Sci Rep. 2025 Aug 4;15(1):28475. doi: 10.1038/s41598-025-11031-9.

ABSTRACT

Falling poses a significant health risk to the elderly, often resulting in severe injuries if not promptly addressed. As the global population increases, the frequency of falls increases along with the associated financial burden. Hence, early detection is crucial for initiating timely medical interventions and minimizing physical, social, and economic harm. With the growing demand for safety monitoring of older adults, particularly those living alone, effective fall detection has become increasingly important for supporting independent living. In this study, we propose a novel deep learning architecture and an optimization algorithm for human fall direction recognition. Subsequently, we developed four novel residual block and self-attention mechanisms, named residual block-deep convolutional neural network (3-RBNet), 5-RBNet, 7-RBNet, and 9-RBNet self-attention models. The models were trained on enhanced images, and deep features were extracted from the self-attention layer. The 7-RBNet and 9-RBNet self-attention models demonstrated superior accuracy and precision rates, leading us to exclude the 3-RBNet self model from further analysis. To optimize feature selection and improve classification performance while reducing computational costs, we employed the tree seed algorithm on the self-attention features of 7-RBNet and 9-RBNet self-attention models. Experiments using the proposed method were performed on a human fall dataset collected from Soonchunhyang University, South Korea. The proposed method achieved maximum accuracies of 93.2% and 92.5%, respectively. Compared with recent techniques, our approach improved accuracy and precision.

PMID:40760069 | DOI:10.1038/s41598-025-11031-9

Commun Med (Lond). 2025 Aug 4;5(1):331. doi: 10.1038/s43856-025-01053-9.

ABSTRACT

BACKGROUND: Ileus, a postoperative complication after colorectal surgery, increases morbidity, costs, and hospital stays. Assessing risk of ileus is crucial, especially with the trend towards early discharge. Prior studies assessed risk of ileus with regression models, the role of deep learning remains unexplored.

METHODS: We evaluated the Gated Recurrent Unit with Decay (GRU-D) for real-time ileus risk assessment in 7349 colorectal surgeries across three Mayo Clinic sites with two Electronic Health Record (EHR) systems. The results were compared with atemporal models on a panel of benchmark metrics.

RESULTS: Here we show that despite extreme data sparsity (e.g., 72.2% of labs, 26.9% of vitals lack measurements within 24 h post-surgery), GRU-D demonstrates improved performance by integrating new measurements and exhibits robust transferability. In brute-force transfer, AUROC decreases by no more than 5%, while multi-source instance transfer yields up to a 2.6% improvement in AUROC and an 86% narrower confidence interval. Although atemporal models perform better at certain pre-surgical time points, their performance fluctuates considerably and generally falls short of GRU-D in post-surgical hours.

CONCLUSIONS: GRU-D's dynamic risk assessment capability is crucial in scenarios where clinical follow-up is essential, warranting further research on built-in explainability for clinical integration.

PMID:40760048 | DOI:10.1038/s43856-025-01053-9

Sci Rep. 2025 Aug 5;15(1):28477. doi: 10.1038/s41598-025-13720-x.

ABSTRACT

This study explores how artificial intelligence technologies can enhance the regulatory capacity for legal risks in internet healthcare based on a machine learning (ML) analytical framework and utilizes data from the health insurance portability and accountability act (HIPAA) database. The research methods include data collection and processing, construction and optimization of ML models, and the application of a risk assessment framework. Firstly, the data are sourced from the HIPAA database, encompassing various data types, such as medical records, patient personal information, and treatment costs. Secondly, to address missing values and noise in the data, preprocessing methods such as denoising, normalization, and feature extraction are employed to ensure data quality and model accuracy. Finally, in the selection of ML models, this study experiments with several common algorithms, including extreme gradient boosting (XGBoost), support vector machine (SVM), random forest (RF), and deep neural network (DNN). Each algorithm has its strengths and limitations depending on the specific legal risk assessment task. RF enhances classification performance by integrating multiple decision trees, while SVM achieves efficient classification by identifying the maximum margin hyperplane. DNN demonstrates strong capabilities in handling complex nonlinear relationships, and XGBoost further improves classification accuracy by optimizing decision tree models through gradient boosting. Model performance is evaluated using metrics such as accuracy, recall, precision, F1 score, and area under curve (AUC) value. The experimental results indicate that the DNN model performs excellently in terms of F1 score, accuracy, and recall, showcasing its efficiency and stability in legal risk assessment. The principal component analysis-random forest (PCA+RF) and RF models also exhibit stable performance, making them suitable for various application scenarios. In contrast, the SVM and K-Nearest Neighbor models perform relatively weaker, although they still retain some validity in certain contexts, their overall performance is inferior to deep learning and ensemble learning methods. This study not only provides effective ML tools for legal risk assessment in internet healthcare but also offers theoretical support and practical guidance for future research in this field.

PMID:40760025 | DOI:10.1038/s41598-025-13720-x

Sci Rep. 2025 Aug 4;15(1):28431. doi: 10.1038/s41598-025-13386-5.

ABSTRACT

This paper presents a novel deep learning approach for the adaptive fusion of multicultural visual elements in cross-cultural visual communication design for interface development. We address the challenge of creating culturally appropriate digital interfaces by developing a comprehensive framework that combines convolutional neural networks, attention mechanisms, and generative adversarial networks to analyze, extract, and adaptively fuse cultural features from diverse visual communication design elements. The proposed algorithm dynamically adjusts color schemes, spatial arrangements, typography, and iconography based on target cultural preferences while maintaining visual communication design coherence and functional clarity. Experimental evaluations conducted across five cultural regions demonstrate that our approach outperforms existing methods in cultural appropriateness (17.3% improvement), aesthetic coherence (12.8% enhancement), and user satisfaction (27.3% increase). Implementation in e-commerce, educational, and financial service applications showed significant improvements in user engagement, task efficiency, and conversion rates. Our research contributes to the advancement of inclusive digital experiences by providing a computational framework for cross-cultural visual communication design that respects cultural diversity while enhancing user experience across cultural boundaries.

PMID:40760013 | DOI:10.1038/s41598-025-13386-5

Sci Rep. 2025 Aug 4;15(1):28371. doi: 10.1038/s41598-025-12464-y.

ABSTRACT

Cross-cultural sentiment analysis in restaurant reviews presents unique challenges due to linguistic and cultural differences across regions. The purpose of this study is to develop a culturally adaptive sentiment analysis model that improves sentiment detection across multilingual restaurant reviews. This paper proposes XLM-RSA, a novel multilingual model based on XLM-RoBERTa with Aspect-Focused Attention, tailored for enhanced sentiment analysis across diverse cultural contexts. We evaluated XLM-RSA on three benchmark datasets: 10,000 Restaurant Reviews, Restaurant Reviews, and European Restaurant Reviews, achieving state-of-the-art performance across all datasets. XLM-RSA attained an accuracy of 91.9% on the Restaurant Reviews dataset, surpassing traditional models such as BERT (87.8%) and RoBERTa (88.5%). In addition to sentiment classification, we introduce an aspect-based attention mechanism to capture sentiment variations specific to key aspects like food, service, and ambiance, yielding aspect-level accuracy improvements. Furthermore, XLM-RSA demonstrated strong performance in detecting cultural sentiment shifts, with an accuracy of 85.4% on the European Restaurant Reviews dataset, showcasing its robustness to diverse linguistic and cultural expressions. An ablation study highlighted the significance of the Aspect-Focused Attention, where XLM-RSA with this enhancement achieved an F1-score of 91.5%, compared to 89.1% with a simple attention mechanism. These results affirm XLM-RSA's capacity for effective cross-cultural sentiment analysis, paving the way for more accurate sentiment-driven insights in globally distributed customer feedback.

PMID:40759996 | DOI:10.1038/s41598-025-12464-y

Nat Methods. 2025 Aug 4. doi: 10.1038/s41592-025-02772-6. Online ahead of print.

ABSTRACT

Recent research in deep-learning-based foundation models promises to learn representations of single-cell data that enable prediction of the effects of genetic perturbations. Here we compared five foundation models and two other deep learning models against deliberately simple baselines for predicting transcriptome changes after single or double perturbations. None outperformed the baselines, which highlights the importance of critical benchmarking in directing and evaluating method development.

PMID:40759747 | DOI:10.1038/s41592-025-02772-6

Sci Rep. 2025 Aug 4;15(1):28405. doi: 10.1038/s41598-025-13949-6.

ABSTRACT

This study presents an Internet of Things (IoT)-enabled Deep Learning Monitoring (IoT-E-DLM) model for real-time Athletic Performance (AP) tracking and feedback in collegiate sports. The proposed work integrates advanced wearable sensor technologies with a hybrid neural network combining Temporal Convolutional Networks, Bidirectional Long Short-Term Memory (TCN + BiLSTM) + Attention mechanisms. It is designed to overcome key challenges in processing heterogeneous, high-frequency sensor data and delivering low-latency, sport-specific feedback. The system deployed edge computing for real-time local processing and cloud setup for high-complexity analytics, achieving a balance between responsiveness and accuracy. Extensive research was tested with 147 student-athletes across numerous sports, including track and field, basketball, soccer, and swimming, over 12 months at Shangqiu University. The proposed model achieved a prediction accuracy of 93.45% with an average processing latency of 12.34 ms, outperforming conventional and state-of-the-art approaches. The system also demonstrated efficient resource usage (CPU: 68.34%, GPU: 72.56%), high data capture reliability (98.37%), and precise temporal synchronization. These results confirm the model's effectiveness in enabling real-time performance monitoring and feedback delivery, establishing a robust groundwork for future developments in Artificial Intelligence (AI)-driven sports analytics.

PMID:40759726 | DOI:10.1038/s41598-025-13949-6

Sci Rep. 2025 Aug 4;15(1):28408. doi: 10.1038/s41598-025-14029-5.

ABSTRACT

In recent years, with the rise of digital twin technology in the field of artificial intelligence and the continuous advancement of hardware imaging equipment, significant progress has been made in the detection of structural damage in buildings and sculptures. Structural damage to cultural heritage buildings poses a major threat to their integrity, making accurate detection of such damage crucial for cultural heritage preservation. However, existing deep learning-based object detection technologies face limitations in achieving full coverage of architectural sculptures and enabling multi-angle, free observation, while also exhibiting substantial detection errors. To address these challenges, this paper proposes a detection method that integrates digital modeling with an improved YOLO algorithm. By scanning architectural scenes to generate digital twin models, this method enables full-angle and multi-seasonal scene transformations. Specifically, the Nanjing Sheli pagoda is selected as the research subject, where drone-based panoramic scanning is employed to create a digitalized full-scene model. The improved YOLO algorithm is then used to evaluate detection performance under varying weather and lighting conditions. Finally, evaluation metrics are utilized to automatically analyze detection accuracy and the extent of damage. Compared to traditional on-site manual measurement methods, the proposed YOLO-based automatic detection technology in digitalized scenarios significantly reduces labor costs while improving detection accuracy and efficiency. This approach provides a highly effective and reliable technical solution for assessing the extent of damage in historical buildings.

PMID:40759712 | DOI:10.1038/s41598-025-14029-5

Nat Commun. 2025 Aug 4;16(1):7150. doi: 10.1038/s41467-025-61880-1.

ABSTRACT

Healthy alveolar repair relies on the ability of alveolar stem cells to differentiate into specialized epithelial cells for gas exchange. In chronic fibrotic lung diseases such as idiopathic pulmonary fibrosis (IPF), this regenerative process is abnormal but the underlying mechanisms remain unclear. Here, using human lung tissue that represents different stages of disease and a 33-plex single-cell imaging mass cytometry (IMC), we present a high-resolution, temporo-spatial cell atlas of the regenerating alveolar niche. With unbiased mathematical methods which quantify statistically enriched interactions, CD206himacrophage subtype and an alveolar basal intermediate epithelial cell emerge as the most statistically robust spatial association in the epithelial and immune cell interactome, found across all stages of disease. Spatially resolved receptor-ligand analysis further offers an in silico mechanism by which these macrophages may influence epithelial regeneration. These findings provide a foundational step toward understanding immune-epithelial dynamics in aberrant alveolar regeneration in IPF.

PMID:40759629 | DOI:10.1038/s41467-025-61880-1

J Exp Clin Cancer Res. 2025 Aug 4;44(1):225. doi: 10.1186/s13046-025-03478-5.

ABSTRACT

BACKGROUND: The metastatic microenvironment is often rich in tumor-associated macrophages (TAMs). In uveal melanoma (UM), high levels of TAMs positively correlate with tumor progression and poorer prognosis. We hypothesize that the immunomodulation of TAMs can remodel the UM tumor microenvironment and make it more susceptible to therapeutic interventions.

METHODS: In our work, we designed a novel computational pipeline that combines single-cell transcriptomics data, network analysis, multicriteria decision techniques, and pharmacophore-based docking simulations to select molecular targets and matching repurposable drugs for TAM immunomodulation. The method generates a ranking of drug-target interactions, the most promising of which are channeled towards experimental validation.

RESULTS: To identify potential immunomodulatory targets, we created a network-based representation of the TAM interactome and extracted a regulatory core conditioned on UM expression data. Further, we selected 13 genes from this core (NLRP3, HMOX1, CASP1, GSTP1, NAMPT, HSP90AA1, B2M, ISG15, LTA4H, PTGS2, CXCL2, PLAUR, ZFP36, TANK) for pharmacophore-based virtual screening of FDA-approved compounds, followed by flexible molecular docking. Based on the ranked docking results, we chose the interaction between caspase-1 and clindamycin for experimental validation. Functional studies on macrophages confirmed that clindamycin inhibits caspase-1 activity and thereby inflammasome activation, leading to a decrease in IL-1β, IL-18, and gasdermin D cleavage products as well as a reduction in pyroptotic cell death. This clindamycin-mediated inhibition of caspase-1 was also observable in TAMs derived from the bone marrow of multiple myeloma patients.

CONCLUSIONS: Our computational workflow for drug repurposing identified clindamycin as an efficacious inhibitor of caspase-1 that suppresses inflammasome activity and pyroptosis in vitro in TAMs.

PMID:40759978 | DOI:10.1186/s13046-025-03478-5

Cell Commun Signal. 2025 Aug 4;23(1):362. doi: 10.1186/s12964-025-02306-9.

ABSTRACT

Candida albicans, responsible for approximately 70% of all Candida infections, is a leading cause of invasive candidiasis and poses a significant global health threat. With the emergence of drug-resistant strains, mortality rates have reached a staggering 63.6% in severe cases, complicating treatment options and demanding the discovery of novel therapeutic targets. To address this pressing need, using a unique multidisciplinary approach, we attempted to identify some the critical metabolic pathways that can be targeted to modulate the virulence of CAL. Condition-specific genome-scale metabolic models (GSMMs), along with a novel integrated host-CAL model developed in this study, highlighted the central role of arginine (Arg) metabolism and uncovered ALT1, an arginine biosynthesis enzyme, as a critical metabolic vulnerability in CAL virulence. Heightened expression of arginine biosynthesis genes indicated that increased arginine synthesis mainly occurred through proline intermediates during host interaction. Significantly impaired virulence and in vivo pathogenicity of ALT1-deleted CAL highlighted the potential of targeting arginine metabolism as a novel strategy to combat antifungal resistance and underscored the power of integrating systems biology with experimental approaches in identifying new therapeutic targets.

PMID:40759957 | DOI:10.1186/s12964-025-02306-9

Inflammopharmacology. 2025 Aug 4. doi: 10.1007/s10787-025-01872-1. Online ahead of print.

ABSTRACT

Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder marked by cognitive decline, cholinergic dysfunction, synaptic loss, and neuroinflammation. Existing therapies such as Donepezil and estrogen replacement offer only symptomatic relief, failing to address the complexity of the disease due to their reductionist, single-targeted approach. In this study, we employed an integrative systems biology framework to evaluate the neurotherapeutic potential of Dioscorea bulbifera (DB), a core component of the US-patented polyherbal formulation BHD (comprising Bacopa monnieri, Hippophae rhamnoides, and DB), which has shown promising neuroprotective properties in preclinical models. We identified active phytoconstituents of DB-including Emodin, Beta-sitosterol, Diosgenin, Stigmasterol, Diosbulbin B, Jarnol, and Myricetin-and systematically assessed their interaction with Alzheimer's-relevant hub-bottleneck (H-B) genes using molecular docking, gene expression integration, network pharmacology, and molecular dynamics simulations. Our findings delineate a dual mechanistic model of DB's action: (1) an Estrogen Signaling Module centered around ESR1 and its key signaling associates (MAPK1, MAPK8, AKT1, EGFR, PIK3CA, and MAP2K1), forming a tightly interconnected, feedback-regulated pathway modulating memory, synaptic plasticity, neuroprotection, and inflammation; and (2) a Cholinergic Module involving direct inhibition of ACHE, providing rapid symptomatic relief. Molecular docking and dynamic simulations confirmed the strong and stable interactions of DB bioactives with both ESR1 and ACHE, showing comparable or superior stability to reference drugs (Estradiol and Donepezil). Regulatory network analysis revealed that ESR1 is one of the most connected genes in hippocampal-specific PPI networks and is co-regulated by numerous miRNAs and transcription factors. Co-expression analysis identified additional AD-relevant genes (e.g., PIK3R1, MAPK14, PTEN, DHODH, CAV1) involved in synaptic signaling, oxidative stress, and neurogenesis, while TF-miRNA coregulatory nodes such as miR-199a-3p, miR-181a-5p, GATA2, CREB1, and HINFP added further mechanistic layers to DB's network modulation. KEGG and GO enrichment analyses mapped DB-targeted genes to critical AD pathways, including Estrogen signaling, MAPK, PI3K-AKT, TNF, FoxO, and the Alzheimer's disease pathway itself. This multi-targeted, systems-level modulation by DB underscores its potential not only as a neuroprotective nutraceutical-especially for postmenopausal women vulnerable to estrogen loss-but also as a promising adjuvant to standard AD therapies.

PMID:40759849 | DOI:10.1007/s10787-025-01872-1

Nat Plants. 2025 Aug 4. doi: 10.1038/s41477-025-02064-z. Online ahead of print.

ABSTRACT

Flowering plants rely on double fertilization for their sexual reproduction. A pair of immotile sperm cells (SCs) are conveyed through a pollen tube into the female gametophyte, where they fertilize the egg cell and central cell to give rise to the embryo and endosperm in seeds, respectively1-4. The SCs and the pollen vegetative nucleus (VN) move as a male germ unit (MGU) in pollen tubes5,6. The tryptophan-proline-proline (WPP) domain-interacting tail-anchored proteins (WITs) and WPP domain-interacting proteins (WIPs) are involved in VN migration, and the cytoskeleton is required for MGU transportation in pollen tubes7-11. Here we report that two kinesins, referred to as HUG1 and HUG2, localize at the VN envelope in a WIT- and WIP-dependent manner and surround the SCs in pollen. Mutation of HUG1 and HUG2 leads to disconnected VN and SCs, impaired MGU transportation and reduced plant fertility, supporting key roles of HUG proteins in MGU formation and transportation in pollen.

PMID:40759770 | DOI:10.1038/s41477-025-02064-z

Nat Plants. 2025 Aug 4. doi: 10.1038/s41477-025-02056-z. Online ahead of print.

ABSTRACT

Auxins are plant hormones that direct the growth and development of organisms on the basis of environmental cues. Indole-3-acetic acid (IAA) is the most abundant auxin in most plants. A variety of membrane transport proteins work together to distribute auxins. These include the AUX/LAX protein family that mediate auxin import from the apoplast to the cytosol. Here we use structural and biophysical approaches combined with molecular dynamics to study transport by Arabidopsis thaliana LAX3, which is essential for plant root formation. Transport assays document high-affinity transport of IAA, as well as competitive behaviour of the synthetic phenoxyacetic acid auxin herbicide 2,4-dichlorophenoxyacetic acid and the auxin transport inhibitors 1-naphthoxyacetic acid and 2-naphthoxyacetic acid. Four cryo-EM structures were solved with resolutions of 2.9-3.4 Å: an inward open apo structure, two inward semi-occluded structures in complex with IAA and 2,4-dichlorophenoxyacetic acid, and a fully occluded structure in complex with 2-naphthoxyacetic acid. Structurally, LAX3 consists of a bundle and a scaffold domain. The ligand-binding site is sandwiched between these domains with two histidines occupying positions analogous to the sodium-binding sites in distantly related sodium:neurotransmitter transporters. This architecture suggests that these histidines couple transport to the proton motive force. Molecular dynamics simulations are used to explore substrate binding and release, including their dependence on specific protonation states. This study advances our understanding of auxin recognition and transport by AUX/LAX, providing insights into a fundamental aspect of plant physiology and development.

PMID:40759769 | DOI:10.1038/s41477-025-02056-z

Sci Rep. 2025 Aug 4;15(1):28458. doi: 10.1038/s41598-025-13146-5.

ABSTRACT

This paper presents an intelligent computational framework for modeling nonlinear irreversible biochemical reactions (NIBR) using artificial neural networks (ANNs). The biochemical reactions are modeled using an extended Michaelis-Menten kinetic scheme involving enzyme-substrate and enzyme-product complexes, expressed through a system of nonlinear ordinary differential equations (ODEs). Datasets were generated using the Runge-Kutta 4th order (RK4) method and used to train a multilayer feedforward ANN employing the Backpropagation Levenberg-Marquardt (BLM) algorithm. The proposed BLM-ANN model is compared with two other training algorithms: Bayesian Regularization (BR) and Scaled Conjugate Gradient (SCG). Six kinetic scenarios, each with four cases of varying reaction rate constants [Formula: see text], were used to validate the models. Performance was evaluated using mean squared error (MSE), absolute error (AE), regression coefficients (R), error histograms, and auto-correlation analysis. Results show that the BLM-ANN model outperforms BR and SCG in terms of accuracy (with MSE as low as [Formula: see text]), convergence speed, and robustness across diverse kinetic profiles. Regression plots confirm high correlation with RK4 solutions, and error distributions validate the model's predictive capability. The comparison between the solution of BLM-ANN and RK4 method of the proposed model. These results demonstrate the high accuracy, reliability, and generalization capability of the proposed framework.

PMID:40759699 | DOI:10.1038/s41598-025-13146-5

Nat Commun. 2025 Aug 4;16(1):7146. doi: 10.1038/s41467-025-62478-3.

ABSTRACT

Parkinson's Disease (PD) is an incurable neurodegenerative disease that causes movement disorders. Neurons in PD aggregate α-synuclein and are depleted from the substantia nigra (SN), which is a movement control hub. The presence of α-synuclein-reactive T cells in PD patient blood suggests a role for adaptive immunity in the pathogenesis of PD. However, the characteristics of this response within the brain are not well understood. Here, we employed single-nucleus RNAseq, spatial transcriptomics, and T cell receptor (TCR) sequencing to analyze T cell and glial cell states in post-mortem PD brain tissue. CD8 + T cells were enriched in the PD SN and characterized by clonal expansion and TCR sequences with homology to those reactive to α-synuclein. Furthermore, PD T cells were spatially correlated with CD44+ astrocytes, which increased in the PD SN. Silencing CD44 in cultured astrocytes attenuated neuroinflammatory signatures, suggesting a potential therapeutic target. These findings provide insight into the neurodegenerative niche underlying T cell-mediated neuroinflammation in PD.

PMID:40759663 | DOI:10.1038/s41467-025-62478-3

Nat Commun. 2025 Aug 4;16(1):7160. doi: 10.1038/s41467-025-62529-9.

ABSTRACT

Engineering mammalian cells with synthetic circuits drives innovation in next-generation biotherapeutics and industrial biotechnology. However, applications often depend on cellular productivity, which is constrained by finite cellular resources. Here, we harness computational biology to identify drugs that boost productivity without additional genetic modifications. We perform RNA-sequencing on cells expressing an incoherent feed-forward loop (iFFL), a genetic circuit that enhances operational capacity. To find drugs that mimic this effect, we use DECCODE (Drug Enhanced Cell COnversion using Differential Expression), an unbiased method that matches our transcriptional data with thousands of drug-induced profiles. Among the compound candidates, we select Filgotinib, that enhances expression of both transiently and stably expressed genetic payloads across various experimental scenarios and cell lines, including AAV and lentivirus transduction. Our results reveal cell-specific responses, underscoring the context dependency of small-molecule treatments. Altogether, we present a versatile tool for biomedical and industrial applications requiring enhanced productivity from engineered cells.

PMID:40759644 | DOI:10.1038/s41467-025-62529-9

J Microbiol Immunol Infect. 2025 Jul 30:S1684-1182(25)00148-3. doi: 10.1016/j.jmii.2025.07.013. Online ahead of print.

ABSTRACT

BACKGROUND: Active tuberculosis drug-safety monitoring and management (aDSM) is recommended in the treatment of rifampicin-resistant tuberculosis. We established comprehensive aDSM and conducted a nationwide multicenter prospective study in Taiwan.

METHODS: We designed a treatment initiation form to capture characteristics of patients at baseline, a treatment review form to monitor symptoms, blood tests, QT intervals, and audiometry during treatment, and an adverse event report form for reporting severe adverse events (grade 3 or more), serious adverse events and adverse events resulting in discontinuation of anti-tuberculosis drugs. Severity of adverse events were categorized by using Common Terminology Criteria for Adverse Events v4.03, and causality was assessed by using the World Health Organization - Uppsala Monitoring Centre system.

RESULTS: Of 333 patients with rifampicin-resistant tuberculosis enrolled from May 2017 to February 2020, 329 (98.8 %) had adverse events and 196 (58.9 %) had severe adverse events during treatment. The top three severe adverse events were metabolism disorders (104, 31.2 %), hearing impairment (102, 30.6 %), and hepatotoxicity (64, 19.2 %). Of 403 severe adverse events reported, 284 (70.5 %) were classified as drug-related. The top five drugs associated with severe adverse events were bedaquiline (27.6 %), clofazimine (26.7 %), kanamycin (25.1 %), pyrazinamide (22.4 %) and linezolid (22.2 %). Forty-four (13.2 %) patients were hospitalized and 15 (4.5 %) had prolonged hospitalization due to adverse events. One death was considered drug-related.

CONCLUSION: Severe adverse events in the treatment of rifampicin-resistant tuberculosis were more frequent than previously reported and needed to be closely monitored and timely managed by systematic and comprehensive aDSM.

PMID:40759626 | DOI:10.1016/j.jmii.2025.07.013

World J Mens Health. 2025 Jul 28. doi: 10.5534/wjmh.250085. Online ahead of print.

ABSTRACT

PURPOSE: This study aimed to evaluate the efficacy and safety of mirabegron and tamsulosin combination therapy compared to tamsulosin monotherapy in benign prostatic hyperplasia (BPH) patients with lower urinary tract symptoms (LUTS).

MATERIALS AND METHODS: This phase 3, randomized, double-blind, placebo-controlled clinical trial evaluated the efficacy and safety of mirabegron/tamsulosin combination therapy versus tamsulosin monotherapy in men with LUTS. The trial, conducted across 25 centers from July 2021 to October 2023. Eligible participants were randomly assigned to either the combination or monotherapy group for 12 weeks. Primary efficacy endpoints included changes in total urinary frequency score (TUFS) and International Prostate Symptom Scores (IPSS), with secondary endpoints evaluating various urinary symptoms and changes in post void residual volume (PVR), maximum urinary flow rate (Qmax), and quality of life scores. Safety assessments included adverse events, PVR, Qmax, vital signs, electrocardiogram, and laboratory tests.

RESULTS: A total of 795 participants were randomized to monotherapy (n=397) and combination therapy (n=398) groups. After 12 weeks, 342 in the monotherapy and 339 in the combination therapy group completed the study, with no significant baseline differences. The combination therapy group showed a greater improvement in TUFS (-11.28) and IPSS (-10.85) scores compared to monotherapy (-8.30 and -9.85, respectively) with significant differences (p<0.0001, p=0.0325). Combination therapy showed significant improvements in storage symptoms and voiding diary variables, including daytime frequency, urgency, and incontinence, compared to monotherapy. The incidence of treatment-emergent adverse events was similar between the groups (13.10% vs 16.58%, p=0.1943), with no serious drug-related adverse events, confirming an acceptable safety profile for combination therapy.

CONCLUSIONS: Combination therapy with mirabegron and tamsulosin is more effective than monotherapy in improving LUTS in patients with BPH, particularly storage symptoms, with a comparable safety profile. A fixed-dose combination formulation in the future may further improve patient adherence and quality of life.

PMID:40759594 | DOI:10.5534/wjmh.250085

Am J Respir Crit Care Med. 2025 Aug 4. doi: 10.1164/rccm.202501-0186OC. Online ahead of print.

ABSTRACT

RATIONALE: Primary ciliary dyskinesia (PCD) is a rare respiratory disorder of motile cilia caused by pathogenic variants in >50 known genes. Genetic testing routinely examines the coding regions of these genes and bi-allelic pathogenic variants are reported in up to 70% of patients. Many patients remain with an incomplete or no genetic diagnosis.

OBJECTIVES: To retrospectively analyse the diagnostic yield in 497 patients referred for genetic testing and the increase in yield by investigating pathogenic DNA variants in the non-coding regions of PCD genes, in 42 patients with an incomplete genetic diagnosis.

METHODS: End-to-end next-generation gene sequencing including coding and non-coding regions of 17 PCD genes was performed, following routine genetic diagnosis of a panel of 46+ genes. Intronic variants were prioritised for pathogenicity using in silico tools to predict splice effects, that were subsequently confirmed in RNA extracted from nasal epithelium.

MAIN RESULTS: 232 of 496 patients (46.8%) had a complete genetic diagnosis of PCD after stringent variant assessment during routine genetic testing. Eighty-six patients (17.3%) had an incomplete genetic diagnosis, 42 of whom had end-to-end gene sequencing. Novel, potentially pathogenic, non-coding variants were identified in 16 of 42 patients (38.1%). Three recurrent deep-intronic variants were found.

CONCLUSION: Diagnostic yield for PCD is increased by end-to-end gene sequencing. Non-coding variants that affect splicing are recurrent and are an important source of pathogenic genomic variation in patients with PCD. This work illustrates the potential clinical utility of end-to-end geneor genome sequencing for PCD.

PMID:40758541 | DOI:10.1164/rccm.202501-0186OC