Sci Adv. 2025 Mar 14;11(11):eadr3757. doi: 10.1126/sciadv.adr3757. Epub 2025 Mar 12.

ABSTRACT

Brain age gap (BAG), the deviation between estimated brain age and chronological age, is a promising marker of brain health. However, the genetic architecture and reliable targets for brain aging remains poorly understood. In this study, we estimate magnetic resonance imaging (MRI)-based brain age using deep learning models trained on the UK Biobank and validated with three external datasets. A genome-wide association study for BAG identified two unreported loci and seven previously reported loci. By integrating Mendelian Randomization (MR) and colocalization analysis on eQTL and pQTL data, we prioritized seven genetically supported druggable genes, including MAPT, TNFSF12, GZMB, SIRPB1, GNLY, NMB, and C1RL, as promising targets for brain aging. We rediscovered 13 potential drugs with evidence from clinical trials of aging and prioritized several drugs with strong genetic support. Our study provides insights into the genetic basis of brain aging, potentially facilitating drug development for brain aging to extend the health span.

PMID:40073132 | DOI:10.1126/sciadv.adr3757

Cancer Chemother Pharmacol. 2025 Mar 12;95(1):40. doi: 10.1007/s00280-025-04756-x.

ABSTRACT

In recent years, assessing dihydropyrimidine dehydrogenase (DPD) activity has become crucial for cancer patients undergoing 5-fluorouracil (5FU)-based chemotherapy due to the life-threatening toxicity associated with reduced DPD function. The methods for evaluating DPD activity have evolved, with the analysis of DPYD polymorphisms in blood samples becoming the preferred approach. As the indications for liver transplantation are increasing-particularly due to a rise in cases of cholangiocarcinoma (CCA) and non-resectable colorectal liver metastasis-more cancer patients with a history of liver transplantation may experience disease relapse. Furthermore, 5-fluorouracil chemotherapy is a standard treatment for both cancers. This growing need to evaluate DPD activity in transplanted livers arises because standard tests conducted on blood samples reflect the activity of native liver tissue and may produce misleading results. This paper presents two clinical cases from 2022 to 2023 involving patients who underwent successful liver transplants but were later diagnosed with intrahepatic CCA in the explanted liver. Both patients were subsequently prescribed capecitabine as adjuvant chemotherapy, making it essential to assess DPD activity in donor liver tissue to ensure safe treatment protocols. However, there are currently no established guidelines for this specific patient group. If we follow standard clinical practice, this critical analysis will be insufficient, as it only describes the DPD activity of the native liver. It is imperative to determine the DPD activity of the transplanted liver. In summary, this case report highlights the importance of managing this complex situation effectively.

PMID:40072607 | DOI:10.1007/s00280-025-04756-x

Br J Radiol. 2025 Mar 12:tqaf047. doi: 10.1093/bjr/tqaf047. Online ahead of print.

ABSTRACT

OBJECTIVES: To develop a deep learning (DL) model based on ultrasound (US) images of lymph nodes for predicting cervical lymph node metastasis (CLNM) in postoperative patients with differentiated thyroid carcinoma (DTC).

METHODS: Retrospective collection of 352 lymph nodes from 330 patients with cytopathology findings between June 2021 and December 2023 at our institution. The database was randomly divided into the training and test cohort at an 8:2 ratio. The DL basic model of longitudinal and cross-sectional of lymph nodes was constructed based on ResNet50 respectively, and the results of the two basic models were fused (1:1) to construct a longitudinal + cross-sectional DL model. Univariate and multivariate analysis were used to assess US features and construct a conventional US model. Subsequently, a combined model was constructed by integrating DL and US.

RESULTS: The diagnostic accuracy of the longitudinal + cross-sectional DL model was higher than that of longitudinal or cross-sectional alone. The AUC of the combined model (US＋DL) was 0.855 (95%CI: 0.767-0.942), and the accuracy, sensitivity and specificity were 0.786 (95%CI: 0.671-0.875), 0.972 (95%CI: 0.855-0.999) and 0.588 (95%CI: 0.407-0.754), respectively. Compared with US and DL models, the IDI and NRI of the combined model are both positive.

CONCLUSIONS: This study preliminary shows that the DL model based on US images of lymph nodes has a high diagnostic efficacy for predicting CLNM in postoperative patients with DTC, and the combined model of US+DL is superior to single conventional US and DL for predicting CLNM in this population.

ADVANCES IN KNOWLEDGE: We innovatively used DL of lymph node US images to predict the status of cervical lymph nodes in postoperative patients with DTC.

PMID:40073229 | DOI:10.1093/bjr/tqaf047

Sci Adv. 2025 Mar 14;11(11):eadr3757. doi: 10.1126/sciadv.adr3757. Epub 2025 Mar 12.

ABSTRACT

Brain age gap (BAG), the deviation between estimated brain age and chronological age, is a promising marker of brain health. However, the genetic architecture and reliable targets for brain aging remains poorly understood. In this study, we estimate magnetic resonance imaging (MRI)-based brain age using deep learning models trained on the UK Biobank and validated with three external datasets. A genome-wide association study for BAG identified two unreported loci and seven previously reported loci. By integrating Mendelian Randomization (MR) and colocalization analysis on eQTL and pQTL data, we prioritized seven genetically supported druggable genes, including MAPT, TNFSF12, GZMB, SIRPB1, GNLY, NMB, and C1RL, as promising targets for brain aging. We rediscovered 13 potential drugs with evidence from clinical trials of aging and prioritized several drugs with strong genetic support. Our study provides insights into the genetic basis of brain aging, potentially facilitating drug development for brain aging to extend the health span.

PMID:40073132 | DOI:10.1126/sciadv.adr3757

Sci Adv. 2025 Mar 14;11(11):eadq8131. doi: 10.1126/sciadv.adq8131. Epub 2025 Mar 12.

ABSTRACT

There is great interest in using genetically tractable organisms such as Drosophila to gain insights into the regulation and function of sleep. However, sleep phenotyping in Drosophila has largely relied on simple measures of locomotor inactivity. Here, we present FlyVISTA, a machine learning platform to perform deep phenotyping of sleep in flies. This platform comprises a high-resolution closed-loop video imaging system, coupled with a deep learning network to annotate 35 body parts, and a computational pipeline to extract behaviors from high-dimensional data. FlyVISTA reveals the distinct spatiotemporal dynamics of sleep and wake-associated microbehaviors at baseline, following administration of the sleep-inducing drug gaboxadol, and with dorsal fan-shaped body drivers. We identify a microbehavior ("haltere switch") exclusively seen during quiescence that indicates a deeper sleep stage. These results enable the rigorous analysis of sleep in Drosophila and set the stage for computational analyses of microbehaviors in quiescent animals.

PMID:40073129 | DOI:10.1126/sciadv.adq8131

Biomacromolecules. 2025 Mar 12. doi: 10.1021/acs.biomac.4c01124. Online ahead of print.

ABSTRACT

We use a combination of Brownian dynamics (BD) simulation results and deep learning (DL) strategies for the rapid identification of large structural changes caused by missense mutations in intrinsically disordered proteins (IDPs). We used ∼6500 IDP sequences from MobiDB database of length 20-300 to obtain gyration radii from BD simulation on a coarse-grained single-bead amino acid model (HPS2 model) used by us and others [Dignon, G. L. PLoS Comput. Biol. 2018, 14, e1005941,Tesei, G. Proc. Natl. Acad. Sci. U.S.A. 2021, 118, e2111696118,Seth, S. J. Chem. Phys. 2024, 160, 014902] to generate the training sets for the DL algorithm. Using the gyration radii ⟨Rg⟩ of the simulated IDPs as the training set, we develop a multilayer perceptron neural net (NN) architecture that predicts the gyration radii of 33 IDPs previously studied by using BD simulation with 97% accuracy from the sequence and the corresponding parameters from the HPS model. We now utilize this NN to predict gyration radii of every permutation of missense mutations in IDPs. Our approach successfully identifies mutation-prone regions that induce significant alterations in the radius of gyration when compared to the wild-type IDP sequence. We further validate the prediction by running BD simulations on the subset of identified mutants. The neural network yields a (104-106)-fold faster computation in the search space for potentially harmful mutations. Our findings have substantial implications for rapid identification and understanding of diseases related to missense mutations in IDPs and for the development of potential therapeutic interventions. The method can be extended to accurate predictions of other mutation effects in disordered proteins.

PMID:40072940 | DOI:10.1021/acs.biomac.4c01124

Gigascience. 2025 Jan 6;14:giae098. doi: 10.1093/gigascience/giae098.

ABSTRACT

Magnetic resonance imaging (MRI) preprocessing is a critical step for neuroimaging analysis. However, the computational cost of MRI preprocessing pipelines is a major bottleneck for large cohort studies and some clinical applications. While high-performance computing and, more recently, deep learning have been adopted to accelerate the computations, these techniques require costly hardware and are not accessible to all researchers. Therefore, it is important to understand the performance bottlenecks of MRI preprocessing pipelines to improve their performance. Using the Intel VTune profiler, we characterized the bottlenecks of several commonly used MRI preprocessing pipelines from the Advanced Normalization Tools (ANTs), FMRIB Software Library, and FreeSurfer toolboxes. We found few functions contributed to most of the CPU time and that linear interpolation was the largest contributor. Data access was also a substantial bottleneck. We identified a bug in the Insight Segmentation and Registration Toolkit library that impacts the performance of the ANTs pipeline in single precision and a potential issue with the OpenMP scaling in FreeSurfer recon-all. Our results provide a reference for future efforts to optimize MRI preprocessing pipelines.

PMID:40072903 | DOI:10.1093/gigascience/giae098

Phys Chem Chem Phys. 2025 Mar 12. doi: 10.1039/d4cp04579c. Online ahead of print.

ABSTRACT

The phosphorylation of residue T177 produces a significant effect on the conformational dynamics of CDK6. Gaussian accelerated molecular dynamics (GaMD) simulations followed by deep learning (DL) are applied to explore the molecular mechanism of the phosphorylation-mediated effect on the conformational dynamics of CDK6 bound by three inhibitors 6ZV, 6ZZ and 0RS, in which 6ZV and 6ZZ have been used to test clinical performance. The DL finds that the β-sheets, αC helix as well as the T-loop are involved in obvious differences of conformation contacts and suggests that the T-loop plays a key role in the function of CDK6. The analyses of free energy landscapes (FELs) reveal that the phosphorylation of T177 leads to alterations of the T-loop conformation and the results from principal component analysis (PCA) indicate that the phosphorylation affects the fluctuation behavior of the β-sheets and the T-loop in CDK6. Interaction networks of inhibitors with CDK6 were analyzed and the information reveals that 6ZV contributes more hydrogen binding interactions (HBIs) and hot interaction spots with CDK6. Our MM-GBSA calculations suggest that the binding ability of 6ZV to CDK6 is stronger than 6ZZ and 0RS. We anticipate that this work could provide useful information for further understanding of CDK6 function and developing new promising inhibitors targeting CDK6.

PMID:40072875 | DOI:10.1039/d4cp04579c

IEEE Trans Biomed Eng. 2025 Mar 12;PP. doi: 10.1109/TBME.2025.3541643. Online ahead of print.

ABSTRACT

OBJECTIVE: To enable fast and stable neonatal brain MR imaging by integrating learned neonate-specific subspace model and model-driven deep learning.

METHODS: Fast data acquisition is critical for neonatal brain MRI, and deep learning has emerged as an effective tool to accelerate existing fast MRI methods by leveraging prior image information. However, deep learning often requires large amounts of training data to ensure stable image reconstruction, which is not currently available for neonatal MRI applications. In this work, we addressed this problem by utilizing a subspace model-assisted deep learning approach. Specifically, we used a subspace model to capture the spatial features of neonatal brain images. The learned neonate-specific subspace was then integrated with a deep network to reconstruct high-quality neonatal brain images from very sparse k-space data.

RESULTS: The effectiveness and robustness of the proposed method were validated using both the dHCP dataset and testing data from four independent medical centers, yielding very encouraging results. The stability of the proposed method has been confirmed with different perturbations, all showing remarkably stable reconstruction performance. The flexibility of the learned subspace was also shown when combined with other deep neural networks, yielding improved image reconstruction performance.

CONCLUSION: Fast and stable neonatal brain MR imaging can be achieved using subspace-assisted deep learning with sparse sampling. With further development, the proposed method may improve the practical utility of MRI in neonatal imaging applications.

PMID:40072865 | DOI:10.1109/TBME.2025.3541643

IEEE Trans Vis Comput Graph. 2025 Mar 12;PP. doi: 10.1109/TVCG.2025.3549147. Online ahead of print.

ABSTRACT

In Augmented Reality (AR), virtual content enhances user experience by providing additional information. However, improperly positioned or designed virtual content can be detrimental to task performance, as it can impair users' ability to accurately interpret real-world information. In this paper we examine two types of task-detrimental virtual content: obstruction attacks, in which virtual content prevents users from seeing real-world objects, and information manipulation attacks, in which virtual content interferes with users' ability to accurately interpret real-world information. We provide a mathematical framework to characterize these attacks and create a custom open-source dataset for attack evaluation. To address these attacks, we introduce ViDDAR (Vision language model-based Task-Detrimental content Detector for Augmented Reality), a comprehensive full-reference system that leverages Vision Language Models (VLMs) and advanced deep learning techniques to monitor and evaluate virtual content in AR environments, employing a user-edge-cloud architecture to balance performance with low latency. To the best of our knowledge, ViDDAR is the first system to employ VLMs for detecting task-detrimental content in AR settings. Our evaluation results demonstrate that ViDDAR effectively understands complex scenes and detects task-detrimental content, achieving up to 92.15% obstruction detection accuracy with a detection latency of 533 ms, and an 82.46% information manipulation content detection accuracy with a latency of 9.62 s.

PMID:40072851 | DOI:10.1109/TVCG.2025.3549147

Jpn J Radiol. 2025 Mar 12. doi: 10.1007/s11604-025-01760-2. Online ahead of print.

ABSTRACT

PURPOSE: Myocardial computed tomography (CT) late enhancement (LE) allows assessment of myocardial scarring. Super-resolution deep learning image reconstruction (SR-DLR) trained on data acquired from ultra-high-resolution CT may improve image quality for CT-LE. Therefore, this study investigated image noise and image quality with SR-DLR compared with conventional DLR (C-DLR) and hybrid iterative reconstruction (hybrid IR).

METHODS AND METHODS: We retrospectively analyzed 30 patients who underwent CT-LE using 320-row CT. The CT protocol comprised stress dynamic CT perfusion, coronary CT angiography, and CT-LE. CT-LE images were reconstructed using three different algorithms: SR-DLR, C-DLR, and hybrid IR. Image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and qualitative image quality scores are in terms of noise reduction, sharpness, visibility of scar and myocardial boarder, and overall image quality. Inter-observer differences in myocardial scar sizing in CT-LE by the three algorithms were also compared.

RESULTS: SR-DLR significantly decreased image noise by 35% compared to C-DLR (median 6.2 HU, interquartile range [IQR] 5.6-7.2 HU vs 9.6 HU, IQR 8.4-10.7 HU; p < 0.001) and by 37% compared to hybrid IR (9.8 HU, IQR 8.5-12.0 HU; p < 0.001). SNR and CNR of CT-LE reconstructed using SR-DLR were significantly higher than with C-DLR (both p < 0.001) and hybrid IR (both p < 0.05). All qualitative image quality scores were higher with SR-DLR than those with C-DLR and hybrid IR (all p < 0.001). The inter-observer differences in scar sizing were reduced with SR-DLR and C-DLR compared with hybrid IR (both p = 0.02).

CONCLUSION: SR-DLR reduces image noise and improves image quality of myocardial CT-LE compared with C-DLR and hybrid IR techniques and improves inter-observer reproducibility of scar sizing compared to hybrid IR. The SR-DLR approach has the potential to improve the assessment of myocardial scar by CT late enhancement.

PMID:40072715 | DOI:10.1007/s11604-025-01760-2

Bioinformatics. 2025 Mar 12:btaf103. doi: 10.1093/bioinformatics/btaf103. Online ahead of print.

ABSTRACT

MOTIVATION: Computational models are crucial for addressing critical questions about systems evolution and deciphering system connections. The pivotal feature of making this concept recognisable from the biological and clinical community is the possibility of quickly inspecting the whole system, bearing in mind the different granularity levels of its components. This holistic view of system behaviour expands the evolution study by identifying the heterogeneous behaviours applicable, for example, to the cancer evolution study.

RESULTS: To address this aspect, we propose a new modelling paradigm, UnifiedGreatMod, which allows modellers to integrate fine-grained and coarse-grained biological information into a unique model. It enables functional studies by combining the analysis of the system's multi-level stable states with its fluctuating conditions. This approach helps to investigate the functional relationships and dependencies among biological entities. This is achieved thanks to the hybridisation of two analysis approaches that capture a system's different granularity levels. The proposed paradigm was then implemented into the open-source, general modelling framework GreatMod, in which a graphical meta-formalism is exploited to simplify the model creation phase and R languages to define user-defined analysis workflows. The proposal's effectiveness was demonstrated by mechanistically simulating the metabolic output of Echerichia coli under environmental nutrient perturbations and integrating a gene expression dataset. Additionally, the UnifiedGreatMod was used to examine the responses of luminal epithelial cells to Clostridium difficile infection.

AVAILABILITY: GreatMod https://qbioturin.github.io/epimod/, epimod_FBAfunctions https://github.com/qBioTurin/epimod_FBAfunctions, first case study E.coli https://github.com/qBioTurin/Ec_coli_modelling, second case study C. difficile https://github.com/qBioTurin/EpiCell_CDifficile.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:40073274 | DOI:10.1093/bioinformatics/btaf103

Elife. 2025 Mar 12;13:RP95802. doi: 10.7554/eLife.95802.

ABSTRACT

Fiber photometry has become a popular technique to measure neural activity in vivo, but common analysis strategies can reduce the detection of effects because they condense within-trial signals into summary measures, and discard trial-level information by averaging across-trials. We propose a novel photometry statistical framework based on functional linear mixed modeling, which enables hypothesis testing of variable effects at every trial time-point, and uses trial-level signals without averaging. This makes it possible to compare the timing and magnitude of signals across conditions while accounting for between-animal differences. Our framework produces a series of plots that illustrate covariate effect estimates and statistical significance at each trial time-point. By exploiting signal autocorrelation, our methodology yields joint 95% confidence intervals that account for inspecting effects across the entire trial and improve the detection of event-related signal changes over common multiple comparisons correction strategies. We reanalyze data from a recent study proposing a theory for the role of mesolimbic dopamine in reward learning, and show the capability of our framework to reveal significant effects obscured by standard analysis approaches. For example, our method identifies two dopamine components with distinct temporal dynamics in response to reward delivery. In simulation experiments, our methodology yields improved statistical power over common analysis approaches. Finally, we provide an open-source package and analysis guide for applying our framework.

PMID:40073228 | DOI:10.7554/eLife.95802

Sci Adv. 2025 Mar 14;11(11):eadt4548. doi: 10.1126/sciadv.adt4548. Epub 2025 Mar 12.

ABSTRACT

Carbohydrate-responsive element binding protein (ChREBP) and Max-like protein X (MLX) form a heterodimeric transcription factor complex that couples intracellular sugar levels to carbohydrate and lipid metabolism. To promote the expression of target genes, two ChREBP-MLX heterodimers form a heterotetramer to bind a tandem element with two adjacent E-boxes, called carbohydrate-responsive element (ChoRE). How the ChREBP-MLX hetero-tetramerization is achieved and regulated remains poorly understood. Here, we show that MLX phosphorylation on an evolutionarily conserved motif is necessary for the heterotetramer formation on the ChoRE and the transcriptional activity of the ChREBP-MLX complex. We identified casein kinase 2 (CK2) and glycogen synthase kinase 3 (GSK3) as MLX kinases. High intracellular glucose-6-phosphate accumulation inhibits MLX phosphorylation and heterotetramer formation on the ChoRE, impairing ChREBP-MLX activity. Physiologically, MLX phosphorylation is necessary in Drosophila to maintain sugar tolerance and lipid homeostasis. Our findings suggest that MLX phosphorylation is a key mechanism for the ChREBP-MLX heterotetramer formation to regulate carbohydrate and lipid metabolism.

PMID:40073115 | DOI:10.1126/sciadv.adt4548

STAR Protoc. 2025 Mar 11;6(1):103657. doi: 10.1016/j.xpro.2025.103657. Online ahead of print.

ABSTRACT

Colostrum and milk from dairy sources consist of whey, casein, and fat, which have notable pharmacological properties due to their proteins and peptides. Here, we present a protocol for isolating, simulating in vitro gastrointestinal digestion, and fractionating colostrum and milk hydrolysates from any dairy source. We also describe steps for nano-liquid chromatography-electrospray ionization-tandem mass spectrometry (nano-LC-ESI-MS/MS) identification of proteins and peptides and in silico system biology-based profiling of the proteins and peptides present in the hydrolysates.

PMID:40073020 | DOI:10.1016/j.xpro.2025.103657

PLoS Comput Biol. 2025 Mar 12;21(3):e1012109. doi: 10.1371/journal.pcbi.1012109. Online ahead of print.

ABSTRACT

Advancements with cost-effective, high-throughput omics technologies have had a transformative effect on both fundamental and translational research in the medical sciences. These advancements have facilitated a departure from the traditional view of human red blood cells (RBCs) as mere carriers of hemoglobin, devoid of significant biological complexity. Over the past decade, proteomic analyses have identified a growing number of different proteins present within RBCs, enabling systems biology analysis of their physiological functions. Here, we introduce RBC-GEM, one of the most comprehensive, curated genome-scale metabolic reconstructions of a specific human cell type to-date. It was developed through meta-analysis of proteomic data from 29 studies published over the past two decades resulting in an RBC proteome composed of more than 4,600 distinct proteins. Through workflow-guided manual curation, we have compiled the metabolic reactions carried out by this proteome to form a genome-scale metabolic model (GEM) of the RBC. RBC-GEM is hosted on a version-controlled GitHub repository, ensuring adherence to the standardized protocols for metabolic reconstruction quality control and data stewardship principles. RBC-GEM represents a metabolic network is a consisting of 820 genes encoding proteins acting on 1,685 unique metabolites through 2,723 biochemical reactions: a 740% size expansion over its predecessor. We demonstrated the utility of RBC-GEM by creating context-specific proteome-constrained models derived from proteomic data of stored RBCs for 616 blood donors, and classified reactions based on their simulated abundance dependence. This reconstruction as an up-to-date curated GEM can be used for contextualization of data and for the construction of a computational whole-cell models of the human RBC.

PMID:40072998 | DOI:10.1371/journal.pcbi.1012109

J Chem Inf Model. 2025 Mar 12. doi: 10.1021/acs.jcim.4c02424. Online ahead of print.

ABSTRACT

The goal of drug repositioning is to expedite the drug development process by finding novel therapeutic applications for approved drugs. Using multifeature learning, different computational drug repositioning techniques have recently been introduced to predict possible drug-disease relationships. Nevertheless, current graph-based methods tend to model drug-disease interaction relationships without considering the semantic influence of node-specific side information on graphs. These approaches also suffer from the noise and sparsity inherent in the data. To address these limitations, we propose MDGCN, a novel drug repositioning method that incorporates multidependency graph convolutional networks and contrastive learning. Based on drug and disease similarity matrices and the drug-disease relationships matrix, this approach constructs multidependency graphs. It subsequently employs graph convolutional networks to spread side information between various graphs in each layer. Meanwhile, the weak supervision of drug-disease connections is effectively addressed by introducing cross-view and cross-layer contrastive learning to align node embedding across various views. Extensive experiments show that MDGCN performs better in drug-disease association prediction than seven advanced methods, offering strong support for investigating novel therapeutic indications for medications of interest.

PMID:40071716 | DOI:10.1021/acs.jcim.4c02424

Rheumatology (Oxford). 2025 Mar 1;64(Supplement_1):i109-i111. doi: 10.1093/rheumatology/keae387.

ABSTRACT

The search for targeted therapies and biomarkers for immune-mediated systemic vasculitis requires detailed understanding of molecular pathogenesis. Whilst candidate approaches have identified new opportunities for drug repurposing, they also miss novel approaches for targeting critical immunological or stromal pathways. On the other hand, bulk transcriptional profiling may fail to capture differences in cellular composition and, depending on the cell source profiled, miss important changes within inflamed vascular tissue. The past decade has seen major advances in both experimental techniques and analytical tools that enable multi-dimensional molecular profiling. Interrogation of the transcriptome and proteome is now possible at a single cell level, or at levels of spatial resolution within tissue that was previously unimaginable. As demonstrated during the presentations in the breakout session of the 21st International Vasculitis Workshop entitled Transcriptomic approaches to the study of systemic vasculitis, these techniques are revealing greater understanding of molecular underpinnings of the systemic vasculitides.

PMID:40071404 | DOI:10.1093/rheumatology/keae387

Chem Biol Drug Des. 2025 Mar;105(3):e70088. doi: 10.1111/cbdd.70088.

ABSTRACT

Methicillin-resistant Staphylococcus aureus (MRSA) achieves high-level resistance against β-lactam antibiotics through the expression of penicillin-binding protein 2a (PBP2a), which features a closed active site that impedes antibiotic binding. Herein, we implemented a strategy that combines drug repurposing with synergistic therapy to identify potential inhibitors targeting PBP2a's allosteric site from an FDA-approved drug database. Initially, retrospective verifications were conducted, employing different Glide docking methods (HTVS, SP, and XP) and two representative PBP2a structures. The combination of Glide SP and one representative PBP2a conformation showed the highest efficacy in identifying active compounds. The optimized parameters were then utilized to screen FDA-approved drugs, and 15 compounds were shortlisted for potential combination therapy with cefazolin, an ineffective cephalosporin against MRSA. Through biological assays-checkerboard, time-kill assays, and live/dead bacterial staining-we discovered that four compounds exhibited robust bactericidal activity (FICI < 0.5) compared to both untreated control and monotherapy with cefazolin alone. Scanning electron microscopy (SEM) confirmed that while cefazolin alone did not cause visible damage to MRSA cells, the combination treatment markedly induced cell lysis. Additional MM-GBSA studies underscored the strong binding affinity of mitoxantrone to the allosteric site. These findings introduce a combination therapy approach that potentially restores MRSA's susceptibility to β-lactam antibiotics.

PMID:40070213 | DOI:10.1111/cbdd.70088

Rheumatology (Oxford). 2025 Mar 1;64(Supplement_1):i82-i84. doi: 10.1093/rheumatology/keae593.

ABSTRACT

Polyarteritis Nodosa (PAN), is the firstly described vasculitis and can be seen in paediatric and adult age. PAN has a heterozygous clinical picture including cutaneous, constitutional, musculoskeletal, gastrointestinal, and renal involvement. Description and splitting of other vasculitis, makes this medium vessel vasculitis, a very rare disease. Additionally, many subgroups of PAN have been defined and this effort let to move Hepatitis B virus-PAN to Vasculitis with probable aetiology. Anyhow, idiopathic PAN still exists and cohorts from various countries such as France, India, and Japan have been published. Rarity of PAN necessities global collaboration to highlight clinical features and genetics studies. GLOBAL-PAN is an ongoing collaborative project of EUVAS, VCRC and many national cohorts. This review covers the recent epidemiological data of PAN along with demographic and clinical characteristics of cohorts from all-over the world and GLOBAL-PAN.

PMID:40071408 | DOI:10.1093/rheumatology/keae593