A Smart Phone App with Geo-Spatial Information for Forecasting COVID-19 from Lung X-ray Images

Background: Presently, the diagnosis of coronavirus-2019 (COVID-19) is a challenging task worldwide as the disease is spreading at a very faster rate when one person with the disease comes into contact with the other. Current information denotes that several people are detected with COVID-19 and the data analyst say that the rate of spread of the disease is increasing exponentially, across many countries in the world. Novelty: This investigation has facilitated the need for diagnosing the disease within a short duration of time by using the X-ray images of the lungs. This scheme deploys artificial intelligence like deep learning algorithms to diagnose COVID-19 among the affected people by maintaining social distancing. Real-time datasets are gathered from the government hospitals for those who are affected by COVID-19 and healthy people. Further investigation can direct the patients themselves to open the smart phone app which will record the respiratory sounds. Followed by this, the features are extracted using Discrete Wavelet Transform (DWT), where a threshold is applied to extract useful coefficients that can be used to train the deep learning neural networks using Fast Recurrent Convolutional Neural Networks (F-RCNN). The respiratory audio signals are captured to detect patients affected by coronavirus by a way of noncontact, nonintrusive approach. The results reported are valued in detection of COVID-19 by using a smart phone app which is available instantly. Objectives: This approach seems to be an indigenous, noninvasive, and cost-effective approach that will relive the patients from trauma of undergoing the swab test and awaiting the laboratory reports, which incurs time delay. Experimental results are obtained from 20,000 samples of patients suffering from COVID-19 and also persons who are normal. This mobile phone app is effective in diagnosing the COVID-19 from the X-ray images of the lungs. Even low-income people can also use this technology. Methods: The effectiveness of the proposed system which uses DWT, thresholding, and deep learning algorithms resulted with a performance whose F-measure is 96–98%. The classification is carried out to classify the COVID-19-positive and COVID-19-negative cases using Fast Recurrent Convolutional Neural Networks (F-RCNN). Expected Outcome: A smart phone app will be developed to detect the COVID-19 by using a noninvasive and easily affordable technique. The forecasted results were in the range of 89–95% for the above said algorithms. It is significant from the above results that the severe impact of COVID-19 can be diagnosed using a noninvasive mobile phone app using X-ray images. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Synthesis of Immucillins BCX‐1777and BCX‐4430 from a Common Precursor

A convergent route for the synthesis of BCX‐1777 and BCX‐4430 from a Boc‐protected 2‐pyrrolidinone, derived from 2,3,5‐tri‐O‐benzyl‐d‐ribonolactone, and a dihalogenated pyrrolopyrimidine as the key starting materials is reported. A chemoselective cross‐coupling was achieved from the two key starting materials in 79 % yield. Luche reduction and mesylation resulted in the stereoselective formation of an advanced intermediate in 77 % yield over two steps, which served as a precursor for synthesizing BCX‐1777 and BCX‐4430 in 38 % (over 10 steps) and 32 % (over 11 steps) overall yields, respectively, from 2,3,5‐tri‐O‐benzyl‐d‐ribonolactone.Dedicated to the students in my lab for their perseverance during the COVID-19 pandemic

The brighter side of home schooling for children with special needs: Learning from COVID-19 lockdown

COVID-19 has resulted in widespread social isolation, quarantines, and suspended academic activity. Children with special needs are socially, financially, and educationally impacted by dramatic changes to laws and restrictions put in place to curtail this devastating global pandemic. This study addresses the beneficial improvements that occurred in the lives of children with special needs during the quarantine period. It led to improved skills, better family dynamics, and an enhancement in technological expertise. Here, we use the case study approach. Twelve parents of children with special needs were selected from the Kozhikode district of Kerala, India. Convenient sampling methods were used for the selection of respondents. The data obtained from the twelve participants were scrutinized. The verbatims were coded with the QDA-Miner Software. Our findings indicate that the lockdown has reinforced family bonds;opened up spaces for homeschooling and digital learning;strengthened relationships with parents, communities, and teachers;and paved the way for technological adaptation. During lockdown, children with special needs continued to learn and build skills in a nurturing familial environment. © Authors.

Dental clinical practice changes needed during the COVID-19 pandemic: The ‘new normal’

In December 2019, a novel coronavirus (2019-nCOV) emerged in Wuhan, China. It has affected the entire globe causing the ongoing pandemic. The SARS-CoV-2 infection could be asymptomatic or mildly symptomatic in most cases of COVID-19. Therefore, there is a difficulty in diagnosis of SARS-Cov-2 infection based only on the clinical findings and hence requires the confirmatory laboratory testing. In dental clinical practice, there is a very high risk of transmission of SARS-Cov-2 infection. Therefore, there is an urgent need to assess and minimize the risk in dental care settings. Each patient in dental clinical practice needs to be considered as the potentially infectious and managed accordingly with the use of appropriate infection prevention and control measures. The approaches and measures for risk alleviation need to be emphasized and practiced appropriately to prevent the infection. This review presents the important changes needed in routine and emergency dental practice in the current pandemic.