ABSTRACT
Numerous anthropogenic activities like the construction of large dams, storages, and barrages changed the watershed characteristics impacting ecosystem health. In this study, the hydrological alterations (HAs) that have occurred in the Bhima River due to the construction of the Ujjani dam were analyzed. The hydraulic analysis is also performed to determine the hydraulic parameter and recommend the lowest flow release from the dam for improving ecosystem health. Fifty-eight years of data starting from the year 1960 to 2018 were gathered at Yadgir station, which is located downstream of the Ujjani dam. The data were divided into pre- and post-construction river flow discharge. To check for the change in the river flow regime for the post-dam construction period, HA was calculated using Flow Health Software (FHS). The results demonstrate that the dam impoundment reduces high flows primarily by storing flood flow for water supply, irrigation, etc. The velocity and depth provided by the environmental design flow for a flow health (FH) score of 0.62 give a very good habitat to fishes. A minimum release of 24.8 m3/s from the dam is recommended. This study will help policymakers mitigate the impacts of degrading ecosystem health of the Bhima River.
ABSTRACT
BACKGROUND: Renal trauma is present in 0.5-5% of patients admitted for trauma. Advancements in radiologic imaging and minimal-invasive techniques have led to decreased need for surgical intervention. We used a large trauma cohort to characterise renal trauma patients, their management and outcomes. METHODS: We analysed "Towards Improved Trauma Care Outcomes in India" cohort from four urban tertiary public hospitals in India between 1st September 2013 and 31st December 2015. The data of patients with renal trauma were extracted using International Classification of Diseases 10 codes and analysed for demographic and clinical details. RESULTS: A total of 16,047 trauma patients were included in this cohort. Abdominal trauma comprised 1119 (7%) cases, of which 144 (13%) had renal trauma. Renal trauma was present in 1% of all the patients admitted for trauma. The mean age was 28 years (SD-14.7). A total of 119 (83%) patients were male. Majority (93%) were due to blunt injuries. Road traffic injuries were the most common mechanism (53%) followed by falls (29%). Most renal injuries (89%) were associated with other organ injuries. Seven of the 144 (5%) patients required nephrectomy. Three patients had grade V trauma; all underwent nephrectomy. The 30-day in-hospital mortality, in patients with renal trauma, was 17% (24/144). CONCLUSION: Most renal trauma patients were managed nonoperatively. 89% of patients with renal trauma had concomitant injuries. The renal trauma profile from this large cohort may be generalisable to urban contexts in India and other low- and middle-income countries.
Subject(s)
Trauma Centers , Wounds, Nonpenetrating , Adult , Cohort Studies , Humans , Injury Severity Score , Kidney/diagnostic imaging , Kidney/injuries , Male , Retrospective Studies , Tertiary Healthcare , Wounds, Nonpenetrating/diagnostic imaging , Wounds, Nonpenetrating/epidemiology , Wounds, Nonpenetrating/therapyABSTRACT
Antimicrobial peptides are host defense peptides being viewed as replacement to broad-spectrum antibiotics due to varied advantages. Hepatitis is the commonest infectious disease of liver, affecting 500 million globally with reported adverse side effects in treatment therapy. Antimicrobial peptides active against hepatitis are called as anti-hepatitis peptides (AHP). In current work, we present Extratrees and Random Forests based Quantitative Structure Activity Relationship (QSAR) regression modeling using extracted sequence based descriptors for prediction of the anti-hepatitis activity. The Extra-trees regression model yielded a very high performance in terms coefficient of determination (R2) as 0.95 for test set and 0.7 for the independent dataset. We hypothesize that the developed model can further be used to identify potentially active anti-hepatitis peptides with a high level of reliability.
ABSTRACT
Hepatitis is an emerging global threat to public health due to associated mortality, morbidity, cancer and HIV co-infection. Available diagnostics and therapeutics are inadequate to intercept the course and transmission of the disease. Antimicrobial peptides (AMP) are widely studied and broad-spectrum host defense peptides are investigated as a targeted anti-viral. Therefore, it is of interest to describe the supervised identification of anti-hepatitis peptides. We used a hybrid Support Vector Machine (SVM) with Ant Colony Optimization (ACO) algorithm for simultaneous classification and domain feature selection. The described model shows a 10 fold cross-validation accuracy of 94 percent. This is a reliable and a useful tool for the prediction and identification of hepatitis specific drug activity.
