Double contrast enema and endoscopy are very important in the diagnosis of adenomas and early cancer of the colon and rectum. These exams can not only detect the presence, but also suggest the histologic diagnosis, of polypoid lesions of the colon. An Olivetti M24 Personal Computer was used to create a software to study the results obtained by double contrast enema, and to compare them with endoscopy and pathology. The data base is formed by 7 files: one anagraphic, 3 collecting the characteristics of the diagnosis--namely the radiologic, the endoscopic and the pathologic one-- and 3 multiple files featuring each lesion, as defined by the three diagnostic techniques. The software allows to evaluate the different lesions that can be detected by the three techniques in the same patient and to compare the diagnosis of presence to the morphologic features of each lesion. False negatives and false positives of each technique are easily recognized. It is also possible to characterize the single morphologic feature leading the radiologist and/or the endoscopist to express an opinion about the histologic diagnosis of each lesion and to compare them with pathological features. The first experience in clinical use of the software, in the analysis of the characters of 336 lesions in 218 patients, is described.
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J Osteopath Med
January 2025
Arizona College of Osteopathic Medicine, Midwestern University, Glendale, AZ, USA.
Context: Point-of-care ultrasound (POCUS) has diverse applications across various clinical specialties, serving as an adjunct to clinical findings and as a tool for increasing the quality of patient care. Owing to its multifunctionality, a growing number of medical schools are increasingly incorporating POCUS training into their curriculum, some offering hands-on training during the first 2 years of didactics and others utilizing a longitudinal exposure model integrated into all 4 years of medical school education. Midwestern University Arizona College of Osteopathic Medicine (MWU-AZCOM) adopted a 4-year longitudinal approach to include POCUS education in 2017.
View Article and Find Full Text PDFJ Clin Tuberc Other Mycobact Dis
December 2024
Department of Microbiology and Virology, School of Medicine, Jiroft University of Medical Sciences, Jiroft, Iran.
Background: Leprosy is a chronic infectious disease caused by () However, the emergence of drug-resistant strains of this bacterium, especially multidrug-resistant (MDR) strains, is a serious concern. This study aimed to evaluate the global prevalence of MDR and its implications.
Methods: Using PRISMA guidelines, we systematically reviewed ISI Web of Science, MEDLINE, and EMBASE up to August 2023 to assess the prevalence of MDR .
J Diabetes Metab Disord
June 2025
Department of Traditional Medicine, School of Persian Medicine, Tabriz University of Medical Sciences, Tabriz, Iran.
Objectives: This study was designed to characterize the prevalence, pattern of herbal use, and related factors among diabetic patients in Tabriz, Iran.
Methods: A descriptive cross-sectional study was carried out on 322 diabetic patients with random cluster sampling of specialized and subspecialized clinics in Tabriz, Iran. Binary logistic regression analysis was performed to evaluate the association between predictor variables (sociodemographic and disease-related characteristics and patient preference for treatment type) with herb use Interviews were conducted using a structured questionnaire from October 1, 2022, to April 23, 2023.
Globally, drug-resistant tuberculosis (DR-TB) is responsible for 13% of mortality attributable to antimicrobial resistance. In Ethiopia, extrapulmonary tuberculosis (EPTB) is a significant public health challenge, and drug resistance (DR) in EPTB is often overlooked. In a cross-sectional study conducted between August 2022 and October 2023, we aimed to explore the magnitude of phenotypic drug resistance and identify genetic mutations linked to resistance using 189 Mycobacterium tuberculosis (MTB) isolates cultured from extrapulmonary clinical specimens.
View Article and Find Full Text PDFMotivation: Artificial intelligence (AI) applications require explainability (XAI) for FAIR, ethical deployment, whether in the clinic or in the laboratory. Richly descriptive XAI metadata representing how pre-model data were obtained, characterized, transformed, and distributed, should be available along with the data prior to training and application of AI models.
Results: The FAIRSCAPE framework generates, packages, and integrates critical pre-model XAI descriptive metadata, including deep provenance graphs and data dictionaries with feature validation on uploaded data, software, and computations, with special reference to biomedical datasets.
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