ABSTRACT
Independent and convenient healthy living is the prime goal of everyone. Unfortunately, there are limitations to this idea due to various factors such as age, illness, and pandemics. Through health-monitoring systems, people can easily and safely monitor their vital health parameters. Modern technologies have enabled people to live a healthy and independent life. Internet of things (IoT)-based health-monitoring systems have evolved to enable healthcare providers and patients to connect and coordinate with each other, allowing patients to monitor their vital health conditions and make informed decisions. IoT technology combined with various other technologies such as blockchain, cloud computing, artificial intelligence, and so on is gradually increasing the grip of smart systems on several other tasks in the healthcare sector. Modern smart healthcare systems are becoming more prevalent due to the emergence of smart home technologies. The healthcare module of the smart home automation system will allow people to receive better care even at home, minimizing the loneliness that often accompanies hospital visits. Also, such systems have facilitated remote healthcare governance with utmost accuracy. Also, in light of COVID-19, the need for such systems is becoming higher. The reality is that such systems are taking the load off medical personnel and providing a safer contact-free environment. Also, when it comes to clerical and administrative tasks, IoT-based smart systems have proved their mettle over the years. Even though such technology is in a stage of rapid growth, the reach is still minimal due to certain challenges that persist when it comes to the implementation side. The future is expected to be controlled by such modular technologies, and thus, extensive research both from the side of academia and industry is expected to revolutionize the technology even more soon. © 2023 selection and editorial matter, Durgesh Srivastava, Neha Sharma, Deepak Sinwar, Jabar H. Yousif, and Hari Prabhat Gupta;individual chapters, the contributors.
ABSTRACT
Background: Thyroid gland expresses ACE-2 receptors. It is thus a potential target for involvement by the COVID virus. Also, the cytokine storm and various drugs used in the treatment for COVID-19 can affect the thyroid function tests. Aim: To study thyroid function abnormalities in hospitalized patients with COVID19 infection. Objective: To study the proportion of patients with thyroid function abnormalities in hospitalized patients with COVID-19 infection. Results: Total 271 Covid -19 patients were included in the study, of which 27 were asymptomatic, 158 mild, 39 moderate and 47 severe categories classified according to the MOHFW criteria. The mean age was 49 ±17 years, 64.9% were male. Abnormal thyroid function test was present in 37.2% (101/271) patients. Low fT3, low fT4 and low TSH were present in 21.03%, 15.9% and 4.5%, respectively. Mean fT3 and fT3/fT4 ratio decreased with increasing severity of COVID-19 illness (p=0.000). In multivariate analysis, low fT3 was associated with increased risk of mortality (OR 12.36, 95% CI: 1.23- 124.19;P=0.033). Conclusion: Low fT3 and fT3/fT4 ratio helps to predicts disease severity. Low fT3 is associated with increased risk of mortality.
ABSTRACT
With the outbreak of the COVID-19 pandemic, classroom environments have been subjected to revolutionary changes via the employment of virtual classrooms and allied technological advancements. The traditional methodologies are proving to be inefficient in such an environment for teaching as well as managerial tasks. Also considering their cumbersome nature, the need for a newer, stronger, and better model is evident. As of now, many Deep Learning techniques have been employed for the purpose, ranging from the usage of standard object detection APIs or even CNNs and their variants. Our study proposes a model based on SVM embedded on top of embedding vectors combined with a Single-shot detector for real-time monitoring of attendance and attentiveness of students in a virtual classroom set up making use of video feed. A small comparative study between the proposed model and dlib, a standard library for the purpose as well is performed. The results show that our model outperforms dlib methodology significantly with high accuracy and performance efficiency. We had done experimentations on the fer2013 dataset particularly for emotion detection and custom datasets in general. Even though the model performs well in our experimentations, the need for a stronger and better dataset is high for evaluating the model and implementing it in real-life scenarios. © 2022 IEEE.
ABSTRACT
Background: Knowledge, attitude, and practice of the community can determine progress from localized to community transmission of COVID-19 in India. Aims and Objectives: To assess the knowledge, attitude and practice levels of the general population during the early phase of COVID-19 pandemic. Materials and Methods: A rapid cross-sectional survey was conducted among hospital visitors of a lead tertiary care hospital. Realtime data collection from 223 respondents was done and analyzed using Stata 12.1 SE. Results: Mean age was 38.2 ±13.5 years, 68.2% were males, mean education was 12.1 ± 3.7 years and 45.3% were gainfully employed. Almost all had heard about CORONA (221, 99.1%). Adequate knowledge was present on: a) mode of spread (48.0%);b) preventive measures (70.9%). Television (77.1%), social media (60.5%) and WhatsApp (44.0%) were sources of information. Positive attitude was seen for: a) wearing a mask (83.9%);b) health-seeking (85%) on presenting with symptoms;c) Self-isolation or home-quarantine (20%). Adequate knowledge towards the mode of spread, symptoms and control measures or positive attitudes for health-seeking were found to positively translate into correct practices (p≤0.001). Conclusion: Attitudes and practices of social distancing, self-isolation or home-quarantine were not adequate to prevent the epidemic progress in India. © 2021, Indian Association of Preventive and Social Medicine. All rights reserved.
ABSTRACT
From the evolution of the mankind, Turmeric has been used in conventional medication. India is in lead for producing, marketing and exporting the Turmeric and its value added products. Curcumalonga (Turmeric) is an Indian rhizomatous medicinal herb from the Zingiberaceae family that is common and widely available across the globe. The components of Turmeric are curcumin, demethoxycurcumin and bisdemethoxycurcumin and these are collectively known as curcuminoids. Curcumin, the active ingredient of Turmeric is generally investigated by the scientific community for its wide range of antioxidant activity, anti-Inflammatory properties and anti-cancer activity, anti-metabolic syndrome activities, neuroprotective activity, antimicrobial effects, anti-arthritis effects, anti-viral effects, anti-asthma and anti-diabetic effects, anti-obesity, cardio and liver toxicity protection activity, anti-depression and anxiety activities. Turmeric has been widely used as a typical household treatment for cough, sore throat, respiratory ailments and could be an effective immunity booster against SARS-CoV-2 therapy during the ongoing pandemic situation. Safety evaluation studies indicate that both turmeric and curcumin are well tolerated at a very high dose without any toxic effects. Thus, turmeric and its constituents have the potential for the development of modern medicine for the treatment of various diseases. So in this review, we describe the various metabolic roles of curcumin and activities for the benefit of human health.
ABSTRACT
COVID-19, the infectious pandemic disease is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This deadly disease was unknown before its catastrophic outbreak of the infection in Wuhan city of China, in December 2019. The pandemic situation has increased the demand of rapid enhancement of the in-vitro diagnostic assays which would enable the mass screening and testing. Several molecular and serological diagnostics assays such as direct viral antigen tests, nucleic acid amplification tests and serological tests were developed. Nucleic acid tests such as RT-PCR. TrueNAT, Feluda Test, loop-mediated isothermal amplification (LAMP) etc. detect the presence of RNA virus in the nasal or throat swab or from saliva. Antigen tests detect the presence of a virus as the antigen, which is a surface protein. Antibody tests such as enzyme-linked immunosorbent assays (ELISA), lateral flow assays (LFA), chemiluminescence assays (CLIA) etc. detect the presence of antibodies generated against SARS-CoV-2 in the blood samples.