J Med Signals Sens. 2025 Feb 28;15:6. doi: 10.4103/jmss.jmss_46_24. eCollection 2025.

ABSTRACT

BACKGROUND: Gastroesophageal reflux disease (GERD) is a prevalent digestive disorder that impacts millions of individuals globally. Multichannel intraluminal impedance-pH (MII-pH) monitoring represents a novel technique and currently stands as the gold standard for diagnosing GERD. Accurately characterizing reflux events from MII data are crucial for GERD diagnosis. Despite the initial introduction of clinical literature toward software advancements several years ago, the reliable extraction of reflux events from MII data continues to pose a significant challenge. Achieving success necessitates the seamless collaboration of two key components: a reflux definition criteria protocol established by gastrointestinal experts and a comprehensive analysis of MII data for reflux detection.

METHOD: In an endeavor to address this challenge, our team assembled a dataset comprising 201 MII episodes. We meticulously crafted precise reflux episode definition criteria, establishing the gold standard and labels for MII data.

RESULT: A variety of signal-analyzing methods should be explored. The first Isfahan Artificial Intelligence Competition in 2023 featured formal assessments of alternative methodologies across six distinct domains, including MII data evaluations.

DISCUSSION: This article outlines the datasets provided to participants and offers an overview of the competition results.

PMID:40191685 | PMC:PMC11970833 | DOI:10.4103/jmss.jmss_46_24

J Med Signals Sens. 2025 Feb 28;15:5. doi: 10.4103/jmss.jmss_55_24. eCollection 2025.

ABSTRACT

BACKGROUND: Multiple sclerosis (MS) is one of the most common reasons of neurological disabilities in young adults. The disease occurs when the immune system attacks the central nervous system and destroys the myelin of nervous cells. This results in appearing several lesions in the magnetic resonance (MR) images of patients. Accurate determination of the amount and the place of lesions can help physicians to determine the severity and progress of the disease.

METHOD: Due to the importance of this issue, this challenge has been dedicated to the segmentation and localization of lesions in MR images of patients with MS. The goal was to segment and localize the lesions in the flair MR images of patients as close as possible to the ground truth masks.

RESULTS: Several teams sent us their results for the segmentation and localization of lesions in MR images. Most of the teams preferred to use deep learning methods. The methods varied from a simple U-net structure to more complicated networks.

CONCLUSION: The results show that deep learning methods can be useful for segmentation and localization of lesions in MR images. In this study, we briefly described the dataset and the methods of teams attending the competition.

PMID:40191684 | PMC:PMC11970832 | DOI:10.4103/jmss.jmss_55_24

Front Genet. 2025 Mar 21;16:1532651. doi: 10.3389/fgene.2025.1532651. eCollection 2025.

ABSTRACT

MOTIVATION: Predicting the response of cell lines to characteristic drugs based on multi-omics gene information has become the core problem of precision oncology. At present, drug response prediction using multi-omics gene data faces the following three main challenges: first, how to design a gene probe feature extraction model with biological interpretation and high performance; second, how to develop multi-omics weighting modules for reasonably fusing genetic data of different lengths and noise conditions; third, how to construct deep learning models that can handle small sample sizes while minimizing the risk of possible overfitting.

RESULTS: We propose an innovative drug response prediction model (NMDP). First, the NMDP model introduces an interpretable semi-supervised weighted SPCA module to solve the feature extraction problem in multi-omics gene data. Next, we construct a multi-omics data fusion framework based on sample similarity networks, bimodal tests, and variance information, which solves the data fusion problem and enables the NMDP model to focus on more relevant genomic data. Finally, we combine a one-dimensional convolution method and Kolmogorov-Arnold networks (KANs) to predict the drug response. We conduct five sets of real data experiments and compare NMDP against seven advanced drug response prediction methods. The results show that NMDP achieves the best performance, with sensitivity and specificity reaching 0.92 and 0.93, respectively-an improvement of 11%-57% compared to other models. Bio-enrichment experiments strongly support the biological interpretation of the NMDP model and its ability to identify potential targets for drug activity prediction.

PMID:40191608 | PMC:PMC11968432 | DOI:10.3389/fgene.2025.1532651

APL Bioeng. 2025 Apr 3;9(2):026104. doi: 10.1063/5.0242570. eCollection 2025 Jun.

ABSTRACT

Current risk assessment models for predicting ischemic stroke (IS) in patients with atrial fibrillation (AF) often fail to account for the effects of medications and the complex interactions between drugs, proteins, and diseases. We developed an interpretable deep learning model, the AF-Biological-IS-Path (ABioSPath), to predict one-year IS risk in AF patients by integrating drug-protein-disease pathways with real-world clinical data. Using a heterogeneous multilayer network, ABioSPath identifies mechanisms of drug actions and the propagation of comorbid diseases. By combining mechanistic pathways with patient-specific characteristics, the model provides individualized IS risk assessments and identifies potential molecular pathways involved. We utilized the electronic health record data from 7859 AF patients, collected between January 2008 and December 2009 across 43 hospitals in Hong Kong. ABioSPath outperformed baseline models in all evaluation metrics, achieving an AUROC of 0.7815 (95% CI: 0.7346-0.8283), a positive predictive value of 0.430, a negative predictive value of 0.870, a sensitivity of 0.500, a specificity of 0.885, an average precision of 0.409, and a Brier score of 0.195. Cohort-level analysis identified key proteins, such as CRP, REN, and PTGS2, within the most common pathways. Individual-level analysis further highlighted the importance of PIK3/Akt and cytokine and chemokine signaling pathways and identified IS risks associated with less-studied drugs like prochlorperazine maleate. ABioSPath offers a robust, data-driven approach for IS risk prediction, requiring only routinely collected clinical data without the need for costly biomarkers. Beyond IS, the model has potential applications in screening risks for other diseases, enhancing patient care, and providing insights for drug development.

PMID:40191603 | PMC:PMC11970939 | DOI:10.1063/5.0242570

R Soc Open Sci. 2024 Oct 23;11(10):240720. doi: 10.1098/rsos.240720. eCollection 2024 Oct.

ABSTRACT

Contemporary drug discovery paradigms rely heavily on binding assays about the bio-physicochemical processes. However, this dominant approach suffers from overlooked higher-order interactions arising from the intricacies of molecular mechanisms, such as those involving cis-regulatory elements. It introduces potential impairments and restrains the potential development of computational methods. To address this limitation, I developed a deep learning model that leverages an end-to-end approach, relying exclusively on therapeutic information about drugs. By transforming textual representations of drug and virus genetic information into high-dimensional latent representations, this method evades the challenges arising from insufficient information about binding specificities. Its strengths lie in its ability to implicitly consider complexities such as epistasis and chemical-genetic interactions, and to handle the pervasive challenge of data scarcity. Through various modeling skills and data augmentation techniques, the proposed model demonstrates outstanding performance in out-of-sample validations, even in scenarios with unknown complex interactions. Furthermore, the study highlights the importance of chemical diversity for model training. While the method showcases the feasibility of deep learning in data-scarce scenarios, it reveals a promising alternative for drug discovery in situations where knowledge of underlying mechanisms is limited.

PMID:40191531 | PMC:PMC11972434 | DOI:10.1098/rsos.240720

Imaging Sci Dent. 2025 Mar;55(1):1-10. doi: 10.5624/isd.20240139. Epub 2025 Jan 15.

ABSTRACT

PURPOSE: This study was performed to assess the performance and accuracy of artificial intelligence (AI) in the detection and diagnosis of maxillary sinus pathologies using computed tomography (CT)/cone-beam computed tomography (CBCT) imaging.

MATERIALS AND METHODS: A comprehensive literature search was conducted across 4 databases: Google Scholar, BioMed Central (BMC), ProQuest, and PubMed. Combinations of keywords such as "DCNN," "deep learning," "convolutional neural network," "machine learning," "predictive modeling," and "data mining" were used to identify relevant articles. The study included articles that were published within the last 5 years, written in English, available in full text, and focused on diagnostic accuracy.

RESULTS: Of an initial 530 records, 12 studies with a total of 3,349 patients (7,358 images) were included. All articles employed deep learning methods. The most commonly tested pathologies were maxillary rhinosinusitis and maxillary sinusitis, while the most frequently used AI models were convolutional neural network architectures, including ResNet and DenseNet, YOLO, and U-Net. DenseNet and ResNet architectures have demonstrated superior precision in detecting maxillary sinus pathologies due to their capacity to handle deeper networks without overfitting. The performance in detecting maxillary sinus pathology varied, with an accuracy ranging from 85% to 97%, a sensitivity of 87% to 100%, a specificity of 87.2% to 99.7%, and an area under the curve of 0.80 to 0.91.

CONCLUSION: AI with various architectures has been used to detect maxillary sinus abnormalities on CT/CBCT images, achieving near-perfect results. However, further improvements are needed to increase accuracy and consistency.

PMID:40191392 | PMC:PMC11966023 | DOI:10.5624/isd.20240139

J Respir Biol Transl Med. 2025 Mar;2(1):10001. doi: 10.70322/jrbtm.2025.10001. Epub 2025 Mar 7.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a progressive, irreversible, and fatal disease with an increasing incidence and limited therapeutic options. It is characterized by the formation and deposition of excess extracellular matrix proteins resulting in the gradual replacement of normal lung architecture by fibrous tissue. The cellular and molecular mechanism of IPF has not been fully understood. A hallmark in IPF is pulmonary fibroblast to myofibroblast transformation (FMT). During excessive lung repair upon exposure to harmful stimuli, lung fibroblasts transform into myofibroblasts under stimulation of cytokines, chemokines, and vesicles from various cells. These mediators interact with lung fibroblasts, initiating multiple signaling cascades, such as TGFβ1, MAPK, Wnt/β-catenin, NF-κB, AMPK, endoplasmic reticulum stress, and autophagy, contributing to lung FMT. Furthermore, single-cell transcriptomic analysis has revealed significant heterogeneity among lung myofibroblasts, which arise from various cell types and are adapted to the altered microenvironment during pathological lung repair. This review provides an overview of recent research on the origins of lung myofibroblasts and the molecular pathways driving their formation, with a focus on the interactions between lung fibroblasts and epithelial cells, endothelial cells, and macrophages in the context of lung fibrosis. Based on these molecular insights, targeting the lung FMT could offer promising avenues for the treatment of IPF.

PMID:40190620 | PMC:PMC11970920 | DOI:10.70322/jrbtm.2025.10001

Int J Stem Cells. 2025 Apr 7. doi: 10.15283/ijsc25011. Online ahead of print.

ABSTRACT

The etiology of chronic and lethal interstitial lung disease, termed idiopathic pulmonary fibrosis (IPF), remains unidentified. IPF induces pathological lung scarring that results in rigidity and impairs gas exchange, eventually resulting in premature mortality. Recent findings indicate that deubiquitinating enzymes play a key role in stabilizing fibrotic proteins and contribute to pulmonary fibrosis. The ubiquitin-specific protease 11 (USP11) promotes pro-fibrotic proteins, and its expression elevated in tissue samples from patients with IPF. Thus, this study aimed to examine the effects of loss of function of USP11 gene on the progression of pulmonary fibrosis by utilizing 3D cell culture alveolar organoids (AOs) that replicate the structure and functions of the proximal and distal airways and alveoli. Here, we applied the CRISPR/Cas9 system to knock out the USP11 gene in human induced pluripotent stem cells (hiPSCs) and then differentiated these hiPSCs into AOs. Loss of USP11 gene resulted in abnormalities in type 2 alveolar epithelial cells in the hiPSC-USP11KO-AOs. Moreover, knock out of the USP11 mitigates pulmonary fibrosis caused by TGF-β in hiPSC-USP11KO-AOs by reducing collagen formation and fibrotic markers, suggesting it has the therapeutic potential to treat IPF patients.

PMID:40189830 | DOI:10.15283/ijsc25011

Oncol Res. 2025 Mar 19;33(4):821-836. doi: 10.32604/or.2024.053772. eCollection 2025.

ABSTRACT

Immunotherapies have demonstrated notable clinical benefits in the treatment of cervical cancer (CC). However, the development of therapeutic resistance and diverse adverse effects in immunotherapy stem from complex interactions among biological processes and factors within the tumor immune microenvironment (TIME). Advanced omic technologies offer novel insights into a more expansive and thorough layer of the TIME. Furthermore, integrating multidimensional omics within the frameworks of systems biology and computational methodologies facilitates the generation of interpretable data outputs to characterize the clinical and biological trajectories of tumor behavior. In this review, we present advanced omics technologies that utilize various clinical samples to address scientific inquiries related to immunotherapies for CC, highlighting their utility in identifying metastasis dissemination, recurrence risk, and therapeutic resistance in patients treated with immunotherapeutic approaches. This review elaborates on the strategy for integrating multi-omics data through artificial intelligence algorithms. Additionally, an analysis of the obstacles encountered in the multi-omics analysis process and potential avenues for future research in this domain are presented.

PMID:40191729 | PMC:PMC11964870 | DOI:10.32604/or.2024.053772

Front Genet. 2025 Mar 21;16:1543056. doi: 10.3389/fgene.2025.1543056. eCollection 2025.

ABSTRACT

INTRODUCTION: Rapid advances in personalized medicine and direct-to-consumer genomic applications could increase the risk that physicians will apply genomic results inappropriately. To address a persistent lack of understanding of genomics, we implemented a pathology-supported genetic testing (PSGT) approach, guided by insights from a clinician needs assessment conducted in 2010.

METHODS: Findings from the previous clinician survey were used to develop a new patient screening tool that integrates non-communicable disease (NCD) and post-COVID-19 care pathways. In parallel to the application of this solution for stratification of patients in different treatment groups, an updated version of the original survey questionnaire was used to reassess the knowledge and willingness of healthcare professionals to apply PSGT.

RESULTS: Thirty-six respondents completed the revised needs assessment survey in October 2022, while attending a genomics session at the Annual General Practitioner Congress, Stellenbosch University, South Africa. Nearly 89% of the respondents reported having insufficient knowledge to offer genetic testing; 80% were supportive of using PSGT to differentiate inherited from lifestyle- or therapy-associated NCDs and 83.3% supported integrating wellness screening with genetic testing to identify high-risk individuals.

DISCUSSION: It appears that while clinicians are interested in learning about genomics, they continue to report significant knowledge deficits in this area, highlighting the need for targeted clinician training and tools like multidisciplinary NCD-COVID pathway analysis to improve clinical decision-making. The co-development of a genomic counseling report for ongoing studies, guided the selection of Long COVID patients for whole-genome sequencing across the illness and wellness domains.

PMID:40191609 | PMC:PMC11970434 | DOI:10.3389/fgene.2025.1543056

NAR Genom Bioinform. 2025 Apr 4;7(2):lqaf031. doi: 10.1093/nargab/lqaf031. eCollection 2025 Jun.

ABSTRACT

Defective interfering particles (DIPs) are viral deletion mutants that hamper virus replication and are, thus, potent novel antiviral agents. To evaluate possible antiviral treatments, we first need to get a deeper understanding of DIP characteristics. Thus, we performed a meta-analysis of 20 already published sequencing datasets of influenza A and B viruses (IAV and IBV) from in vivo and in vitro experiments. We analyzed each dataset for characteristics, such as deletion-containing viral genome (DelVG) length distributions, direct repeats, and nucleotide enrichment at the deletion site. Our analysis suggests differences in the length of the 3'- and 5'-end retained in IAV and IBV viral sequences upon deletion. Moreover, in vitro DelVGs tend to be shorter than those in vivo, which is a novel finding with potential implications for future DIP treatment design. Additionally, our analysis demonstrates the presence of DelVGs with longer than expected sequences, possibly related to an alternative mechanism of DelVG formation. Finally, a joint ranking of DelVGs originating from 7 A/Puerto Rico/8/1934 datasets revealed 11 highly abundant, yet unnoticed, candidates. Together, our study highlights the importance of meta-analyses to uncover yet unknown DelVG characteristics and to pre-select candidates for antiviral treatment design.

PMID:40191586 | PMC:PMC11970370 | DOI:10.1093/nargab/lqaf031

Front Psychol. 2025 Mar 21;16:1587001. doi: 10.3389/fpsyg.2025.1587001. eCollection 2025.

ABSTRACT

[This corrects the article DOI: 10.3389/fpsyg.2025.1548905.].

PMID:40191577 | PMC:PMC11970553 | DOI:10.3389/fpsyg.2025.1587001

Front Psychol. 2025 Feb 19;16:1548905. doi: 10.3389/fpsyg.2025.1548905. eCollection 2025.

ABSTRACT

INTRODUCTION: The present study aimed to evaluate the impact of time-out on power-play outcomes both in elite senior and youth matches and in relation to final (margin of victory, MoV) and current (margin of advantage, MoA) match scores (i.e., winning in unbalanced games, MW; winning-draw-losing in close games, W-D-L; losing in unbalanced games, ML).

MATERIALS AND METHODS: A total of 97 (seniors, n = 50; youth, n = 47) European Championship matches were analyzed, comparing power-plays preceded or not by a time-out in relation to the following offensive indicators: goal, exclusion, penalty, and no-goal.

RESULTS: The results reported that both senior and youth levels have been characterized by better power-play outcomes without time-out (higher goals scored: senior, p ≤ 0.01, youth, p ≤ 0.001; and lower "no goal" events: p ≤ 0.01, youth, p ≤ 0.01). Similar trends were observed with respect to the MoV. Specifically, in senior close games, there were both significantly higher goals scored (p ≤ 0.05) and fewer 'no goal' events (p ≤ 0.05), and these patterns were also evident among youth losing teams in unbalanced games. Differently, for MoA, both higher goals scored (p ≤ 0.01) and lower "no goal" events (p ≤ 0.01) emerged for senior losing teams in unbalanced games and youth close games (higher goals scored, p ≤ 0.01; and lower "no goal" events, p ≤ 0.05).

DISCUSSION: Therefore, the present study demonstrated that time-out tends to limit the success of the following power-play action and that MoV and MoA approaches do not overlap. As a consequence, coaches could benefit from these findings by being more aware of the actual time-out consequences on the following power-play as well as their defensive potentialities when the opponents call time-out.

PMID:40191572 | PMC:PMC11970554 | DOI:10.3389/fpsyg.2025.1548905

Res Pharm Sci. 2025 Feb 20;20(1):121-141. doi: 10.4103/RPS.RPS_128_23. eCollection 2025 Feb.

ABSTRACT

BACKGROUND AND PURPOSE: Colorectal cancer (CRC) holds the position of being the third most prevalent cancer and the second primary cause of cancer-related fatalities on a global scale. Approximately 65% of CRC patients survive for 5 years following diagnosis. Metastasis and recurrence frequently occur in half of CRC patients diagnosed at the late stage. This study used bioinformatics analysis to identify key signaling pathways, hub genes, transcription factors, and protein kinases involved in transforming primary CRC with liver metastasis potential. Prognostic markers in CRC were also identified.

EXPERIMENTAL APPROACH: The GSE81582 dataset was re-analyzed to identify differentially expressed genes (DEGs) in early CRC compared to non-tumoral tissues. A protein interaction network (PIN) was constructed, revealing significant modules and hub genes. Prognostic markers, transcription factors, and protein kinases were determined. Boxplot and gene set enrichment analyses were performed.

FINDINGS/RESULTS: This study identified 1113 DEGs in primary CRC compared to healthy controls. PIN analysis revealed 75 hub genes and 8 significant clusters associated with early CRC. The down-regulation of SUCLG2 and KPNA2 correlated with poor prognosis. SIN3A and CDK6 played crucial roles in early CRC transformation, affecting rRNA processing pathways.

CONCLUSION AND IMPLICATIONS: This study demonstrated several pathways, biological processes, and genes mediating the malignant transformation of healthy colorectal tissues to primary CRC and may help the prognosis and treatment of patients with early CRC.

PMID:40190820 | PMC:PMC11972027 | DOI:10.4103/RPS.RPS_128_23

J Sci Med Sport. 2025 Mar 14:S1440-2440(25)00067-2. doi: 10.1016/j.jsams.2025.03.004. Online ahead of print.

ABSTRACT

OBJECTIVES: To analyze the effects of different PA intensities during active learning on cognitive performance and physical fitness in preschool children.

DESIGN: Cluster randomized controlled trial.

METHODS: Four classrooms (n = 99 children aged 3-6 years) were randomly allocated to two intervention groups that performed either light PA (LPA, n = 26) or moderate-to-vigorous PA (MVPA, n = 25) during foreign language (English) lessons, or to a control group (n = 48) that maintained their usual sedentary lessons. The intervention consisted of two 45-min lessons per week and was performed over a 10-week period. Children's PA levels and intensity during sessions were assessed through accelerometry. Primary outcomes included the retention of foreign language vocabulary (free- and cued-recall tests), cognitive performance (BENCI battery), and physical fitness (PREFIT battery).

RESULTS: Both LPA and particularly MVPA groups resulted in greater total PA levels and intensity compared with the control group (p < 0.001) and provided significantly larger benefits in the free-recall test and verbal memory (all p < 0.05 compared to the control group). Additionally, MVPA group provided larger benefits in the free- and cued-recall tests, speed agility and cardiorespiratory fitness (all p < 0.05 compared to LPA).

CONCLUSIONS: Physically active learning appears as an effective strategy for enhancing foreign language vocabulary, cognitive performance, and physical fitness in preschool children. Increasing PA intensity seems to maximize these benefits.

PMID:40189956 | DOI:10.1016/j.jsams.2025.03.004

J Chem Theory Comput. 2025 Apr 6. doi: 10.1021/acs.jctc.5c00121. Online ahead of print.

ABSTRACT

We have developed and tested an absolute free energy perturbation (FEP) protocol, which combines all-atom molecular dynamics, replica exchange with solute tempering (REST) enhanced sampling, and a spherical harmonic restraint applied to a ligand. Our objective was to compute the binding free energy together with the underlying binding mechanism for a ligand, which binds diffusively to a protein. Such ligands represent nearly impossible targets for traditional FEP simulations. To test our FEP/REST protocol, we selected a conserved motif peptide KKPK termed minNLS from the nuclear localization signal sequence of the Venezuelan equine encephalitis virus capsid protein. This peptide fragment binds diffusively to importin-α transport protein without forming well-defined poses. Our FEP/REST simulations with a spherical restraint provided a converged estimate of minNLS binding free energy. We found that minNLS binds with moderate affinity to importin-α utilizing an unusual, purely entropic mechanism in which binding free energy is determined by favorable entropic gain. For this cationic minNLS peptide, a favorable binding entropic gain is primarily associated with the release of water from the solvation shells of charged amino acids. We demonstrated that FEP/REST simulations sample the KKPK bound ensemble well, allowing us to characterize the distribution of bound structures, binding interactions, and locations on the importin-α surface. Analysis of experimental studies offered support to our rationale behind the KKPK entropic binding mechanism.

PMID:40189800 | DOI:10.1021/acs.jctc.5c00121

Front Pharmacol. 2025 Mar 21;16:1549526. doi: 10.3389/fphar.2025.1549526. eCollection 2025.

ABSTRACT

Drug-induced liver injury (DILI) results from the liver toxicity caused by drugs or their metabolites. Gallic acid (GA) is a naturally occurring secondary metabolite found in many fruits, plants, and nuts. Recently, GA has drawn increasing attention due to its potent pharmacological properties, particularly its anti-inflammatory and antioxidant capabilities. To the best of our knowledge, this is the first review to focus on the pharmacological properties of GA and related molecular activation mechanisms regarding protection against hepatotoxicity. We also provide a thorough explanation of the physicochemical properties, fruit sources, toxicity, and pharmacokinetics of GA after reviewing a substantial number of studies. Pharmacokinetic studies have shown that GA is quickly absorbed and eliminated when taken orally, which restricts its use in development. However, the bioavailability of GA can be increased by optimizing its structure or changing its form of administration. Notably, according to toxicology studies conducted on a range of animals and clinical trials, GA rarely exhibits toxicity or side effects. The antioxidation mechanisms mainly involved Nrf2, while anti-inflammatory mechanisms involved MAPKs and NF-κB signaling pathways. Owing to its marked pharmacological properties, GA is a prospective candidate for the management of diverse xenobiotic-induced hepatotoxicity. We also discuss the applications of cutting-edge technologies (nano-delivery systems, network pharmacology, and liver organoids) in DILI. In addition to guiding future research and development of GA as a medicine, this study offers a theoretical foundation for its clinical application.

PMID:40191418 | PMC:PMC11968354 | DOI:10.3389/fphar.2025.1549526

Curr Med Res Opin. 2025 Apr 7:1-17. doi: 10.1080/03007995.2025.2470755. Online ahead of print.

ABSTRACT

Some current prostate cancer (PCa) treatment regimens are known to have adverse effects on bone, for example androgen deprivation therapy (ADT), and on cardiovascular health, for example ADT and antiandrogen therapy. Strengthened recommendations for the practical assessment and management of bone and cardiovascular health in men with PCa are needed. This review aims to provide practical guidance for healthcare providers along the continuum of patient care on the management of bone and cardiovascular health in men with PCa undergoing ADT and antiandrogen therapy based on real-world evidence. Evidence was identified by searching PubMed for publications that reported the effects of PCa treatment on bone or cardiovascular health in a real-world setting and were published between January 2017 and August 2023. Review articles were excluded. The evidence identified indicates that ADT decreases bone mineral density (BMD) and increases the risk of osteoporosis and fractures. Bone-protecting agents (BPAs) are effective at improving bone health in patients undergoing ADT and antiandrogen therapy at all stages of the PCa pathway. Despite this, the use and timing of initiation of BPAs are variable. Furthermore, real-world studies have confirmed an association between ADT and cardiovascular risk. As survival outcomes improve, maintenance of bone and cardiovascular health is increasingly important in men with PCa. Risk is a continuous variable that must be assessed throughout the continuum of PCa treatment. Therefore, all men starting ADT should be assessed for bone and cardiovascular risk. Lifestyle adjustments, dietary supplementation and pharmacological intervention may be advised.

PMID:40190143 | DOI:10.1080/03007995.2025.2470755

Sci Rep. 2025 Apr 6;15(1):11776. doi: 10.1038/s41598-025-90533-y.

ABSTRACT

Accurate segmentation of biomedical time-series, such as intracardiac electrograms, is vital for understanding physiological states and supporting clinical interventions. Traditional rule-based and feature engineering approaches often struggle with complex clinical patterns and noise. Recent deep learning advancements offer solutions, showing various benefits and drawbacks in segmentation tasks. This study evaluates five segmentation algorithms, from traditional rule-based methods to advanced deep learning models, using a unique clinical dataset of intracardiac signals from 100 patients. We compared a rule-based method, a support vector machine (SVM), fully convolutional semantic neural network (UNet), region proposal network (Faster R-CNN), and recurrent neural network for electrocardiographic signals (DENS-ECG). Notably, Faster R-CNN has never been applied to 1D signals segmentation before. Each model underwent Bayesian optimization to minimize hyperparameter bias. Results indicated that deep learning models outperformed traditional methods, with UNet achieving the highest segmentation score of 88.9 % (root mean square errors for onset and offset of 8.43 ms and 7.49 ms), closely followed by DENS-ECG at 87.8 %. Faster R-CNN and SVM showed moderate performance, while the rule-based method had the lowest accuracy (77.7 %). UNet and DENS-ECG excelled in capturing detailed features and handling noise, highlighting their potential for clinical application. Despite greater computational demands, their superior performance and diagnostic potential support further exploration in biomedical time-series analysis.

PMID:40189617 | DOI:10.1038/s41598-025-90533-y

J Microbiol Methods. 2025 Apr 4:107125. doi: 10.1016/j.mimet.2025.107125. Online ahead of print.

ABSTRACT

Conventional clinical microbiological techniques are enhanced by the introduction of artificial intelligence (AI). Comprehensive data processing and analysis enabled the development of curated datasets that has been effectively used in training different AI algorithms. Recently, a number of machine learning (ML) and deep learning (DL) algorithms are developed and evaluated using diverse microbiological datasets. These datasets included spectral analysis (Raman and MALDI-TOF spectroscopy), microscopic images (Gram and acid fast stains), and genomic and protein sequences (whole genome sequencing (WGS) and protein data banks (PDBs)). The primary objective of these algorithms is to minimize the time, effort, and expenses linked to conventional analytical methods. Furthermore, AI algorithms are incorporated with quantitative structure-activity relationship (QSAR) models to predict novel antimicrobial agents that address the continuing surge of antimicrobial resistance. During the COVID-19 pandemic, AI algorithms played a crucial role in vaccine developments and the discovery of new antiviral agents, and introduced potential drug candidates via drug repurposing. However, despite their significant benefits, the implementation of AI encounters various challenges, including ethical considerations, the potential for bias, and errors related to data training. This review seeks to provide an overview of the most recent applications of artificial intelligence in clinical microbiology, with the intention of educating a wider audience of clinical practitioners regarding the current uses of machine learning algorithms and encouraging their implementation. Furthermore, it will discuss the challenges related to the incorporation of AI into clinical microbiology laboratories and examine future opportunities for AI within the realm of infectious disease epidemiology.

PMID:40188989 | DOI:10.1016/j.mimet.2025.107125