An overview of regulation for nutraceuticals and concept of personalized nutraceuticals

Nutraceuticals are essentially nutritional components that have a vital role in developing and maintaining the body's regular functions, which keeps people healthy. The nutraceutical sector is also primarily driven by the existing global population and trends. Examples of foods considered as nutraceuticals include prebiotics, fibre, polyunsaturated fatty acids, probiotics, antioxidants, and other natural or herbal foods. Some of the most serious health problems of the 20th century, like COVID-19 and diabetes mellitus, are managed with the help of the preceding nutraceuticals. As we move into a time of health and medicine, the food industry as a whole has become more focused on research.

COVIZONE: A Deep Transfer Learning-Based System for Automatic Detection of Covid-19 using Chest X-Ray

With increasing rise of COVID-19 infected patients in India and worldwide, examining and detecting COVID-19 among such large number of populations is becoming a humongous task for the medical practitioners and civic authorities. RT-PCR, real time reverse transcription-polymerase chain reaction technique is widely accepted and one of the reliable methods for detection of novel COVID-19.However, being a time consuming, laborious and expensive method for declaring results for the patients in over 6-8 hours to even 3 days in remote places, this technique is not being widely used. The high and very fast spread rate of COVID-19 and low availability of RT-PCR kit, is making the use of computer assisted technologies an inevitable and a potentially faster response mechanism catering to a large population with least human error and a cost-effective solution. Therefore, an intelligent system COVIZONE has been presented, in the proposed work, designed using state of the art pre-trained CNN model to analyze and detect COVID-19 presence in the lungs using Chest X-Ray and CT-Scan Images. In the proposed work, a multi-class classification (Normal, Pneumonic and COVID-19) of patients using ResNet and ResNext CNN model has been done. Both the models show similar performance with high accuracy of 96% and 97% respectively on public dataset of COVID-19, Pneumonia and Normal CXR and CT-Scans. To avoid skewness due to lesser number of COVID-19 CXR images, dataset used has limited Pneumonia and Normal CXR images to train the system and achieved noticeable high accuracy. The proposed COVID-19 detection model i.e. COVIZONE, even if not used as a primary Covid testing and detection tool, can still be a very helpful tool for screening potentially infected persons and help the physicians who are yet not trained for this pandemic diagnosis. © 2021 IEEE

Identification of Unmasked Faces: Protection in Public Places from COVID-19 and Other Infections

The 3rd wave of Coronavirus is hitting the world. The doctors and scientists are also in their last phase of developing a vaccine. But it is challenging to get the vaccine for everyone due to several factors such as production, supply chain, cold chain and price. Till then, the only way to prevent infection is to wear a mark and follow social distancing. This paper describes how to identify mask and no-mask faces in public areas using image processing algorithms. One of the significant steps taken by people around the world to stay safe is wearing masks. This research-based project gets a camera-based solution to detect faces and recognize them if they exist in the database. If the live stream has unmasked faces, which aren't present in the existing database, give out an alarm to alert the administrator or user. It is a significant project for times like these because now, with the lockdown ending in the world because of financial meltdowns, most public places like malls, temples, shops, grocery stores, and medical stores are opening. A concern for the safety and well-being of everyone around us because the virus can spread at a logarithmic rate and cause mass damage in the form of large bills and some cases, death. To avoid this, we always have to wear masks when in public places, and this is what the project does. The crucial technologies used in our manuscript are Face Detection, Face Recognition Identification, and Feature Detection in the face. © 2021 IEEE.

Essential oils for the treatment of skin anomalies: Scope and potential

Skin diseases contribute significantly to worldwide morbidity and mortality. It is the most common of all human diseases which can affect people of any age group. Most importantly, it is seen that the COVID-19 pandemic have further detrimentally contributed to dermatological manifestations. Due to the enormous socioeconomic burden created by skin disorders, the dermatological treatments have been added in the WHO List of Essential Medicines. Some of the major predominant diseases are acne, psoriasis, eczema, fungal infections and skin carcinoma. As a matter of fact, focus on treatment of skin diseases should be arguably considered as a matter of global urgency. Although treatments are available, they face numerous challenges which limit patient acceptability. Essential oils have a long history of pharmacological use;however their role in the treatment of dermatological disorders is vague. Therefore, in this review, the potential and mechanism of different essential oils obtained from various sources in the treatment of major dermal disorders has been summarized. This will help the formulation scientists and the clinicians to develop suitable formulation strategies for the prevention and cure of skin diseases. © 2021 SAAB

Predictive Model for COVID-19 Using Deep Learning

The Coronavirus has now taken more than 2.4 million lives and infected more than 100.2 million people. The spread of Coronavirus has had an adverse effect on global health and economy. The Coronavirus pandemic puts healthcare systems worldwide under immense pressure. With advancement in Machine Learning and, in particular Artificial Intelligence, early detection of Covid-19 can assist in rapid recovery and help to relieve strain from healthcare systems. Early results indicate that there are abnormalities in patients’ chest X-rays infected with Coronavirus. In this Review paper, an extensive and exhaustive guide to identify COVID virus using Chest X-Ray samples in an effective and cheap method has been presented. It highlights different CNN Architectures and Gradient Class Activation as the main approach to analyze and detect the infection. A significant amount of training and validation data/images are required for training neural networks such as Convolutional Neural Networks (CNNs) for accurate predictions on test data. Generative Adversarial Networks (GANs) especially ACGANs and Cycle GANs were used to create new images for the training dataset, which helped generalizing the classification model. The paper exhibits the application of different Convolutional Neural Networks architectures for transfer learning, including Inception and ResNet50. The paper then presents the combination of GAN and deep learning models for precise identification of COVID-19 infection. With so much research going on to detect COVID-19, this paper will help all researchers and doctors in the future. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Preparedness to face the COVID-19 pandemic in palliative care services in the asia-pacific region: A rapid online survey

Background: Palliative care improves COVID-19 patient management who are highly vulnerable and facing higher risk of dying. However, evidence regarding palliative care preparedness and response plans is limited in the Asia-Pacific region. Aim: To evaluate the preparedness of palliative care services in the Asia- Pacific region to respond to the COVID-19 pandemic. Method: An online cross-sectional survey was developed based on prior methodology guidance and the International Health Regulations. It was emailed to the Asia-Pacific Hospice and Palliative Care Network subscribers (n=1551) and organisational members (n=185) from 10th June to 1st July 2020. Descriptive analysis was used for reporting. Results: Ninety-seven respondents completed the survey. Around half of services were hospital-based (n=47, 48%), and public-funded (n=46, 47%). Half of services reported to have confirmed cases (n=47, 49%). Staff perceived moderate stress of being infected by COVID-19 (median: median: 7 on a 1-10 scale). > 85% of respondents reported they had up-to-date contact list for staff and patients, however one-third revealed challenges to keep record of relatives (n=30, 31%) and patients visited in communities (n=29, 30%). Majority of services (60%) obtained adequate infection control resources except face mask. More than half had no guidance on Do Not Resuscitate orders (n=59, 66%) and bereavement care (n=44, 51%). One-third considered they had capacity to train nonpalliative care specialists in using protocols. Conclusion: Recommendations to strengthen the palliative care preparedness include: 1) improving the access to infection control materials such as face mask and hand disinfectants;2) acquiring stress management protocols for staff when unavailable;3) reinforcing the contact tracing system for relatives and patients visited in the community and 4) developing guidance on patient and family care before and after patients' deaths.

Cohorting to Isolate asymptomatic spreaders: An agent-based simulation study on the Mumbai Suburban Railway

This paper studies cohorting in public transit systems and its usefulness in mitigating disease transmission. The Mumbai suburban railway system is used as a case study. © 2021 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved.

New directions for apprenticeships

Apprenticeship has proven its effectiveness in equipping young persons with the skills needed by the labor market. However, in the context of learning societies, questions are being raised about the effectiveness of the apprenticeship model for reskilling and upskilling of adults and older workers. This chapter explores promising practices that have the potential to give apprenticeships a new direction and make them attractive and effective for adults and older workers, as well as for millennials and digital natives. These include using intermediaries to increase the participation of employers, in particular small and medium-sized enterprises;improving the management of apprenticeship training with the help of digital technology;bridging the skills gap in digital economy;introducing higher-level or degree-level apprenticeships;making apprenticeship programs accessible and effective for adults and older workers;and providing quality learning opportunities in the informal sector. © The Author(s) 2021.

Medical education focus in published articles related to COVID-19.

OBJECTIVE: The ongoing pandemic of Coronavirus Disease 2019 (COVID-19) has affected >2 million patients with approximately 900,000 cases in the United States alone. Medical education has possibly suffered during this time, due to disease mitigation strategies and stress on both students and teachers. We aimed to quantify the publications describing the impact of COVID-19 on medical education in the early stage of the pandemic. MATERIALS AND METHODS: Abstract and titles related to COVID-19 listed in PubMed were reviewed for an educational focus. Articles with an educational focus were further evaluated for characteristics of content. RESULTS: Out of a total of 3641 articles related to COVID-19, only 26 (0.7%) articles had an educational theme. Thirteen articles were pertaining to residency and fellowship education and twelve articles were focused on medical student education. Only one article referred to nursing education. CONCLUSIONS: Publications pertaining to medical education during the ongoing COVID-19 pandemic are few. There is an urgent need for scholarship to understand the best processes and develop innovative methods that can help improve the educational experience of students and trainees during this extraordinary time.

Representation of specialties in Coronavirus Disease 2019 (COVID-19) research: analysis of published literature in the first 3 months.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) has infected more than 1.5 million patients worldwide and is associated with high morbidity and mortality. The volume of research published in a specialty, which may shed light on the perceived impact that a topic plays within that discipline, is unknown for the present pandemic. MATERIALS AND METHODS: We analyzed all articles published in the first 3 months on COVID-19 and categorized them by journal type and article content. RESULTS: There was a ~100-fold difference in articles published on COVID when categorized by journal type and ~65-fold difference when grouped by article content. CONCLUSIONS: There is a wide variation in research published withing various specialties regarding COVID-19. Researchers across specialties need to urgently realize the impact of COVID-19 and publish articles that can help improve understanding of this ongoing pandemic.