ABSTRACT
Introduction: IL-6 is the key molecule of cytokine storm in COVID-19. Dyslipidemia is a common complication in patients with Coronavirus disease 2019 (COVID-19), but the association of dyslipidemia with the severity of COVID-19 is still unclear. In this study, we aimed to investigate the biochemical alterations of High-Density Lipoprotein Cholesterol (HDL-C), and Interleukin-6 (IL-6) in COVID-19 patients and their relationships with the disease severity. Material(s) and Method(s): We conducted a retrospective single-institutional study of 99 consecutive confirmed cases of COVID-19. Serum IL-6 and HDL-C concentrations, demographic and clinical profile were collected during hospital stay. Duration of study was from September 2020 to August 2021. Descriptive statistics were applied to summarize the demographic data. Results are reported as mean with standard deviation. Receiver operating characteristic curve (ROC) analysis was used to compare biochemical markers. Result(s): Serum HDL-C levels had a significant positive correlation with SpO2 with correlation coefficient r = 0.589. Serum IL-6 had a negative correlation with SpO2 with correlation coefficient r =-0.632. The AUC for IL6 and HDL-C in predicting COVID severity is 0.982 and 0.985 respectively. Conclusion(s): HDL-C is decreased and IL-6 is increased with the disease severity.Copyright © 2023, Dr Yashwant Research Labs Pvt Ltd. All rights reserved.
ABSTRACT
In this computer world, huge data are generated in several fields. Statistics in the healthcare engineering provides data about many diseases and corresponding patient's information. These data help to evaluate a huge amount of data for identifying the unknown patterns in the diseases and are also utilized for predicting the disease. Hence, this work is to plan and implement a new computer-aided technique named modified Ensemble Learning with Weighted RBM Features (EL-WRBM). Data collection is an initial process, in which the data of various diseases are gathered from UCI repository and Kaggle. Then, the gathered data are pre-processed by missing data filling technique. Then, the pre-processed data are performed by deep belief network (DBN), in which the weighted features are extracted from the RBM regions. Then, the prediction is made by ensemble learning with classifiers, namely, support vector machine (SVM), recurrent neural network (RNN), and deep neural network (DNN), in which hyper-parameters are optimized by the adaptive spreading rate-based coronavirus herd immunity optimizer (ASR-CHIO). At the end, the simulation analysis reveals that the suggested model has implications to support doctor diagnoses.
ABSTRACT
Introduction: The Covid-19 virus has been postulated to interact with Angiotensin-converting enzyme 2 (ACE2) receptors in the gut and possibly cause malabsorption. Our study aimed to determine the incidence of diarrhea in patients hospitalized with Covid-19 and determine if there is any association in clinical outcomes. Methods: This retrospective study included 2711 patients ages 18 years or older that were hospitalized and tested positive for SARS-COV2 from 3/1/2020 through 4/1/2021 at a public hospital in New York City. We examined baseline characteristics and comorbidities. T-test for age and Pearson's Chi squared test for statistical analysis were used for the primary outcome of mortality and secondary outcomes of mechanical ventilation, vasopressor use and acute kidney injury. Results: Clinical outcomes of 279 Covid-19 hospitalized patients with diarrhea was compared to 2432 patients without diarrhea. 279/2711 Covid-19 patients were positive for diarrhea (incidence of 10.3%). Overall sample characteristics included mean age inyears (62.81, SD 18.40) and 64.09 (SD 19.25) (p< 0.293) for diarrhea and non-diarrhea groups, respectively. Males comprised 143 (51.3%) in the diarrhea group and 1324 (54.4%) in the non-diarrhea group (p< 0.312). Baseline characteristics and multiple comorbidities (Diabetes, Chronic Kidney Disease, Chronic Obstructive Pulmonary Disease, Heart Failure, Hypertension, and Coronary Artery Disease) were not statistically different (p >0.063) between groups. Overall mortality in the diarrhea group was 71(25.4%) and in the nondiarrhea group was 608 (25%) with odds ratio of 1.024 (0.770-1.36, p< 0.870). For secondary outcomes, we found acute kidney injury odds ratio of 1.131 (0.882 - 1.452, p< 0.332), shock requiring vasopressors odds ratio of 1.010 (0.751-1.358, p< 0.948), and mechanical ventilation odds ratio of 1.150 (0.849 - 1.556, (p< 0.366). Conclusion: Baseline characteristics and comorbidities were not different in patients with and without diarrhea. Even though diarrhea is prevalent in Covid-19 patients, our data suggests that there is no statistically significant difference in primary outcome ofmortality and secondary outcomes of AKI, Vasopressor use, and Mechanical ventilation. These findings could be due to small sample size and future research with a larger population is needed. However, patients should still be treated with supportive care for symptomatic relief.
ABSTRACT
The coronavirus (COVID-19) pandemic has severely affected the supply chain all over. A major challenge for the supply chain (SC) is to address this disruption risk and bring sustainability to SC. The objective of this paper is to identify the stakeholders' requirements and critical success factors (CSFs) for the sustainability initiative in SC during this pandemic situation. Three potential stakeholders' requirements and a total of 16 critical success factors have been identified by taking inputs from experts and decision-makers. Further, these critical factors are analyzed and ranked based on a hybrid quality function deployment (QFD)-best-worst methodology (BWM). The QFD method has been used to identify the stakeholder’ requirements. And, the BWM has been adopted to prioritize the CSFs. The scientific value of the study is the contribution of the framework model for the sustainable initiatives in the SC during the COVID-19 pandemic outbreak, identification of stakeholders’ requirements and CSFs, and prioritizes these CSFs. The top three most critical success factors are found to be social distancing, emergency logistics systems, and emergency backup facilities. The proposed framework provides a roadmap to operation and supply chain managers to come up with good solutions for sustainability initiatives in the supply chain during and after the pandemic outbreak.