Sci Rep. 2025 May 20;15(1):17507. doi: 10.1038/s41598-025-01999-9.

ABSTRACT

Enterprise knowledge retrieval faces challenges like sparse data and inefficient cross-domain knowledge transfer, hindering traditional methods. To address this, we develop a cross-domain recommendation model (CDR-VAE), combining a hybrid autoencoder with domain alignment, and test its effectiveness on an enterprise dataset and the Movies&Books benchmark. At a top-5 recommendation length, CDR-VAE scores HR = 0.642, Recall = 0.432, NDCG = 0.715, outperforming existing models. Removing shared latent representations reduces HR to 0.701, proving their necessity for cross-domain learning. In enterprise applications, high-activity users favor technical reports (0.903), while low-activity users shift toward cross-domain content like industry standards (0.701), confirming the model's robustness in sparse scenarios. CDR-VAE successfully tackles sparsity and cross-domain barriers, advancing enterprise knowledge management. This work provides theoretical and practical insights for deep learning-based recommendation systems in data-scarce environments.

PMID:40394166 | DOI:10.1038/s41598-025-01999-9

Sci Rep. 2025 May 20;15(1):17516. doi: 10.1038/s41598-025-02152-2.

ABSTRACT

The swift development of the Internet of Things (IoT) devices has created a pressing need for effective cybersecurity measures. They are vulnerable to different cyber threats that can compromise the functionality and security of urban systems. Distributed Denial of Service (DDoS) attacks are among IoT networks' most challenging and destructive cyber threats. With the rapid growth in IoT devices and users, the vulnerability of IoT devices to such attacks has enhanced significantly, making DDoS attacks a predominant threat. This work introduces several approaches for effectively detecting IoT-based DDoS threats. Classical machine learning (ML) techniques mostly face difficulty in managing real-world traffic characteristics effectually, making them less appropriate for detecting DDoS attacks. In contrast, Artificial Intelligence (AI)-based methods have proven more effective in detecting cyber-attacks than conventional approaches. This manuscript proposes an effective Feature Pruning with Optimal Deep Learning-based DDoS Attack Detection (FPODL-DDoSAD) technique in the IoT framework. The FPODL-DDoSAD technique initially uses a min-max scalar for the data scaling into the standard layout. Besides, the feature pruning process is performed using an improved pelican optimization algorithm (IPOA), which enables the choice of an optimal subset of features. Meanwhile, DDoS attacks are recognized using a sparse denoising autoencoder (SDAE) model. Furthermore, the parameter tuning of the SDAE classifier is accomplished by utilizing the Fish Migration Optimizer (FMO) technique. The experimental values of the FPODL-DDoSAD approach are assessed on the benchmark BoT-IoT dataset. The comparison study of the FPODL-DDoSAD method demonstrates a superior accuracy value of 99.80% over existing techniques.

PMID:40394115 | DOI:10.1038/s41598-025-02152-2

Sci Rep. 2025 May 20;15(1):17531. doi: 10.1038/s41598-025-97718-5.

ABSTRACT

Breast cancer represents a significant global health challenge, which makes it essential to detect breast cancer early and accurately to improve patient prognosis and reduce mortality rates. However, traditional diagnostic processes relying on manual analysis of medical images are inherently complex and subject to variability between observers, highlighting the urgent need for robust automated breast cancer detection systems. While deep learning has demonstrated potential, many current models struggle with limited accuracy and lack of interpretability. This research introduces the Deep Neural Breast Cancer Detection (DNBCD) model, an explainable AI-based framework that utilizes deep learning methods for classifying breast cancer using histopathological and ultrasound images. The proposed model employs Densenet121 as a foundation, integrating customized Convolutional Neural Network (CNN) layers including GlobalAveragePooling2D, Dense, and Dropout layers along with transfer learning to achieve both high accuracy and interpretability for breast cancer diagnosis. The proposed DNBCD model integrates several preprocessing techniques, including image normalization and resizing, and augmentation techniques to enhance the model's robustness and address class imbalances using class weight. It employs Grad-CAM (Gradient-weighted Class Activation Mapping) to offer visual justifications for its predictions, increasing trust and transparency among healthcare providers. The model was assessed using two benchmark datasets: Breakhis-400x (B-400x) and Breast Ultrasound Images Dataset (BUSI) containing 1820 and 1578 images, respectively. We systematically divided the datasets into training (70%), testing (20%,) and validation (10%) sets, ensuring efficient model training and evaluation obtaining accuracies of 93.97% for B-400x dataset having benign and malignant classes and 89.87% for BUSI dataset having benign, malignant, and normal classes for breast cancer detection. Experimental results demonstrate that the proposed DNBCD model significantly outperforms existing state-of-the-art approaches with potential uses in clinical environments. We also made all the materials publicly accessible for the research community at: https://github.com/romzanalom/XAI-Based-Deep-Neural-Breast-Cancer-Detection .

PMID:40394112 | DOI:10.1038/s41598-025-97718-5

Nat Commun. 2025 May 20;16(1):4686. doi: 10.1038/s41467-025-59819-7.

ABSTRACT

In anti-neutrophil cytoplasmic antibody-associated vasculitis (AAV) and systemic lupus erythematosus (SLE), glomerulonephritis is a severe kidney complication driven by immune cells, including T cells. However, the mechanisms underlying T cell activation in these contexts remain elusive. Here we report that in patients with AAV and SLE, type I interferon (IFN-I) induces T cell differentiation into interferon-stimulated genes-expressing T (ISG-T) cells, which are characterized by an elevated IFN-I signature, an immature phenotype, and cytotoxicity in inflamed tissue. Mechanistically, IFN-I stimulates the expression of interferon regulatory factor 7 (IRF7) in T cells, which in turn induces granzyme B production. In mice, blocking IFN-I signaling reduces IRF7 and granzyme B expression in T cells, thus ameliorating glomerulonephritis. In parallel, spatial transcriptomic analyses of kidney biopsies from patients with AAV or SLE reveal an elevated ISG signature and the presence of ISG-T cells in close proximity to plasmacytoid dendritic cells, the primary producers of IFN-I. Our results from both patients and animal models thus suggest that IFN-I production in inflamed tissue may drive ISG-T cell differentiation to expand the pool of cytotoxic T cells in autoimmune diseases.

PMID:40393992 | DOI:10.1038/s41467-025-59819-7

Nat Commun. 2025 May 20;16(1):4441. doi: 10.1038/s41467-025-59654-w.

ABSTRACT

Despite years of intense investigation, the mechanisms underlying neuronal death in Alzheimer's disease, remain incompletely understood. To define relevant pathways, we conducted an unbiased, genome-scale forward genetic screen for age-associated neurodegeneration in Drosophila. We also measured proteomics, phosphoproteomics, and metabolomics in Drosophila models of Alzheimer's disease and identified Alzheimer's genetic variants that modify gene expression in disease-vulnerable neurons in humans. We then used a network model to integrate these data with previously published Alzheimer's disease proteomics, lipidomics and genomics. Here, we computationally predict and experimentally confirm how HNRNPA2B1 and MEPCE enhance toxicity of the tau protein, a pathological feature of Alzheimer's disease. Furthermore, we demonstrated that the screen hits CSNK2A1 and NOTCH1 regulate DNA damage in Drosophila and human stem cell-derived neural progenitor cells. Our study identifies candidate pathways that could be targeted to ameliorate neurodegeneration in Alzheimer's disease.

PMID:40393985 | DOI:10.1038/s41467-025-59654-w

Nat Commun. 2025 May 20;16(1):4670. doi: 10.1038/s41467-025-59937-2.

ABSTRACT

Accurately predicting the effect of missense variants is important in discovering disease risk genes and clinical genetic diagnostics. Commonly used computational methods predict pathogenicity, which does not capture the quantitative impact on fitness in humans. We develop a method, MisFit, to estimate missense fitness effect using a graphical model. MisFit jointly models the effect at a molecular level ( d ) and a population level (selection coefficient, s ), assuming that in the same gene, missense variants with similar d have similar s . We train it by maximizing probability of observed allele counts in 236,017 individuals of European ancestry. We show that s is informative in predicting allele frequency across ancestries and consistent with the fraction of de novo mutations in sites under strong selection. Further, s outperforms previous methods in prioritizing de novo missense variants in individuals with neurodevelopmental disorders. In conclusion, MisFit accurately predicts s and yields new insights from genomic data.

PMID:40393980 | DOI:10.1038/s41467-025-59937-2

Trends Parasitol. 2025 May 19:S1471-4922(25)00104-7. doi: 10.1016/j.pt.2025.04.010. Online ahead of print.

ABSTRACT

The microbiological paradigm for surveillance of diverse pathogens requires knowledge of the variation of the major surface antigen under the most intense immune selection as immune responses to these antigens drive transmission dynamics. This creates a pathway for population genetics/genomics to be combined with mathematical modelling to describe transmission dynamics to inform public health policy. Here we consider how we can bring population genetics and population dynamics together for a highly recombining pathogen like Plasmodium falciparum. We do this through the lens of what has been recently learnt about the population genetics of the var multigene family encoding the major surface antigen of the blood stages of Plasmodium falciparum, known as PfEMP1.

PMID:40393890 | DOI:10.1016/j.pt.2025.04.010

Annu Rev Plant Biol. 2025 May;76(1):25-52. doi: 10.1146/annurev-arplant-083123-053039.

ABSTRACT

Plant nitrogen nutrition is an essential and energy-costly component of terrestrial food chains. Understanding nitrate sensing in plants can lead to improved crop yields and nutrient use efficiency, directly impacting food security and agricultural sustainability. Herein, we review and present a comprehensive framework for understanding nitrate sensing in plants, integrating molecular, genetic, and physiological aspects. We begin by detailing the primary nitrate response and nitrate starvation response, which are central to the plant's ability to sense and respond to nitrate availability. We then explore the intricate interactions between nitrate signaling and other nutritional pathways such as those for carbon, phosphorus, potassium, and sulfur assimilation and reactive oxygen species (ROS) handling, and how it unfolds in long-distance systemic communication between roots and shoots. Finally, evolutionary insights are provided by comparing nitrate-sensing mechanisms across different plant species as well as Bacteria, Archaea, Chlorophyta, Charophyta (algae), and Fungi, revealing how these mechanisms may have evolved in diverse ecological niches. This review not only provides a framework to project our present and future understanding of plant nitrate and nitrogen nutrition but also offers potential strategies for improving nutrient use efficiency in crops through genetic and biotechnological interventions.

PMID:40393732 | DOI:10.1146/annurev-arplant-083123-053039

Cell Rep Methods. 2025 May 15:101052. doi: 10.1016/j.crmeth.2025.101052. Online ahead of print.

ABSTRACT

Quantitative comparison of ChIP-seq profiling between experimental conditions or samples remains technically challenging for the epigenetics field. Here, we report a strategy combining the use of well-defined cellular spike-in ratios of orthologous species' chromatin and a bioinformatic analysis pipeline to facilitate highly quantitative comparisons of 2D chromatin sequencing across experimental conditions. We find that the PerCell methodology results in efficient and consistent levels of spike-in vs. experimental genomic reads. We demonstrate use of the method and pipeline to enable quantitative, internally normalized chromatin sequencing on zebrafish embryos and human cancer cells. Overall, we propose the PerCell method to enable cross-species comparative epigenomics and promote uniformity of data analyses and sharing across labs.

PMID:40393455 | DOI:10.1016/j.crmeth.2025.101052

BMC Psychiatry. 2025 May 20;25(1):509. doi: 10.1186/s12888-025-06983-1.

ABSTRACT

BACKGROUND: Treating depression in children and adolescents has always been a challenge in clinical pharmacotherapy. Vortioxetine, as a new type of antidepressant, is considered to have the potential for use in the treatment of depression in children and adolescents. This study aimed to evaluate the usage of vortioxetine and its efficacy and tolerability in children and adolescents with major depressive disorder in a real-world study.

METHODS: A retrospective survey of vortioxetine treatment was conducted at a Class A tertiary mental health hospital. Data regarding the demographic and clinical characteristics were collected among children and adolescents with major depressive disorder from electronic medical record system.

RESULTS: The study included a total of 253 depressive patients, comprising 96 males and 157 females, who were prescribed vortioxetine at any time during the research period. One hundred and twenty-three patients (43.62%) received vortioxetine treatment at the initial visit. Of the total patients, 27 (10.67%) reported side effect, such as nausea, vomiting, dizziness, palpitations, diarrhea, drowsiness, and itching. Additionally, 20 (7.91%) discontinued medical treatment due to adverse effect. No significant difference was found between males and females in drug-related adverse events (X2 = 0.56, P = 0.454). Furthermore, 96 (37.94%) reported relief from their symptoms in all patients, with a significant difference observed between males and females in reporting symptom relief (X2 = 3.934, P = 0.047). But this difference disappeared in patients who took vortioxetine alone and those who took it for more than three months.

CONCLUSION: There exists a certain proportion of children and adolescents suffering from depression who are prescribed vortioxetine in an off-label manner in psychiatric clinics. Vortioxetine demonstrates well tolerability in clinical practice. However, the proportion of self-report symptom alleviation is comparatively unsatisfactory. Furthermore, gender appears influence on self-report symptom relief.

PMID:40394553 | DOI:10.1186/s12888-025-06983-1

ChemMedChem. 2025 May 20:e202500117. doi: 10.1002/cmdc.202500117. Online ahead of print.

ABSTRACT

Cathepsins are lysosomal proteases with well-documented roles in the progression of various cancers. Among them, cathepsin B (CTSB), a cysteine protease, is notably involved in the development of breast cancer and neuroblastoma. In this study, we explored the potential of darifenacin as a repurposed therapeutic targeting CTSB, using molecular docking and simulation studies which demonstrated a significantly lower binding energy against CTSB (-456.268 kJ/mol) compared to its known inhibitor, aloxistatin (-36.601 kJ/mol). The cytotoxic efficacy of darifenacin was evaluated on IMR-32 (neuroblastoma) and MCF-7 (breast cancer) cells, yielding half-maximal inhibitory concentrations (IC50) of 38.14 and 39.96 µM, respectively. Darifenacin effectively inhibited CTSB enzymatic activity by ~1.82 and ~1.75-fold in IMR-32 and MCF-7 cells, respectively, triggering intracellular ROS generation, mitochondrial membrane potential depolarization, and cell cycle arrest. These events culminated in apoptosis-mediated cell death, with apoptotic populations reaching 51.39% in IMR-32 and 40.6% in MCF-7 cells, respectively. Additionally, darifenacin disrupted lipid droplet accumulation, cellular migration, and colony and sphere-forming abilities in both cell lines. Overall, this study identifies darifenacin as a promising therapeutic agent against CTSB-driven cancer progression.

PMID:40393029 | DOI:10.1002/cmdc.202500117

PLoS One. 2025 May 20;20(5):e0323761. doi: 10.1371/journal.pone.0323761. eCollection 2025.

ABSTRACT

Parkinson's disease (PD) is an increasingly prevalent neurologic condition for which symptomatic, but not preventative, treatment is available. Drug repurposing is an innovate drug discovery method that uncovers existing therapeutics to treat or prevent conditions for which they are not currently indicated, a method that could be applied to incurable diseases such as PD. A knowledge graph artificial intelligence prediction system was used to select potential drugs that could be used to treat or prevent PD, and amphetamine was identified as the strongest candidate. Retrospective cohort analysis on a large, electronic health record database of deidentified patients with attention deficit hyperactive disorder (the main diagnosis for which amphetamine is prescribed) revealed a significantly reduced hazard of developing PD in patients prescribed amphetamine versus patients not prescribed amphetamine at 2, 4, and 6 years: Hazard Ratio (95% Confidence Interval) = 0.59 (0.36, 0.98), 0.63 (0.42, 0.93), and 0.55 (0.38, 0.79). Pathway enrichment analysis confirmed that amphetamine targets many of the biochemical processes implicated in PD, such as dopaminergic synapses and neurodegeneration. Together, these observational findings suggest that therapeutic and legal amphetamine use may reduce the risk of developing PD, in contrast to previous work that found the inverse relationship in patients using amphetamine recreationally.

PMID:40392924 | DOI:10.1371/journal.pone.0323761

IEEE J Biomed Health Inform. 2025 May 20;PP. doi: 10.1109/JBHI.2025.3571801. Online ahead of print.

ABSTRACT

Drug repositioning, which identifies new therapeutic potential of approved drugs, is pivotal in accelerating drug discovery. Recently, growing efforts are devoted to applying graph neural networks (GNNs) for effectively modeling drug-disease associations (DDAs). However, current GNN-based methods are generally designed for unsigned graphs and fail to gain complementary insights provided by negative links. Despite the proposal of sign-aware GNNs in general fields, there exist two intractable challenges when indiscriminately deploying prior solutions into drug repositioning. (i) How to explicitly connect the nodes within the same set (disease-disease and drug-drug)? (ii) How to design the contrastive learning objective for signed graphs? To this end, we propose a novel sign-aware graph contrastive learning approach, namely SIGDR, which takes both the positive and negative links from signed biological networks into consideration to identify underlying DDAs. To handle the first challenge, we measure the drug and disease similarity and form signed unipartite graphs according to similarity scores. For the second challenge, a signed bipartite graph is then constructed from the annotated DDA dataset. Through dividing above obtained signed graphs into positive and negative subgraphs respectively, we devise the inter-view contrastive learning auxiliary task to enhance the consistency of node representations derived from partitioned subgraphs with the same link type. Extensive experiments conducted on three benchmarks under 10-fold cross-validation demonstrate the model effectiveness. Source code and datasets are available at https://github.com/OleCui/paper_SIGDR.

PMID:40392637 | DOI:10.1109/JBHI.2025.3571801

J Neurooncol. 2025 May 20. doi: 10.1007/s11060-025-05082-1. Online ahead of print.

ABSTRACT

The presence of hormonal receptors in meningiomas has been known for decades. More recently, evidence has shown increased prevalence of meningiomas in patients taking certain types of hormonal treatments, such as oral contraceptives, progestins or hormone replacement therapy. Epidemiological evidence suggests that patients undergoing hormonal therapy harbor higher mutational rates of the oncogene PIK3CA. Due to the relative paucity of literature describing the intersection of hormone therapy and mutated PIK3CA pathways in meningioma, we have conducted a narrative review on this topic. Similarly, the clinical trial landscape for hormonal therapies for meningioma currently focuses on somatostatin receptor-targeted therapies and peptide receptor radionucleotide therapy, and the PIK3CA-hormonal signaling axis has not been explicitly targeted. Given the role of PIK3CA mutations in promoting cancer progression in other hormone-sensitive tumors, such as breast and prostate cancer, exploring this axis could inform drug repurposing including hormonal therapy specifically for these tumors.

PMID:40392516 | DOI:10.1007/s11060-025-05082-1

Bioinformatics. 2025 May 20:btaf311. doi: 10.1093/bioinformatics/btaf311. Online ahead of print.

ABSTRACT

MOTIVATION: Single-cell and spatial transcriptomics provide unprecedented insight into diseases. Pharmacotranscriptomic approaches are powerful tools that leverage gene expression data for drug repurposing and discovery. Multiple databases attempt to connect human cellular transcriptional responses to small molecules for use in transcriptome-based drug discovery efforts. However, preclinical research often requires in vivo experiments in non-human species, which makes utilizing such valuable resources difficult. To facilitate both human orthologous conversion of non-human transcriptomes and the application of pharmacotranscriptomic databases to pre-clinical research models, we introduce OrthologAL. OrthologAL interfaces with BioMart to access different gene sets from the Ensembl database, allowing for ortholog conversion without the need for user-generated code.

RESULTS: Researchers can input their single-cell or other high-dimensional gene expression data from any species as a Seurat object, and OrthologAL will output a human ortholog-converted Seurat object for download and use. To demonstrate the utility of this application, we tested OrthologAL using single-cell, single-nuclei, and spatial transcriptomic data derived from common preclinical models, including patient-derived orthotopic xenografts of medulloblastoma, and mouse and rat models of spinal cord injury. OrthologAL can convert these data types efficiently to that of corresponding orthologs while preserving the dimensional architecture of the original non-human expression data. OrthologAL will be broadly useful for the simple conversion of Seurat objects and for applying preclinical, high-dimensional transcriptomics data to functional human-derived small molecule predictions.

AVAILABILITY: OrthologAL is available for download as an R package with functions to launch the Shiny GUI at https://github.com/AyadLab/OrthologAL or via Zenodo at https://doi.org/10.5281/zenodo.15225041. The medulloblastoma single-cell transcriptomics data were downloaded from the NCBI Gene Expression Omnibus with the identifier GSE129730. 10X Visium data of medulloblastoma PDX mouse models from Vo et al. were acquired by contacting the authors, and the raw data are available from ArrayExpress under the identifier E-MTAB-11720. The single-cell and single-nuclei transcriptomics data of rat and mouse spinal-cord injury were acquired from the Gene Expression Omnibus under the identifiers GSE213240 and GSE234774.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:40392208 | DOI:10.1093/bioinformatics/btaf311

Future Med Chem. 2025 May 20:1-17. doi: 10.1080/17568919.2025.2507564. Online ahead of print.

ABSTRACT

The Janus kinase (JAK)/Signal Transducer and Activator of Transcription (STAT) signaling pathway is essential for controlling immune function, blood cell formation, and cell growth. Dysregulation of this pathway is implicated in various diseases, including hematologic malignancies, autoimmune disorders, and chronic inflammatory conditions. This review provides a comprehensive overview of the structural and functional aspects of JAK/STAT signaling, with a particular focus on the role of JAK2. This manuscript explores the primary regulators of the JAK/STAT pathway, such as Suppressors Of Cytokine Signaling (SOCS), Protein Inhibitors of Activated STATs (PIAS), and Protein Tyrosine Phosphatases (PTPs), which play a crucial role in maintaining cellular balance and stability. Additionally, the therapeutic landscape of JAK2 inhibitors is explored, covering both approved and investigational drugs, including their mechanisms of action, efficacy, and safety profiles. Emerging strategies such as drug repositioning using computational approaches and experimental validation are also highlighted. By integrating insights from molecular docking studies, machine learning models, and kinase assays, this review emphasizes the potential of JAK2 inhibitors in disease management.

PMID:40392133 | DOI:10.1080/17568919.2025.2507564

J Antimicrob Chemother. 2025 May 20:dkaf157. doi: 10.1093/jac/dkaf157. Online ahead of print.

ABSTRACT

BACKGROUND: Bacterial vaginosis (BV) is associated with significant reproductive health risks and high recurrence rates after standard antibiotic treatment. Sulfadoxine/pyrimethamine, an antimalarial drug, demonstrated unexpected clearance of BV in clinical trials, suggesting potential antimicrobial effects. Drug repurposing, which leverages existing drugs for new therapeutic applications, offers a promising approach to address the challenges of antimicrobial resistance and high recurrence rates in BV.

OBJECTIVE: To determine the in vitro activity of sulfadoxine/pyrimethamine and its components, sulfadoxine and pyrimethamine, on key species associated with BV.

METHODS: Minimum inhibitory concentration (MIC) and minimum bactericidal concentration were determined for sulfadoxine/pyrimethamine (20:1 ratio), sulfadoxine, pyrimethamine, and standard-of-care antibiotics, metronidazole and clindamycin, against BV-associated species (Gardnerella spp., Fannyhessea vaginae, Prevotella bivia) and Lactobacillus crispatus (vaginal health marker). Gardnerella biofilms were also exposed to sulfadoxine/pyrimethamine, pyrimethamine, or metronidazole, and biofilm biomass and biofilm cells culturability were assessed.

RESULTS: Sulfadoxine had no effect, while pyrimethamine inhibited all Gardnerella strains with MIC values ranging from 0.125 to 4 mg/L, lower than MICs observed for metronidazole (2-128 mg/L). Pyrimethamine also outperformed metronidazole in inhibiting biofilm mass accumulation and reducing biofilm culturable cells in 3/4 Gardnerella strains. Sulfadoxine/pyrimethamine presented lower MICs than metronidazole for 5/8 Gardnerella strains. Sulfadoxine, pyrimethamine, and sulfadoxine/pyrimethamine showed no activity against other BV-associated species or L. crispatus.

CONCLUSIONS: These findings suggest that pyrimethamine (and sulfadoxine/pyrimethamine) could be promising alternative or adjuvant therapies for BV, warranting further clinical trials.

PMID:40391646 | DOI:10.1093/jac/dkaf157

Database (Oxford). 2025 May 20;2025:baaf008. doi: 10.1093/database/baaf008.

ABSTRACT

We previously described Graph2VR, a prototype that enables researchers to use virtual reality (VR) to explore and navigate through Linked Data graphs using SPARQL queries (see https://doi.org/10.1093/database/baae008). Here we evaluate the use of Graph2VR in three realistic life science use cases. The first use case visualizes metadata from large-scale multi-center cohort studies across Europe and Canada via the EUCAN Connect catalogue. The second use case involves a set of genomic data from synthetic rare disease patients, which was processed through the Variant Interpretation Pipeline and then converted into Resource Description Format for visualization. The third use case involves enriching a graph with additional information, in this case, the Dutch Anatomical Therapeutic Chemical code Ontology with the DrugID from Drugbank. These examples collectively showcase Graph2VR's potential for data exploration and enrichment, as well as some of its limitations. We conclude that the endless three-dimensional space provided by VR indeed shows much potential for the navigation of very large knowledge graphs, and we provide recommendations for data preparation and VR tooling moving forward. Database URL: https://doi.org/10.1093/database/baaf008.

PMID:40392751 | DOI:10.1093/database/baaf008

PLoS One. 2025 May 20;20(5):e0324360. doi: 10.1371/journal.pone.0324360. eCollection 2025.

ABSTRACT

BACKGROUND: Dolutegravir (DTG)-based antiretroviral therapy has demonstrated superior efficacy, tolerability, and durability when compared to other HIV treatment regimens. However, monitoring viral kinetics is critical for determining treatment efficacy and making sound judgments. The purpose of this study was to assess viral kinetics in people living with HIV (PLWH) on DTG-based ART and identify characteristics related to virologic response in the Cape Coast Metropolis, Ghana.

METHODS: Among people living with HIV (PLWH) attending HIV clinics between January 2020 and December 2023, a prospective and retrospective analysis of viral kinetics and clinical data were carried out. Data on viral loads, clinical laboratory results, ART regimen, and sociodemographic data were gathered. Viral loads analysis was undertaken using the COBAS AmpliPrep/COBAS TaqMan HIV-1 test, v2.0. Univariate and multivariate analyses were carried out to assess the variables related to virologic response.

RESULTS: Complete data was obtained for a total of 902 PLWH in this study. The average age was 45 ± 15.30 years, and 72.62% were female. The majority, 89.02% (835/902), had been on the DTG+3TC+TDF regimen. Over 60% had undetectable viral loads (<50 copies/mL). Univariate analysis shows a significant relationship between gender and virologic response, with females having a lower likelihood of virologic failure (OR: 0.60, 95% CI: 0.39-0.93, p-value = 0.024). In multivariate analysis, the duration of ART had various relationships with virologic response, with the odds ratio for two years reaching near significance (OR: 1.88, 95% CI: 0.98-3.59, p = 0.057). PLWH with viral loads >1000 copies/mL were 11.20% (101/902) while viral suppression, which was at detectable limits (>50 - ≤ 1000 cp/mL), was 13.08% (118/902) showing high rates of viral suppression.

CONCLUSION: The presence of virologic failures was of concern despite the high rates of viral suppression that DTG-based ART demonstrated. Undetectable viral suppression was higher than detectable viral suppression. Regular monitoring of viral kinetics, adherence, and comorbidities is essential to meeting the United Nations program on HIV/AIDS (UNAIDS) 95-95-95 targets and providing efficient therapeutic approaches for PLWH.

PMID:40392867 | DOI:10.1371/journal.pone.0324360

mBio. 2025 May 20:e0054125. doi: 10.1128/mbio.00541-25. Online ahead of print.

ABSTRACT

The opportunistic pathogen Pseudomonas aeruginosa causes debilitating lung infections in people with cystic fibrosis, as well as eye, burn, and wound infections in otherwise immunocompetent individuals. Many of P. aeruginosa's virulence factors are regulated by environmental cues, such as temperature and cell density. One such virulence factor is protease IV. Prior studies have shown that piv expression is higher at ambient temperatures (22°C-28°C) compared to human body temperature (37°C) and also upregulated by the LasRI quorum sensing system, although it is unclear how. We found that piv expression was thermoregulated at stationary phase, but not exponential phase, and that piv is thermoregulated at the level of transcription. Using a transcriptional reporter for piv, we show that LasR activates piv expression more at 25°C at stationary phase than at 37°C. We show that key components of the LasRI quorum sensing system are not upregulated at 25°C, suggesting that LasR regulatory activity is not higher intrinsically at this temperature. We also identified sequences within the piv promoter that are important for its thermoregulation. We propose that LasR upregulates piv more at 25°C than at 37°C. The finding that temperature controls LasR regulation of piv highlights the complex nature of gene regulatory systems in P. aeruginosa.IMPORTANCEPseudomonas aeruginosa is a versatile opportunistic pathogen capable of causing many different types of infections that are often difficult to treat, such as lung infections in people with cystic fibrosis. Temperature regulates the expression of many virulence factors that contribute to P. aeruginosa's ability to cause infection, yet our mechanistic understanding of virulence factor thermoregulation is poor. In this study, we show that the virulence factor protease IV is thermoregulated at the level of transcription through the quorum sensing regulator, LasR. Mechanistic studies of virulence factor thermoregulation will expand our understanding of how P. aeruginosa experiences different environments, including the mammalian host. Our work also highlights the importance of growth conditions in studying gene regulation, as it better elucidates the regulation of protease IV by LasR, which was previously not well understood.

PMID:40391957 | DOI:10.1128/mbio.00541-25