Introduction:Diabetic foot ulcer and infections are one of the major complications in diabetic patients leading to frequent hospitalization and increased mortality. Knowledge about the microbes that cause infections will be helpful for providing appropriate antimicrobial therapy.
Aim:To evaluate the bacteriological profile of patients with diabetic foot ulcers and their antibiotic susceptibility pattern.
Methodology:A cross-sectional study was carried out for a period of eight months in the Department of surgery in patients with diabetic foot ulcer at a tertiary care teaching hospital. Patient data relevant to the study were collected using a standard data collection form designed as per the need of the study. Details of the organisms isolated and susceptibility pattern were collected from microbiology department.
Results:A total of 122 pathogens were identified from 71 patients with male (63.38%) predominance over females (36.61%). Out of the 71 patients, 38 (53.52%) patients had monomicrobial infections and 33 (46.47%) patients had polymicrobial infections. Of the total 122 organisms, 79(64.75%) organisms were found to be gram negative organisms and 43(35.24%) were gram positive. Pseudomonas aeruginosa found in 22 (18.03%) patients was the predominant pathogen isolated followed by Klebsiella pneumonia found in 18 (14.75%) patients. The gram-positive organisms isolated showed maximum susceptibility towards antibiotics Teicoplanin and Linezolid while the gram-negative organisms showed susceptibility to Imipenem, Meropenem, and Piperacillin/Tazobactum combination.
Conclusion:The study showed a preponderance of gram-negative bacilli among the isolates from the diabetic foot ulcers. It is recommended that antimicrobial sensitivity testing is necessary for initiating appropriate antibiotic regimen which will help to reduce the drug resistance and minimize the healthcare costs.
More than a year has passed since the report of the first case of coronavirus disorder 2019 (COVID), and increasing deaths hold to occur. Minimizing the time required for useful resource allocation and medical choice making, together with triage, desire of air flow modes and admission to the intensive care unit is essential. system learning strategies are acquiring an increasingly more sought-after role in predicting the outcome of COVID sufferers. in particular, the use of baseline gadget learning techniques is swiftly growing in COVID mortality prediction, in view that a mortality prediction model could unexpectedly and effectively help medical choice-making for COVID sufferers at approaching threat of demise. latest research reviewed predictive fashions for SARS-CoV-2 prognosis, severity, period of sanatorium live, extensive care unit admission or mechanical ventilation modes results; however, systematic opinions centered on prediction of COVID mortality outcome with machine gaining knowledge of methods are missing within the literature. the present evaluation appeared into the studies that carried out gadget mastering, inclusive of deep studying, methods in COVID mortality prediction as a result trying to gift the prevailing posted literature and to offer feasible causes of the pleasant effects that the research received. The have a look at also mentioned hard components of cutting-edge research, supply in guidelines for destiny developments.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.