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Extending the MST Model to Large Biomolecular Systems: Parametrization of the ddCOSMO-MST Continuum Solvation Model.
J Comput Chem
January 2025
Departament de Farmàcia i Tecnologia Farmacèutica, i Fisicoquímica, Facultat de Farmàcia i Ciències de l'Alimentació, Universitat de Barcelona (UB), Barcelona, Spain.
Continuum solvation models such as the polarizable continuum model and the conductor-like screening model are widely used in quantum chemistry, but their application to large biosystems is hampered by their computational cost. Here, we report the parametrization of the Miertus-Scrocco-Tomasi (MST) model for the prediction of hydration free energies of neutral and ionic molecules based on the domain decomposition formulation of COSMO (ddCOSMO), which allows a drastic reduction of the computational cost by several orders of magnitude. We also introduce several novelties in MST, like a new definition of atom types based on hybridization and an automatic setup of the cavity for charged regions.
View Article and Find Full Text PDFLeveraging Transformers-based models and linked data for deep phenotyping in radiology.
Comput Methods Programs Biomed
January 2025
Laberit, Avda. de Catalunya, 9, València, 46020, Spain.
Background And Objective: Despite significant investments in the normalization and the standardization of Electronic Health Records (EHRs), free text is still the rule rather than the exception in clinical notes. The use of free text has implications in data reuse methods used for supporting clinical research since the query mechanisms used in cohort definition and patient matching are mainly based on structured data and clinical terminologies. This study aims to develop a method for the secondary use of clinical text by: (a) using Natural Language Processing (NLP) for tagging clinical notes with biomedical terminology; and (b) designing an ontology that maps and classifies all the identified tags to various terminologies and allows for running phenotyping queries.
View Article and Find Full Text PDFOut-of-distribution generalization for segmentation of lymph node metastasis in breast cancer.
Sci Rep
January 2025
Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, Toronto, ON, Canada.
Pathology provides the definitive diagnosis, and Artificial Intelligence (AI) tools are poised to improve accuracy, inter-rater agreement, and turn-around time (TAT) of pathologists, leading to improved quality of care. A high value clinical application is the grading of Lymph Node Metastasis (LNM) which is used for breast cancer staging and guides treatment decisions. A challenge of implementing AI tools widely for LNM classification is domain shift, where Out-of-Distribution (OOD) data has a different distribution than the In-Distribution (ID) data used to train the model, resulting in a drop in performance in OOD data.
View Article and Find Full Text PDFThree-Dimensional Ultrasound for Physical and Virtual Fetal Heart Models: Current Status and Future Perspectives.
J Clin Med
December 2024
Discipline of Woman Health, Municipal University of São Caetano do Sul (USCS), São Caetano do Sul 09521-160, SP, Brazil.
Congenital heart defects (CHDs) are the most common congenital defect, occurring in approximately 1 in 100 live births and being a leading cause of perinatal morbidity and mortality. Of note, approximately 25% of these defects are classified as critical, requiring immediate postnatal care by pediatric cardiology and neonatal cardiac surgery teams. Consequently, early and accurate diagnosis of CHD is key to proper prenatal and postnatal monitoring in a tertiary care setting.
View Article and Find Full Text PDFThe study of heat tolerance in Drosophila melanogaster has been of particular interest to researchers for decades, with a common approach to assessing heat tolerance being to monitor the time to knockdown (TKD) after exposure to an elevated temperature. Classically, flies are housed in individual vials and placed inside a heated water bath. TKD is then monitored manually by researchers.
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