Cornea. 2025 May 2. doi: 10.1097/ICO.0000000000003877. Online ahead of print.

NO ABSTRACT

PMID:40315261 | DOI:10.1097/ICO.0000000000003877

Am J Respir Crit Care Med. 2025 Apr 30. Online ahead of print.

NO ABSTRACT

PMID:40315143

Am J Respir Crit Care Med. 2025 May 2. Online ahead of print.

ABSTRACT

RATIONALE: Hyper IgE syndrome (STAT3-HIES), also known as Job's syndrome, is a rare immunodeficiency disease typically caused by dominant-negative STAT3 mutations. STAT3-HIES is characterized by chronic pulmonary infection and inflammation, suggesting impaired innate host defense.

OBJECTIVES: To identify airway epithelial host defense defects caused by STAT3 mutations that, together with immune dysfunction, contribute to recurrent pulmonary infections in STAT3-HIES.

METHODS: STAT3-HIES sputum was analyzed for biochemical and biophysical properties. STAT3-HIES excised lungs were harvested for histology; and bronchial brush samples were collected for RNA sequencing and in vitro culture. A STAT3-HIES-specific R382W mutation, expressed via lentivirus, and STAT3 knockout (CRISPR/Cas9), were studied in normal human bronchial epithelial cells under basal or inflammatory (IL1β)-stimulated conditions. Effects of STAT3 deficiency on transcriptomics, epithelial ion channel, secretory, antimicrobial, and ciliary functions were assessed.

MEASUREMENTS AND MAIN RESULTS: STAT3-HIES sputum showed increased mucus concentration and viscoelasticity. STAT3-HIES excised lungs exhibited mucus obstruction and elevated IL1β expression. STAT3 mutations reduced CFTR mRNA and protein levels, impaired CFTR-dependent fluid and mucin secretion, suppressed antimicrobial peptide, cytokine, and chemokine expression, and acidified airway surface liquid at baseline and post-IL1β exposure. Notably, mutant STAT3 suppressed IL1R1 expression. Furthermore, STAT3 mutations impaired multiciliogenesis by blocking commitment to ciliated cell lineages through inhibition of HES6, leading to defective mucociliary transport. Administration of a γ-secretase inhibitor restored HES6 expression, improved ciliogenesis in STAT3 R382W mutant cells.

CONCLUSIONS: STAT3 dysfunction leads to multi-component defects in airway epithelial innate defense, which, in conjunction with immune deficiency, contributes to chronic pulmonary infection in STAT3-HIES.

PMID:40315437

PLoS One. 2025 May 2;20(5):e0322911. doi: 10.1371/journal.pone.0322911. eCollection 2025.

ABSTRACT

BACKGROUND: The current study aimed to explore patient and provider perspectives of the impact of the pandemic on cystic fibrosis healthcare access and service delivery.

METHODS: We used Interpretive Description, a qualitative approach with the end-goal of informing decisions and actions in clinical practice by generating findings that are clinically meaningful and useful. Levesque et al.'s "Conceptual framework of access to health care" informed the development of our interview guides. Interviews were conducted via telephone or Zoom and confidentially transcribed verbatim. Data generation and analysis occurred concurrently to allow for iterative refinements of the interview guides. Analysis was informed by Braun and Clarke's six phases of reflexive thematic analysis. Strategies to enhance rigour and trustworthiness of the findings were utilized.

RESULTS: We completed 13 interviews: 8 with patients and 5 with providers. Three key themes were generated: (a) Tensions due to infection prevention at micro- meso-, and macro- levels; (b) Modifying aspects of person-focused care can bolster perceived quality of clinical encounters; and (c) Accessibility of appropriate healthcare services could improve efficiency of service delivery. Infection prevention at the individual level was not found to be burdensome. Society's compliance with public health measures, or lack thereof, impacted the level of stigma and anxiety experienced by patients with cystic fibrosis. A changed model of care reliant on patient self-report instead of clinician-led testing and in-person assessment due to the transition to virtual care was associated with mixed perceptions since patients with cystic fibrosis were comfortable making care decisions but many participants (patient and provider) felt challenged by the lack of objective data for decision-making. It was essential for patients with cystic fibrosis to feel known, heard, and seen by their providers in order to feel the care was effective. Finally, critical insights around the need for a balance of in-person and virtual care as well as the need for mental health supports were offered.

CONCLUSIONS: The learnings from this study could be translated into practical strategies for improving cystic fibrosis care during the pandemic and beyond. We recommend: (1) a hybrid approach to care moving forward, (2) each patient having a lead physician with others filling in as necessary when scheduling demands, and (3) a reallocation of resources to fund a mental health practitioner position.

PMID:40315223 | DOI:10.1371/journal.pone.0322911

Brief Bioinform. 2025 May 1;26(3):bbaf160. doi: 10.1093/bib/bbaf160.

ABSTRACT

Single-cell sequencing has advanced our understanding of cellular heterogeneity and disease pathology, offering insights into cellular behavior and immune mechanisms. However, extracting meaningful phenotype-related features is challenging due to noise, batch effects, and irrelevant biological signals. To address this, we introduce Deep scSTAR (DscSTAR), a deep learning-based tool designed to enhance phenotype-associated features. DscSTAR identified HSP+ FKBP4+ T cells in CD8+ T cells, which linked to immune dysfunction and resistance to immune checkpoint blockade in non-small cell lung cancer. It has also enhanced spatial transcriptomics analysis of renal cell carcinoma, revealing interactions between cancer cells, CD8+ T cells, and tumor-associated macrophages that may promote immune suppression and affect outcomes. In hepatocellular carcinoma, it highlighted the role of S100A12+ neutrophils and cancer-associated fibroblasts in forming tumor immune barriers and potentially contributing to immunotherapy resistance. These findings demonstrate DscSTAR's capacity to model and extract phenotype-specific information, advancing our understanding of disease mechanisms and therapy resistance.

PMID:40315434 | DOI:10.1093/bib/bbaf160

Brief Bioinform. 2025 May 1;26(3):bbaf199. doi: 10.1093/bib/bbaf199.

ABSTRACT

RNA torsion and pseudo-torsion angles are critical in determining the three-dimensional conformation of RNA molecules, which in turn governs their biological functions. However, current methods are limited by RNA's structural complexity as well as flexibility, with experimental techniques being costly and computational approaches struggling to capture the intricate sequence dependencies needed for accurate predictions. To address these challenges, we introduce DeepRNA-Twist, a novel deep learning framework designed to predict RNA torsion and pseudo-torsion angles directly from sequence. DeepRNA-Twist utilizes RNA language model embeddings, which provides rich, context-aware feature representations of RNA sequences. Additionally, it introduces 2A3IDC module (Attention Augmented Inception Inside Inception with Dilated CNN), combining inception networks with dilated convolutions and multi-head attention mechanism. The dilated convolutions capture long-range dependencies in the sequence without requiring a large number of parameters, while the multi-head attention mechanism enhances the model's ability to focus on both local and global structural features simultaneously. DeepRNA-Twist was rigorously evaluated on benchmark datasets, including RNA-Puzzles, CASP-RNA, and SPOT-RNA-1D, and demonstrated significant improvements over existing methods, achieving state-of-the-art accuracy. Source code is available at https://github.com/abrarrahmanabir/DeepRNA-Twist.

PMID:40315431 | DOI:10.1093/bib/bbaf199

PLoS One. 2025 May 2;20(5):e0320656. doi: 10.1371/journal.pone.0320656. eCollection 2025.

ABSTRACT

With the acceleration of urbanization and the increase in traffic volume, frequent traffic accidents have significantly impacted public safety and socio-economic conditions. Traditional methods for predicting traffic accidents often overlook spatiotemporal features and the complexity of traffic networks, leading to insufficient prediction accuracy in complex traffic environments. To address this, this paper proposes a deep learning model that combines Convolutional Neural Networks (CNN), Long Short-Term Memory networks (LSTM), and Graph Neural Networks (GNN) for traffic accident risk prediction using vehicle spatiotemporal trajectory data. The model extracts spatial features such as vehicle speed, acceleration, and lane-changing distance through CNN, captures temporal dependencies in trajectories using LSTM, and effectively models the complex spatial structure of traffic networks with GNN, thereby improving prediction accuracy.The main contributions of this paper are as follows: First, an innovative combined model is proposed, which comprehensively considers spatiotemporal features and road network relationships, significantly improving prediction accuracy. Second, the model's strong generalization ability across multiple traffic scenarios is validated, enhancing the accuracy of traditional prediction methods. Finally, a new technical approach is provided, offering theoretical support for the implementation of real-time traffic accident warning systems. Experimental results demonstrate that the model can effectively predict accident risks in various complex traffic scenarios, providing robust support for intelligent traffic management and public safety.

PMID:40315419 | DOI:10.1371/journal.pone.0320656

Sci Adv. 2025 May 2;11(18):eadu4888. doi: 10.1126/sciadv.adu4888. Epub 2025 May 2.

ABSTRACT

Squeezing states are crucial for advancing quantum metrology beyond the classical limit. Despite this, generating high-performance squeezed light with long-term stability remains a challenge due to system complexity and quantum fragility. We experimentally achieved a record-breaking squeezing level of 4.3 decibels (lossless, 5.9 decibels) using polarization self-rotation (PSR) in atomic vapor, maintaining stability for hours despite environmental disturbances. Overcoming the limitations of the PSR theory model's optimization guidance, which arises from the mutual interference of multiple parameters at this squeezing level, we developed an artificial intelligence (AI) control (AIC) system that harnesses deep learning to discern and manage these complex relationships, thereby enabling self-adapted to external environments. This integrated approach represents a concrete step for the actual application of quantum metrology and information processing, illustrating the synergy between AI and fundamental science in breaking complexity constraints.

PMID:40315327 | DOI:10.1126/sciadv.adu4888

Bioinformatics. 2025 May 2:btaf141. doi: 10.1093/bioinformatics/btaf141. Online ahead of print.

ABSTRACT

MOTIVATION: The biosecurity issue arises as the capability of deep learning-based protein design has rapidly increased in recent years. Current regulation procedures for DNA synthesizing focus on the biosecurity but ignore the data privacy.

RESULTS: We propose a general framework for adding watermarks to protein sequences designed by various autoregressive deep learning models. Compared to current regulation procedures, watermarks also ensure robust traceability to achieve biosecurity but maintain privacy of designed sequences by local verification. Benchmarked with other watermarking techniques, the watermark detection efficiency of our method is substantially increased to be more practical in real-world scenarios. Moreover, it provides a convenient way for researchers to claim their own intellectual property since the designer's information could be embedded into the sequence with our framework.

AVAILABILITY AND IMPLEMENTATION: The implementation of the protein watermark framework is freely available to non-commercial users at https://github.com/poseidonchan/ProteinWatermark.

CONTACT AND SUPPLEMENTARY INFORMATION: Contact authors: Yanshuo Chen (cys@umd.edu) and Heng Huang (heng@umd.edu). The step-by-step tutorials of adding and detecting watermarks are also included in the repository at: https://github.com/poseidonchan/ProteinWatermark/tree/main/tutorials.

PMID:40315154 | DOI:10.1093/bioinformatics/btaf141

IEEE Trans Pattern Anal Mach Intell. 2025 May 2;PP. doi: 10.1109/TPAMI.2025.3566620. Online ahead of print.

ABSTRACT

With the recent advances in technology, a wide range of systems continue to collect a large amount of data over time and thus generate time series. Time-Series Anomaly Detection (TSAD) is an important task in various time-series applications such as e-commerce, cybersecurity, vehicle maintenance, and healthcare monitoring. However, this task is very challenging as it requires considering both the intra-variable dependency (relationships within a variable over time) and the inter-variable dependency (relationships between multiple variables) existing in time-series data. Recent graph-based approaches have made impressive progress in tackling the challenges of this field. In this survey, we conduct a comprehensive and up-to-date review of TSAD using graphs, referred to as G-TSAD. First, we explore the significant potential of graph representation for time-series data and and its contributions to facilitating anomaly detection. Then, we review state-of-the-art graph anomaly detection techniques, mostly leveraging deep learning architectures, in the context of time series. For each method, we discuss its strengths, limitations, and the specific applications where it excels. Finally, we address both the technical and application challenges currently facing the field, and suggest potential future directions for advancing research and improving practical outcomes.

PMID:40315075 | DOI:10.1109/TPAMI.2025.3566620

Phys Rev Lett. 2025 Apr 18;134(15):158401. doi: 10.1103/PhysRevLett.134.158401.

ABSTRACT

Spatial information from cell-surface receptors is crucial for processes that require signal processing and sensing of the environment. Here, we investigate the optimal placement of such receptors through a theoretical model that minimizes uncertainty in gradient estimation. Without requiring a priori knowledge of the physical limits of sensing or biochemical processes, we reproduce the emergence of clusters that closely resemble those observed in real cells. On perfect spherical surfaces, optimally placed receptors spread uniformly. When perturbations break their symmetry, receptors cluster in regions of high curvature, massively reducing estimation uncertainty. This agrees in many scenarios with mechanistic models that minimize elastic preference discrepancies between receptors and cell membranes. We further extend our model to motile receptors responding to cell-shape changes and external fluid flow, demonstrating the biological relevance of our model. Our findings provide a simple and utilitarian explanation for receptor clustering at high-curvature regions when high sensing accuracy is paramount.

PMID:40315515 | DOI:10.1103/PhysRevLett.134.158401

PLoS Pathog. 2025 May 2;21(5):e1013156. doi: 10.1371/journal.ppat.1013156. Online ahead of print.

ABSTRACT

Motile bacteria use chemotaxis to navigate complex environments like the mammalian gut. These bacteria sense a range of chemoeffector molecules, which can either be of nutritional value or provide a cue for the niche best suited for their survival and growth. One such cue molecule is the intra- and interspecies quorum sensing signaling molecule, autoinducer-2 (AI-2). Apart from controlling collective behavior of Escherichia coli, chemotaxis towards AI-2 contributes to its ability to colonize the murine gut. However, the impact of AI-2-dependent niche occupation by E. coli on interspecies interactions in vivo is not fully understood. Using the C57BL/6J mouse infection model, we show that chemotaxis towards AI-2 contributes to nutrient competition and thereby affects colonization resistance conferred by E. coli against the enteric pathogen Salmonella enterica serovar Typhimurium (S. Tm). Like E. coli, S. Tm also relies on chemotaxis, albeit not towards AI-2, to compete against residing E. coli in a gut inflammation-dependent manner. Finally, utilizing a barcoded S. Tm mutant pool, we investigated the impact of AI-2 signaling in E. coli on S. Tm's carbohydrate utilization and central metabolism. Interestingly, AI-2-dependent niche colonization by E. coli was highly specific, impacting only a limited number of S. Tm mutants at distinct time points during infection. Notably, it significantly altered the fitness of mutants deficient in mannose utilization (ΔmanA, early stage infection) and, to a lesser extent, fumarate respiration (ΔdcuABC, late stage infection). The role of quorum sensing and chemotaxis in metabolic competition among bacteria remains largely unexplored. Here, we provide initial evidence that AI-2-dependent nutrient competition occurs between S. Tm and E. coli at specific time points during infection. These findings represent a crucial step toward understanding how bacteria navigate the gastrointestinal tract and engage in targeted nutrient competition within this complex three-dimensional environment.

PMID:40315408 | DOI:10.1371/journal.ppat.1013156

Jpn J Radiol. 2025 May 2. doi: 10.1007/s11604-025-01792-8. Online ahead of print.

ABSTRACT

OBJECTIVE: To investigate which brain tissue component volume (BTCV) biomarkers may be more effective than hormones in influencing bone age development in central precocious puberty (CPP).

METHODS: This retrospective study included 84 children with CPP and 84 controls. Data on cranial synthetic magnetic resonance (SyMR), X-ray bone age, and three hormones were collected. BTCVs-myelin content (MyC), white matter (WM), gray matter (GM), cerebrospinal fluid (CSF), and non-WM/GM/MyC/CSF (NoN)-were obtained from SyMRI. A deep learning model assessed Tanner-Whitehouse III (TW3) bone age scores (TW3-RUS, TW3-Carpal). We evaluated the correlation between BTCVs, bone age scores, luteinizing hormone (LH), LH after gonadotropin-releasing hormone (GnRH) stimulation, and follicle-stimulating hormone (FSH).

RESULTS: Children with CPP had lower MyC, WM, and GM than controls. The TW3-RUS score did not correlate with BTCVs or hormones. The TW3-Carpal score was positively correlated with MyC (r = 0.397, P < 0.001) but not with WM, GM, CSF, NoN, or hormones. The regression model showed a positive correlation between the TW3-Carpal score and MyC (β = 0.077, P < 0.001), while LH correlated with GM and NoN (β = - 16.66, P = 0.019; β = 24.62, P = 0.019).

CONCLUSION: The TW3-Carpal score in CPP positively correlates with MyC, while two TW3 scores do not correlate with hormone levels, suggesting myelin has a greater impact on bone age development than hormones. MyC may serve as a potential biomarker in BTCVs for CPP.

PMID:40314875 | DOI:10.1007/s11604-025-01792-8

Eur J Nucl Med Mol Imaging. 2025 May 2. doi: 10.1007/s00259-025-07314-y. Online ahead of print.

NO ABSTRACT

PMID:40314811 | DOI:10.1007/s00259-025-07314-y

Eur Radiol. 2025 May 2. doi: 10.1007/s00330-025-11623-z. Online ahead of print.

ABSTRACT

OBJECTIVES: To develop a deep neural network for automatic bowel segmentation and assess its applicability for estimating large bowel length (LBL) in individuals with constipation.

MATERIALS AND METHODS: We utilized contrast-enhanced and non-enhanced abdominal, chest, and whole-body CT images for model development. External testing involved paired pre- and post-contrast abdominal CT images from another hospital. We developed 3D nnU-Net models to segment the gastrointestinal tract and separate it into the esophagus, stomach, small bowel, and large bowel. Segmentation accuracy was evaluated using the Dice similarity coefficient (DSC) based on radiologists' segmentation. We employed the network to estimate LBL in individuals having abdominal CT for health check-ups, and the height-corrected LBL was compared between groups with and without constipation.

RESULTS: One hundred thirty-three CT scans (88 patients; age, 63.6 ± 10.6 years; 39 men) were used for model development, and 60 for external testing (30 patients; age, 48.9 ± 15.8 years; 16 men). In the external dataset, the mean DSC for the entire gastrointestinal tract was 0.985 ± 0.008. The mean DSCs for four-part separation exceeded 0.95, outperforming TotalSegmentator, except for the esophagus (DSC, 0.807 ± 0.173). For LBL measurements, 100 CT scans from 51 patients were used (age, 67.0 ± 6.9 years; 59 scans from men; 59 with constipation). The height-corrected LBL were significantly longer in the constipation group on both per-exam (79.1 ± 12.4 vs 88.8 ± 15.8 cm/m, p = 0.001) and per-subject basis (77.6 ± 13.6 vs 86.9 ± 17.1 cm/m, p = 0.04).

CONCLUSION: Our model accurately segmented the entire gastrointestinal tract and its major compartments from CT scans and enabled the noninvasive estimation of LBL in individuals with constipation.

KEY POINTS: Questions Automated bowel segmentation is a first step for algorithms, including bowel tracing and length measurement, but the complexity of the gastrointestinal tract limits its accuracy. Findings Our 3D nnU-Net model showed high performance in segmentation and four-part separation of the GI tract (DSC > 0.95), except for the esophagus. Clinical relevance Our model accurately segments the gastrointestinal tract and separates it into major compartments. Our model potentially has use in various clinical applications, including semi-automated measurement of LBL in individuals with constipation.

PMID:40314787 | DOI:10.1007/s00330-025-11623-z

Pancreas. 2025 May 1;54(5):e430-e441. doi: 10.1097/MPA.0000000000002463.

ABSTRACT

BACKGROUND AND OBJECTIVES: Accurate survival prediction for pancreatic ductal adenocarcinoma (PDAC) is crucial for personalized treatment strategies. This study aims to construct a novel pathomics indicator using hematoxylin and eosin-stained whole slide images and deep learning to enhance PDAC prognosis prediction.

METHODS: A retrospective, 2-center study analyzed 864 PDAC patients diagnosed between January 2015 and March 2022. Using weakly supervised and multiple instance learning, pathologic features predicting 2-year survival were extracted. Pathomics features, including probability histograms and TF-IDF, were selected through random survival forests. Survival analysis was conducted using Kaplan-Meier curves, log-rank tests, and Cox regression, with AUROC and C-index used to assess model discrimination.

RESULTS: The study cohort comprised 489 patients for training, 211 for validation, and 164 in the neoadjuvant therapy (NAT) group. A pathomics score was developed using 7 features, dividing patients into high-risk and low-risk groups based on the median score of 131.11. Significant survival differences were observed between groups (P<0.0001). The pathomics score was a robust independent prognostic factor [Training: hazard ratio (HR)=3.90; Validation: HR=3.49; NAT: HR=4.82; all P<0.001]. Subgroup analyses revealed higher survival rates for early-stage low-risk patients and NAT responders compared to high-risk counterparts (both P<0.05 and P<0.0001). The pathomics model surpassed clinical models in predicting 1-, 2-, and 3-year survival.

CONCLUSIONS: The pathomics score serves as a cost-effective and precise prognostic tool, functioning as an independent prognostic indicator that enables precise stratification and enhances the prediction of prognosis when combined with traditional pathologic features. This advancement has the potential to significantly impact PDAC treatment planning and improve patient outcomes.

PMID:40314741 | DOI:10.1097/MPA.0000000000002463

Med Biol Eng Comput. 2025 May 2. doi: 10.1007/s11517-025-03363-5. Online ahead of print.

ABSTRACT

Defects to human crania are one kind of head bone damages, and cranial implants can be used to repair the defected crania. The automation of the implant design process is crucial in reducing the corresponding therapy time. Taking the cranial implant design problem as a special kind of shape completion task, an automatic cranial implant design workflow is proposed, which consists of a deep neural network for the direct shape prediction of the missing part of the defective cranium and conventional post-processing steps to refine the automatically generated implant. To evaluate the proposed workflow, we employ cross-validation and report an average Dice Similarity Score and boundary Dice Similarity Score of 0.81 and 0.81, respectively. We also measure the surface distance error using the 95th quantile of the Hausdorff Distance, which yields an average of 3.01 mm. Comparison with the manual cranial implant design procedure also revealed the convenience of the proposed workflow. In addition, a plugin is developed for 3D Slicer, which implements the proposed automatic cranial implant design workflow and can facilitate the end-users.

PMID:40314711 | DOI:10.1007/s11517-025-03363-5

Int J Comput Assist Radiol Surg. 2025 May 2. doi: 10.1007/s11548-025-03361-w. Online ahead of print.

ABSTRACT

PURPOSE: The study aims to develop an automatic method to align ultrasound images of the distal forearm for diagnosing pediatric fractures. This approach seeks to bypass the reliance on X-rays for fracture diagnosis, thereby minimizing radiation exposure and making the process less painful, as well as creating a more child-friendly diagnostic pathway.

METHODS: We present a fully automatic pipeline to align paired POCUS images. We first leverage a deep learning model to delineate bone boundaries, from which we obtain key anatomical landmarks. These landmarks are finally used to guide the optimization-based alignment process, for which we propose three optimization constraints: aligning specific points, ensuring parallel orientation of the bone segments, and matching the bone widths.

RESULTS: The method demonstrated high alignment accuracy compared to reference X-rays in terms of boundary distances. A morphology experiment including fracture classification and angulation measurement presents comparable performance when based on the merged ultrasound images and conventional X-rays, justifying the effectiveness of our method in these cases.

CONCLUSIONS: The study introduced an effective and fully automatic pipeline for aligning ultrasound images, showing potential to replace X-rays for diagnosing pediatric distal forearm fractures. Initial tests show that surgeons find many of our results sufficient for diagnosis. Future work will focus on increasing dataset size to improve diagnostic accuracy and reliability.

PMID:40314702 | DOI:10.1007/s11548-025-03361-w

J Chem Inf Model. 2025 May 2. doi: 10.1021/acs.jcim.5c00271. Online ahead of print.

ABSTRACT

Antimicrobial resistance (AMR) is a global threat, with Burkholderia species contributing significantly to difficult-to-treat infections. The Pen family of β-lactamases are produced by all Burkholderia spp., and their mutation or overproduction leads to the resistance of β-lactam antibiotics. Here we investigate the dynamic differences among four Pen β-lactamases (PenA, PenI, PenL and PenP) using machine learning driven enhanced sampling molecular dynamics simulations, Markov State Models (MSMs), convolutional variational autoencoder-based deep learning (CVAE) and the BindSiteS-CNN model. In spite of sharing the same catalytic mechanisms, these enzymes exhibit distinct dynamic features due to low sequence identity, resulting in different substrate profiles and catalytic turnover. The BindSiteS-CNN model further reveals local active site dynamics, offering insights into the Pen β-lactamase evolutionary adaptation. Our findings reported here identify critical mutations and propose new hot spots affecting Pen β-lactamase flexibility and function, which can be used to fight emerging resistance in these enzymes.

PMID:40314617 | DOI:10.1021/acs.jcim.5c00271

Radiol Imaging Cancer. 2025 May;7(3):e250141. doi: 10.1148/rycan.250141.

NO ABSTRACT

PMID:40314587 | DOI:10.1148/rycan.250141