Quantification of SARS-CoV-2 in wastewater samples from hospitals treating COVID-19 patients during the first wave of the pandemic in Brazil.

The COVID-19 pandemic has caused a global health crisis, and wastewater-based epidemiology (WBE) has emerged as an important tool to assist public health decision-making. Recent studies have shown that the SARS-CoV-2 RNA concentration in wastewater samples is a reliable indicator of the severity of the pandemic for large populations. However, few studies have established a strong correlation between the number of infected people and the viral concentration in wastewater due to variations in viral shedding over time, viral decay, infiltration, and inflow. Herein we present the relationship between the number of COVID-19-positive patients and the viral concentration in wastewater samples from three different hospitals (A, B, and C) in the city of Belo Horizonte, Minas Gerais, Brazil. A positive and strong correlation between wastewater SARS-CoV-2 concentration and the number of confirmed cases was observed for Hospital B for both regions of the N gene (R = 0.89 and 0.77 for N1 and N2, respectively), while samples from Hospitals A and C showed low and moderate correlations, respectively. Even though the effects of viral decay and infiltration were minimized in our study, the variability of viral shedding throughout the infection period and feces dilution due to water usage for different activities in the hospitals could have affected the viral concentrations. These effects were prominent in Hospital A, which had the smallest sewershed population size, and where no correlation between the number of defecations from COVID-19 patients and viral concentration in wastewater was observed. Although we could not determine trends in the number of infected patients through SARS-CoV-2 concentrations in hospitals' wastewater samples, our results suggest that wastewater monitoring can be efficient for the detection of infected individuals at a local level, complementing clinical data.

Comparison of chest CT findings in outpatient and hospitalized COVID-19 RT-PCR positive patients of Shiraz.

INTRODUCTION: Chest CT provides valuable information regarding coronavirus disease 2019 (COVID-19) during the treatment process. The present study aimed to assess the distribution of chest CT findings in outpatient (OPD) and hospitalized corona patients. MATERIAL AND METHOD: This was a retrospective study. Archived corona patient's data on the picture archiving and communication system (PACS) was assessed in terms of demographic data and patients' lungs' radiologic features. The OPD and hospitalized patients referred to University hospitals from February 20 to the study's date were evaluated. Data were analyzed using independent chi-square and t-test. RESULTS: Five hundred and fifty nine patients, including 187 OPD and 372 hospitalized patients, were analyzed. The frequency of normal chest CT, typical, and possible corona features was 37.4%, 40.8%, and 14.3%. The normal chest CT rate was significantly higher in outpatient versus hospitalized patients (P<0.001). Consolidation and/or ground-glass opacity were seen in 61% of patients, considerably higher in hospitalized patients (P<0.001). 2% and 15% OPD and hospitalized patients had more than 25% lung involvement, respectively. The frequency of other signs such as Crazy Paving, atoll sign, subpleural band/distortion also was significantly higher in hospitalized patients (P<0.001). CONCLUSION: Most OPD patients had less than 5% lung involvement or normal chest CT. The typical features of lung involvement in COVID-19 were significantly higher in hospitalized patients.

Mental Health in Hospitalised COVID 19 Patients in Quarantine During Second Wave in a South Indian Private Teaching Hospital.

BACKGROUND: Covid 19 lockdown measures were taken all of a sudden during the devastating second wave in India, when there was a considerable loss and suffering in the country. The coronavirus disease 2019 (COVID 19) pandemic has led to unprecedented hazards to mental well-being globally. PURPOSE: To assess the prevalence and evaluate risk factors of depression, insomnia or sleep disturbances and suicidal ideation among covid 19 positive patients admitted in covid wards with mild-to-moderate disease. MATERIALS AND METHODS: A total of 635 hospitalised patients who were covid-19 positive were requested to fill an online quality of life pre-validated questionnaire comprising of 4 sections - the sociodemographic information section, health care assessment related to depression symptoms, insomnia assessment and assessment of suicidal ideation. The survey comprised of pre-validated questions on sociodemographics, knowledge of covid 19, fear of covid 19, insomnia, feeling of sadness, depression, feeling of rejection and suicidal ideation among the covid 19 positive inpatients in quarantine due to mild or moderate covid 19 disease. RESULTS: The prevalence of depression and insomnia or sleep disturbances after being diagnosed as covid 19 positive and hospitalized was nearly 40% and 28.8%, respectively, among the inpatients. Depression was significantly observed in female group (p < 0.001), unmarried or separated individuals (p < 0.001), housewives (p < 0.001) and patients with comorbidities (p < 0.001). Insomnia was more likely to be present in elderly covid positive patients (p < 0.001) and separated or divorced group of participants (p < 0.001). The prevalence of suicidal ideation was 5% of the total covid 19 positive patients participated in this study, and it was significantly observed among separated or divorced patients, cancer patients, patients from suburban residence and among graduates (p < 0.001). CONCLUSION: Covid 19 is associated with major psychological impact among the patients suffering from thus warrants counselling.

Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review.

The worldwide health crisis caused by the SARS-Cov-2 virus has resulted in>3 million deaths so far. Improving early screening, diagnosis and prognosis of the disease are critical steps in assisting healthcare professionals to save lives during this pandemic. Since WHO declared the COVID-19 outbreak as a pandemic, several studies have been conducted using Artificial Intelligence techniques to optimize these steps on clinical settings in terms of quality, accuracy and most importantly time. The objective of this study is to conduct a systematic literature review on published and preprint reports of Artificial Intelligence models developed and validated for screening, diagnosis and prognosis of the coronavirus disease 2019. We included 101 studies, published from January 1st, 2020 to December 30th, 2020, that developed AI prediction models which can be applied in the clinical setting. We identified in total 14 models for screening, 38 diagnostic models for detecting COVID-19 and 50 prognostic models for predicting ICU need, ventilator need, mortality risk, severity assessment or hospital length stay. Moreover, 43 studies were based on medical imaging and 58 studies on the use of clinical parameters, laboratory results or demographic features. Several heterogeneous predictors derived from multimodal data were identified. Analysis of these multimodal data, captured from various sources, in terms of prominence for each category of the included studies, was performed. Finally, Risk of Bias (RoB) analysis was also conducted to examine the applicability of the included studies in the clinical setting and assist healthcare providers, guideline developers, and policymakers.