ABSTRACT
Background: The novel coronavirus disease (COVID-19) has affected over 50 million people and has inflicted more than 1.2 million casualties ever since its inception in December 2019. Besides, multiple hematological and biochemical parameters have emerged as potential biomarkers to predict severe disease and mortality in COVID-19. One such biochemical biomarker is hypocalcemia. Hypocalcemia is associated with severe disease, organ failure, increased likelihood of hospitalization, admission to the intensive care unit, need for mechanical ventilation, and death from COVID-19. Hence, the present study was undertaken to compare the serum total calcium in patients infected with COVID-19 and a normal healthy population. Aim(s): To compare serum total calcium in patients infected with COVID-19 and normal healthy populations. Material(s) and Method(s): This is a case-control study with 50 COVID-19 patients and 50 normal healthy individuals as controls. Serum calcium was determined by Arsenazo III method using Vitros 5600 autoanalyser. Result(s): Chi-square analysis was done and the p-value between cases and control was < 0.05 which is significant. 20% of the COVID -19 patients had very severe hypocalcemia ranging between 4.5-6 mg/dl, 47% of the COVID-19 patients had moderate to severe hypocalcemia with values between 6.6-8.5 mg/dl, and 33% of the COVID -19 patients had normal calcium levels ranging from 8.6-10 mg/dl. Conclusion(s): Hypocalcemia is highly prevalent in COVID-19 patients implying that hypocalcemia is intrinsic to the disease. Prospective studies with a larger number of patients are required to prove this hypothesis and unravel the underlying pathophysiological mechanisms. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.
ABSTRACT
Face recognition is an important feature of computer vision. It is used to detect a face and recognize a person and verify the person correctly. Face recognition technology plays an essential role in our everyday lives like in passport checking, smart door, access control, voter verification, criminal investigation, and system to secure public places such as parks, airports, bus stations, and railway stations, etc and many other purposes. While going through the pandemic and the post pandemic situations wearing a mask are compulsory for everyone in order to prevent the transmission of corona virus. This resulted in ineffectiveness of the existing conventional face recognition systems. Hence it is required to improvise the existing systems to get the desired results to detect the masked face at the earliest. This system works in three processes that are image pre-processing, image detection, and image classification. The main aim is to identify that whether a person’s face is covered with mask or not as per the CCTV camera surveillance or a webcam recording. It keeps on checking if a person is wearing mask or not. For classification, feature extraction and detection of the masked faces, Convolutional Neural Network (CNN) and Caffe models are used. These help in easy detection of masked faces with higher accuracy in a very less time and with high security. © 2021 IEEE.
ABSTRACT
Corona virus disease (COVID19) is a hastily spreadable disease that is wreaking havoc on medicalcare systems all over the world. Due to the drawbacks of rear transcription-polymerase chain reaction (RT-PCR)based tests for COVID19 detection, a count of recent modules have proposed radiology imaging-based ideas.CT imaging is critical for detecting COVID-19-related lung manifestations, and segmenting infected parts from CT scans is critical for quantitative disease progression estimation in exact and correct diagnosis and follow throughassessment.In this research, we use a Convolution Neural Network to predict COVID19 disease in chest CT scan images. It's an advanced lung infection detection system based on computed tomography (CT) images that has a lot of potential as a complement to the COVID-19 treatment strategy.