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Smartphone digital image colorimetry couple with chemometric approach for determination of boron in nuts prior to deep eutectic solvent liquid-liquid microextraction: a first application of hybrid chemometrics in SDIC.
Anal Sci
January 2025
Department of Analytical Chemistry, Faculty of Pharmacy, Near East University, TRNC, Mersin 10, 99138, Nicosia, Turkey.
In this research, a green approach utilizing deep eutectic solvent liquid-liquid microextraction is combined with smartphone digital image colorimetry for the determination of boron in nut samples. A smartphone camera was used to capture the image of the analyte extract located in a custom-made colorimetric box. Using ImageJ software, the images were split into RGB channels, with the green channel identified as the optimum.
View Article and Find Full Text PDFInteraction of human gut microbiota and local immune system in progression of colorectal adenoma (MIMICA-1): a protocol for a prospective, observational cohort study.
Front Oncol
January 2025
Clinic of Gastroenterology, Nephro-Urology, and Surgery, Institute of Clinical Medicine, Faculty of Medicine, Vilnius University, Vilnius, Lithuania.
Introduction: The current understanding of colorectal carcinogenesis is based on the adenoma-carcinoma sequence, where genetics, intestinal microbiota changes and local immunity shifts seem to play the key roles. Despite the emerging evidence of dysbiotic intestinal state and immune-cell infiltration changes in patients with colorectal adenocarcinoma, early and advanced adenoma as precursors of colorectal cancer, and carcinoma as the following progression, are rather less studied. The newly colon-site adapted AI-based analysis of immune infiltrates is able to predict long-term outcomes of colon carcinoma.
View Article and Find Full Text PDFFunctional gastrointestinal disorders predictors in neonates and toddlers: A machine learning approach to risk assessment.
Heliyon
January 2025
Department of Medical and Surgical Sciences, University of Bologna, Bologna, Italy.
Background: Functional Gastrointestinal Disorders (FGIDs) can pose a great burden on affected children, their families, and the healthcare system. Due to the lack of knowledge about the precise pathophysiology of FGIDs, a proper identification of children at risk to develop FGIDs has never been attempted. The research aims to identify early-life risk factors for FGIDs such as infantile colic, regurgitation, and functional constipation, within the first year of life.
View Article and Find Full Text PDFThe efficiency of artificial intelligence for management and clinical decision-making in the identification of patients with undiagnosed HCV infection (Intelligen-C strategy).
Gastroenterol Hepatol
January 2025
Pharmacoeconomics & Outcomes Research Iberia (PORIB), Madrid, España. Electronic address:
Introduction: Artificial intelligence (AI) allows the optimization of diagnostic processes for hepatitis C virus (HCV) patients. Our objective was to evaluate the clinical, economic, and management benefits of an AI-based clinical decision support system (Intelligen-C strategy).
Methods: The Intelligen-C strategy consisted of (1) a retrospective phase (Dec 2013-Sep 2021), in which medical records were reviewed to search for anti-HCV-positive and/or HCV-RNA-positive patients lost in the system, and (2) a prospective phase (Feb 2022-Jan 2023), in which automated screening (40-70 years) and routine testing for risk factors were performed in patients who were admitted to the emergency department or were hospitalized.
Enhancing panoramic dental imaging with AI-driven arch surface fitting: Achieving improved clarity and accuracy through an optimal reconstruction zone.
Dentomaxillofac Radiol
January 2025
Department of Oral and Maxillofacial Radiology, School of Dentistry, Pusan National University, Yangsan, 50612, Korea.
Objectives: This study aimed to develop an automated method for generating clearer, well-aligned panoramic views by creating an optimized three-dimensional (3D) reconstruction zone centered on the teeth. The approach focused on achieving high contrast and clarity in key dental features, including tooth roots, morphology, and periapical lesions, by applying a 3D U-Net deep learning model to generate an arch surface and align the panoramic view.
Methods: This retrospective study analyzed anonymized cone-beam CT (CBCT) scans from 312 patients (mean age 40 years; range 10-78; 41.
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