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Accurate simulation of respiratory dynamics is essential for advancing the diagnosis and treatment of pulmonary diseases. This review analyzes current methodologies for modelling lung mechanics during insufflation and exsufflation, focusing on airflow simulations in the tracheobronchial tree. 45 studies were selected through a structured screening process and evaluated based on modelling approaches, simulation techniques, boundary conditions, and clinical applicability. The review identifies three main strategies for 3D TB model generation: segmentation of DICOM images, CAD-based geometries, and hybrid methods. While DICOM segmentation ensures anatomical realism, it is limited in generational depth. Conversely, CAD and hybrid approaches extend model coverage but may compromise subject specificity. Simulation methods include Computational Fluid Dynamics, Fluid-Structure Interaction, biomechanical, structural and statistical models, MR-Linac workflows, and neural networks. Among these, CFD remains the most widely adopted due to its accessibility and maturity, whereas FSI and hybrid CFD-FSI models offer superior physiological fidelity. The review wants to highlight the importance of combining detailed anatomical modelling with dynamic simulation frameworks to improve clinical interventions, particularly in lung surgery. Future work should focus on integrating patient-specific imaging, advanced boundary conditions, and multiscale modelling to enable more precise and scalable respiratory simulations.
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http://dx.doi.org/10.1080/03091902.2025.2543503 | DOI Listing |
BMC Glob Public Health
September 2025
Kenya Medical Research Institute (KEMRI) - Wellcome Trust Research Programme (KWTRP), Kilifi, Kenya.
Background: Between November 2023 and March 2024, coastal Kenya experienced another wave of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections detected through our continued genomic surveillance. Herein, we report the clinical and genomic epidemiology of SARS-CoV-2 infections from 179 individuals (a total of 185 positive samples) residing in the Kilifi Health and Demographic Surveillance System (KHDSS) area (~ 900 km).
Methods: We analyzed genetic, clinical, and epidemiological data from SARS-CoV-2 positive cases across pediatric inpatient, health facility outpatient, and homestead community surveillance platforms.
Diagn Pathol
September 2025
Department of Gastrointestinal Medical Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.
Background: Gastric cancer is one of the most common cancers worldwide, with its prognosis influenced by factors such as tumor clinical stage, histological type, and the patient's overall health. Recent studies highlight the critical role of lymphatic endothelial cells (LECs) in the tumor microenvironment. Perturbations in LEC function in gastric cancer, marked by aberrant activation or damage, disrupt lymphatic fluid dynamics and impede immune cell infiltration, thereby modulating tumor progression and patient prognosis.
View Article and Find Full Text PDFSubst Abuse Treat Prev Policy
September 2025
Centre for Interdisciplinary Addiction Research (ZIS), Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf (UKE), Martinistraße 52, 20246, Hamburg, Germany.
Background: Alcohol use disorder (AUD) is conceptualized as a dimensional phenomenon in the DSM-5, but electronic health records (EHRs) rely on binary AUD definitions according to the ICD-10. The present study classifies AUD severity levels using EHR data and tests whether increasing AUD severity levels are linked with increased comorbidity.
Methods: Billing data from two German statutory health insurance companies in Hamburg included n = 21,954 adults diagnosed with alcohol-specific conditions between 2017 and 2021.
BMC Oral Health
September 2025
Oral and Maxillofacial Radiology Department, Cairo university, Cairo, Egypt.
Aim: The purpose of this study was to assess the accuracy of a customized deep learning model based on CNN and U-Net for detecting and segmenting the second mesiobuccal canal (MB2) of maxillary first molar teeth on cone beam computed tomography (CBCT) scans.
Methodology: CBCT scans of 37 patients were imported into 3D slicer software to crop and segment the canals of the mesiobuccal (MB) root of the maxillary first molar. The annotated data were divided into two groups: 80% for training and validation and 20% for testing.