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An study of FIKK9.5 protein of for identification of therapeutics.
J Biomol Struct Dyn
December 2024
School of Biotechnology, KIIT Deemed To be University, Bhubaneswar, Odisha, India.
The FIKK protein family, encompassing 21 serine-threonine protein kinases, is a distinctive cluster exclusive to the Apicomplexa phylum. Predominantly located in which is a malarial parasite, with a solitary gene identified in a distinct apicomplexan species, this family derives its nomenclature from - phenylalanine, isoleucine, lysine, lysine (FIKK), a conserved amino acid motif. Integral to the parasite's life cycle and consequential to malaria pathogenesis, the absence of orthologous proteins in eukaryotic organisms designates it as a promising antimalarial drug target.
View Article and Find Full Text PDFDeciphering distinct pathogenic mechanisms of ankylosing spondylitis and systemic sclerosis via shared biomarkers ZSWIM6 and CCL3L3: Insights from an integrative bioinformatics approach.
Autoimmunity
December 2025
Spine Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi, People's Republic of China.
Ankylosing Spondylitis (AS) and Systemic Sclerosis (SSc) are both autoimmune diseases, albeit with distinct anatomical targets. AS primarily affects the spine and sacroiliac joints, triggering inflammation and eventual fusion of the vertebrae. SSc predominantly impacts the skin and connective tissues, leading to skin fibrosis, thickening, and potential damage to vital organs such as the lungs, heart, and kidneys.
View Article and Find Full Text PDFIdentification of patients with unstable angina based on coronary CT angiography: the application of pericoronary adipose tissue radiomics.
Front Cardiovasc Med
December 2024
Cardiovascular Medicine Department, Affiliated Hospital of North Sichuan Medical College, Nanchong, China.
Objective: To explore whether radiomics analysis of pericoronary adipose tissue (PCAT) captured by coronary computed tomography angiography (CCTA) could discriminate unstable angina (UA) from stable angina (SA).
Methods: In this single-center retrospective case-control study, coronary CT images and clinical data from 240 angina patients were collected and analyzed. Patients with unstable angina ( = 120) were well-matched with those having stable angina ( = 120).
Identification of key factors for malnutrition diagnosis in chronic gastrointestinal diseases using machine learning underscores the importance of GLIM criteria as well as additional parameters.
Front Nutr
December 2024
Department of Systems Biology and Bioinformatics, Institute of Computer Science, University of Rostock, Rostock, Germany.
Introduction: Disease-related malnutrition is common but often underdiagnosed in patients with chronic gastrointestinal diseases, such as liver cirrhosis, short bowel and intestinal insufficiency, and chronic pancreatitis. To improve malnutrition diagnosis in these patients, an evaluation of the current Global Leadership Initiative on Malnutrition (GLIM) diagnostic criteria, and possibly the implementation of additional criteria, is needed.
Aim: This study aimed to identify previously unknown and potentially specific features of malnutrition in patients with different chronic gastrointestinal diseases and to validate the relevance of the GLIM criteria for clinical practice using machine learning (ML).
SHAPE: A visual computing pipeline for interactive landmarking of 3D photograms and patient reporting for assessing craniosynostosis.
Comput Graph
December 2024
Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, 13001 East 17th Place, Aurora, CO 80045, USA.
3D photogrammetry is a cost-effective, non-invasive imaging modality that does not require the use of ionizing radiation or sedation. Therefore, it is specifically valuable in pediatrics and is used to support the diagnosis and longitudinal study of craniofacial developmental pathologies such as craniosynostosis - the premature fusion of one or more cranial sutures resulting in local cranial growth restrictions and cranial malformations. Analysis of 3D photogrammetry requires the identification of craniofacial landmarks to segment the head surface and compute metrics to quantify anomalies.
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