Aim and Objective:To assess the correlations between clinical and biochemical parameters with radiological severity of acute pyelonephritis. Methodology: It was a descriptive analytical study. All patients admitted to Narayana Medical College and Hospital, Nellore, from March 2019 to December 2020 with a CT proven diagnosis of acute pyelonephritis. All the patients diagnosed to have APN based on clinical and/or radiological findings were included in this study. Diagnosis of APN was based on both clinical and radiological criteria. Clinical criteria include the presence of "classical" symptoms of APN. Results: A sample size of 100 patients was considered for the study. But, in view of COVID pandemic, sample collection as limited to 62mong 62 patients with pyelonephritis, 30(48.4%) were males, and 32(51.6%) were females. Males were older when compared to females. The mean age of 62 patients with acute pyelonephritis was 55.47 ± 12.82 years. The total number of patients with diabetes mellitus were 19(30.65%). Patients with hypertension were 18(29.03%). 4(26.67%) patients in group 1, 9(28.13%) patients in group 2, and 5(33.33%) patients in group 3 were found to have hypertension. Classical triad of pyelonephritis was seen in 51 (82.3%) patients and was absent in 11 (17.7%) of patients. There was an association between inotrope use and severe CT grading by using the likelihood ratio test, which was statistically significant with a p-value of 0.015. Ultrasound was found to detect pyelonephritis in 18(29.03%) patients. In 44(70.97%) patients, ultrasound was found to be normal despite the presence of clinical features. HbA1C levels were similar among the three groups with a mean value of 6.48±0.65, 6.53±0.62, and 6.59±0.77 in group 1, group 2, and group 3 patients, respectively. Conclusion: This study showed a good correlation between clinical and radiological severity in adult patients with APN. Duration of hospital stay, presence of hypotension, and leukocytosis were associated with severe pyelonephritis.
With the Advent of advancement in the field of Artificial Intelligence the computer is made more intelligent and can enable to think and make prediction accurately. The machine learning being a subfield of Artificial Intelligence is used in numerous research works. Different analysts feel that enormous data generated in field of biology have to be sorted in an intelligent way to yield best model. There are numerous kinds of Machine Learning Techniques like Unsupervised, Semi Supervised, Supervised, Reinforcement, and Evolutionary Learning and Deep Learning. These learning’s are used to classify huge data at a rapid pace. This paper discusses about the wide spectrum of Biology and the process of pre-processing data and the best suitable Machine learning model for each of them.
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