Arterioscler Thromb Vasc Biol. 2025 Feb 27. doi: 10.1161/ATVBAHA.124.322145. Online ahead of print.

ABSTRACT

BACKGROUND: FXIa (coagulation factor XIa) is considered as a promising antithrombotic target with reduced hemorrhagic liabilities. The objective of this study was to identify a small-molecule inhibitor of FXIa as a potential low-hemorrhagic anticoagulant.

METHODS: A high-throughput virtual screening was conducted using a drug repurposing library with the catalytic domain of FXIa as the bait. The identified inhibitor's anticoagulant activity was evaluated in vitro and in both arterial and venous murine thrombotic models. The dependency of the inhibitor on FXIa was further examined using FXI-/- mice. Hemorrhagic risks were subsequently evaluated in models of both localized and major bleeding.

RESULTS: Virtual screening led to the identification of montelukast, a commonly used antiasthmatic drug, as a potent and specific FXIa inhibitor (IC50, 0.17 μmol/L). MK exhibited anticoagulant effects comparable to those of 2 mostly prescribed anticoagulants (warfarin and apixaban) in both arterial and venous thrombotic models. Notably, in stark contrast to the pronounced hemorrhagic risks of warfarin and apixaban, MK did not measurably increase the tendency of localized or major bleeding. Furthermore, MK did not prolong the time to arterial thrombotic occlusion in FXI-/- mice, while effectively inhibited arterial occlusion induced by the reinfusion of recombinant FXIa, confirming that MK's anticoagulant activity is mediated by plasma FXIa. Additionally, MK ameliorated inflammation levels and mitigated pulmonary microthrombus formation in a septic mouse model. Moreover, combination therapy with MK enhanced the antithrombotic effects of antiplatelets without an obvious increase of hemorrhage.

CONCLUSIONS: This proof-of-concept study suggests the potent low-hemorrhage antithrombotic effect of MK by targeting FXIa and unveiling a new therapeutic application of MK.

PMID:40013360 | DOI:10.1161/ATVBAHA.124.322145

Front Oncol. 2025 Feb 11;15:1550672. doi: 10.3389/fonc.2025.1550672. eCollection 2025.

NO ABSTRACT

PMID:40012550 | PMC:PMC11861434 | DOI:10.3389/fonc.2025.1550672

Toxicol Res. 2024 Dec 21;41(2):123-129. doi: 10.1007/s43188-024-00271-y. eCollection 2025 Mar.

ABSTRACT

This review investigates the correlation between prenatal tobacco exposure and the risk of liver diseases in offspring. By synthesizing data from clinical trials and animal studies, it provides a comprehensive overview of the potential mechanisms underlying this association. This review begins by analyzing the prevalence of maternal smoking and its impact on fetal development. It then discusses specific liver diseases observed in offspring exposed prenatally to tobacco, such as acute liver injuries and metabolic dysfunction-associated fatty liver disease, and discusses the underlying pathophysiological pathways. Current evidence indicates that altered fetal liver development, oxidative stress, and genetic modifications may predispose offspring to liver diseases. Furthermore, this review highlights the gaps in current research and the need for longitudinal studies to better understand the long-term effects of prenatal tobacco exposure on the liver. The review concludes with recommendations for public health policies aimed at enhancing our understanding of maternal smoking and mitigating its adverse effects on offspring, emphasizing the importance of smoking cessation during pregnancy.

PMID:40013082 | PMC:PMC11850666 | DOI:10.1007/s43188-024-00271-y

J Transl Gastroenterol. 2024 Jun;2(2):125-130. doi: 10.14218/JTG.2024.00017. Epub 2024 Jun 28.

ABSTRACT

The brain-gut axis represents a bidirectional communication network that integrates neural, hormonal, and immunological signaling between the central nervous system and the gastrointestinal tract. Adverse childhood experiences (ACEs) have increasingly been recognized for their profound impact on this axis, with implications for both mental and physical health outcomes. This mini-review explores the emerging field of epigenomics-specifically, how epigenetic modifications incurred by ACEs can influence the brain-gut axis and contribute to the pathophysiology of various disorders. We examine the evidence linking epigenetic mechanisms such as DNA methylation, histone modifications, and non-coding RNAs to the modulation of gene expression involved in stress responses, neurodevelopment, and immune function-all of which intersect at the brain-gut axis. Additionally, we discuss the emerging potential of the gut microbiome as both a target and mediator of epigenetic changes, further influencing brain-gut communication in the context of ACEs. The methodological and therapeutic challenges posed by these insights are significant. The reversibility of epigenetic marks and the long-term consequences of early life stress require innovative and comprehensive approaches to intervention. This underscores the need for comprehensive strategies encompassing psychosocial, pharmacological, neuromodulation, and lifestyle interventions tailored to address ACEs' individualized and persistent effects. Future directions call for a multi-disciplinary approach and longitudinal studies to uncover the full extent of ACEs' impact on epigenetic regulation and the brain-gut axis, with the goal of developing targeted therapies to mitigate the long-lasting effects on health.

PMID:40012740 | PMC:PMC11864786 | DOI:10.14218/JTG.2024.00017

J Cereb Blood Flow Metab. 2025 Feb 27:271678X251322032. doi: 10.1177/0271678X251322032. Online ahead of print.

ABSTRACT

The impact of DNA methylation (DNAm) on epigenetics has gained prominence in recent years due to its potential influence on ischemic stroke (IS) and treatment outcomes. DNAm is reversible and a better understanding of its role in IS could help identify novel therapeutic targets. The aim of this systematic review was to compile the available data on DNAm in the risk and prognosis of IS and to explore its therapeutic potential. The review process followed the PRISMA criteria. We searched the Pubmed and Cochrane databases to identify studies that used hypothesis free methodological approaches. Of the 459 studies identified, 34 met the inclusion criteria. The studies were categorized as follows: risk of IS; outcomes; and DNAm age. Most studies used genotyping array technology rather than whole-genome sequencing. DNAm testing was mainly based on blood samples. Most studies involved European cohorts. Most of the studies were performed at a single-center with recruitment at the time of stroke. In a few studies, health status was determined longitudinally. This systematic review shows that IS patients are biologically older than expected and present characteristic DNAm patterns related to stroke risk and outcomes. These patterns could be used to develop new treatments with epidrugs.

PMID:40012472 | DOI:10.1177/0271678X251322032

ERJ Open Res. 2025 Feb 25;11(1):00445-2024. doi: 10.1183/23120541.00445-2024. eCollection 2025 Jan.

ABSTRACT

BACKGROUND: Cystic fibrosis transmembrane conductance regulator (CFTR) triple modulator therapy, elexacaftor-tezacaftor-ivacaftor (ETI) has transformed care for people with cystic fibrosis (CF) who have eligible mutations. It is, however, not curative. Response to treatment also varies and lung disease, although slowed, remains progressive. We have previously demonstrated inhibition of the epithelial sodium channel (ENaC) by selective furin inhibition to be an alternative, mutation-agnostic approach that can enhance airways hydration and restore mucociliary transport (MCT) in CF. Inhibition of furin therefore, offers a potential therapeutic strategy for those ineligible, intolerant or nonresponsive to ETI and may provide a further opportunity for clinical benefit for those currently treated with ETI. The aim of this study was to determine the impact of furin inhibition on ETI responses to assess its utility as an adjunct therapy.

METHODS: Differentiated primary CF human bronchial epithelial cells (HBECs) were treated with the highly selective furin inhibitor BOS-318 and with ETI. Ion channel function was measured using a 24-channel Transepithelial Current Clamp (TECC-24) system and airways surface hydration was investigated by measuring airway surface liquid (ASL) height and MCT rate.

RESULTS: The presence of BOS-318 had no effect on the ability of ETI to stimulate CFTR-mediated Cl- secretion but contributed a reduced Na+ transport via robust inhibition of ENaC. This altered ion transport profile effected an improved ASL height and MCT rate, which were significantly greater than improvements observed with ETI alone, demonstrating the benefits of the dual approach.

CONCLUSIONS: Selective furin inhibition has the potential to further improve clinical outcomes for all people with CF and offers opportunity as an adjunct to improve responses to currently available CFTR modulator therapies.

PMID:40013020 | PMC:PMC11863070 | DOI:10.1183/23120541.00445-2024

J Thorac Imaging. 2025 Feb 27. doi: 10.1097/RTI.0000000000000827. Online ahead of print.

ABSTRACT

PURPOSE: To assess the performance of a newly introduced deep learning-based reconstruction algorithm, namely the artificial intelligence iterative reconstruction (AIIR), in reducing the dose of pediatric chest CT by using the image data of below 3-year-old patients with congenital heart disease (CHD).

MATERIALS AND METHODS: The lung image available from routine-dose cardiac CT angiography (CTA) on below 3 years patients with CHD was employed as a reference for evaluating the paired low-dose chest CT. A total of 191 subjects were prospectively enrolled, where the dose for chest CT was reduced to ~0.1 mSv while the cardiac CTA protocol was kept unchanged. The low-dose chest CT images, obtained with the AIIR and the hybrid iterative reconstruction (HIR), were compared in image quality, ie, overall image quality and lung structure depiction, and in diagnostic performance, ie, severity assessment of pneumonia and airway stenosis.

RESULTS: Compared with the reference, lung image quality was not found significantly different on low-dose AIIR images (all P>0.05) but obviously inferior with the HIR (all P<0.05). Compared with the HIR, low-dose AIIR images also achieved a closer pneumonia severity index (AIIR 4.32±3.82 vs. Ref 4.37±3.84, P>0.05; HIR 5.12±4.06 vs. Ref 4.37±3.84, P<0.05) and airway stenosis grading (consistently graded: AIIR 88.5% vs. HIR 56.5% ) to the reference.

CONCLUSIONS: AIIR has the potential for large dose reduction in chest CT of patients below 3 years of age while preserving image quality and achieving diagnostic results nearly equivalent to routine dose scans.

PMID:40013381 | DOI:10.1097/RTI.0000000000000827

J Evid Based Med. 2025 Mar;18(1):e70005. doi: 10.1111/jebm.70005.

ABSTRACT

OBJECTIVE: Leukemia is a type of blood cancer that begins in the bone marrow and results in high numbers of abnormal white blood cells. Automated detection and classification of leukemia and its subtypes using artificial intelligence (AI) and machine learning (ML) algorithms plays a significant role in the early diagnosis and treatment of this fatal disease. This study aimed to review and synthesize research findings on AI-based approaches in leukemia detection and classification from peripheral blood smear images.

METHODS: A systematic literature search was conducted across four e-databases (Web of Science, PubMed, Scopus, and IEEE Xplore) from January 2015 to March 2023 by searching the keywords "Leukemia," "Machine Learning," and "Blood Smear Image," as well as their synonyms. All original journal articles and conference papers that used ML algorithms in detecting and classifying leukemia were included. The study quality was assessed using the Qiao Quality Assessment tool.

RESULTS: From 1325 articles identified through a systematic search, 190 studies were eligible for this review. The mean validation accuracy (ACC) of the ML methods applied in the reviewed studies was 95.38%. Among different ML methods, modern techniques were mostly considered to detect and classify leukemia (60.53% of studies). Supervised learning was the dominant ML paradigm (79% of studies). Studies utilized common ML methodologies for leukemia detection and classification, including preprocessing, feature extraction, feature selection, and classification. Deep learning (DL) techniques, especially convolutional neural networks, were the most widely used modern algorithms in the mentioned methodologies. Most studies relied on internal validation (87%). Moreover, K-fold cross-validation and train/test split were the commonly employed validation strategies.

CONCLUSION: AI-based algorithms are widely used in detecting and classifying leukemia with remarkable performance. Future studies should prioritize rigorous external validation to evaluate generalizability.

PMID:40013326 | DOI:10.1111/jebm.70005

IEEE Int Conf Connect Health Appl Syst Eng Technol. 2023 Jun;2023:68-79. doi: 10.1145/3580252.3586974. Epub 2024 Jan 22.

ABSTRACT

Social presence has been known to impact eating behavior among people with obesity; however, the dual study of eating behavior and social presence in real-world settings is challenging due to the inability to reliably confirm the co-occurrence of these important factors. High-resolution video cameras can detect timing while providing visual confirmation of behavior; however, their potential to capture all-day behavior is limited by short battery lifetime and lack of autonomy in detection. Low-resolution infrared (IR) sensors have shown promise in automating human behavior detection; however, it is unknown if IR sensors contribute to behavior detection when combined with RGB cameras. To address these challenges, we designed and deployed a low-power, and low-resolution RGB video camera, in conjunction with a low-resolution IR sensor, to test a learned model's ability to detect eating and social presence. We evaluated our system in the wild with 10 participants with obesity; our models displayed slight improvement when detecting eating (5%) and significant improvement when detecting social presence (44%) compared with using a video-only approach. We analyzed device failure scenarios and their implications for future wearable camera design and machine learning pipelines. Lastly, we provide guidance for future studies using low-cost RGB and IR sensors to validate human behavior with context.

PMID:40013103 | PMC:PMC11864367 | DOI:10.1145/3580252.3586974

Open Mind (Camb). 2025 Feb 16;9:305-324. doi: 10.1162/opmi_a_00189. eCollection 2025.

ABSTRACT

Humans make rich inferences about the geometry of the visual world. While deep neural networks (DNNs) achieve human-level performance on some psychophysical tasks (e.g., rapid classification of object or scene categories), they often fail in tasks requiring inferences about the underlying shape of objects or scenes. Here, we ask whether and how this gap in 3D shape representation between DNNs and humans can be closed. First, we define the problem space: after generating a stimulus set to evaluate 3D shape inferences using a match-to-sample task, we confirm that standard DNNs are unable to reach human performance. Next, we construct a set of candidate 3D-aware DNNs including 3D neural field (Light Field Network), autoencoder, and convolutional architectures. We investigate the role of the learning objective and dataset by training single-view (the model only sees one viewpoint of an object per training trial) and multi-view (the model is trained to associate multiple viewpoints of each object per training trial) versions of each architecture. When the same object categories appear in the model training and match-to-sample test sets, multi-view DNNs approach human-level performance for 3D shape matching, highlighting the importance of a learning objective that enforces a common representation across viewpoints of the same object. Furthermore, the 3D Light Field Network was the model most similar to humans across all tests, suggesting that building in 3D inductive biases increases human-model alignment. Finally, we explore the generalization performance of multi-view DNNs to out-of-distribution object categories not seen during training. Overall, our work shows that multi-view learning objectives for DNNs are necessary but not sufficient to make similar 3D shape inferences as humans and reveals limitations in capturing human-like shape inferences that may be inherent to DNN modeling approaches. We provide a methodology for understanding human 3D shape perception within a deep learning framework and highlight out-of-domain generalization as the next challenge for learning human-like 3D representations with DNNs.

PMID:40013087 | PMC:PMC11864798 | DOI:10.1162/opmi_a_00189

Digit Health. 2025 Feb 16;11:20552076251320726. doi: 10.1177/20552076251320726. eCollection 2025 Jan-Dec.

ABSTRACT

OBJECTIVE: To develop an enhanced deep learning model, MRpoxNet, based on a modified ResNet50 architecture for the early detection of monkeypox from digital skin lesion images, ensuring high diagnostic accuracy and clinical reliability.

METHODS: The study utilized the Kaggle MSID dataset, initially comprising 1156 images, augmented to 6116 images across three classes: monkeypox, non-monkeypox, and normal skin. MRpoxNet was developed by extending ResNet50 from 177 to 182 layers, incorporating additional convolutional, ReLU, dropout, and batch normalization layers. Performance was evaluated using metrics such as accuracy, precision, recall, F1 score, sensitivity, and specificity. Comparative analyses were conducted against established models like ResNet50, AlexNet, VGG16, and GoogleNet.

RESULTS: MRpoxNet achieved a diagnostic accuracy of 98.1%, outperforming baseline models in all key metrics. The enhanced architecture demonstrated superior robustness in distinguishing monkeypox lesions from other skin conditions, highlighting its potential for reliable clinical application.

CONCLUSION: MRpoxNet provides a robust and efficient solution for early monkeypox detection. Its superior performance suggests readiness for integration into diagnostic workflows, with future enhancements aimed at dataset expansion and multimodal adaptability to diverse clinical scenarios.

PMID:40013075 | PMC:PMC11863262 | DOI:10.1177/20552076251320726

Biophys Rev (Melville). 2025 Feb 21;6(1):011307. doi: 10.1063/5.0249920. eCollection 2025 Mar.

ABSTRACT

Accurately predicting mutation-caused binding free energy changes (ΔΔGs) on protein interactions is crucial for understanding how genetic variations affect interactions between proteins and other biomolecules, such as proteins, DNA/RNA, and ligands, which are vital for regulating numerous biological processes. Developing computational approaches with high accuracy and efficiency is critical for elucidating the mechanisms underlying various diseases, identifying potential biomarkers for early diagnosis, and developing targeted therapies. This review provides a comprehensive overview of recent advancements in predicting the impact of mutations on protein interactions across different interaction types, which are central to understanding biological processes and disease mechanisms, including cancer. We summarize recent progress in predictive approaches, including physicochemical-based, machine learning, and deep learning methods, evaluating the strengths and limitations of each. Additionally, we discuss the challenges related to the limitations of mutational data, including biases, data quality, and dataset size, and explore the difficulties in developing accurate prediction tools for mutation-induced effects on protein interactions. Finally, we discuss future directions for advancing these computational tools, highlighting the capabilities of advancing technologies, such as artificial intelligence to drive significant improvements in mutational effects prediction.

PMID:40013003 | PMC:PMC11857871 | DOI:10.1063/5.0249920

Front Med (Lausanne). 2025 Feb 12;12:1507258. doi: 10.3389/fmed.2025.1507258. eCollection 2025.

ABSTRACT

INTRODUCTION: Pulmonary granulomatous nodules (PGN) often exhibit similar CT morphological features to solid lung adenocarcinomas (SLA), making preoperative differentiation challenging. This study aims to address this diagnostic challenge by developing a novel deep learning model.

METHODS: This study proposes MAEMC-NET, a model integrating generative (Masked AutoEncoder) and contrastive (Momentum Contrast) self-supervised learning to learn CT image representations of intra- and inter-solitary nodules. A generative self-supervised task of reconstructing masked axial CT patches containing lesions was designed to learn intra- and inter-slice image representations. Contrastive momentum is used to link the encoder in axial-CT-patch path with the momentum encoder in coronal-CT-patch path. A total of 494 patients from two centers were included.

RESULTS: MAEMC-NET achieved an area under curve (95% Confidence Interval) of 0.962 (0.934-0.973). These results not only significantly surpass the joint diagnosis by two experienced chest radiologists (77.3% accuracy) but also outperform the current state-of-the-art methods. The model performs best on medical images with a 50% mask ratio, showing a 1.4% increase in accuracy compared to the optimal 75% mask ratio on natural images.

DISCUSSION: The proposed MAEMC-NET effectively distinguishes between benign and malignant solitary pulmonary nodules and holds significant potential to assist radiologists in improving the diagnostic accuracy of PGN and SLA.

PMID:40012977 | PMC:PMC11861088 | DOI:10.3389/fmed.2025.1507258

IEEE Robot Autom Lett. 2024 May;9(5):3988-3994. doi: 10.1109/lra.2024.3374189. Epub 2024 Mar 6.

ABSTRACT

Deep learning offers a potentially powerful alternative to hand-tuned control of active lower-limb prostheses, being capable of generating continuous joint-level assistance end-to-end. This eliminates the need for conventional task classification, state machines and mid-level control equations by collapsing the entire control problem into a deep neural network. In this letter, sensor data and conventional commanded torque from an open-source powered knee-ankle prosthesis (OSL) were collected across five locomotion modes: level ground, ramp incline/decline and stair ascent/descent. Reference commanded torques were generated using an expert-tuned finite state machine-based impedance controller for each mode and transfemoral amputee participant (N = 12). Stance phases of the output were then estimated using a temporal convolutional network (TCN), which produced mode- and user-independent knee and ankle torques with RMSE of 0.154 ± 0.06 and 0.106 ± 0.06 Nm/kg, respectively. Training the model on mode-specific data only produced significant reductions in stair descent, lowering knee and ankle RMSE by 0.06 ± 0.028 and 0.033 ± 0.008 Nm/kg respectively (p < 0.05). In addition, the TCN adapted to walking speed and slope shifts in reference commanded torque. These results demonstrate that this deep learning model not only removes the need for heuristic state machines and mode classification but can also reduce or remove the need for prosthesis assistance tuning entirely.

PMID:40012860 | PMC:PMC11864809 | DOI:10.1109/lra.2024.3374189

Microb Cell. 2025 Feb 20;12:1-8. doi: 10.15698/mic2025.02.841. eCollection 2025.

ABSTRACT

Molecular de-extinction has emerged as a novel strategy for studying biological molecules throughout evolutionary history. Among the myriad possibilities offered by ancient genomes and proteomes, antimicrobial peptides (AMPs) stand out as particularly promising alternatives to traditional antibiotics. Various strategies, including software tools and advanced deep learning models, have been used to mine these host defense peptides. For example, computational analysis of disulfide bond patterns has led to the identification of six previously uncharacterized β-defensins in extinct and critically endangered species. Additionally, artificial intelligence and machine learning have been utilized to uncover ancient antibiotics, revealing numerous candidates, including mammuthusin, and elephasin, which display inhibitory effects toward pathogens in vitro and in vivo. These innovations promise to discover novel antibiotics and deepen our insight into evolutionary processes.

PMID:40012704 | PMC:PMC11853161 | DOI:10.15698/mic2025.02.841

NPJ Biol Phys Mech. 2025;2(1):5. doi: 10.1038/s44341-025-00009-3. Epub 2025 Feb 24.

ABSTRACT

Biological systems are complex networks where measurable functions emerge from interactions among thousands of components. Many studies aim to link biological function with molecular elements, yet quantifying their contributions simultaneously remains challenging, especially at the single-cell level. We propose a machine-learning approach that integrates faceted data subsets to reconstruct a complete view of the system using conditional distributions. We develop both polynomial regression and neural network models, validated with two examples: a mechanical spring network under external forces and an 8-dimensional biological network involving the senescence marker P53, using single-cell data. Our results demonstrate successful system reconstruction from partial datasets, with predictive accuracy improving as more variables are measured. This approach offers a systematic method to integrate fragmented experimental data, enabling unbiased and holistic modeling of complex biological functions.

PMID:40012561 | PMC:PMC11850287 | DOI:10.1038/s44341-025-00009-3

New Phytol. 2025 Feb 27. doi: 10.1111/nph.20434. Online ahead of print.

ABSTRACT

The increased positive impact of plant diversity on ecosystem functioning is often attributed to the accumulation of mutualists and dilution of antagonists in diverse plant communities. While increased plant diversity alters traits related to resource acquisition, it remains unclear whether it reduces defence allocation, whether this reduction differs between roots and leaves, or varies among species. To answer these questions, we assessed the effect of plant species richness, plant species identity and their interaction on the expression of 23 physical and chemical leaf and fine root defence traits of 16 plant species in a 19-yr-old biodiversity experiment. Only leaf mass per area, leaf and root dry matter content and root nitrogen, traits associated with both, resource acquisition and defence, responded consistently to species richness. However, species richness promoted a decoupling of these defences in leaves and fine roots, possibly in response to resource limitations in diverse communities. Species-specific responses were rare and related to chemical defence and mutualist collaboration, likely responding to species-specific antagonists' dilution and mutualists' accumulation. Overall, our study suggests that resource limitation in diverse communities might mediate the relationship between plant defence traits and antagonist dilution.

PMID:40013369 | DOI:10.1111/nph.20434

Front Syst Biol. 2024;4:1368555. doi: 10.3389/fsysb.2024.1368555. Epub 2024 Apr 8.

ABSTRACT

Bone remodeling is an essential, delicately balanced physiological process of coordinated activity of bone cells that remove and deposit new bone tissue in the adult skeleton. Due to the complex nature of this process, many mathematical models of bone remodeling have been developed. Each of these models has unique features, but they have underlying patterns. In this review, the authors highlight the important aspects frequently found in mathematical models for bone remodeling and discuss how and why these aspects are included when considering the physiology of the bone basic multicellular unit, which is the term used for the collection of cells responsible for bone remodeling. The review also emphasizes the view of bone remodeling from a systems biology perspective. Understanding the systemic mechanisms involved in remodeling will help provide information on bone pathology associated with aging, endocrine disorders, cancers, and inflammatory conditions and enhance systems pharmacology. Furthermore, some features of the bone remodeling cycle and interactions with other organ systems that have not yet been modeled mathematically are discussed as promising future directions in the field.

PMID:40012834 | PMC:PMC11864782 | DOI:10.3389/fsysb.2024.1368555

Bioessays. 2025 Feb 26:e202500020. doi: 10.1002/bies.202500020. Online ahead of print.

NO ABSTRACT

PMID:40012408 | DOI:10.1002/bies.202500020

Clin Mol Hepatol. 2025 Feb 27. doi: 10.3350/cmh.2024.0333e. Online ahead of print.

NO ABSTRACT

PMID:40012401 | DOI:10.3350/cmh.2024.0333e