Biomol Biomed. 2025 May 16. doi: 10.17305/bb.2025.12475. Online ahead of print.

ABSTRACT

The preoperative human epidermal growth factor receptor type 2 (HER2) status of breast cancer is typically determined by pathological examination of a core needle biopsy, which influences the efficacy of neoadjuvant chemotherapy (NAC). However, the highly heterogeneous nature of breast cancer and the limitations of needle aspiration biopsy increase the instability of pathological evaluation. The aim of this study was to predict HER2 status in preoperative breast cancer using deep learning (DL) models based on ultrasound (US) and magnetic resonance imaging (MRI). The study included women with invasive breast cancer who underwent US and MRI at our institution between January 2021 and July 2024. US images and dynamic contrast-enhanced T1-weighted MRI images were used to construct DL models (DL-US: the DL model based on US; DL-MRI: the model based on MRI; and DL-MRI&US: the combined model based on both MRI and US). All classifications were based on postoperative pathological evaluation. Receiver operating characteristic analysis and the DeLong test were used to compare the diagnostic performance of the DL models. In the test cohort, DL-US differentiated the HER2 status of breast cancer with an AUC of 0.842 (95% CI: 0.708-0.931), and sensitivity and specificity of 89.5% and 79.3%, respectively. DL-MRI achieved an AUC of 0.800 (95% CI: 0.660-0.902), with sensitivity and specificity of 78.9% and 79.3%, respectively. DL-MRI&US yielded an AUC of 0.898 (95% CI: 0.777-0.967), with sensitivity and specificity of 63.2% and 100.0%, respectively.

PMID:40392960 | DOI:10.17305/bb.2025.12475

PLoS One. 2025 May 20;20(5):e0322711. doi: 10.1371/journal.pone.0322711. eCollection 2025.

ABSTRACT

The fish market is a crucial industry for both domestic economies and the global seafood trade. Accurate fish species classification (FSC) plays a significant role in ensuring sustainability, improving food safety, and optimizing market efficiency. This study introduces automatic FSC using Swin Transformer (ST) through transfer learning (SwinFishNet), which proposes an innovative approach to FSC by leveraging the ST model, a cutting-edge architecture known for its exceptional performance in computer vision tasks. The ST's unique ability to capture both local and global features through its hierarchical structure enhances its effectiveness in complex image classification tasks. The model utilizes three distinct datasets: the 12-class BD-Freshwater-Fish dataset, the 10-class SmallFishBD dataset, and the 20-class FishSpecies dataset, focusing on image processing-based classification. Images were preprocessed by resizing to 224 [Formula: see text] 224 pixels, normalizing, and converting to tensor format for compatibility with deep learning models. Transfer learning was applied using the ST, which was fine-tuned on these datasets and optimized with the AdamW algorithm. The model's performance was evaluated using classification accuracy (CA), F1-score, recall, precision, Matthews correlation coefficient, Cohen's kappa and confusion matrix metrics. The results yielded promising CAs: 0.9847 for BD-Freshwater-Fish, 0.9964 for SmallFishBD, and 0.9932 for the FishSpecies dataset. These results underscore the potential of the SwinFishNet in automating FSC and demonstrate its significant contributions to improving sustainability, market efficiency, and food safety in the seafood industry. This work offers a novel methodology with broad applications in both commercial and research settings, advancing the role of artificial intelligence in the fish market.

PMID:40392913 | DOI:10.1371/journal.pone.0322711

PLoS One. 2025 May 20;20(5):e0323776. doi: 10.1371/journal.pone.0323776. eCollection 2025.

ABSTRACT

To investigate the potential of artificial intelligence (AI) in Cervical Vertebral Maturation (CVM) staging, we developed and compared AI-based qualitative CVM and AI-based quantitative QCVM methods. A dataset of 3,600 lateral cephalometric images from 6 medical centers was divided into training, validation, and testing sets in an 8:1:1 ratio. The QCVM approach categorized images into six stages (QCVM I-IV) based on measurements from 13 cervical vertebral landmarks, while the qualitative method identified six stages (CS1-CS6) through morphological assessment of three cervical vertebrae. Statistical analyses evaluated the methods' performance, including the Pearson correlation coefficient, mean square error (MSE), success detection rate (SDR), precision-recall metrics, and the F1 score. For landmark prediction, our AI model demonstrated remarkable performance, achieving an SDR (error threshold of ≤ 1.0 mm) of 97.14% and with the mean prediction error across thirteen landmarks ranging narrowly from 0.17 to 0.55 mm. Based on the AI-predicted landmarks, the cervical vertebral measurements showed strong agreement with orthodontists, as indicated by a Pearson correlation coefficient of 0.98 and an MSE of 0.004. Besides, the CVM method attained an overall classification accuracy of 71.11%, while the QCVM method showed a higher accuracy of 78.33%. These findings suggest that the AI-based quantitative QCVM method offers superior performance, with higher agreement rates and classification accuracy compared to the AI-based qualitative CVM approach, indicating the fully automated QCVM model could give orthodontists a powerful tool to enhance cervical vertebral maturation staging.

PMID:40392884 | DOI:10.1371/journal.pone.0323776

PLoS One. 2025 May 20;20(5):e0323735. doi: 10.1371/journal.pone.0323735. eCollection 2025.

ABSTRACT

The role of resources and capabilities in shaping and implementing a firm's strategy is paramount. The COVID-19 pandemic underscored the necessity for managers to possess a decision-making model that facilitates the selection of resources and capabilities in a real-time, dynamic, adaptive, and iterative manner. However, the dynamic capabilities framework, which serves as a decision-making model, faces three significant issues when selecting resources and capabilities within complex contexts. These issues, identified as research gaps, include context mismatch, inappropriate treatment, and strategy alignment. These gaps serve as the foundation for decision making models. This study aims to develop a decision-making model for determining banking resources and capabilities. The novelty of this study is encapsulated in the proposed decision-making model for resource and capability determination in complex contexts. Furthermore, this study employed a methodology adapted from the International Society of Pharmacoeconomics and Outcomes Research-Society of Medical Decision Making (ISPOR-SMDM). The research methodology was conducted in ten stages to develop a decision-making model. This study used qualitative methods, a case study strategy, and an abductive approach. The research sample consists of Indonesian State-Owned Banks (SOB). This research culminated in a proposed decision-making model that includes seven managerial decisions: probe, sense, structuring, bundling, building, leverage, and reconfiguring. This model integrates fuzzy preference judgments as inputs, deep learning analytics (predictive analysis) as processes, and success rate predictions as outputs. Theoretically, this research contributes to the enhancement of dynamic capabilities through the complex domains of the cynefin framework. Practically, it offers a decision-making model for the board of directors (BOD) to determine resources and capabilities amid complex environmental changes.

PMID:40392866 | DOI:10.1371/journal.pone.0323735

J Comput Biol. 2025 May 20. doi: 10.1089/cmb.2025.0075. Online ahead of print.

ABSTRACT

It is estimated that approximately 15% of cancers worldwide can be linked to viral infections. The viruses that can cause or increase the risk of cancer include human papillomavirus, hepatitis B and C viruses, Epstein-Barr virus, and human immunodeficiency virus, to name a few. The computational analysis of the massive amounts of tumor DNA data, whose collection is enabled by the advancements in sequencing technologies, has allowed studies of the potential association between cancers and viral pathogens. However, the high diversity of oncoviral families makes reliable detection of viral DNA difficult, and the training of machine learning models that enable such analysis computationally challenging. We introduce XVir, a data pipeline that deploys a transformer-based deep learning architecture to reliably identify viral DNA present in human tumors. XVir is trained on a mix of sequencing reads coming from viral and human genomes, resulting in a model capable of robust detection of potentially mutated viral DNA across a range of experimental settings. Results on semi-experimental data demonstrate that XVir is able to achieve high classification accuracy, generally outperforming state-of-the-art competing methods. In particular, it retains high accuracy even when faced with diverse viral populations while being significantly faster to train than other large deep learning-based classifiers.

PMID:40392695 | DOI:10.1089/cmb.2025.0075

J Med Internet Res. 2025 May 20;27:e71560. doi: 10.2196/71560.

ABSTRACT

BACKGROUND: With the rapid development of digital biomarkers in Parkinson disease (PD) research, it has become increasingly important to explore the current research trends and key areas of focus.

OBJECTIVE: This study aimed to comprehensively evaluate the current status, hot spots, and future trends of global PD biomarker research, and provide a systematic review of deep learning models for freezing of gait (FOG) digital biomarkers.

METHODS: This study used bibliometric analysis based on the Web of Science Core Collection database to conduct a comprehensive analysis of the multidimensional landscape of Parkinson digital biomarkers. After identifying research hot spots, the study also followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines for a scoping review of deep learning models for FOG from 5 databases: Web of Science, PubMed, IEEE Xplore, Embase, and Google Scholar.

RESULTS: A total of 750 studies were included in the bibliometric analysis, and 40 studies were included in the scoping review. The analysis revealed a growing number of related publications, with 3700 researchers contributing. Neurology had the highest average annual participation rate (12.46/19, 66%). The United States contributed the most research (192/1171, 16.4%), with 210 participating institutions, which was the highest among all countries. In the study of deep learning models for FOG, the average accuracy of the models was 0.92, sensitivity was 0.88, specificity was 0.90, and area under the curve was 0.91. In addition, 31 (78%) studies indicated that the best models were primarily convolutional neural networks or convolutional neural networks-based architectures.

CONCLUSIONS: Research on digital biomarkers for PD is currently at a stable stage of development, with widespread global interest from countries, institutions, and researchers. However, challenges remain, including insufficient interdisciplinary and interinstitutional collaboration, as well as a lack of corporate funding for related projects. Current research trends primarily focus on motor-related studies, particularly FOG monitoring. However, deep learning models for FOG still lack external validation and standardized performance reporting. Future research will likely progress toward deeper applications of artificial intelligence, enhanced interinstitutional collaboration, comprehensive analysis of different data types, and the exploration of digital biomarkers for a broader range of Parkinson symptoms.

TRIAL REGISTRATION: Open Science Foundation (OSF Registries) OSF.IO/RG8Y3; https://doi.org/10.17605/OSF.IO/RG8Y3.

PMID:40392578 | DOI:10.2196/71560

Mol Divers. 2025 May 20. doi: 10.1007/s11030-025-11211-9. Online ahead of print.

ABSTRACT

MicroRNAs (miRNAs) are evolutionarily conserved small regulatory elements that are ubiquitous in cells and are found to be abnormally expressed during the onset and progression of several human diseases. miRNAs are increasingly recognized as potential diagnostic and therapeutic targets that could be inhibited by small molecules (SMs). The knowledge of SM-miRNA associations (SMAs) is sparse, mainly because of the dynamic and less predictable 3D structures of miRNAs that restrict the high-throughput screening of SMs. Toward augmenting the costly and laborious experiments determining the SM-miRNA interactions, machine learning (ML) has emerged as a cost-effective and efficient platform. In this article, various aspects associated with the ML-guided predictions of SMAs are thoroughly reviewed. Firstly, a detailed account of the SMA data resources useful for algorithms training is provided, followed by an elaboration of various feature extraction methods and similarity measures utilized on SMs and miRNAs. Subsequent to a summary of the ML algorithms basics and a brief description of the performance measures, an exhaustive census of all the 32 ML-based SMA prediction methods developed so far is outlined. Distinctive features of these methods have been described by classifying them into six broad categories, namely, classical ML, deep learning, matrix factorization, network propagation, graph learning, and ensemble learning methods. Trend analyses are performed to investigate the patterns in ML algorithms usage and performance achievement in SMA prediction. Outlining key principles behind the up-to-date methodologies and comparing their accomplishments, this review offers valuable insights into critical areas for future research in ML-based SMA prediction.

PMID:40392452 | DOI:10.1007/s11030-025-11211-9

J Imaging Inform Med. 2025 May 20. doi: 10.1007/s10278-025-01504-8. Online ahead of print.

ABSTRACT

Deep learning methods provide enormous promise in automating manually intense tasks such as medical image segmentation and provide workflow assistance to clinical experts. Deep neural networks (DNN) require a significant amount of training examples and a variety of expert opinions to capture the nuances and the context, a challenging proposition in oncological studies (H. Wang et al., Nature, vol. 620, no. 7972, pp. 47-60, Aug 2023). Inter-reader variability among clinical experts is a real-world problem that severely impacts the generalization of DNN reproducibility. This study proposes quantifying the variability in DNN performance using expert opinions and exploring strategies to train the network and adapt between expert opinions. We address the inter-reader variability problem in the context of prostate gland segmentation using a well-studied DNN, the 3D U-Net model. Reference data includes magnetic resonance imaging (MRI, T2-weighted) with prostate glandular anatomy annotations from two expert readers (R#1, n = 342 and R#2, n = 204). 3D U-Net was trained and tested with individual expert examples (R#1 and R#2) and had an average Dice coefficient of 0.825 (CI, [0.81 0.84]) and 0.85 (CI, [0.82 0.88]), respectively. Combined training with a representative cohort proportion (R#1, n = 100 and R#2, n = 150) yielded enhanced model reproducibility across readers, achieving an average test Dice coefficient of 0.863 (CI, [0.85 0.87]) for R#1 and 0.869 (CI, [0.87 0.88]) for R#2. We re-evaluated the model performance across the gland volumes (large, small) and found improved performance for large gland size with an average Dice coefficient to be at 0.846 [CI, 0.82 0.87] and 0.872 [CI, 0.86 0.89] for R#1 and R#2, respectively, estimated using fivefold cross-validation. Performance for small gland sizes diminished with average Dice of 0.8 [0.79, 0.82] and 0.8 [0.79, 0.83] for R#1 and R#2, respectively.

PMID:40392414 | DOI:10.1007/s10278-025-01504-8

Int J Surg. 2025 May 20. doi: 10.1097/JS9.0000000000002488. Online ahead of print.

ABSTRACT

BACKGROUND: Glioblastoma (GBM) is a highly aggressive brain tumor with poor prognosis. This study aimed to construct and validate a radiomics-based machine learning model for predicting overall survival (OS) in IDH-wildtype GBM after maximal safe surgical resection using magnetic resonance imaging.

METHODS: A total of 582 patients were retrospectively enrolled, comprising 301 in the training cohort, 128 in the internal validation cohort, and 153 in the external validation cohort. Volumes of interest (VOIs) from contrast-enhanced T1-weighted imaging (CE-T1WI) were segmented into three regions: contrast-enhancing tumor, necrotic non-enhancing core, and peritumoral edema using an ResNet-based segmentation network. A total of 4,227 radiomic features were extracted and filtered using LASSO-Cox regression to identify signatures. The prognostic model was constructed using the Mime prediction framework, categorizing patients into high- and low-risk groups based on the median OS. Model performance was assessed using the concordance index (CI) and Kaplan-Meier survival analysis. Independent prognostic factors were identified through multivariable Cox regression analysis, and a nomogram was developed for individualized risk assessment.

RESULTS: The Step Cox [backward] + RSF model achieved CIs of 0.89, 0.81, and 0.76 in the training, internal and external validation cohorts. Log-rank tests demonstrated significant survival differences between high- and low-risk groups across all cohorts (P < 0.05). Multivariate Cox analysis identified age (HR: 1.022; 95% CI: 0.979, 1.009, P < 0.05), KPS score (HR: 0.970, 95% CI: 0.960, 0.978, P < 0.05), rad-scores of the necrotic non-enhancing core (HR: 8.164; 95% CI: 2.439, 27.331, P < 0.05), and peritumoral edema (HR: 3.748; 95% CI: 1.212, 11.594, P < 0.05) as independent predictors of OS. A nomogram integrating these predictors provided individualized risk assessment.

CONCLUSION: This deep learning segmentation-based radiomics model demonstrated robust performance in predicting OS in GBM after maximal safe surgical resection. By incorporating radiomic signatures and advanced machine learning algorithms, it offers a non-invasive tool for personalized prognostic assessment and supports clinical decision-making.

PMID:40391963 | DOI:10.1097/JS9.0000000000002488

Adv Sci (Weinh). 2025 May 20:e2416161. doi: 10.1002/advs.202416161. Online ahead of print.

ABSTRACT

The clinical benefits of neoadjuvant chemoimmunotherapy (NACI) are demonstrated in patients with bladder cancer (BCa); however, more than half fail to achieve a pathological complete response (pCR). This study utilizes multi-center cohorts of 2322 patients with pathologically diagnosed BCa, collected between January 1, 2014, and December 31, 2023, to explore the correlation between tumor budding (TB) status and NACI response and disease prognosis. A deep learning model is developed to noninvasively evaluate TB status based on CT images. The deep learning model accurately predicts the TB status, with area under the curve values of 0.932 (95% confidence interval: 0.898-0.965) in the training cohort, 0.944 (0.897-0.991) in the internal validation cohort, 0.882 (0.832-0.933) in external validation cohort 1, 0.944 (0.908-0.981) in the external validation cohort 2, and 0.854 (0.739-0.970) in the NACI validation cohort. Patients predicted to have a high TB status exhibit a worse prognosis (p < 0.05) and a lower pCR rate of 25.9% (7/20) than those predicted to have a low TB status (pCR rate: 73.9% [17/23]; p < 0.001). Hence, this model may be a reliable, noninvasive tool for predicting TB status, aiding clinicians in prognosis assessment and NACI strategy formulation.

PMID:40391846 | DOI:10.1002/advs.202416161

Bioelectrochemistry. 2025 May 16;165:109006. doi: 10.1016/j.bioelechem.2025.109006. Online ahead of print.

ABSTRACT

Aptasensors utilize aptamers as recognition elements, which offer high sensitivity, selectivity, ease of modification, outstanding stability, real-time detection, biocompatibility, cost-effectiveness, and good integration. Utilizing metal-organic framework (MOF)-based electrochemical aptasensors is a promising approach that is drawing significant attention. MOF-based aptasensors for cancer detection present a promising avenue, yet they face several challenges. The optimization of sensitivity and selectivity in the MOF-aptamer system is complex, requiring a delicate balance to distinguish the target analyte from interfering substances in physiological environments. Efficient immobilization of aptamers on MOF surfaces while maintaining their conformation and stability under physiological conditions poses a crucial challenge for robust sensing. Integrating MOFs and aptamers with transduction systems, such as electrodes in electrochemical sensors, is critical for achieving efficient signal transduction and maintaining sensor stability. Herein, the latest developments in MOF-based electrochemical aptasensors for detecting tumor markers will be discussed. Focus will be given on single metallic, bimetallic, and calcinated-based MOFs and their strengths and weaknesses will be summarized. Further, the synthesis strategy of electrochemical sensors is analyzed to meet the requirements of selectivity and sensitivity. Finally, the future perspectives for developing and applying electrochemical aptasensors are also discussed.

PMID:40393088 | DOI:10.1016/j.bioelechem.2025.109006

Genetics. 2025 May 20:iyaf095. doi: 10.1093/genetics/iyaf095. Online ahead of print.

ABSTRACT

The nematode Caenorhabditis elegans exhibits an invariant cell lineage during its development, where the gene-molecular network that regulates the development is crucial for the biological process. While there are many molecular cell atlases describing the phenomena and key molecules involved in cell transformation, the underlying mechanisms from a systems biology perspective have received less attention. Based on an endogenous molecular-cellular theory that relates the molecular mechanisms to biological phenotypes, we constructed a model of the core endogenous network to describe the early stages of embryonic development of the C. elegans. Different cell types and intermediate cell states during development from zygotes to founder cells correspond to the steady states of the network as a nonlinear stochastic dynamical system. Connections between steady states form a topological landscape that encompasses known developmental lineage trajectories. By regulating the expression of agents in the network, we quantitatively simulated the effects of the Wnt and Notch signaling pathway on cell fate transitions and predicted the possible trajectories of transdifferentiation of the AB cell across the lineage. The success of the current study may help advance our understanding of the fundamental principles of developmental biology and cell fate determination, offering an effective tool for the quantitative analysis of cellular processes.

PMID:40393069 | DOI:10.1093/genetics/iyaf095

Sci Signal. 2025 May 20;18(887):eadk0922. doi: 10.1126/scisignal.adk0922. Epub 2025 May 20.

ABSTRACT

The extension of lamellipodia, which are thin, fanlike projections at the cell periphery, requires the assembly of branched actin networks under the control of the small GTPase Rac1. In melanoma, a hyperactive P29S Rac1 mutant is associated with resistance to inhibitors that target the kinases BRAF and MAPK and with more aggressive disease because it sequesters and inactivates the tumor suppressor merlin (encoded by NF2) inside abnormally large lamellipodia. Here, we investigated how these merlin-inactivating lamellipodia are maintained using quantitative, live cell imaging of cell morphology and signaling dynamics. We showed that Rac1 and merlin activity were regulated in spatially confined regions or microdomains within the lamellipodium. The role of merlin as a proliferation-limiting tumor suppressor required its ability to inhibit lamellipodial extension and to locally inhibit Rac1 signaling. Conversely, local inactivation of merlin in lamellipodia released these restraints on morphology and signaling, leading to enhanced proliferation. Merlin and Rac1 are thus in a morphologically and dynamically regulated double-negative feedback loop, a signaling motif that can amplify and stabilize modest stimuli of lamellipodia extensions that enable melanoma to sustain mitogenic signaling under growth challenge. This represents an example of how acute oncogenicity is promoted by collaborations between cell morphological programs and biochemical signaling.

PMID:40392939 | DOI:10.1126/scisignal.adk0922

Elife. 2025 May 20;14:RP106537. doi: 10.7554/eLife.106537.

ABSTRACT

Mitosis in eukaryotes involves reorganisation of the nuclear envelope (NE) and microtubule-organising centres (MTOCs). During male gametogenesis in Plasmodium, the causative agent of malaria, mitosis is exceptionally rapid and highly divergent. Within 8 min, the haploid male gametocyte genome undergoes three replication cycles (1N to 8N), while maintaining an intact NE. Axonemes assemble in the cytoplasm and connect to a bipartite MTOC-containing nuclear pole (NP) and cytoplasmic basal body, producing eight flagellated gametes. The mechanisms coordinating NE remodelling, MTOC dynamics, and flagellum assembly remain poorly understood. We identify the SUN1-ALLAN complex as a novel mediator of NE remodelling and bipartite MTOC coordination during Plasmodium berghei male gametogenesis. SUN1, a conserved NE protein, localises to dynamic loops and focal points at the nucleoplasmic face of the spindle poles. ALLAN, a divergent allantoicase, has a location like that of SUN1, and these proteins form a unique complex, detected by live-cell imaging, ultrastructural expansion microscopy, and interactomics. Deletion of either SUN1 or ALLAN genes disrupts nuclear MTOC organisation, leading to basal body mis-segregation, defective spindle assembly, and impaired spindle microtubule-kinetochore attachment, but axoneme formation remains intact. Ultrastructural analysis revealed nuclear and cytoplasmic MTOC miscoordination, producing aberrant flagellated gametes lacking nuclear material. These defects block development in the mosquito and parasite transmission, highlighting the essential functions of this complex.

PMID:40392232 | DOI:10.7554/eLife.106537

mBio. 2025 May 20:e0093025. doi: 10.1128/mbio.00930-25. Online ahead of print.

ABSTRACT

Dose-limiting toxicities pose a major barrier to cancer treatment. While preclinical studies show that the gut microbiota influences and is influenced by anticancer drugs, data from patients paired with careful side effect monitoring remains limited. Here, we investigate capecitabine (CAP)-microbiome interactions through longitudinal metagenomic sequencing of stool from 56 advanced colorectal cancer patients. CAP significantly altered the gut microbiome, enriching for menaquinol (vitamin K2) biosynthesis genes. Transposon library screens, targeted gene deletions, and media supplementation revealed that menaquinol biosynthesis protects Escherichia coli from drug toxicity. Stool menaquinol gene and metabolite levels were associated with decreased peripheral sensory neuropathy. Machine learning models trained in this cohort predicted toxicities in an independent cohort. Taken together, these results suggest treatment-associated increases in microbial vitamin biosynthesis serve a chemoprotective role for bacterial and host cells. Further, our findings provide a foundation for in-depth mechanistic dissection, human intervention studies, and extension to other cancer treatments.IMPORTANCESide effects are common during the treatment of cancer. The trillions of microbes found within the human gut are sensitive to anticancer drugs, but the effects of treatment-induced shifts in gut microbes for side effects remain poorly understood. We profiled gut microbes in colorectal cancer patients treated with capecitabine and carefully monitored side effects. We observed a marked expansion in genes for producing vitamin K2 (menaquinone). Vitamin K2 rescued gut bacterial growth and was associated with decreased side effects in patients. We then used information about gut microbes to develop a predictive model of drug toxicity that was validated in an independent cohort. These results suggest that treatment-associated increases in bacterial vitamin production protect both bacteria and host cells from drug toxicity, providing new opportunities for intervention and motivating the need to better understand how dietary intake and bacterial production of micronutrients like vitamin K2 influence cancer treatment outcomes.

PMID:40391895 | DOI:10.1128/mbio.00930-25

Brief Bioinform. 2025 May 1;26(3):bbaf220. doi: 10.1093/bib/bbaf220.

ABSTRACT

Prostate cancer (PCa) is observed with high incidence in men worldwide. Ferroptosis, occurred from disorders in a series of gene and pathway regulation, is an emerging target against cancer. However, most of the computational approaches solely treated ferroptosis-related genes (FRGs) as independent variables in model training, and the interactions among FRGs and other candidates were not fully deciphered in a disease-specific content. In this study, a novel network-based and knowledge-guided bioinformatics model was proposed by integrating ferroptosis-related prior knowledge with topological and functional characterization on a protein-protein interaction network for biomarker discovery in PCa development and ferroptosis. The model started at a random walk with restart algorithm for weighting genes close to known FRGs in the PCa-specific network to extract a core subnetwork for robustness and vulnerability analysis. Then key regulatory modules and a candidate gene, i.e. PRKCA, were respectively identified using a multi-level prioritization strategy with hub-bottleneck node filtering, edge-based gene co-expression measuring, community module detecting and a newly defined Ferr.neighbor functional score. The experimental validation using human clinical samples, cell lines, and nude mice convinced the role of PRKCA as a latent biomarker and a tumor suppressor in PCa carcinogenesis with a potential mechanism on triggering GPX4-mediated ferroptosis of PCa cells. This study provides a general-purpose systems biology framework for significant FRG screening, and future translational perspectives of PRKCA as a novel diagnostic and therapeutic signature for PCa management should be explored.

PMID:40391833 | DOI:10.1093/bib/bbaf220

Adv Sci (Weinh). 2025 May 20:e02278. doi: 10.1002/advs.202502278. Online ahead of print.

ABSTRACT

In the field of triboelectric nanogenerators (TENGs), the application of a thin lubricant layer on the contact surface and its maintenance for long-term cycling remain important challenges for improving the mechanical-electrical stability of TENGs. Herein, a simple and innovative approach is proposed to solve this dilemma using commercial oil-absorbing sheets and oil infusion steps. In particular, a wind-driven nano-oil-barrier-based fluttering triboelectric nanogenerator (NF-TENG) is developed. The nano-oil barrier (of nanoscale thickness) of NF-TENG is thoroughly analyzed using atomic force microscopy imaging and electrical-mechanical measurement/calculation results. Compared with other control groups, only NF-TENG maintains 95% output performance from 100% initial output performance, and device damage is minimized even after 970,000 cycles. The mechanism of NF-TENG and its differences from previous studies are established. NF-TENG is optimized and studied for various design variables and wind speeds. NF-TENG generated a peak power of 468 µW with 100 Hz and an average power of 166 µW at optimum load resistance, under a breeze wind speed of 6 m s-1. NF-TENG demonstrates its applications in two real-life scenarios: 1) wind harvesting at a rooftop vent pipe for outdoor temperature-humidity sensing, and 2) wind harvesting during bicycle riding for safety light illumination.

PMID:40391798 | DOI:10.1002/advs.202502278

JACC Clin Electrophysiol. 2025 Apr 16:S2405-500X(25)00272-5. doi: 10.1016/j.jacep.2025.04.015. Online ahead of print.

ABSTRACT

BACKGROUND: Vagal responses (VR) are frequently observed during pulmonary vein isolation (PVI) with pulsed field ablation (PFA). Our aim was to compare the effectiveness of two different anticholinergic (AC) medications, namely Glycopyrrolate (GLY) or Atropine (ATP), for VR prophylaxis in patients undergoing PVI via a pentaspline PFA catheter.

METHODS: Consecutive AF patients undergoing first-time PVI with PFA were prospectively enrolled at four centres between April 2023 and March 2024. Intravenous GLY 0.2mg [Group GLY] or Atropine 1mg [Group ATP] were administered prophylactically before transseptal access. Clinically relevant VRs included sinus bradycardia (<40 beats/min), asystole (>6 sec), atrioventricular block (AVB), need for temporary backup pacing. The incidence of periprocedural VRs was compared with that of patients without prophylactic AC drug administration (Group noAC). Drug-related adverse events were compared between the two anticholinergic drugs.

RESULTS: We enrolled 240 (61±12 years, 60.0% males) patients (GLY:80 patients; ATP: 80 patients; noAC: 80 patients). Intraprocedural VRs were observed in 65 (27.1%) patients. GLY and ATP effectively reduced overall VRs (GLY: 7.5% vs. ATP: 11.3% vs. noAC:62.5%; p<0.001), asystole (GLY: 1.3% vs. ATP: 2.5% vs. noAC: 33.8%; p<0.001), and need for temporary backup pacing (GLY: 1.3% vs. ATP: 5.0% vs. noAC: 23.8%; p<0.001). The risk of overall drug-related adverse events (8.8% vs 0%; p=0.007) and drug-induced AF (5% vs 0%; p=0.043) was significantly higher with ATP.

CONCLUSIONS: Prophylactic AC drug administration effectively prevented clinically relevant VRs in patients undergoing PVI with PFA. Both AC drugs were equally highly effective, but ATP showed a significantly higher rate of drug-induced adverse events.

PMID:40392662 | DOI:10.1016/j.jacep.2025.04.015

Urolithiasis. 2025 May 20;53(1):95. doi: 10.1007/s00240-025-01760-x.

ABSTRACT

This study was designed to assess the safety and efficacy of Silodosin as a medical expulsive therapy following shockwave lithotripsy (SWL) in paediatric patients with renal stones. In this prospective randomized controlled study conducted at Tanta University Hospital from January 2022 to March 2024, thirty children with a single de novo radiopaque renal pelvic stone less than 2 cm scheduled for SWL were randomized into two equal groups. Group A (n = 15) received Silodosin 4 mg once daily after the first SWL session, and Group B (n = 15) received a matching placebo. The first dose was administered on the night of the initial SWL session and continued until stone-free status was confirmed, for a maximum of 4 weeks. The stone expulsion time was set as a primary outcome, while the secondary outcomes were one-session stone-free rate (SFR), postoperative pain scores, and Silodosin related adverse events. The results showed that the mean stone expulsion time in group A (11.4 ± 1.8 days) was significantly shorter compared to group B (16.4 ± 1.6 days; P < 0.0001). One-session SFR was 86.6% in Silodosin group compared to 73.3% in Placebo group (P = 0.6). Pain visual analogue scores were significantly lower in group A (2.31 ± 1.75) than in group B (5.08 ± 2.43; P = 0.003). No severe drug-related adverse effects were reported in either group. In conclusion, Silodosin appears to be a safe and effective adjunct to SWL in paediatric patients, significantly reducing stone expulsion time and postoperative pain. Larger studies are needed to confirm these findings.

PMID:40392272 | DOI:10.1007/s00240-025-01760-x

Hum Vaccin Immunother. 2025 Dec;21(1):2506294. doi: 10.1080/21645515.2025.2506294. Epub 2025 May 20.

ABSTRACT

This study aims to evaluate the immunogenicity and safety of hepatitis A vaccine administered with one or two doses among adults. Participants aged 18 y and above were recruited, with blood samples collected prior to vaccination for anti-HAV antibodies screening. All participants received a single dose of hepatitis A vaccine. Participants who tested negative for anti-HAV antibodies before vaccination were randomly assigned to four groups to receive the second dose at different intervals. Blood samples were collected for antibody testing. Adverse events were reported within 28 d after each vaccination for safety assessment. A total of 1,042 participants were included in study analysis. The seroprevalence of anti-HAV antibodies was 52.56%, with the lowest seroprevalence observed among adults aged 36-40 y. The overall seroconversion rate 1 month after the first dose of hep A vaccine was 67.68%. For participants in group A, the second dose was administered at a 6-month interval, both the seropositivity and seroconversion rates reached 100%, with a GMC of 3602.44 IU/L 1 months after the second vaccination. Difference of GMCs had no statistical significance across age groups. The incidence of adverse reactions (ARs) within 28 d after second vaccination in group A was 3.85%. No serious adverse events (SAEs) related to vaccination were reported. This interim analysis highlights the susceptibility of adults to hepatitis A virus (HAV). One or two doses of hepatitis A vaccine demonstrated good immunogenicity and safety in adults.

PMID:40391688 | DOI:10.1080/21645515.2025.2506294