Comput Struct Biotechnol J. 2024 Sep 27;23:3575-3583. doi: 10.1016/j.csbj.2024.09.018. eCollection 2024 Dec.

ABSTRACT

Accessible and easy-to-use standardized bioinformatics workflows are necessary to advance microbiome research from observational studies to large-scale, data-driven approaches. Standardized multi-omics data enables comparative studies, data reuse, and applications of machine learning to model biological processes. To advance broad accessibility of standardized multi-omics bioinformatics workflows, the National Microbiome Data Collaborative (NMDC) has developed the Empowering the Development of Genomics Expertise (NMDC EDGE) resource, a user-friendly, open-source web application (https://nmdc-edge.org). Here, we describe the design and main functionality of the NMDC EDGE resource for processing metagenome, metatranscriptome, natural organic matter, and metaproteome data. The architecture relies on three main layers (web application, orchestration, and execution) to ensure flexibility and expansion to future workflows. The orchestration and execution layers leverage best practices in software containers and accommodate high-performance computing and cloud computing services. Further, we have adopted a robust user research process to collect feedback for continuous improvement of the resource. NMDC EDGE provides an accessible interface for researchers to process multi-omics microbiome data using production-quality workflows to facilitate improved data standardization and interoperability.

PMID:39963423 | PMC:PMC11832004 | DOI:10.1016/j.csbj.2024.09.018

Small Methods. 2025 Feb 17:e2402111. doi: 10.1002/smtd.202402111. Online ahead of print.

ABSTRACT

Spatially resolved transcriptomics (SRT) has emerged as a transformative technology for elucidating cellular organization and tissue architecture. However, a significant challenge remains in identifying pathology-relevant spatial functional landscapes within the tissue microenvironment, primarily due to the limited integration of cell-cell communication dynamics. To address this limitation, SpaDCN, a Spatially Dynamic graph Convolutional Network framework is proposed, which aligns cell-cell communications and gene expression within a spatial context to reveal the spatial functional regions with the coherent cellular organization. To effectively transfer the influence of cell-cell communications on expression variation, SpaDCN respectively generates the node layer and edge layer of spatial graph representation from expression data and the ligand-receptor complex contributions and then employs a dynamic graph convolution to switch the propagation of node graph and edge graph. It is demonstrated that SpaDCN outperforms existing methods in identifying spatial domains and denoising expression across various platforms and species. Notably, SpaDCN excels in identifying marker genes with significant prognostic potential in cancer tissues. In conclusion, SpaDCN offers a powerful and precise tool for spatial domain detection in spatial transcriptomics, with broad applicability across various tissue types and research disciplines.

PMID:39962819 | DOI:10.1002/smtd.202402111

Oncologist. 2025 Feb 6;30(2):oyae320. doi: 10.1093/oncolo/oyae320.

ABSTRACT

BACKGROUND: The impact of concurrent proton pump inhibitors (PPIs) use on the prognosis of patients with breast cancer undergoing cyclin-dependent kinase inhibitors (CDKIs) treatment is currently uncertain. Considerable divergence exists regarding the clinical studies. In this study, we aim to perform a comprehensive analysis to evaluate the influence of concomitant PPI use on the effectiveness and adverse effects of CDKIs in patients with breast cancer.

METHODS: This study encompassed all pertinent clinical studies published up to the present, following the PRISMA guidelines. The study used hazard ratio (HR) or odds ratio (OR) as a summary statistic and used fixed or random effects models for pooled estimation.

RESULTS: This study incorporated 10 research articles involving 2993 participants. Among patients with breast cancer undergoing treatment with CDKIs, the simultaneous administration of PPIs was associated with a notable reduction in overall survival (HR = 2.00; 95% CI, 1.35-2.96). Nevertheless, no substantial correlation was observed between the simultaneous utilization of PPIs and the progression-free survival (PFS) of patients (HR = 1.30; 95% CI, 0.98-1.74). PFS did not change significantly when considering different drugs, treatment lines, or regions alone. Furthermore, the simultaneous administration of PPIs was found to result in a notable decrease in the incidence of grades 3/4 risk factors (OR = 0.63, 95% CI, 0.46-0.85).

CONCLUSION: The concurrent administration of PPIs did not result in significant alterations in the risk of disease advancement among patients with breast cancer undergoing CDKIs treatment. The utilization of PPIs led to a decrease in the adverse effects linked to the administration of CDKIs.

PMID:39963828 | DOI:10.1093/oncolo/oyae320

Eur Heart J Cardiovasc Pharmacother. 2025 Feb 17:pvaf014. doi: 10.1093/ehjcvp/pvaf014. Online ahead of print.

ABSTRACT

AIMS: Managing patients with atherosclerotic cardiovascular disease (ASCVD) and heart failure (HF) is challenging. While sodium-glucose cotransporter 2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1 RA) show cardiovascular benefits, the impact of combining these agents is unclear. This study evaluated whether adding GLP-1 RA to SGLT2i provides additional benefits in patients with both ASCVD and HF.

METHODS AND RESULTS: This retrospective observational study utilized the TriNetX database to analyze patients with ASCVD and HF who initiated GLP-1 RA with SGLT2i or SGLT2i alone from August 1, 2016, to September 30, 2024. A total of 2 797 317 patients were identified, with 96 051 patients meeting inclusion criteria. After propensity score matching (PSM), 5 272 patients in each group were analyzed. Primary outcomes included mortality or hospitalization within one year; secondary outcomes examined mortality, hospitalization, and heart failure exacerbation (HFE). Patients receiving GLP-1RA and SGLT2i therapies had significantly lower risk of mortality or hospitalization (HR 0.78; 95% CI 0.74-0.83), mortality (HR 0.72; 95% CI 0.62-0.84), hospitalization (HR 0.78; 95% CI 0.73-0.83), and HFE (HR 0.77; 95% CI 0.72-0.83) versus SGLT2i alone. Subgroup analyses showed consistent benefits in patients with HFpEF, HFrEF, patients with diabetes, obesity, chronic kidney disease, or those using semaglutide or dulaglutide, while liraglutide use showed a neutral effect. Drug-related side effects were monitored as safety outcomes, which showed no significant differences between groups.

CONCLUSIONS: In ASCVD and HF patients, adding GLP-1 RA to SGLT2i reduces one-year mortality and hospitalization, warranting further investigation in diverse settings.

PMID:39963713 | DOI:10.1093/ehjcvp/pvaf014

BMC Bioinformatics. 2025 Feb 17;26(1):54. doi: 10.1186/s12859-025-06053-z.

ABSTRACT

Adverse drug reactions (ADRs) are among the global public health events that seriously endanger human life and cause high economic burdens. Therefore, predicting the possibility of their occurrence and taking early and effective response measures is of great significance. Constructing a correlation matrix between drugs and their adverse reactions, followed by effective correlation data mining, is one of the current strategies to predict ADRs using accessible public data. Since the number of known ADRs in real-world data is far less than the number of their unknown counterparts, the drug-ADR association matrix is very sparse, which greatly affects the classification performance of machine learning methods. To effectively address the problem of sparsity, we proposed a novel weighted pseudo-labeling framework that mines potential unknown drug-ADR pairs by integrating multiple weighted matrix factorization (MF) models and treating them as pseudo-labeled drug-ADR pairs. Pseudo-labeled data is added to the training set, and the MF model is fine-tuned to improve the classification performance. To prevent overfitting to easily found pseudo-labels and improve the quality of pseudo-labels, a novel weighting approach for pseudo-labels was adopted. This paper reproduces the baselines under the same experimental conditions to evaluate the performance of the proposed method on sparse data from the Side Effect Resource (SIDER) database. Experimental results showed that our method outperformed other baselines in the Area Under Precision-Recall and F1-scores and still maintained the best performance in sparser scenarios. Furthermore, we conducted a case study, and the results showed that our proposed framework efficiently predicted ADRs in the real world.

PMID:39962381 | DOI:10.1186/s12859-025-06053-z

JAMA Netw Open. 2025 Feb 3;8(2):e2460033. doi: 10.1001/jamanetworkopen.2024.60033.

ABSTRACT

IMPORTANCE: Donation after circulatory death (DCD) heart procurement has increased, but concerns remain about the effect of simultaneous heart and lung procurement, particularly with thoracoabdominal normothermic regional perfusion (TA-NRP), on the use of DCD lungs. Previous analyses exclude critical donor factors and organ nonuse, and rapidly rising DCD use may bias comparisons to historical controls.

OBJECTIVE: To use validated risk-adjusted models to assess whether DCD heart procurement via TA-NRP and direct procurement is associated with lung use.

DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study involved adult DCD donors between January 1, 2019, and September 30, 2024, listed in the Scientific Registry of Transplant Recipients (SRTR). The SRTR deceased donor yield model was used to develop an observed to expected (O:E) yield ratio of lung use obtained through DCD among 4 cohorts: cardiac DCD donors vs noncardiac DCD donors and cardiac DCD donors undergoing TA-NRP vs direct procurement. Temporal trends in O:E ratios were analyzed with the Cochran-Armitage test.

MAIN OUTCOMES AND MEASURES: The O:E ratios of DCD lung use.

RESULTS: Among 24 431 DCD donors (15 878 [65.0%] male; median [IQR] age, 49.0 [37.0-58.0] years), 22 607 were noncardiac DCD (14 375 [63.6%] male; median [IQR] age, 51.0 [39.0-58.0] years) and 1824 were cardiac DCD (1503 [82.4%] male; median [IQR] age, 32.0 [26.0-38.0] years) donors; noncardiac DCD donors were more likely to be smokers (6873 [30.4%] vs 227 [12.4%]; P < .001). Among cardiac DCD donors, 325 underwent TA-NRP, while 712 underwent direct procurement. TA-NRP donors had shorter median (IQR) lung ischemic times (6.07 [4.38-9.56] hours vs 8.12 [6.16-12.00] hours; P < .001) and distances to recipient hospitals (222 [9-626] nautical miles vs 331 [159-521] nautical miles; P = .050) than direct procurement donors. Lung use was higher among cardiac DCD donations compared with noncardiac DCD donations (16.7% vs 4.4%, P < .001). Within the cardiac DCD cohort, lung use was similar between TA-NRP and direct procurement (19.1% vs 18.7%; P = .88) cohorts. Both noncardiac DCD and cardiac DCD donors had observed lung yields greater than expected (O:E, 1.29 [95% CI, 1.21-1.35] and 1.79 [95% CI, 1.62-1.96]; both P < .001), although cardiac DCD yield was significantly higher than noncardiac DCD yield (P < .001). Both TA-NRP and direct procurement lung yields were greater than expected (O:E, 2.00 [95% CI, 1.60-2.43] and 1.77 [95% CI, 1.52-1.99]; both P < .001) but were not significantly different from each other (P = .83). The O:E ratios did not change significantly over time across all cohorts. Among recipients, the TA-NRP cohort experienced significantly better 90-day mortality (0 of 62 vs 9 of 128 patients [7.0%]; P = .03) and overall survival (4 of 62 patients [6.5%] vs 21 of 128 patients [16.4%]; P = .04) rates compared with the direct procurement cohort.

CONCLUSIONS AND RELEVANCE: In this cohort study of DCD donors, concomitant heart procurement provided better-than-expected rates of lung use as assessed with validated O:E use ratios regardless of procurement technique. The findings also suggest a survival benefit with improved 90-day and overall survival rates for the TA-NRP cohort compared with the direct procurement cohort. Policies should be developed to maximize the benefits of these donations.

PMID:39960670 | PMC:PMC11833517 | DOI:10.1001/jamanetworkopen.2024.60033

Sci Rep. 2025 Feb 17;15(1):5693. doi: 10.1038/s41598-025-85802-9.

ABSTRACT

Recently, deep learning-based medical image classification models have made substantial advancements. However, many existing models prioritize performance at the cost of efficiency, limiting their practicality in clinical use. Traditional Convolutional Neural Network (CNN)-based methods, Transformer-based methods, and hybrid approaches combining these two struggle to balance performance and model complexity. To achieve efficient predictions with a low parameter count, we propose DenseAttentionNetwork (DANet), a lightweight model for Alzheimer's disease detection in 3D MRI images. DANet leverages dense connections and a linear attention mechanism to enhance feature extraction and capture long-range dependencies. Its architecture integrates convolutional layers for localized feature extraction with linear attention for global context, enabling efficient multi-scale feature reuse across the network. By replacing traditional self-attention with a parameter-efficient linear attention mechanism, DANet overcomes some limitations of standard self-attention. Extensive experiments across multi-institutional datasets demonstrate that DANet achieves the best performance in area under the receiver operating characteristic curve (AUC), which underscores the model's robustness and effectiveness in capturing relevant features for Alzheimer's disease detection while also attaining a strong accuracy structure with fewer parameters. Visualizations based on activation maps further verify the model's ability to highlight AD-relevant regions in 3D MRI images, providing clinically interpretable insights into disease progression.

PMID:39962113 | DOI:10.1038/s41598-025-85802-9

Sci Rep. 2025 Feb 17;15(1):5815. doi: 10.1038/s41598-025-85703-x.

ABSTRACT

Alzheimer's Disease (AD) is a progressive condition of a neurological brain disorder recognized by symptoms such as dementia, memory loss, alterations in behaviour, and impaired reasoning abilities. Recently, many researchers have been working to develop an effective AD recognition system using deep learning (DL) based convolutional neural network (CNN) model aiming to deploy the automatic medical image diagnosis system. The existing system is still facing difficulties in achieving satisfactory performance in terms of accuracy and efficiency because of the lack of feature ineffectiveness. This study proposes a lightweight Stacked Convolutional Neural Network with a Channel Attention Network (SCCAN) for MRI based on AD classification to overcome the challenges. In the procedure, we sequentially integrate 5 CNN modules, which form a stack CNN aiming to generate a hierarchical understanding of features through multi-level extraction, effectively reducing noise and enhancing the weight's efficacy. This feature is then fed into a channel attention module to select the practical features based on the channel dimension, facilitating the selection of influential features. . Consequently, the model exhibits reduced parameters, making it suitable for training on smaller datasets. Addressing the class imbalance in the Kaggle MRI dataset, a balanced distribution of samples among classes is emphasized. Extensive experiments of the proposed model with the ADNI1 Complete 1Yr 1.5T, Kaggle, and OASIS-1 datasets showed 99.58%, 99.22%, and 99.70% accuracy, respectively. The proposed model's high performance surpassed state-of-the-art (SOTA) models and proved its excellence as a significant advancement in AD classification using MRI images.

PMID:39962097 | DOI:10.1038/s41598-025-85703-x

Insights Imaging. 2025 Feb 17;16(1):44. doi: 10.1186/s13244-025-01911-z.

ABSTRACT

OBJECTIVE: To assess the accuracy of deep learning reconstruction (DLR) technique on synthetic MRI (SyMRI) including T2 measurements and diagnostic performance of DLR synthetic MRI (SyMRIDL) in patients with knee osteoarthritis (KOA) using conventional MRI as standard reference.

MATERIALS AND METHODS: This prospective study recruited 36 volunteers and 70 patients with suspected KOA from May to October 2023. DLR and non-DLR synthetic T2 measurements (T2-SyMRIDL, T2-SyMRI) for phantom and in vivo knee cartilage were compared with multi-echo fast-spin-echo (MESE) sequence acquired standard T2 values (T2MESE). The inter-reader agreement on qualitative evaluation of SyMRIDL and the positive percent agreement (PPA) and negative percentage agreement (NPA) were analyzed using routine images as standard diagnosis.

RESULTS: DLR significantly narrowed the quantitative differences between T2-SyMRIDL and T2MESE for 0.8 ms with 95% LOA [-5.5, 7.1]. The subjective assessment between DLR synthetic MR images and conventional MRI was comparable (all p > 0.05); Inter-reader agreement for SyMRIDL and conventional MRI was substantial to almost perfect with values between 0.62 and 0.88. SyMRIDL MOAKS had substantial inter-reader agreement and high PPA/NPA values (95%/99%) using conventional MRI as standard reference. Moreover, T2-SyMRIDL measurements instead of non-DLR ones significantly differentiated normal-appearing from injury-visible cartilages.

CONCLUSION: DLR synthetic knee MRI provided both weighted images for clinical diagnosis and accurate T2 measurements for more confidently identifying early cartilage degeneration from normal-appearing cartilages.

CRITICAL RELEVANCE STATEMENT: One-acquisition synthetic MRI based on deep learning reconstruction provided an accurate quantitative T2 map and morphologic images in relatively short scan time for more confidently identifying early cartilage degeneration from normal-appearing cartilages compared to the conventional morphologic knee sequences.

KEY POINTS: Deep learning reconstruction (DLR) synthetic knee cartilage T2 values showed no difference from conventional ones. DLR synthetic T1-, proton density-, STIR-weighted images had high positive percent agreement and negative percentage agreement using MRI OA Knee Score features. DLR synthetic T2 measurements could identify early cartilage degeneration from normal-appearing ones.

PMID:39961957 | DOI:10.1186/s13244-025-01911-z

Med Biol Eng Comput. 2025 Feb 17. doi: 10.1007/s11517-025-03311-3. Online ahead of print.

ABSTRACT

Aortic aneurysms pose a significant risk of rupture. Previous research has shown that areas exposed to low wall shear stress (WSS) are more prone to rupture. Therefore, precise WSS determination on the aneurysm is crucial for rupture risk assessment. Computational fluid dynamics (CFD) is a powerful approach for WSS calculations, but they are computationally intensive, hindering time-sensitive clinical decision-making. In this study, we propose a deep learning (DL) surrogate, MultiViewUNet, to rapidly predict time-averaged WSS (TAWSS) distributions on abdominal aortic aneurysms (AAA). Our novel approach employs a domain transformation technique to translate complex aortic geometries into representations compatible with state-of-the-art neural networks. MultiViewUNet was trained on 23 real and 230 synthetic AAA geometries, demonstrating an average normalized mean absolute error (NMAE) of just 0.362 % in WSS prediction. This framework has the potential to streamline hemodynamic analysis in AAA and other clinical scenarios where fast and accurate stress quantification is essential.

PMID:39961912 | DOI:10.1007/s11517-025-03311-3

Med Biol Eng Comput. 2025 Feb 17. doi: 10.1007/s11517-025-03318-w. Online ahead of print.

ABSTRACT

Caries segmentation holds significant clinical importance in medical image analysis, particularly in the early detection and treatment of dental caries. However, existing deep learning segmentation methods often struggle with accurately segmenting complex caries boundaries. To address this challenge, this paper proposes a novel network, named AEDD-Net, which combines an attention mechanism with a dual-decoder structure to enhance the performance of boundary segmentation for caries. Unlike traditional methods, AEDD-Net integrates atrous spatial pyramid pooling with cross-coordinate attention mechanisms to effectively fuse global and multi-scale features. Additionally, the network introduces a dedicated boundary generation module that precisely extracts caries boundary information. Moreover, we propose an innovative boundary loss function to further improve the learning of boundary features. Experimental results demonstrate that AEDD-Net significantly outperforms other comparison networks in terms of Dice coefficient, Jaccard similarity, precision, and sensitivity, particularly showing superior performance in boundary segmentation. This study provides an innovative approach for automated caries segmentation, with promising potential for clinical applications.

PMID:39961911 | DOI:10.1007/s11517-025-03318-w

Eur Radiol. 2025 Feb 17. doi: 10.1007/s00330-025-11450-2. Online ahead of print.

ABSTRACT

OBJECTIVES: This study proposes a multimodal deep learning (DL) approach to investigate the impact of tumors and visceral fat on occult peritoneal metastasis in colorectal cancer (CRC) patients.

METHODS: We developed a DL model named Multi-scale Feature Fusion Network (MSFF-Net) based on ResNet18, which extracted features of tumors and visceral fat from the longest diameter tumor section and the third lumbar vertebra level (L3) in preoperative CT scans of CRC patients. Logistic regression analysis was applied to patients' clinical data that integrated with DL features. A random forest (RF) classifier was established to evaluate the MSFF-Net's performance on internal and external test sets and compare it with radiologists' performance.

RESULTS: The model incorporating fat features outperformed the single tumor modality in the internal test set. Combining clinical information with DL provided the best diagnostic performance for predicting peritoneal metastasis in CRC patients. The AUCs were 0.941 (95% CI: [0.891, 0.986], p = 0.03) for the internal test set and 0.911 (95% CI: [0.857, 0.971], p = 0.013) for the external test set. CRC patients with peritoneal metastasis had a higher visceral adipose tissue index (VATI) compared to those without. Maximum tumor diameter and VATI were identified as independent prognostic factors for peritoneal metastasis. Grad-CAM decision regions corresponded with the independent prognostic factors identified by logistic regression analysis.

CONCLUSION: The study confirms the network features of tumors and visceral fat significantly enhance predictive performance for peritoneal metastasis in CRC. Visceral fat is a meaningful imaging biomarker for peritoneal metastasis's early detection in CRC patients.

KEY POINTS: Question Current research on predicting colorectal cancer with peritoneal metastasis mainly focuses on single-modality analysis, while studies based on multimodal imaging information are relatively scarce. Findings The Multi-scale Feature Fusion Network, constructed based on ResNet18, can utilize CT images of tumors and visceral fat to detect occult peritoneal metastasis in colorectal cancer. Clinical relevance This study identified independent prognostic factors for colorectal cancer peritoneal metastasis and combines them with tumor and visceral fat network features, aiding early diagnosis and accurate prognostic assessment.

PMID:39961863 | DOI:10.1007/s00330-025-11450-2

Med Biol Eng Comput. 2025 Feb 17. doi: 10.1007/s11517-025-03325-x. Online ahead of print.

ABSTRACT

Computational drug repositioning approaches should be investigated for the identification of new treatments for Alzheimer's patients as a huge amount of omics data has been produced during pre-clinical and clinical studies. Here, we investigated a gene network in Alzheimer's patients to detect a proper therapeutic target. We screened the targets of different drugs (34,006 compounds) using data available in the Connectivity Map database. Then, we analyzed transcriptome profiles of Alzheimer's patients to discover a network of gene-drugs based on mutual information, representing an index of dependence among genes. This study identified a network consisting of 25 genes and compounds and interconnected biological processes using computational approaches. The results also highlight the diagnostic role of the 25 genes since we obtained good classification performances using a neural network model. We also suggest 12 repurposable drugs (like KU-60019, AM-630, CP55940, enflurane, ginkgolide B, linopirdine, apremilast, ibudilast, pentoxifylline, roflumilast, acitretin, and tamibarotene) interacting with 6 genes (ATM, CNR1, GLRB, KCNQ2, PDE4B, and RARA), that we linked to retrograde endocannabinoid signaling, synaptic vesicle cycle, morphine addiction, and homologous recombination.

PMID:39961913 | DOI:10.1007/s11517-025-03325-x

J Bioinform Comput Biol. 2024 Dec;22(6):2450028. doi: 10.1142/S0219720024500288. Epub 2025 Feb 1.

ABSTRACT

The prediction of drug-target affinity (DTA) is crucial for efficiently identifying potential targets for drug repurposing, thereby reducing resource wastage. In this paper, we propose a novel graph-based deep learning model for DTA that leverages adaptive structure-aware pooling for graph processing. Our approach integrates a self-attention mechanism with an enhanced graph neural network to capture the significance of each node in the graph, marking a significant advancement in graph feature extraction. Specifically, adjacent nodes in the 2D molecular graph are aggregated into clusters, with the features of these clusters weighted according to their attention scores to form the final molecular representation. In terms of model architecture, we utilize both global and hierarchical pooling, and assess the performance of the model on multiple benchmark datasets. The evaluation results on the KIBA dataset show that our model achieved the lowest mean squared error (MSE) of 0.126, which is a 0.5% reduction compared to the best-performing baseline method. Additionally, to validate the generalization capabilities of the model, we conduct comparative experiments on regression and binary classification tasks. The results demonstrate that our model outperforms previous models in both types of tasks.

PMID:39961610 | DOI:10.1142/S0219720024500288

Ageing Res Rev. 2025 Feb 15:102693. doi: 10.1016/j.arr.2025.102693. Online ahead of print.

ABSTRACT

Repurposing the existing drugs for the management of both common and rare diseases is increasingly appealing due to challenges such as high attrition rates, economic, and the slow pace of discovering and improving new drugs. Drug repurposing involves the utilization of existing medications to treat diseases for which they were not originally intended. Despite encountering scientific and economic challenges, the pharmaceutical industry is intrigued by the potential to uncover new indications for medications. Medication repurposing is applicable across different stages of drug development, with the greatest potential observed when the drug has undergone prior safety testing. In this review, strategies for repurposing drugs for Parkinson's disease (PD) are outlined, a neurodegenerative disorder predominantly impacting dopaminergic neurons in the substantia nigra pars compacta region. PD is a debilitating neurodegenerative condition marked by an amalgam of motor and non-motor symptoms. Despite the availability of certain symptomatic treatments, particularly targeting motor symptoms, there remains a lack of established drugs capable of modifying the course of PD, leading to its unchecked progression. Although standard drug discovery initiatives focusing on treatments that relieve diseases have yielded valuable understanding into the underlying mechanisms of PD, none of the numerous promising candidates identified in preclinical studies have successfully transitioned into clinically effective medications. Due to the substantial expenses associated with drug discovery endeavors, it is understandable that there has been a notable shift towards reprofiling strategies. Assessing the efficacy of an existing medication offers the additional advantage of circumventing the requirement for preclinical safety assessments and formulation enhancements, consequently streamlining the process and reducing both the duration of time and financial investments involved in bringing a treatment to clinical fruition. Furthermore, repurposed drugs may benefit from lower rates of failure, presenting an additional potential advantage. various strategies for repurposing drugs are available to researchers in the field of PD. Some of these strategies have demonstrated effectiveness in identifying appropriate drugs for clinical trials, thereby providing validation for such techniques. This review provides an overview of the diverse strategies employed for drug reprofiling from approaches that emphasise on single-gene transcriptional investigations to comprehensive epidemiological correlation analysis. Additionally, instances of previous or current research endeavors employing each strategy has been discussed. For strategies not yet implemented in PD research, their efficacy is demonstrated using examples from other disorders. In this review, we assess the safety and efficacy potential of prominent candidates repurposed as potential treatments for modifying the course of PD undergoing advanced clinical trials.

PMID:39961372 | DOI:10.1016/j.arr.2025.102693

Sci Data. 2025 Feb 17;12(1):282. doi: 10.1038/s41597-025-04580-1.

ABSTRACT

The Provenance Ontology (PROV-O) is a World Wide Web Consortium (W3C) recommended ontology used to structure data about provenance across a wide variety of domains. Basic Formal Ontology (BFO) is a top-level ontology ISO/IEC standard used to structure a wide variety of ontologies, such as the OBO Foundry ontologies and the Common Core Ontologies (CCO). To enhance interoperability between these two ontologies, their extensions, and data organized by them, a mapping methodology and set of alignments are presented according to specific criteria which prioritize semantic and logical principles. The ontology alignments are evaluated by checking their logical consistency with canonical examples of PROV-O instances and querying terms that do not satisfy the alignment criteria as formalized in SPARQL. A variety of semantic web technologies are used in support of FAIR (Findable, Accessible, Interoperable, Reusable) principles.

PMID:39962095 | DOI:10.1038/s41597-025-04580-1

J Biomed Inform. 2025 Feb 15:104804. doi: 10.1016/j.jbi.2025.104804. Online ahead of print.

ABSTRACT

OBJECTIVE: Visual representations generally serve as supplements to information, rather than as bearers of computable information themselves. Our objective is to develop a method for creating semantically-augmented graphic libraries that will serve as visual standards and can be implemented as visual assets in intelligent information systems.

METHODS: Graphics were developed using a composable approach and specified using SVG. OWL was used to represent the entities of our system, which include elements, units, graphics, graphic libraries, and library collections. A graph database serves as our data management system. Semantics are applied at multiple levels: (a) each element is associated with a semantic style class to link visual style to semantic meaning, (b) graphics are described using object properties and data properties, (c) relationships are specified between graphics, and (d) mappings are made between the graphics and outside resources.

RESULTS: The Graphic Library web application enables users to browse the libraries, view information pages for each graphic, and download individual graphics. We demonstrate how SPARQL can be employed to query the graphics database and the APIs can be used to retrieve the graphics and associated data for applications. In addition, this work shows that our method of designing composable graphics is well-suited to depicting variations in human anatomy.

CONCLUSION: This work provides a bridge between visual communication and the field of knowledge representation. We demonstrate a method for creating visual standards that are compatible with practices in biomedical ontology and implement a system for making them accessible to information systems.

PMID:39961540 | DOI:10.1016/j.jbi.2025.104804

Pharmacol Res. 2025 Feb 15:107660. doi: 10.1016/j.phrs.2025.107660. Online ahead of print.

ABSTRACT

Atypical teratoid/rhabdoid tumor (AT/RT) is a highly malignant embryonal brain tumor driven by genetic alterations inactivating the SMARCB1 or, less commonly, the SMARCA4 gene. Large-scale molecular profiling studies have identified distinct molecular subtypes termed AT/RT-TYR, -SHH and -MYC. Despite the increasing knowledge of AT/RT biology, curative treatment options are still lacking for certain risk groups and outcomes of these patients remain poor. We performed an in vitro high-throughput drug screen of 768 small molecule drugs covering conventional chemotherapeutic agents and late-stage developmental drugs in 13 AT/RT cell lines and determined intra- and inter-entity differential responses to unravel specific vulnerabilities. Our data demonstrated in vitro preferential activity of mitogen-activated protein kinase kinase (MEK) and mouse double minute 2 homolog (MDM2) inhibitors in AT/RT cell lines compared to other high-grade brain tumor cell lines including medulloblastoma and malignant glioma models. Moreover, we were able to link distinct drug response patterns to AT/RT molecular subtypes through integration of drug response data with large-scale DNA methylation and RNASeq-based expression profiles. Subtype-dependent drug response profiles demonstrated sensitivity of AT/RT-SHH cell lines to B-cell lymphoma 2 (BCL2) and heat shock protein 90 (HSP90) inhibitors, and increased activity of microtubule inhibitors, kinesin spindle protein (KSP) inhibitors, and the eukaryotic translation initiation factor 4E (eIF4E) inhibitor briciclib in a subset of AT/RT-MYC cell lines. In summary, our in vitro pharmacogenomic approach revealed preclinical evidence of tumor type- and subtype-specific therapeutic vulnerabilities in AT/RT cell lines that may inform future in vivo and clinical evaluations of novel pharmacological strategies.

PMID:39961404 | DOI:10.1016/j.phrs.2025.107660

Respir Med. 2025 Feb 15:107995. doi: 10.1016/j.rmed.2025.107995. Online ahead of print.

NO ABSTRACT

PMID:39961395 | DOI:10.1016/j.rmed.2025.107995

Sci Rep. 2025 Feb 17;15(1):5759. doi: 10.1038/s41598-025-90430-4.

ABSTRACT

The concept of a smart city has spread as a solution ensuring wider availability of data and services to citizens, apart from as a means to lower the environmental footprint of cities. Crowd density monitoring is a cutting-edge technology that enables smart cities to monitor and effectively manage crowd movements in real time. By utilizing advanced artificial intelligence and video analytics, valuable insights are accumulated from crowd behaviour, assisting cities in improving operational efficiency, improving public safety, and urban planning. This technology also significantly contributes to resource allocation and emergency response, contributing to smarter, safer urban environments. Crowd density classification in smart cities using deep learning (DL) employs cutting-edge NN models to interpret and analyze information from sensors such as IoT devices and CCTV cameras. This technique trains DL models on large datasets to accurately count people in a region, assisting traffic management, safety, and urban planning. By utilizing recurrent neural networks (RNNs) for time-series data and convolutional neural networks (CNNs) for image processing, the model adapts to varying crowd scenarios, lighting, and angles. This manuscript presents a Deep Convolutional Neural Network-based Crowd Density Monitoring for Intelligent Urban Planning (DCNNCDM-IUP) technique on smart cities. The proposed DCNNCDM-IUP technique utilizes DL methods to detect crowd densities, which can significantly assist in urban planning for smart cities. Initially, the DCNNCDM-IUP technique performs image preprocessing using Gaussian filtering (GF). The DCNNCDM-IUP technique utilizes the SE-DenseNet approach, which effectually learns complex feature patterns for feature extraction. Moreover, the hyperparameter selection of the SE-DenseNet approach is accomplished by using the red fox optimization (RFO) methodology. Finally, the convolutional long short-term memory (ConvLSTM) methodology recognizes varied crowd densities. A comprehensive simulation analysis is conducted to demonstrate the improved performance of the DCNNCDM-IUP technique. The experimental validation of the DCNNCDM-IUP technique portrayed a superior accuracy value of 98.40% compared to existing DL models.

PMID:39962323 | DOI:10.1038/s41598-025-90430-4