BMJ Open Qual. 2025 May 22;14(2):e003230. doi: 10.1136/bmjoq-2024-003230.

ABSTRACT

BACKGROUND: Surveys are widely used in healthcare to gather knowledge and information about services provided. There is a recognised gap between survey findings and their impact on practice, particularly for standardised surveys conducted at the national or organisational level. Findings are more likely to be acted on where there is a culture and infrastructure supportive of quality improvement (QI), but little is known about the experiences of local QI teams designing and using surveys in practice.

OBJECTIVE: To understand the experiences of QI teams designing and using surveys within a national QI collaborative, including perceived value and challenges.

METHODS: Using an interactive research approach, 14 semistructured interviews were conducted with members of the Cystic Fibrosis Lung Transplant Transition Learning and Leadership Collaborative. Data were analysed through multiple rounds of coding and inductive thematic analysis.

RESULTS: Collaborative participants viewed surveys positively as an improvement tool. The design and use of surveys was a team-based effort, embedded within the structure of the collaborative. Surveys illuminated local, microsystem and mesosystem data and provided patient and staff insights. As one step in the QI journey, surveys helped shape the direction of local QI work, resulting in positive changes in areas such as working relationships, patient interactions, staff education and work processes.Challenges experienced included: response rates and survey design, inability to act on findings, issues of sensitivity and anonymity, expertise to design surveys, time requirements, and survey fatigue.

CONCLUSIONS: Surveys played a crucial role in driving QI efforts, leading to impactful changes in practice. Used within a supportive collaborative context, surveys became an essential tool for ongoing learning and improvement, highlighting the distinct needs of surveys used in QI compared with research.

PMID:40404211 | DOI:10.1136/bmjoq-2024-003230

Anal Chem. 2025 May 22. doi: 10.1021/acs.analchem.4c06905. Online ahead of print.

ABSTRACT

Light scattering is a powerful tool that has been widely applied in various scenarios, such as nanoparticle analysis, single-cell measurement, and blood flow monitoring. However, noise is always a concerning and challenging issue in light scattering imaging (LSI) due to the complexity of noise sources. In this work, a deep learning-based adaptive denoising framework has been established to explore the temporal information on LSI videos, aiming to provide an unsupervised and self-learning denoising strategy for various application scenarios of LSI. This novel framework consists of three stages: noise distribution maps for describing the characteristics of LSI noise, video denoising based on the unsupervised learning of the FastDVDNet network, and denoising effect discrimination to screen the best denoised result for further processing. The denoising performance is validated by two common LSI applications: nanoparticle analysis and label-free identification of single cells. The result shows that our method compares favorably to existing methods in suppressing the background noise and enhancing the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of LSI. Consequently, the successful analysis of both particle size distribution and cell classification can be notably improved. The proposed unsupervised adaptive denoising method is expected to offer a powerful tool toward a fully automated denoising and improved accuracy in extensive applications of LSI.

PMID:40405330 | DOI:10.1021/acs.analchem.4c06905

J Cheminform. 2025 May 22;17(1):80. doi: 10.1186/s13321-025-01004-5.

ABSTRACT

The field of molecular representation has witnessed a shift towards models trained on molecular structures represented by strings or graphs, with chemical information encoded in nodes and bonds. Graph-based representations offer a more realistic depiction and support 3D geometry and conformer-based augmentation. Graph Neural Networks (GNNs) and Graph-based Transformer models (GTs) represent two paradigms in this field, with GT models emerging as a flexible alternative. In this study, we compare the performance of GT models against GNN models on three datasets. We explore the impact of training procedures, including context-enriched training through pretraining on quantum mechanical atomic-level properties and auxiliary task training. Our analysis focuses on sterimol parameters estimation, binding energy estimation, and generalization performance for transition metal complexes. We find that GT models with context-enriched training provide on par results compared to GNN models, with the added advantages of speed and flexibility. Our findings highlight the potential of GT models as a valid alternative for molecular representation learning tasks.

PMID:40405272 | DOI:10.1186/s13321-025-01004-5

Biomed Eng Online. 2025 May 22;24(1):64. doi: 10.1186/s12938-025-01396-3.

ABSTRACT

BACKGROUND: Neurological disorders, ranging from common conditions like Alzheimer's disease that is a progressive neurodegenerative disorder and remains the most common cause of dementia worldwide to rare disorders such as Angelman syndrome, impose a significant global health burden. Altered facial expressions are a common symptom across these disorders, potentially serving as a diagnostic indicator. Deep learning algorithms, especially convolutional neural networks (CNNs), have shown promise in detecting these facial expression changes, aiding in diagnosing and monitoring neurological conditions.

OBJECTIVES: This systematic review and meta-analysis aimed to evaluate the performance of deep learning algorithms in detecting facial expression changes for diagnosing neurological disorders.

METHODS: Following PRISMA2020 guidelines, we systematically searched PubMed, Scopus, and Web of Science for studies published up to August 2024. Data from 28 studies were extracted, and the quality was assessed using the JBI checklist. A meta-analysis was performed to calculate pooled accuracy estimates. Subgroup analyses were conducted based on neurological disorders, and heterogeneity was evaluated using the I2 statistic.

RESULTS: The meta-analysis included 24 studies from 2019 to 2024, with neurological conditions such as dementia, Bell's palsy, ALS, and Parkinson's disease assessed. The overall pooled accuracy was 89.25% (95% CI 88.75-89.73%). High accuracy was found for dementia (99%) and Bell's palsy (93.7%), while conditions such as ALS and stroke had lower accuracy (73.2%).

CONCLUSIONS: Deep learning models, particularly CNNs, show strong potential in detecting facial expression changes for neurological disorders. However, further work is needed to standardize data sets and improve model robustness for motor-related conditions.

PMID:40405223 | DOI:10.1186/s12938-025-01396-3

BMC Public Health. 2025 May 22;25(1):1884. doi: 10.1186/s12889-025-23059-7.

ABSTRACT

INTRODUCTION: Epidemic modeling is crucial for understanding and predicting infectious disease spread. To capture the complexity of real-world transmission, dynamic interactions between individuals with spatial heterogeneity must be considered. This modeling requires high-dimensional epidemic parameters, which can lead to unidentifiability; therefore, integrating various data types for inference is essential to effectively address these challenges.

METHODS: We introduce a novel hybrid framework, Multi-Patch Model Update with Graph Attention Network (MPUGAT), that combines a multi-patch compartmental model with a spatio-temporal deep learning model. MPUGAT employs a GAT (Graph Attention Mechanism) to transform static traffic matrices into dynamic transmission matrices by analyzing patterns in diverse time series data from each city.

RESULTS: We demonstrate the effectiveness of MPUGAT through its application to COVID-19 data from South Korea. By accurately estimating time-varying transmission rates, MPUGAT outperforms traditional models and aligns with actual policies such as social distancing.

CONCLUSION: MPUGAT offers a novel approach for effectively integrating easily accessible, low-dimensional, non-epidemic-related data into epidemic modeling frameworks. Our findings highlight the importance of incorporating dynamic data and utilizing graph attention mechanisms to enhance accuracy of infectious disease modeling and the analysis of policy interventions. This study underscores the potential of leveraging diverse data sources and advanced deep learning techniques to improve epidemic forecasting and inform public health strategies.

PMID:40405112 | DOI:10.1186/s12889-025-23059-7

Eur Radiol. 2025 May 22. doi: 10.1007/s00330-025-11696-w. Online ahead of print.

ABSTRACT

OBJECTIVES: To evaluate the variability of fully automated airway quantitative CT (QCT) measures caused by different kernels and the effect of kernel conversion.

MATERIALS AND METHODS: This retrospective study included 96 patients who underwent non-enhanced chest CT at two centers. CT scans were reconstructed using four kernels (medium soft, medium sharp, sharp, very sharp) from three vendors. Kernel conversion targeting the medium soft kernel as reference was applied to sharp kernel images. Fully automated airway quantification was performed before and after conversion. The effects of kernel type and conversion on airway quantification were evaluated using analysis of variance, paired t-tests, and concordance correlation coefficient (CCC).

RESULTS: Airway QCT measures (e.g., Pi10, wall thickness, wall area percentage, lumen diameter) decreased with sharper kernels (all, p < 0.001), with varying degrees of variability across variables and vendors. Kernel conversion substantially reduced variability between medium soft and sharp kernel images for vendors A (pooled CCC: 0.59 vs. 0.92) and B (0.40 vs. 0.91) and lung-dedicated sharp kernels of vendor C (0.26 vs. 0.71). However, it was ineffective for non-lung-dedicated sharp kernels of vendor C (0.81 vs. 0.43) and showed limited improvement in variability of QCT measures at the subsegmental level. Consistent airway segmentation and identical anatomic labeling improved subsegmental airway variability in theoretical tests.

CONCLUSION: Deep learning-based kernel conversion reduced the measurement variability of airway QCT across various kernels and vendors but was less effective for non-lung-dedicated kernels and subsegmental airways. Consistent airway segmentation and precise anatomic labeling can further enhance reproducibility for reliable automated quantification.

KEY POINTS: Question How do different CT reconstruction kernels affect the measurement variability of automated airway measurements, and can deep learning-based kernel conversion reduce this variability? Findings Kernel conversion improved measurement consistency across vendors for lung-dedicated kernels, but showed limited effectiveness for non-lung-dedicated kernels and subsegmental airways. Clinical relevance Understanding kernel-related variability in airway quantification and mitigating it through deep learning enables standardized analysis, but further refinements are needed for robust airway segmentation, particularly for improving measurement variability in subsegmental airways and specific kernels.

PMID:40405045 | DOI:10.1007/s00330-025-11696-w

Int J Comput Assist Radiol Surg. 2025 May 22. doi: 10.1007/s11548-025-03406-0. Online ahead of print.

ABSTRACT

PURPOSE: Assessment of intraoperative surgical skill is necessary to train surgeons and certify them for practice. The generalizability of deep learning models for video-based assessment (VBA) of surgical skill has not yet been evaluated. In this work, we evaluated one unsupervised domain adaptation (UDA) and three semi-supervised (SSDA) methods for generalizability of models for VBA of surgical skill in capsulorhexis by training on one dataset and testing on another.

METHODS: We used two datasets, D99 and Cataract-101 (publicly available), and two state-of-the-art models for capsulorhexis. The models include a convolutional neural network (CNN) to extract features from video images, followed by a long short-term memory (LSTM) network or a transformer. We augmented the CNN and the LSTM with attention modules. We estimated accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).

RESULTS: Maximum mean discrepancy (MMD) did not improve generalizability of CNN-LSTM but slightly improved CNN transformer. Among the SSDA methods, Group Distributionally Robust Supervised Learning improved generalizability in most cases.

CONCLUSION: Model performance improved with the domain adaptation methods we evaluated, but it fell short of within-dataset performance. Our results provide benchmarks on a public dataset for others to compare their methods.

PMID:40405033 | DOI:10.1007/s11548-025-03406-0

Sci Rep. 2025 May 22;15(1):17875. doi: 10.1038/s41598-025-02889-w.

ABSTRACT

Sepsis remains a leading cause of mortality in critical care settings, necessitating timely and accurate risk stratification. However, existing machine learning models for sepsis prediction often suffer from poor interpretability, limited generalizability across diverse patient populations, and challenges in handling class imbalance and high-dimensional clinical data. To address these gaps, this study proposes a novel framework that integrates bio-inspired feature selection and graph-based deep learning for enhanced sepsis risk prediction. Using the MIMIC-IV dataset, we employ the Wolverine Optimization Algorithm (WoOA) to select clinically relevant features, followed by a Generative Pre-Training Graph Neural Network (GPT-GNN) that models complex patient relationships through self-supervised learning. To further improve predictive accuracy, the TOTO metaheuristic algorithm is applied for model fine-tuning. SMOTE is used to balance the dataset and mitigate bias toward the majority class. Experimental results show that our model outperforms traditional classifiers such as SVM, XGBoost, and LightGBM in terms of accuracy, AUC, and F1-score, while also providing interpretable mortality indicators. This research contributes a scalable and high-performing decision support tool for sepsis risk stratification in real-world clinical environments.

PMID:40404796 | DOI:10.1038/s41598-025-02889-w

Sci Rep. 2025 May 22;15(1):17851. doi: 10.1038/s41598-025-00873-y.

ABSTRACT

In the financial world, Credit card fraud is a budding apprehension in the banking sector, necessitating the development of efficient detection methods to minimize financial losses. The usage of credit cards is experiencing a steady increase, thereby leading to a rise in the default rate that banks encounter. Although there has been much research investigating the efficacy of conventional Machine Learning (ML) models, there has been relatively less emphasis on Deep Learning (DL) techniques. In this article, a machine learning-based system to detect fraudulent transactions using a publicly available dataset of credit card transactions. The dataset, highly imbalanced with fraudulent transactions representing less than 0.2% of the total, was processed using techniques like Synthetic Minority Over-sampling Technique (SMOTE) to handle class imbalance. To predict credit card default, this study evaluates the efficacy of a DL (Deep Learning) model and compares it to other ML models, such as Decision Tree (DT) and Adaboost. The objective of this research is to identify the specific DL parameters that contribute to the observed enhancements in the accuracy of credit card default prediction. This research makes use of the UCI ML repository to access the credit card defaulted customer dataset. Subsequently, various techniques are employed to pre-process the unprocessed data and visually present the outcomes through the use of exploratory data analysis (EDA). Furthermore, the algorithms are hyper tuned to evaluate the enhancement in prediction. We used standard evaluation metrics to evaluate all the models. The evaluation indicates that the Adaboost and DT exhibit the highest accuracy rate of 82 ​% in predicting credit card default, surpassing the accuracy of the ANN model, which is 78 ​%. Several classification algorithms, comprising Logistic Regression, Random Forest, and Neural Networks, were evaluated to determine their effectiveness in identifying fraudulent activities. The Random Forest model emerged as the best performing algorithm with an accuracy of 99.5% and a high recall score, indicating its robustness in detecting fraudulent transactions. This system can be deployed in real-time financial systems to enhance fraud prevention mechanisms and ensure secure financial transactions.

PMID:40404766 | DOI:10.1038/s41598-025-00873-y

Sci Data. 2025 May 22;12(1):843. doi: 10.1038/s41597-025-05211-5.

ABSTRACT

In recent years, deep learning techniques have been extensively used for the identification and classification of lithium-ion batteries. However, these models typically require a costly and labor-intensive labeling process, often influenced by commercial or proprietary concerns. In this study, we introduce RecyBat24, a publicly accessible image dataset for the detection and classification of three battery types: Pouch, Prismatic, and Cylindrical. Our dataset is designed to support both academic research and industrial applications, closely replicating real-world scenarios during the acquisition process and employing data augmentation techniques to simulate various external conditions. Additionally, we demonstrate how the RecyBat24's detection-oriented annotations can be used to create a second version of RecyBat24for instance-segmentation tasks. Finally, we demonstrate that recent lightweight machine learning models achieve high accuracy, highlighting their potential for classification and segmentation applications where computational resources are constrained.

PMID:40404746 | DOI:10.1038/s41597-025-05211-5

Sci Rep. 2025 May 22;15(1):17791. doi: 10.1038/s41598-025-02375-3.

ABSTRACT

This article introduces a novel deep-learning based framework, Super-resolution/Denoising network (SDNet), for simultaneous denoising and super-resolution of swept-source optical coherence tomography (SS-OCT) images. The novelty of this work lies in the hybrid integration of data-driven deep-learning with a model-informed noise representation, specifically designed to address the very low signal-to-noise ratio (SNR) and low-resolution challenges in SS-OCT imaging. SDNet introduces a two-step training process, leveraging noise-free OCT references to simulate low-SNR conditions. In the first step, the network learns to enhance noisy images by combining denoising and super-resolution within noise-corrupted reference domain. To refine its performance, the second step incorporates Principle Component Analysis (PCA) as self-supervised denoising strategy, eliminating the need for ground-truth noisy image data. This unique approach enhances SDNet's adaptability and clinical relevance. A key advantage of SDNet is its ability to balance contrast-texture by adjusting the weights of the two training steps, offering clinicians flexibility for specific diagnostic needs. Experimental results across diverse datasets demonstrate that SDNet surpasses traditional model-based and data-driven methods in computational efficiency, noise reduction, and structural fidelity. The framework excels in improving both image quality and diagnostic accuracy. Additionally, SDNet shows promising adaptability for analyzing low-resolution, low-SNR OCT images, such as those from patients with diabetic macular edema (DME). This study establishes SDNet as a robust, efficient, and clinically adaptable solution for OCT image enhancement addressing critical limitations in contemporary imaging workflows.

PMID:40404743 | DOI:10.1038/s41598-025-02375-3

BMC Pulm Med. 2025 May 22;25(1):253. doi: 10.1186/s12890-025-03713-x.

ABSTRACT

BACKGROUND: Antifibrotics have been approved for use in many countries, including Japan, based on the results of several phase III clinical trials in patients with IPF, SSc-ILD, and PPF, which showed slower lung function decline with antifibrotic treatment. There is a paucity of information on the real-world use of antifibrotics in clinical practice.

METHODS: Baseline characteristics, comorbidities, and drugs used prior to and concomitant with antifibrotics were collected for patients with IPF, SSc-ILD, and PPF using a health insurance claims database in Japan from 1 January 2013 to 30 June 2023. Descriptive statistics were generated for all study variables.

RESULTS: This study included 657 nintedanib users with IPF; 418 pirfenidone users with IPF; 4160 nintedanib users with PPF; 18,403 users of glucocorticoids/immunosuppressants for ILD treatment with PPF; 676 nintedanib users with SSc-ILD; and 698 users of glucocorticoids/immunosuppressants for ILD treatment with SSc-ILD. At index, pirfenidone users with IPF were the oldest (mean [SD] 74.8 [7.3] years), and nintedanib users with SSc-ILD were the youngest (mean [SD] 65.6 [11.7] years). In nintedanib users with IPF, 76.7% were prescribed nintedanib as monotherapy, and 75.6% of pirfenidone users were prescribed pirfenidone, as monotherapy. In patients with IPF, 75.2% were prescribed nintedanib, and 76.1% were prescribed pirfenidone, as first-line therapy. In patients with SSc-ILD, 34.9% were prescribed nintedanib as monotherapy for ILD treatment, and 38.6% as first-line therapy. Approximately half of patients with PPF were prescribed nintedanib concomitantly with other glucocorticoids/immunosuppressant drugs, and after one or more glucocorticoids/immunosuppressant drugs. The most common concomitant drug in all patient groups was glucocorticoids. In patients with IPF, 18.6% of nintedanib users and 18.2% of pirfenidone users were prescribed glucocorticoids concomitantly. Concomitant glucocorticoid use was 52.7% for nintedanib users with SSc-ILD, and 44.1% for nintedanib users with PPF.

CONCLUSIONS: These results provide real-world evidence of antifibrotic use in clinical practice. Most patients with IPF were prescribed antifibrotics as monotherapy for ILD treatment whereas antifibrotics were used concomitantly with glucocorticoids/immunosuppressants in many patients with SSc-ILD and PPF. While most patients with IPF were prescribed antifibrotics as first-line therapy, patients with SSc-ILD and PPF were more likely to be prescribed nintedanib as second-line or later-line treatment after glucocorticoids/immunosuppressants.

PMID:40405141 | DOI:10.1186/s12890-025-03713-x

BMJ Open Respir Res. 2025 May 22;12(1):e002614. doi: 10.1136/bmjresp-2024-002614.

ABSTRACT

BACKGROUND: Advanced interstitial lung disease (ILD) often necessitates lung transplantation, and identifying accessible prognostic markers is essential for effective management. However, the link between serum tumour markers and survival in waitlisted lung transplant candidates with advanced ILD remains underexplored.

OBJECTIVE: To evaluate associations between serum tumour marker levels and long-term survival in lung transplant candidates with advanced ILD.

METHODS: This study included 282 patients with end-stage ILD who were waitlisted for lung transplantation from November 2012 to March 2021. Baseline data and serum tumour marker levels were assessed before listing. Vital status and transplant outcomes were retrospectively reviewed as of 31 May 2023. Associations between tumour markers, clinical variables and mortality were analysed using Cox proportional hazards models with competing risk regression.

RESULTS: During a median wait time of 17.8 months (IQR: 7.8-44.1), 107 patients received transplants, 38 survived on the list and 137 died while waiting. Multivariable analysis identified higher CA-125 levels (HR 1.03, 95% CI 1.01 to 1.06, p=0.001), older age (HR 1.03, 95% CI 1.01 to 1.06, p=0.001), female gender (HR 1.43, 95% CI 1.01 to 2.04, p<0.04), elevated C-reactive protein (HR 1.17, 95% CI 1.03 to 1.34, p=0.01) and cerebrovascular disease (HR 2.03, 95% CI 1.38 to 2.98, p=0.01) as significant predictors of mortality.

CONCLUSION: Among waitlisted lung transplant candidates with advanced ILD, elevated serum carbohydrate antigen (CA)-125 and CA19-9 levels are associated with higher mortality risk. Routine assessment of these markers may enhance risk stratification for this patient population.

PMID:40404187 | DOI:10.1136/bmjresp-2024-002614

BMJ Open Respir Res. 2025 May 22;12(1):e002566. doi: 10.1136/bmjresp-2024-002566.

ABSTRACT

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a progressive disease found primarily in older people, with the use of systemic steroids linked to poor outcomes. However, the role of inhaled corticosteroids (ICSs) in IPF remains unclear. This study investigated the association between ICS use and IPF risk using national insurance data, particularly in individuals with chronic airway diseases.

METHODS: Using the National Health Insurance Service-National Sample Cohort database, our study included patients diagnosed with chronic obstructive pulmonary disease or asthma. ICS exposure was assessed via treatment claims, and IPF cases were identified using broad and narrow criteria. We used inverse probability of treatment weighting (IPTW) with propensity scores for balanced covariate analysis.

RESULTS: Of 57 456 patients (mean age: 55.9 years, 42.3% men), 16.5% used ICS and 83.5% did not. ICS users showed higher rates of broad (0.98 vs 0.41 per 1000) and narrow IPF (0.61 vs 0.21 per 1000) than non-users. Pre-IPTW, ICS use was associated with increased IPF risk; however, this was not significant post-IPTW. Post-IPTW, both ICS dose as a continuous variable (broad adjusted HR per 100 µg/day: 1.03, 95% CI: 1.02 to 1.04; narrow adjusted HR per 100 µg/day: 1.03, 95% CI: 1.01 to 1.04 post-IPTW) and high-dose ICS (≥1000 µg/day) (broad adjusted HR: 3.89, 95% CI: 1.61 to 9.41; narrow adjusted HR: 3.99, 95% CI: 1.19 to 13.41) use correlated with an elevated IPF risk.

CONCLUSION: While no overall significant association between ICS use and IPF risk was observed post-IPTW, there may be an increased risk in patients using high-dose ICS.

PMID:40404186 | DOI:10.1136/bmjresp-2024-002566

BMJ Open Respir Res. 2025 May 22;12(1):e003038. doi: 10.1136/bmjresp-2024-003038.

ABSTRACT

INTRODUCTION: Proof-of-concept (POC) studies are vital in determining the feasibility of further drug development, primarily by assessing preliminary efficacy signals with credible endpoints. However, traditional POC studies in idiopathic pulmonary fibrosis (IPF) can suffer from low credibility due to small sample sizes and short durations, leading to non-replicable results in larger phase III trials. To address this, we are conducting a 24-week POC study with 120 patients with IPF, using a statistically supported sample size and incorporating exploratory CT-based imaging biomarkers, to support decision-making in the case of non-significant primary endpoint results. This approach aims to provide data to enable a robust decision-making process for advancing clinical development of BBT-877.

METHODS AND ANALYSIS: In this phase II, double-blind, placebo-controlled study, approximately 120 patients with IPF will be randomised in a 1:1 ratio to receive placebo or 200 mg of BBT-877 two times per day over 24 weeks, with stratification according to background use of an antifibrotic treatment (pirfenidone background therapy, nintedanib background therapy or no background therapy). The primary endpoint is absolute change in forced vital capacity (FVC) (mL) from baseline to week 24. Key secondary endpoints include change from baseline to week 24 in %-predicted FVC, diffusing capacity of the lung for carbon monoxide, 6 min walk test, patient-reported outcomes, pharmacokinetics and safety, and tolerability. Key exploratory endpoints include eLung-based CT evaluation and biomarker-based assessment of pharmacodynamics.

ETHICS AND DISSEMINATION: This study is being conducted following the Declaration of Helsinki principles, Good Clinical Practice guidance, applicable local regulations and local ethics committees. An independent data monitoring committee unblinded to individual subject treatment allocation will evaluate safety and efficacy data on a regular basis throughout the study. The results of this study will be presented at scientific conferences and peer-review publications.

TRIAL REGISTRATION NUMBER: NCT05483907.

PMID:40404183 | DOI:10.1136/bmjresp-2024-003038

Nature. 2025 May 22. doi: 10.1038/s41586-025-09124-6. Online ahead of print.

NO ABSTRACT

PMID:40404940 | DOI:10.1038/s41586-025-09124-6

Sci Rep. 2025 May 22;15(1):17813. doi: 10.1038/s41598-025-01819-0.

ABSTRACT

α-Glucosidase is a key enzyme responsible for controlling the blood glucose, making a pivotal target in the treatment of type 2 diabetes mellitus. Present work introduces1,2,4triazolo[1,5-a]pyridine as a novel, potent scaffold for α-glucosidase inhibition. A diverse scope of targeted compounds was prepared through an efficient, straightforward synthetic protocol. A series of compounds (15a-15v) were synthesized using a simple and efficient protocol, all showing notable inhibitory activity. Among them, compound 15j exhibited the best inhibition potency (IC₅₀ = 6.60 ± 0.09 µM), acting as a competitive and selective α-glucosidase inhibitor with no effect on α-amylase. Moreover, comprehensive computational studies were performed to validate the in vitro results and provide insight into compounds' binding interactions within the α-glucosidase's active site. The machine learning model, trained with the Estate fingerprint, achieved an AUC score of 0.65, demonstrating its utility in predicting α-glucosidase inhibition. Random Forest was identified as the most suitable model, and the dataset with the highest R² value was selected for further feature selection and model improvement. Molecular docking studies demonstrated that compound 15j had a strong binding affinity toward α-glucosidase, with a docking score of - 10.04 kcal/mol, and formed several remarkable interactions, particularly three key hydrogen bonds with TYR158, GLN353, and GLU411, contributing to its high inhibitory efficacy. The results of the molecular dynamics simulation demonstrated that the 15j-α-glucosidase complex exhibits high stability and effectively maintains its binding without causing significant structural changes in the enzyme, confirming the stable interaction and selective inhibition of this compound at the enzyme's active site.

PMID:40404778 | DOI:10.1038/s41598-025-01819-0

NPJ Aging. 2025 May 22;11(1):38. doi: 10.1038/s41514-025-00236-x.

ABSTRACT

DNA methylation clocks have been widely used for accurate age prediction, but most studies have been carried out on mammals. Here we present an epigenetic clock for the aquatic frog Xenopus tropicalis, a widely used model organism in developmental biology and genomics. To construct the clock, we collected DNA methylation data from 192 frogs using targeted bisulfite sequencing at genomic regions containing CpG sites previously shown to have age-associated methylation in Xenopus. We found highly positively and negatively age-correlated CpGs are enriched in heterochromatic regions marked with H4K20me3 and H3K9me3. Positively age-correlated CpGs are enriched in bivalent chromatin and gene bodies with H3K36me3, and tend to be proximal to lowly expressed genes. These epigenetic features of aging are similar to those found in mammals, suggesting evolutionary conservation of epigenetic aging mechanisms. Our clock enables future aging biology experiments that leverage the unique properties of amphibians.

PMID:40404700 | DOI:10.1038/s41514-025-00236-x

Sci Rep. 2025 May 22;15(1):17735. doi: 10.1038/s41598-025-02444-7.

ABSTRACT

Breast cancer is a complex and challenging disease to treat, and despite progress in combating it, drug resistance remains a significant hindrance. Drug combinations have shown promising results in improving therapeutic outcomes, and many machine learning models have been proposed to identify potential drug combinations. Recently, there has been a growing emphasis on enhancing the interpretability of machine learning models to improve our biological understanding of the drug mechanisms underlying the predictions. In this study, we developed a random forest model using simulated protein activities derived from Boolean modeling of breast cancer signaling pathways as input features. The model demonstrates a moderate Pearson's correlation coefficient of 0.40 between the predicted and experimentally observed synergistic scores, with the area under the curve (AUC) of 0.67. Despite its moderate performance, the model offers insights into the interpretable mechanisms behind its predictions. The model's input features consist solely of the individual protein activities simulated in response to drug treatments. Therefore, the framework allows for the analysis of each protein's contribution to the synergy level of each drug pair, enabling a direct interpretation of the drugs' actions on the signaling networks of breast cancer. We demonstrated the interpretability of our approach by identifying proteins responsible for drug resistance and sensitivity in specific cell lines. For example, the analysis revealed that the combination of MEK and STAT3 inhibitors exhibits only a moderate synergistic effect on MDA-MB-468 due to the negative contributions of mTORC1 and NF-κB that diminish the efficacy of the drug pair. The model further predicted that hyperactive PTEN would sensitize the cells to the drug pair. Our framework enhances the understanding of drug mechanisms at the level of the signaling pathways, potentially leading to more effective treatment designs.

PMID:40404689 | DOI:10.1038/s41598-025-02444-7

Nat Commun. 2025 May 23;16(1):4786. doi: 10.1038/s41467-025-60064-1.

ABSTRACT

High-throughput massively parallel reporter assays (MPRAs) and phenotype-rich in vivo transgenic mouse assays are two potentially complementary ways to study the impact of noncoding variants associated with psychiatric diseases. Here, we investigate the utility of combining these assays. Specifically, we carry out an MPRA in induced human neurons on over 50,000 sequences derived from fetal neuronal ATAC-seq datasets and enhancers validated in mouse assays. We also test the impact of over 20,000 variants, including synthetic mutations and 167 common variants associated with psychiatric disorders. We find a strong and specific correlation between MPRA and mouse neuronal enhancer activity. Four out of five tested variants with significant MPRA effects affected neuronal enhancer activity in mouse embryos. Mouse assays also reveal pleiotropic variant effects that could not be observed in MPRA. Our work provides a catalog of functional neuronal enhancers and variant effects and highlights the effectiveness of combining MPRAs and mouse transgenic assays.

PMID:40404660 | DOI:10.1038/s41467-025-60064-1