Diagnostic tools in diagnosing acute appendicitis - Alvarado Score, CRP, USG, and CT (Abdomen)

Aims. To evaluate scope of diagnosing tools-Alvarado score, CRP, USG, and CT in acute appendicitis. Method. Conducted observational study of 152 patients in Department of Emergency Medicine, Sri Ramachandra Medical College and Research Institute, Chennai, India between January to December 2022. The diagnostic tool’s (Alvarado score, CRP, USG, CT (abdomen), sensitivity, specificity, accuracy, and ROC were analyzed to diagnose acute appendicitis. Results. Among 152 study patients, males - 86, females - 66, higher number of age group was <30 years, abnormal variables in study patients are BP - 79%, HR - 80%, RFP pain - 57%, anoxia - 78%, nausea/ vomiting - 68%, RIF tenderness - 69%, rebound tenderness - 63.8%, elevated temperature - 62%, pain - 44.7%, leukocytosis - 70.7%, and left shift - 38.2%. In comparison, Alvarado scores-identified 98% patients, (7-61.2%) (0.0271), CRP - identified 95.1% (<0.001), USG identified (group 1-33%, group 2-12.2%, group 3-11.3%, and group 4-43.5%, and CT identified 152/152 (100%) patients with acute appendicitis. The odds ratio/95% CI of diagnostic tools (USG - 0.878, 0.66, CRP - 7.337, 2.623, Alvarado score - 0.81, 0.687). Sensitivity (Alvarado's score - 84.74%, USG - 83.33%, CRP - 76.43%), and specificity was (Alvarado's score - 84.32, USG - 72.97%, CRP-83.86%. The PPV (Alvarado's score - 74.56%, USG -75.5%, CRP - 33.16%), NPV (Alvarado's score - 32.5%, USG - 79.1%, CRP - 81.03%), and diagnostic accuracy (Alvarado's score - 72.01%, USG - 73.05%, CRP - 68.81%). ROC in individual tools-Alvarado score was specific than USG, and CRP. ROC in combination tools Alvarado score and USG was specific than USG, and CRP. Conclusion. Among the diagnostic tools tested, as individual tool-Alvarado score was specific, in combination, and Alvarado score and USG were accurate, specific, sensitive, hence combination of tools will identify acute appendicitis early to reduce mortality by undiagnosed or late diagnosed

Prevalence of lower urinary tract infection in South Indian type 2 diabetic subjects

This study was done to determine the prevalence of lower urinary tract infection (UTI), the causative pathogens, their antimicrobial pattern, and the recurrence of infection in type 2 diabetic subjects. A total of 1157 (M: F 428: 729) type 2 diabetic subjects were selected for this study. Midstream urine specimens were collected and the culture tests were done by a quantitative method whereas antimicrobial sensitivity was determined by using the Kirby-Bauer method. A significant colony count was seen in 495 (42.8%) subjects and an insignificant count in 350 (30.3%) subjects; there were a few cases of recurrent UTI. Women (47.9%) had a significantly higher prevalence of UTI than men (34.1%) (χ2 = 20.3, P < 0.0001). Except for BMI, UTI was significantly associated with age, duration of diabetes, and poor glycemic control in both sexes. About 533 pathogens of gram positive and gram negative bacilli were isolated from 495 subjects in this study. Escherichea coli (E. coli) was the most commonly found organism. Gram negative pathogens were found to be highly sensitive to sulbactum / cefoperazone and piperacillin / tazobactum. The prevalence of UTI was significantly higher in women than men with E. coli being the major isolated pathogen. Gram negative pathogens were highly sensitive to sulbactum / cefoperazone and piperacillin / tazobactum

COSMOS: a platform for real-time morphology-based, label-free cell sorting using deep learning

Abstract Cells are the singular building blocks of life, and a comprehensive understanding of morphology, among other properties, is crucial to the assessment of underlying heterogeneity. We developed Computational Sorting and Mapping of Single Cells (COSMOS), a platform based on Artificial Intelligence (AI) and microfluidics to characterize and sort single cells based on real-time deep learning interpretation of high-resolution brightfield images. Supervised deep learning models were applied to characterize and sort cell lines and dissociated primary tissue based on high-dimensional embedding vectors of morphology without the need for biomarker labels and stains/dyes. We demonstrate COSMOS capabilities with multiple human cell lines and tissue samples. These early results suggest that our neural networks embedding space can capture and recapitulate deep visual characteristics and can be used to efficiently purify unlabeled viable cells with desired morphological traits. Our approach resolves a technical gap in the ability to perform real-time deep learning assessment and sorting of cells based on high-resolution brightfield images