ABSTRACT
Objectives: To determine the rate of coinfections and its subsequent impact on hospitalization and mortality rate in Indian COVID-19 patients. Method(s): A systematic literature search was performed on PubMed, Cochrane, WHO COVID-19 database, Google Scholar and assisted by MaiA tool at various steps. The studies were retrieved and included based on JBI's CoCoPop framework. Meta-analysis was not performed due to a limited number of studies and high heterogeneity. Descriptive statistics were summarized based on the retrieved coinfections data. The protocol was registered with PROSPERO - CRD42021275644. Result(s): A total of 2418 patients were included from eight studies. The prevalence of coinfections ranged from 4% - 46%. Pathogen-specific data showed highest prevalence of bacterial (57.3%), followed by parasitic (21.1.%), viral (14.6%), and fungal coinfections (6.9%). About 60% - 80% of the patients with coinfections required ICU admissions. Among coinfected COVID-19 patients, the average length of hospital stay was 13.67+/-3.51 days. The mortality rate of COVID-19 patients with coinfections ranged from 9%-65%. Conclusion(s): Bacterial coinfections have the highest prevalence among COVID-19 patients. A causal relationship between coinfections and mortality rate in COVID-19 patients remains unexplored. This brings up the need for comprehensive data recording practices and meticulous reporting. Further, large-scale epidemiologic studies are the need of the hour to determine the nationwide burden of coinfections in the COVID-19 pandemic.Copyright © 2022
ABSTRACT
In the course of an epidemic season of COVID-19, the CT image framework is expedient for detecting the COVID-19 suspected patients. The process of identification of CT images because of COVID-19 is a two-step process. Firstly, we apply to pre-process on input COVID-19 CT images. Secondly, we evaluated individual deep CNN models on a pre-processed image is COVID-19 positive or negative then compared the execution of the individual deep CNN models. Finally, we identified that the InceptionV3 model is best among all the deep CNN models, including InceptionV3, MobileNet, MobileNetV2, ResNet50, ResNet50V2, and Xception. It can be witnessed that the InceptionV3 prototype has provided high accuracy among all the prototypes. InceptionV3 has provided accuracy, f1-score, and AUC as 0.864864, 0.873417, and 0.86425, respectively. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
ABSTRACT
Background:The recent severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) pandemic has struck with the pre-existing HIV pandemic. Information about the frequency and seriousness of COVID-19 infection among immuno- compromised HIV patients is still unclear. To our knowledge till date, clinical data about COVID -19 in HIV infected pa- tients are limited with only few case series. Here, we present cases of two HIV patient with COVID-19 infection admitted at Silchar Medical College & Hospital (SMCH), Silchar, south-eastern Assam, India. Methods:This retrospective study was conducted in VRDL, Silchar Medical College, Silchar among COVID -19 positive patient for the duration of three months from 30th March to 29th June 2020. The aim of the study was to evaluate the scenario of HIV reactive cases among COVID-19 positive population. Results:Between 30th March to 29th June 2020, a total of 727 COVID-19 positive cases were diagnosed at VRDL, SMCH. During these three months only two HIV patients were diagnosed with SARS-COV-2 infection. Patient-1 was 20-year old male (unaware of his HIV status) while Patient-2 was 52 year old army personnel (on ART therapy). In patient-2 HIV was well suppressed with sufficiently high CD4 counts. Fortunately, both the patients were asymptomatic and healthy. The mainstay of treatment given for the patients consisted of hydroxychloroquine, multi-vitamins in addition to the ART drugs (only patient 2). Both patients had stable health conditions, did not have any complications during their entire stay in health care facility for COVID-19. Both were treated and were tested negative by RTPCR within 14-15 days like any other general patient. Conclusions:Although, HIV patients are considered as immunocompromised, but our experience suggests that well con- trolled HIV patients with COVID-19 may have satisfactory prognosis following proper medical care. Further, enlarged cohort studies are needed to better understand risk and clinical course of COVID-19 among HIV-infected people.
ABSTRACT
Technology plays a crucial role in fighting COVID-19 pandemic. The COVID-19 pandemic impacts all facets of human life, including food preservation and supply chain. The changes in the food processing and preservation brings changes to the global food choice. In the age of the COVID-19 pandemic scare, the present article explores the framework for food security. It offers insights into food security with a focus on renewable energy to help rural farmers, besides, exploring the possibility of the diffusion of COVID-19 via food chain. This article indicates that the implementation of stand-alone and hybrid renewable energy systems is relatively fresh in food processing, preservation, and transportation chain. Green food preservation may be a revolutionary idea to address the challenges of the future to secure both producers and customers, besides, to improve the attractiveness of the ecological, economic, and creative sectors. This study reveals that the transformation can be achieved from a technological and economic point of view with the key steps to fulfill this goal in a cost-effective manner. The observation also shows a suitable methodology to identify the best energy, economic and environmental scenario towards fulfilling the energy needs in isolated areas.