mBio. 2025 Apr 2:e0037425. doi: 10.1128/mbio.00374-25. Online ahead of print.

ABSTRACT

Staphylococcus aureus is one of the most common pathogens isolated from the lungs of people with cystic fibrosis (CF), but little is known about its ability to colonize this niche. We performed a transposon-sequencing (Tn-seq) screen to identify genes necessary for S. aureus growth in media prepared from ex vivo CF sputum. We identified 19 genes that were required for growth in all sputum media tested and dozens more that were required for growth in at least one sputum medium. Depleted mutants of interest included insertions in many genes important for surviving metal starvation, as well as the primary regulator of cysteine metabolism, cymR. To investigate the mechanisms by which these genes contribute to S. aureus growth in sputum, we quantified low-molecular-weight thiols, nutrient transition metals, and the host metal-sequestration protein calprotectin in sputum from 11 individuals with CF. In all samples, the abundance of calprotectin exceeded nutrient metal concentration, explaining the S. aureus requirement for metal-starvation genes. Furthermore, all samples contain potentially toxic quantities of cysteine and sufficient glutathione to satisfy the organic sulfur requirements of S. aureus. Deletion of the cysteine importer genes tcyA and tcyP in the ∆cymR background restored growth to wild-type levels in CF sputum, suggesting that the mechanism by which cymR is required for growth in sputum is to prevent uncontrolled import of cysteine or cystine from this environment. Overall, this work demonstrates that calprotectin and cysteine limit S. aureus growth in CF sputum.IMPORTANCEStaphylococcus aureus is a major cause of lung infections in people with cystic fibrosis (CF). This work identifies genes required for S. aureus growth in this niche, which represent potential targets for anti-Staphylococcal treatments. We show that genes involved in surviving metal starvation are required for growth in CF sputum. We also found that the primary regulator of cysteine metabolism, CymR, plays a critical role in preventing cysteine intoxication during growth in CF sputum. To support these models, we analyzed sputum from 11 individuals with CF to determine concentrations of calprotectin, nutrient metals, and low-molecular-weight thiols, which have not previously been quantified together in the same samples.

PMID:40172197 | DOI:10.1128/mbio.00374-25

Mol Divers. 2025 Apr 2. doi: 10.1007/s11030-025-11145-2. Online ahead of print.

ABSTRACT

Farnesoid X receptor (FXR) is a key regulator of bile acid, lipid, and glucose homeostasis, making it a promising target for treating metabolic diseases. FXR antagonists have shown therapeutic potential in cholestasis, metabolic disorders, and certain cancers, while clinically approved FXR antagonists remain unavailable and underrepresented in current treatment strategies. To address this, we developed deep learning models for predicting FXR antagonistic activity (ANTCL) and toxicity (TOXCL). Screening 217,345 compounds from the HMDB database identified eleven human metabolite candidates with significant FXR binding potential. Molecular dynamics simulations and binding free energy calculations revealed five more stable complexes compared to the reference compound Gly-MCA, with HMDB0253354 (Fulvestrant) and HMDB0242367 (ZM 189154) standing out for their binding free energies. Hydrophobic interactions, particularly involving residues MET328, PHE329, and ALA291, contributed to their stability. These results demonstrate the effectiveness of deep learning in FXR antagonist discovery and highlight the potential of HMDB0253354 and HMDB0242367 as promising candidates for metabolic disease treatment.

PMID:40172823 | DOI:10.1007/s11030-025-11145-2

Med Biol Eng Comput. 2025 Apr 2. doi: 10.1007/s11517-025-03347-5. Online ahead of print.

ABSTRACT

Fetal congenital heart disease (FCHD) represents a serious and prevalent congenital malformation. However, there exist notable regional disparities in the detection rates of fetal heart abnormalities. To enhance the diagnostic capabilities of ultrasound physicians in primary hospitals regarding fetal heart structures, the adoption of artificial intelligence technology to assist in acquiring high-quality, standard fetal echocardiographic images is of paramount importance. Currently, primary hospitals face challenges in recognizing standard views in fetal echocardiography, particularly under resource-constrained conditions. Efficient and accurate identification of fetal heart structures has become an urgent issue to address. Despite existing research efforts dedicated to the recognition of standard views in fetal echocardiography, current methods still suffer from limitations in computational complexity, feature extraction capabilities, and long-distance feature capturing, hindering their widespread application in ultrasound diagnosis at primary hospitals. Specifically, the literature lacks an efficient and robust model that can effectively balance high accuracy in standard view recognition with low computational complexity and fast inference times. The need for a model that can accurately capture long-distance features while maintaining efficiency is particularly acute in the context of primary hospitals, where resources are limited and the demand for accurate fetal heart assessments is high. To address these issues, the present study proposes an efficient network based on a state-space model trained with evidence for standard view recognition in fetal echocardiography. This method integrates a visual state space (VSS) model, which boasts powerful feature extraction capabilities and effective long-distance feature capturing, while significantly reducing computational complexity and facilitating efficient model inference. In the collected dataset, the proposed model achieved an accuracy of 99.32% and an F1-score of 99.29% in identifying eight standard views of fetal echocardiography. Furthermore, the model exhibited the lowest floating point operations per second (FLOPs), parameters, and inference time, while achieving the highest frames per second (FPS). This achievement not only provides a solid technical foundation for intelligent diagnosis of FCHD but also serves as an auxiliary tool for junior or novice sonographers at primary hospitals in acquiring basic views of fetal heart structures.

PMID:40172789 | DOI:10.1007/s11517-025-03347-5

Med Biol Eng Comput. 2025 Apr 2. doi: 10.1007/s11517-025-03323-z. Online ahead of print.

ABSTRACT

Ultrasound (US) imaging enables the evaluation of vascular structures in real time, and it can provide morphological and pathological information during US-guided procedures. Automatic prediction of vascular structure boundaries can help clinicians in locating and measuring target structures more accurately and efficiently. Most existing US segmentation methods use per-pixel classification or regression, which require post-processing to obtain contour coordinates. In this work, we present ACE-Net, a novel approach that directly predicts the contour coordinates for every scanning line (A-line) in US images. ACE-Net combines two main modules: a boundary regression module that predicts the upper and lower coordinates of the target area for each A-line, and an A-line classification module that determines whether an A-line belongs to the target area or not. We evaluated our method on three clinical US datasets using, among others, dice similarity coefficient (DSC) and inference time as performance metrics. Our method outperformed state-of-the-art segmentation methods in inference time while achieving superior or comparable performance in DSC. ACE-Net is publicly available at https://github.com/bfarolabarata/ace-net .

PMID:40172788 | DOI:10.1007/s11517-025-03323-z

Eur Radiol. 2025 Apr 2. doi: 10.1007/s00330-025-11552-x. Online ahead of print.

NO ABSTRACT

PMID:40172639 | DOI:10.1007/s00330-025-11552-x

Eur Radiol. 2025 Apr 2. doi: 10.1007/s00330-025-11550-z. Online ahead of print.

NO ABSTRACT

PMID:40172638 | DOI:10.1007/s00330-025-11550-z

An Acad Bras Cienc. 2025 Mar 31;97(2):e20240623. doi: 10.1590/0001-3765202520240623. eCollection 2025.

ABSTRACT

The detection and re-identification of vehicles in dynamic environments, such as highways monitored by a swarm of drones, presents significant challenges, particularly due to the variability of images captured from different angles and under various conditions. This scenario necessitates the development of suitable methods that integrate appropriate computational techniques, such as convolutional neural networks (CNN) to address the diversity of drone captures and improve accuracy in detection and re-identification. In this paper, a solution for vehicle detection and Re-ID is proposed, combining CNN techniques VGG16, VGG19, ResNet50, InceptionV3 and EfficientNetV2L. YOLOv4 was selected for detection, while the DeepSORT algorithm was chosen for tracking. The proposed solution considers the generalization capabilities of these techniques with varied images from different drones in different positions. Two datasets were employed: the first is a public dataset from Mendeley used for method evaluation, while the second consists of images and data collected by a swarm of drones. In the first experiment, the best performing network was ResNet50, with an average accuracy of 55%. In the second experiment, the highest accuracy CNN was VGG19, with 91% accuracy. Overall, the techniques were able to distinguish vehicles of different models and adapted to the data captured by drones.

PMID:40172334 | DOI:10.1590/0001-3765202520240623

Radiol Artif Intell. 2025 Apr 2:e240628. doi: 10.1148/ryai.240628. Online ahead of print.

ABSTRACT

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To assess the prognostic value of an open-source deep learning-based chest radiographs (CXR) algorithm, CXR-Lung-Risk, for stratifying respiratory disease mortality risk among an Asian health screening population using baseline and follow-up CXRs. Materials and Methods This single-center, retrospective study analyzed CXRs from individuals who underwent health screenings between January 2004 and June 2018. The CXR-Lung-Risk scores from baseline CXRs were externally tested for predicting mortality due to lung disease or lung cancer, using competing risk analysis, with adjustments made for clinical factors. The additional value of these risk scores beyond clinical factors was evaluated using the likelihood ratio test. An exploratory analysis was conducted on the CXR-Lung-Risk trajectory over a three-year follow-up period for individuals in the highest quartile of baseline respiratory disease mortality risk, using a time-series clustering algorithm. Results Among 36,924 individuals (median age, 58 years [interquartile range: 53-62 years]; 22,352 male), 264 individuals (0.7%) died of respiratory illness, over a median follow-up period of 11.0 years (interquartile range: 7.8- 12.7 years). CXR-Lung-Risk predicted respiratory disease mortality (adjusted hazard ratio [HR] per 5 years: 2.01, 95% CI: 1.76-2.39, P < .001), offering a prognostic improvement over clinical factors (P < .001). The trajectory analysis identified a subgroup with a continuous increase in CXR-Lung-Risk, which was associated with poorer outcomes (adjusted HR for respiratory disease mortality: 3.26, 95% CI: 1.20-8.81, P = .02) compared with the subgroup with a continuous decrease in CXR-Lung-Risk. Conclusion The open-source CXR-Lung-Risk model predicted respiratory disease mortality in an Asian cohort, enabling a two-layer risk stratification approach through an exploratory longitudinal analysis of baseline and follow-up CXRs. ©RSNA, 2025.

PMID:40172326 | DOI:10.1148/ryai.240628

Radiol Artif Intell. 2025 Apr 2:e240507. doi: 10.1148/ryai.240507. Online ahead of print.

ABSTRACT

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop an unsupervised deep learning framework for generalizable blood-brain barrier (BBB) leakage detection using dynamic contrast-enhanced (DCE) MRI, without requiring pharmacokinetic (PK) models and arterial input function (AIF) estimation. Materials and Methods This retrospective study included data from patients who underwent DCE MRI between April 2010 and December 2020. An autoencoder-based anomaly detection (AEAD) identified 1D voxel-wise time-series abnormal signals through reconstruction residuals, separating them into residual leakage signals (RLS) and residual vascular signals (RVS). The RLS maps were evaluated and compared with the volume transfer constant (Ktrans) using the structural similarity index (SSIM) and correlation coefficient (r). Generalizability was tested on subsampled data, and IDH status classification performance was assessed using areas under the receiver operating characteristic curves (AUCs). Results A total of 274 patients were included (164 male; mean age 54.23 ± [SD] 14.66 years). RLS showed high structural similarity (SSIM = 0.91 ± 0.02) and correlation (r = 0.56, P < .001) with Ktrans. On subsampled data, RLS maps showed better correlation with RLS values from original data (0.89 versus 0.72, P < .001), higher PSNR (33.09 dB versus 28.94 dB, P < .001), and higher SSIM (0.92 versus 0.87, P < .001) compared with Ktrans maps. RLS maps also outperformed Ktrans maps in predicting IDH mutation status (AUC = 0.87 [95% CI: 0.83-0.91] versus 0.81 [95% CI: 0.76-0.85], P = .02). Conclusion The unsupervised framework effectively detected blood-brain barrier leakage without PK models and AIF. ©RSNA, 2025.

PMID:40172325 | DOI:10.1148/ryai.240507

JASA Express Lett. 2025 Apr 1;5(4):045201. doi: 10.1121/10.0036356.

ABSTRACT

Laser doppler vibrometers (LDVs) facilitate noncontact speech acquisition; however, they are prone to material-dependent spectral distortions and speckle noise, which degrade intelligibility in noisy environments. This study proposes a data augmentation method that incorporates material-specific and impulse noises to simulate LDV-induced distortions. The proposed approach utilizes a gated convolutional neural network with HiFi-GAN to enhance speech intelligibility across various material and low signal-to-noise ratio (SNR) conditions, achieving a short-time objective intelligibility score of 0.76 at 0 dB SNR. These findings provide valuable insights into optimized augmentation and deep-learning techniques for enhancing LDV-based speech recordings in practical applications.

PMID:40172315 | DOI:10.1121/10.0036356

AJR Am J Roentgenol. 2025 Apr 2. doi: 10.2214/AJR.25.33012. Online ahead of print.

NO ABSTRACT

PMID:40172167 | DOI:10.2214/AJR.25.33012

Proc Natl Acad Sci U S A. 2025 Apr 8;122(14):e2412310122. doi: 10.1073/pnas.2412310122. Epub 2025 Apr 2.

ABSTRACT

High tumor mutational burden (TMB-H) is a predictive biomarker for the responsiveness of cancer to immune checkpoint inhibitor (ICI) therapy that indicates whether immune cells can sufficiently recognize cancer cells as nonself. However, about 30% of all cancers from The Cancer Genome Atlas (TCGA) are classified as immune-desert tumors lacking T cell infiltration despite TMB-H. Since the underlying mechanism of these immune-desert tumors has yet to be unraveled, there is a pressing need to transform such immune-desert tumors into immune-inflamed tumors and thereby enhance their responsiveness to anti-PD1 therapy. Here, we present a systems framework for identifying immuno-oncotargets, based on analysis of gene regulatory networks, and validating the effect of these targets in transforming immune-desert into immune-inflamed tumors. In particular, we identify DEAD-box helicases 54 (DDX54) as a master regulator of immune escape in immune-desert lung cancer with TMB-H and show that knockdown of DDX54 can increase immune cell infiltration and lead to improved sensitivity to anti-PD1 therapy.

PMID:40172969 | DOI:10.1073/pnas.2412310122

Elife. 2025 Apr 2;13:RP92536. doi: 10.7554/eLife.92536.

ABSTRACT

In eukaryotes, protein kinase signaling is regulated by a diverse array of post-translational modifications, including phosphorylation of Ser/Thr residues and oxidation of cysteine (Cys) residues. While regulation by activation segment phosphorylation of Ser/Thr residues is well understood, relatively little is known about how oxidation of cysteine residues modulate catalysis. In this study, we investigate redox regulation of the AMPK-related brain-selective kinases (BRSK) 1 and 2, and detail how broad catalytic activity is directly regulated through reversible oxidation and reduction of evolutionarily conserved Cys residues within the catalytic domain. We show that redox-dependent control of BRSKs is a dynamic and multilayered process involving oxidative modifications of several Cys residues, including the formation of intramolecular disulfide bonds involving a pair of Cys residues near the catalytic HRD motif and a highly conserved T-loop Cys with a BRSK-specific Cys within an unusual CPE motif at the end of the activation segment. Consistently, mutation of the CPE-Cys increases catalytic activity in vitro and drives phosphorylation of the BRSK substrate Tau in cells. Molecular modeling and molecular dynamics simulations indicate that oxidation of the CPE-Cys destabilizes a conserved salt bridge network critical for allosteric activation. The occurrence of spatially proximal Cys amino acids in diverse Ser/Thr protein kinase families suggests that disulfide-mediated control of catalytic activity may be a prevalent mechanism for regulation within the broader AMPK family.

PMID:40172959 | DOI:10.7554/eLife.92536

Bioinformatics. 2025 Apr 2:btaf149. doi: 10.1093/bioinformatics/btaf149. Online ahead of print.

ABSTRACT

SUMMARY: In single-cell transcriptomics, inconsistent cell type annotations due to varied naming conventions and hierarchical granularity impede data integration, machine learning applications, and meaningful evaluations. To address this challenge, we developed the unified Hierarchical Annotation Framework (uHAF), which includes organ-specific hierarchical cell type trees (uHAF-T) and a mapping tool (uHAF-Agent) based on large language models. uHAF-T provides standardized hierarchical references for 38 organs, allowing for consistent label unification and analysis at different levels of granularity. uHAF-Agent leverages GPT-4 to accurately map diverse and informal cell type labels onto uHAF-T nodes, streamlining the harmonization process. By simplifying label unification, uHAF enhances data integration, supports machine learning applications, and enables biologically meaningful evaluations of annotation methods. Our framework serves as an essential resource for standardizing cell type annotations and fostering collaborative refinement in the single-cell research community.

AVAILABILITY AND IMPLEMENTATION: uHAF is publicly available at: https://uhaf.unifiedcellatlas.org and https://github.com/SuperBianC/uhaf.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

PMID:40172934 | DOI:10.1093/bioinformatics/btaf149

Mycorrhiza. 2025 Apr 2;35(2):26. doi: 10.1007/s00572-025-01188-6.

ABSTRACT

Like other plant-microbe symbioses, the establishment of orchid mycorrhiza (ORM) is likely to require specific communication and metabolic adjustments between the two partners. However, while modulation of plant and fungal metabolism has been investigated in fully established mycorrhizal tissues, the molecular changes occurring during the pre-symbiotic stages of the interaction remain largely unexplored in ORM. In this study, we investigated the pre-symbiotic responses of the ORM fungus Tulasnella sp. SV6 to plantlets of the orchid host Serapias vomeracea in a dual in vitro cultivation system. The fungal mycelium was harvested prior to physical contact with the orchid roots and the fungal transcriptome and metabolome were analyzed using RNA-seq and untargeted metabolomics approaches. The results revealed distinct transcriptomic and metabolomic remodelling of the ORM fungus in the presence of orchid plantlets, as compared to the free-living condition. The ORM fungus responds to the presence of the host plant with a significant up-regulation of genes associated with protein synthesis, amino acid and lipid biosynthesis, indicating increased metabolic activity. Metabolomic analysis supported the RNA-seq data, showing increased levels of amino acids and phospholipids, suggesting a remodelling of cell structure and signalling during the pre-symbiotic interaction. In addition, we identified an increase of transcripts of a small secreted protein that may play a role in early symbiotic signalling. Taken together, our results suggest that Tulasnella sp. SV6 may perceive information from orchid roots, leading to a readjustment of its transcriptomic and metabolomic profiles.

PMID:40172721 | DOI:10.1007/s00572-025-01188-6

Philos Trans A Math Phys Eng Sci. 2025 Apr 2;383(2293):20240412. doi: 10.1098/rsta.2024.0412. Epub 2025 Apr 2.

ABSTRACT

During infectious disease outbreaks, the time-dependent reproduction number ([Formula: see text]) can be estimated to monitor pathogen transmission. In previous work, we developed a simulation-based method for estimating [Formula: see text] from temporally aggregated disease incidence data (e.g. weekly case reports). While that approach is straightforward to use, it assumes implicitly that all cases are reported and the computation can be slow when applied to large datasets. In this article, we extend our previous approach and develop a computationally efficient simulation-based method for estimating [Formula: see text] in real-time accounting for both temporal aggregation of incidence data and under-reporting (with a fixed reporting probability per case). Using simulated data, we show that failing to consider stochastic under-reporting can lead to inappropriately precise estimates, including scenarios in which the true [Formula: see text] value lies outside inferred credible intervals more often than expected. We then apply our approach to data from the 2018 to 2020 Ebola outbreak in the Democratic Republic of the Congo (DRC), again exploring the effects of case under-reporting. Finally, we show how our method can be extended to account for temporal variations in reporting. Given information about the level of case reporting, our framework can be used to estimate [Formula: see text] during future outbreaks with under-reported and temporally aggregated case data.This article is part of the theme issue 'Uncertainty quantification for healthcare and biological systems (Part 2)'.

PMID:40172553 | DOI:10.1098/rsta.2024.0412

Infect Immun. 2025 Apr 2:e0033324. doi: 10.1128/iai.00333-24. Online ahead of print.

ABSTRACT

Babesia microti is the primary cause of human babesiosis in North America. Despite the emergence of the disease in recent years, the pathogenesis and immune response to B. microti infection remain poorly understood. Studies in laboratory mice have shown a critical role for macrophages in the elimination of parasites and infected red blood cells (iRBCs). Importantly, the underlying mechanisms that activate macrophages are still unknown. Recent evidence identified the release of extracellular vesicles (EVs) from Babesia iRBCs. EVs are spherical particles released from cell membranes under natural or pathological conditions that have been suggested to play roles in host-pathogen interactions among diseases caused by protozoan parasites. The present study examined whether EVs released from cultured Babesia iRBCs could activate macrophages and alter cytokine secretion. An analysis of vesicle size in EV fractions from Babesia iRBCs showed diverse populations in the <100 nm size range compared to EVs from uninfected RBCs. In co-culture experiments, EVs released by B. microti iRBCs appeared to be associated with macrophage membranes and cytoplasm, indicating uptake of these vesicles in vitro. Interestingly, the incubation of macrophages with EVs isolated from Babesia iRBC culture supernatants resulted in the activation of NF-κB and modulation of pro-inflammatory cytokines. These results support a role for Babesia-derived EVs in macrophage activation and provide new insights into the mechanisms involved in the induction of the innate immune response during babesiosis.

PMID:40172538 | DOI:10.1128/iai.00333-24

Adv Sci (Weinh). 2025 Apr 2:e2412775. doi: 10.1002/advs.202412775. Online ahead of print.

ABSTRACT

Chronic noncommunicable diseases (NCDS) are often characterized by gradual onset and slow progression, but the difficulty in early prediction remains a substantial health challenge worldwide. This study aims to explore the interconnectedness of disease occurrence through multi-omics studies and validate it in large-scale electronic health records. In response, the research examined multi-omics data from 160 sub-healthy individuals at high altitude and then a deep learning model called Omicsformer is developed for detailed analysis and classification of routine blood samples. Omicsformer adeptly identified potential risks for nine diseases including cancer, cardiovascular conditions, and psychiatric conditions. Analysis of risk trajectories from 20 years of large clinical patients confirmed the validity of the group in preclinical risk assessment, revealing trends in increased disease risk at the time of onset. Additionally, a straightforward NCDs risk prediction system is developed, utilizing basic blood test results. This work highlights the role of multiomics analysis in the prediction of chronic disease risk, and the development and validation of predictive models based on blood routine results can help advance personalized medicine and reduce the cost of disease screening in the community.

PMID:40171841 | DOI:10.1002/advs.202412775

Ir J Med Sci. 2025 Apr 1. doi: 10.1007/s11845-025-03948-x. Online ahead of print.

ABSTRACT

BACKGROUND: This study aims to conduct a systematic review and meta-analysis of the currently present literature analyzing the effectiveness and safety profile of prusogliptin, a novel dipeptidyl peptidase-IV (DPP-4) inhibitor, as compared to placebo in type 2 diabetes mellitus (T2DM) patients.

METHODS: This systemic review and meta-analysis complied with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The search strategy based on various MeSH terms was run on: PubMed/Medline, SCOPUS, and Cochrane Central, which were then systematically searched from inception till March 2024 to select all relevant Randomized Control Trials (RCT).

RESULTS: The analysis of the findings from three RCTs with 957 patients revealed that prusogliptin reduced Hemoglobin A1c (HbA1c)% levels in T2DM patients significantly [Mean Difference (MD): -0.62, 95% Confidence Interval (CI): -0.74 to -0.50, I2 = 0%, p < 0.001] and led to more patients with a HbA1c% ≤ 7% [Odds Ratio (OR): 2.65, 95%CI: 1.94 to 3.61, I2 = 0%, p < 0.00001]. However, prusogliptin led to a non-significant increase in weight when compared with placebo (MD: 0.22, 95% CI: -0.50 to 0.93, I2 = 60%, p = 0.551). The safety profile of prusogliptin revealed a non-significant decrease in treatment-emergent adverse events (OR: 0.90, 95% CI: 0.59 to 1.38, I2 = 43%, p = 0.64) and a non-significant increase in treatment-emergent serious adverse events (OR: 1.02, 95% CI: 0.43 to 2.44, I2 = 0%, p = 0.96) and drug-related adverse events (OR: 1.07, 95%CI: 0.68 to 1.69, I2 = 0%, p = 0.76).

CONCLUSION: Prusogliptin has a favorable efficacy in attaining glycemic control in patients with T2DM. However, its safety profile yields uncertain outcomes. More literature is required for a definitive result.

PMID:40172782 | DOI:10.1007/s11845-025-03948-x

Front Microbiol. 2025 Mar 18;16:1534659. doi: 10.3389/fmicb.2025.1534659. eCollection 2025.

ABSTRACT

INTRODUCTION: Streptococcus pneumoniae is a Gram-positive bacterium responsible for severe infections such as meningitis and pneumonia. The increasing prevalence of antibiotic resistance necessitates the identification of new therapeutic targets. This study aimed to discover potential drug targets against S. pneumoniae using an in silico subtractive genomics approach.

METHODS: The S. pneumoniae genome was compared to the human genome to identify non-homologous sequences using CD-HIT and BLASTp. Essential genes were identified using the Database of Essential Genes (DEG), with consideration for human gut microflora. Protein-protein interaction analyses were conducted to identify key hub genes, and gene ontology (GO) studies were performed to explore associated pathways. Due to the lack of crystal structure data, a potential target was modeled in silico and subjected to structure-based virtual screening.

RESULTS: Approximately 2,000 of the 2,027 proteins from the S. pneumoniae genome were identified as non-homologous to humans. The DEG identified 48 essential genes, which was reduced to 21 after considering human gut microflora. Key hub genes included gpi, fba, rpoD, and trpS, associated with 20 pathways. Virtual screening of 2,509 FDA-approved compounds identified Bromfenac as a leading candidate, exhibiting a binding energy of -26.335 ± 29.105 kJ/mol.

DISCUSSION: Bromfenac, particularly when conjugated with AuAgCu2O nanoparticles, has demonstrated antibacterial and anti-inflammatory properties against Staphylococcus aureus. This suggests that Bromfenac could be repurposed as a potential therapeutic agent against S. pneumoniae, pending further experimental validation. The approach highlights the potential for drug repurposing by targeting proteins essential in pathogens but absent in the host.

PMID:40170924 | PMC:PMC11958985 | DOI:10.3389/fmicb.2025.1534659