Salivary Diagnosis in Covid-19 Infection-A Brief Review

Introduction: SARS-CoV-2 viral infection and the consequent COVID-19 disease rolled over the globe sweeping human lives and national health systems. Early diagnosis plays an important role in stopping its further escalation. Saliva as a Diagnostic Tool: Reverse transcription polymerase chain reaction (RT-PCR) remains the gold standard in the diagnosis of COVID-19 disease. Nasopharyngeal/oropharyngeal swabs are the recommended specimen types for identification of viral RNA. However, false negative results may occur due to inadequate or improper oropharyngeal sampling. Saliva, as a promising alternative, circumvents the limitations associated with the use of nasopharyngeal/oropharyngeal swabs and lessens the exposure risk of health care professionals. Salivaomics or salivary diagnostics includes the study of salivary proteins, salivary RNAs, salivary metabolites, salivary microRNAs and salivary microbiota. Saliva sample collection is easy, non-invasive and more acceptable for repeat testing and can be performed by non-healthcare professionals or even be self-sampled. Recent studies suggest that the sensitivity of saliva-based SARS-CoV-2 RNA detection methods seem to be comparable to or better than that of nasopharyngeal swabs. Conclusion:This paper reviews the role of saliva in the diagnosis of covid-19 infection, with special emphasis on its advantages, limitations and clinical implications.

ARIMA Model to Predict the Covid-19 Pandemic Cases in Telangana State

According to World Health Organization, India faced 28,307,832 confirmed cases as on June 2, 2021. The first wave of Covid-19 has taught new lessons to human lives in the year 2020. The second wave enveloped the India keeping the nation in second place globally with the confirm cases. Telangana has cases of 583,228 as of June 2, 2021. In this study ARIMA (1, 0, 17) model was found to be the best model among the other two to predict the cases. © 2021 IEEE.

Machine learning and deep learning algorithms in disease prediction: Future trends for the healthcare system

This chapter discusses machine learning (ML) and deep learning (DL) models for health care prospects. In clinical research, statistical algorithms in ML provide an effective way to analyze large datasets. Health centers generate huge data daily. ML algorithms need a vast amount of data to generate effective findings to provide significant information and immediate health results to patients. The significance of ML in health is that it processes large datasets beyond the scope of human ability and provides better health to patients at a lower cost. ML algorithms can improve efficiency, reliability, and accuracy in the outcomes obtained. This chapter highlights statistical algorithms of ML. Logistic regression on respiratory disease, generalized linear models, and Poisson regression were implemented to analyze mobile radiation the human body and on COVID-19 datasets, and k-means clustering on brain tumors. The statistical methods of ML shed light on the results of pathology data and assist pathologists. DL algorithms such as artificial neuron networks to diagnose diabetic retinopathy and deep convolutional neural networks for fetal ultrasound are explored. The chapter also describes You Only Look Once, Region Based Convolutional Neural Networks (RCNN) masking, and fully convolutional network algorithms for brain tumors in DL to detect objects in various fields. © 2022 Elsevier Inc. All rights reserved.

Oral Manifestations of COVID-19 Patients: A Systematic Review

Introduction: The emergence of coronavirus disease 2019 (COVID-19) crisis has evoked an exigent need to explicate the association between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and oral mucosal lesions. The present systematic review aims to elucidate the recent literature on oral manifestations related to COVID-19 so as to help the dental professionals for better screening and early diagnosis of the disease. Materials and Methods: A comprehensive literature search on PubMed, Science direct, Scopus, and Embase databases was carried out from December 2019 to March 2021 using keywords 'Coronavirus,' 'COVID-19,' 'SARSCoV-2,' 'Oral mucosal lesions,' and 'Oral manifestation.' Additional information was obtained from Cochrane, World Health Organization, and Medscape. The full text articles of case reports and cross-sectional studies were analyzed and included. The review included 25 articles. Results: Four most common oral manifestations were found: gustatory and olfactory dysfunction, xerostomia, oral mucosal lesions, and salivary gland diseases. Vasculitis, opportunistic infections, drug eruption secondary to administration of Non-Steroidal Anti-inflammatory Drugs (NSAIDs), stress, immunosuppression, and hyperinflammatory immune response secondary to COVID-19 might be some of the relevant predisposing factors responsible for the onset of oral manifestations in patients with COVID-19. Conclusion: The early detection of oral symptoms of SARS-CoV-2 infection could help the clinicians to perform a better screening, and in recognizing early manifestations of the disease. However, the oral manifestations might be misdiagnosed due to subsequent challenge of undergoing oral examinations, hence diverse studies should be undertaken by the researchers to gain a better insight into the topic. © 2021 Wolters Kluwer Medknow Publications. All rights reserved.

Post Covid-19 water and waste water management to protect public health and geoenvironment

The coronavirus disease 2019 pandemic has posed severe threats to humans and the geoenvironment. The findings of severe acute respiratory syndrome coronavirus 2 (Sars-CoV-2) traces in waste water and the practice of disinfecting outdoor spaces in several cities in the world, which can result into the entry of disinfectants and their by-products into storm drainage systems and their subsequent discharge into rivers and coastal waters, raise the issue of environmental, ecological and public health effects. The aims of the current paper are to investigate the potential of water and waste water to operate as transmission routes for Sars-CoV-2 and the risks of this to public health and the geoenvironment. Additionally, several developing countries are characterised by low water-related disaster resilience and low household water security, with measures for protection of water resources and technologies for clean water and sanitation being substandard or not in place. To mitigate the impact of the pandemic in such cases, practical recommendations are provided herein. The paper calls for the enhancement of research into the migration mechanisms of viruses in various media, as well as in the formation of trihalomethanes and other disinfectant by-products in the geoenvironment, in order to develop robust solutions to combat the effects of the current and future pandemics. © 2021 ICE Publishing: All rights reserved.

Impact of covid-19 on commercial laboratory testing for hiv in the United States

Background: On March 13, 2020, the United States declared a national emergency to combat coronavirus disease 2019 (COVID-19). Many states and localities issued shelter-in-place or stay-at-home orders to reduce the spread of COVID-19, limiting movement outside the home to essential activities. Since that time the pandemic has been associated with documented disruptions in routine preventive and other nonemergency care. Screening for HIV infection as well as HIV-1 viral load monitoring for persons living with HIV have likely been affected by the pandemic. Laboratory data from the National Syndromic Surveillance Program provide one way to assess the impact of the COVID-19 pandemic on HIV screening, HIV diagnoses and HIV-1 viral load monitoring. Methods: Using data reported daily to CDC from a large commercial laboratory, we identified lab test reports for HIV screening or HIV-1 viral load testing. For reports with HIV screening test results, we assessed how often the final HIV test algorithm result was confirmed positive. We plotted daily counts of each of the three HIV test types and 7-day moving averages. We also calculated the difference in the number of each type of test performed between March 13, 2019 and September 30, 2019 from those performed during the same time period in 2020. Results: Compared with number of tests performed in 2019, there were 669,847 fewer HIV screening tests, 4,910 fewer confirmed HIV-1 diagnoses, and 67,694 fewer HIV-1 viral load tests performed during March 13 to September 30, 2020. The 7-day average number of HIV tests performed dropped dramatically after March 13, 2020 and did not recover to 2019 levels by September 30, 2020 (Figure). Conclusion: During the national COVID-19 emergency, routine screening for HIV and HIV-1 viral load monitoring may have been delayed or foregone by many patients and clinicians. Undiagnosed HIV infection and higher viral loads could have led to increased morbidity and transmission. Although the number of tests being performed has partially recovered from a nadir this spring, testing at this commercial lab has not yet rebounded to make up what was lost. Healthcare system adaptations including home testing, home sample collection, and telemedicine visits for HIV care can help to address this shortfall as the COVID-19 pandemic persists in the US.