representations from Transformers (BERT) pretraining method to embed these features and constructed a prediction model based on BERT. This model aims to predict AIS codes and comprehensively evaluate its performance through a 5-fold cross-validation. We compared the BERT model with previous research results and current mainstream machine learning methods to verify its advantages in prediction tasks. In addition, we also conducted external validation of the model using 244 external data points from the Chongqing Emergency Center.

RESULTS: The BERT model proposed in this paper performs significantly better than the comparison model on independent test datasets with an accuracy of 0.8971, which surpassed the previous study by 10 % points. In addition, the area under the curve (AUC value of the BERT model is 0.9970, and the F1-score is 0.8434. In the external dataset, the accuracy, AUC, and F1-score results of the model are 0.7131, 0.8586, and 0.6801, respectively. These results indicate that our model has high generalization ability and prediction accuracy.

CONCLUSIONS: The BERT model we proposed is mainly based on diagnostic information to predict AIS codes, and its prediction accuracy is superior to previous investigations and current mainstream machine learning methods. It has a high generalization ability in external datasets.

PMID:40440586 | DOI:10.2196/67311

Science. 2025 May 29:eads7373. doi: 10.1126/science.ads7373. Online ahead of print.

ABSTRACT

Only a minority of patients with rare genetic diseases are currently diagnosed by exome sequencing, suggesting that additional unrecognized pathogenic variants may reside in non-coding sequence. Here, we describe PromoterAI, a deep neural network that accurately identifies non-coding promoter variants which dysregulate gene expression. We show that promoter variants with predicted expression-altering consequences produce outlier expression at both RNA and protein levels in thousands of individuals, and that these variants experience strong negative selection in human populations. We observe that clinically relevant genes in rare disease patients are enriched for such variants and validate their functional impact through reporter assays. Our estimates suggest that promoter variation accounts for 6% of the genetic burden associated with rare diseases.

PMID:40440429 | DOI:10.1126/science.ads7373

Expert Rev Respir Med. 2025 May 29. doi: 10.1080/17476348.2025.2513512. Online ahead of print.

ABSTRACT

BACKGROUND: Fibrosing Interstitial Lung Diseases (F-ILDs) lead to reduced exercise capacity and quality of life. Pulmonary Rehabilitation (PR) exercise programs have shown potential in improving symptoms and functional capacity in these patients. This study aimed to compare the effectiveness of different PR exercise approaches in patients with F-ILDs.

RESEARCH DESIGN AND METHODS: This randomized, three-arm controlled trial included F-ILD patients divided into three groups: hospital-based supervised(HGr), synchronized-online(SOGr) with live video calls, and video-based (VGr) with recorded exercise videos. All groups underwent an 8-week program combining aerobic and resistance training. Clinical parameters assessed included 6-minute walking distance(6MWD), modified medical research council dyspnea score(mMRC), respiratory function tests, Saint George Respiratory Questionnaire (SGRQ), International Physical Activity Questionnaire Short Form(IPAQ-SF), fatigue severity scale (FSS), and muscle strength.

RESULTS: Of the 75 patients, 65 completed the study, with comparable demographic and baseline characteristics. Significant improvements in 6MWD, mMRC, maximal inspiratory pressure, IPAQ-SF, SGRQ, and peripheral muscle strength were seen in all groups. Post-hoc analysis showed HGr had greater improvements in forced vital capacity and FSS compared to SOGr.

CONCLUSION: Hospital-based, synchronized-online, and video-based PR programs all improve clinical outcomes in patients with F-ILDs. However, supervised in-hospital PR yielded greater benefits in lung function and fatigue reduction compared to the online approaches.

CLINICALTRIAL REGISTRATION: https://clinicaltrials.gov/study/NCT05166057.

PMID:40440705 | DOI:10.1080/17476348.2025.2513512

Plant Physiol Biochem. 2025 May 23;226:110066. doi: 10.1016/j.plaphy.2025.110066. Online ahead of print.

ABSTRACT

The BBX family of transcription factors regulate several physiological processes during plant vegetative and reproductive development, bridging light signaling to hormones metabolism. Here, we do an in-depth functional characterization of the tomato SlBBX26 (Solyc10g006750) gene by generating and phenotyping CRISPR/Cas9-mediated genome-edited and RNAi-mediated knockdown lines. We demonstrate that SlBBX26 regulates the negative factor of light signaling, PHYTOCHROME INTERACTING PROTEIN 4 (PIF4), which in turn modulates vegetative growth and flowering by controlling gibberellins (GAs) biosynthetic genes expression and signaling, respectively. Moreover, SlBBX26 regulates fruit growth modulating the expression of cell division- and expansion-related genes. SlBBX26 also influences fruit chloroplast ultrastructure and metabolism in an SlPIF4-mediated manner, leading to alterations in thylakoid stacking, plastoglobuli size, and chlorophyll content, through the regulation of genes involved in chloroplast differentiation and chlorophyll degradation. Finally, SlBBX26-SlPIF4 heterodimer is required to control GA and auxin signaling cascades, triggering the onset of fruit ripening. As such, our findings unveil how BBX proteins contribute to the regulation of main agronomic traits.

PMID:40441101 | DOI:10.1016/j.plaphy.2025.110066

Science. 2025 May 29:eadq7408. doi: 10.1126/science.adq7408. Online ahead of print.

ABSTRACT

The maintenance of H3K9me3 involves the recognition of pre-existing modifications by HP1, which recruits methyltransferase SUV39H1 to methylate the adjacent newly incorporated histones, thereby establishing a positive feedback loop. However, how this positive feedback is restricted to maintain H3K9me3 homeostasis remains largely unknown. Here, we performed an unbiased genome-scale CRISPR-Cas9 screen and identified CUL5ASB7 E3 ubiquitin ligase as a negative regulator of H3K9me3. ASB7 is recruited to heterochromatin by HP1 and promotes SUV39H1 degradation. During mitosis, CDK1 phosphorylates ASB7, preventing its interaction with SUV39H1, leading to SUV39H1 stabilization and H3K9me3 restoration. Our findings reveal a dynamic circuit involving HP1, SUV39H1, and ASB7 that governs H3K9me3 homeostasis, thereby ensuring faithful epigenetic inheritance and preventing excessive heterochromatin formation.

PMID:40440427 | DOI:10.1126/science.adq7408

Cell Rep. 2025 May 28;44(6):115769. doi: 10.1016/j.celrep.2025.115769. Online ahead of print.

ABSTRACT

The ribonuclease DIS3 interacts through its PIN domain with the nuclear exosome and degrades linear RNA substrates using its exoribonuclease domain. However, the PIN domain is also an active endoribonuclease, but cellular substrates are largely unknown. Here, we use a biochemical strategy to find ribonucleases that could degrade circular RNAs (circRNAs). Due to the lack of accessible ends, circRNAs are resistant to exonucleolytic cleavage and are thus more stable than linear RNAs. Using biochemical assays, we identify DIS3 as a candidate for circRNA degradation and demonstrate that it partially resides in the cytoplasm, where circRNAs are degraded. DIS3 shows cleavage activity toward a number of circRNAs and functions independently of the exosome core in vitro. Upon knockdown of DIS3 in cell lines, selected circRNAs are moderately stabilized. We thus propose that cytoplasmic DIS3 functions as a stand-alone enzyme independently of the exosome core and may contribute to circRNA turnover.

PMID:40440169 | DOI:10.1016/j.celrep.2025.115769