Spectrum and Trends of Intestinal Parasitic Infections at a Tertiary Care Hospital during Pandemic Times: A Laboratory-Based Retrospective Study

Background Intestinal parasitic infections continue to loom in developing countries with low sanitation and socioeconomic conditions. Pandemic times are especially important to study the prevalence of these pathogens since the focus of all healthcare services was coronavirus disease 2019 (COVID-19). This study aimed to evaluate the prevalence and time-trend of intestinal parasitic infections in the capital region of India during the pandemic times.Methods In this cross-sectional study, a retrospective review based on data from the past 2 years in the post-COVID-19 pandemic was used. Descriptive and time-trend analyses were applied to the data. Time series analysis was analyzed using the best fit autoregressive integrated moving average (ARIMA) model to look for seasonality in trends and forecasting.Results A total of 7267 patients' stool samples over a 2-year pandemic period were included in the study. Intestinal parasites were detected in 11.18% (813/7276) patients. Giardia lamblia (2.28%) and Blastocystis hominis (3.78%) were the predominant ones. Time-trend analysis from 2020 to 2021 using ARIMA model predicted an increasing trend with waning of pandemic. The most prevalent infection was found in the monsoon and autumn months.Conclusion Rates of infection with Giardia lamblia and Blastocystis hominis have increased in comparison to other protozoan infections like Entamoeba histolytica when compared with prepandemic hospital-based studies. With fading of the pandemic, further increasing trends are predicted.

An inexpensive and rapid diagnostic method for detection of SARS-CoV-2 RNA by loop-mediated isothermal amplification (LAMP).

SARS-CoV-2 is a public pandemic health concern globally. Nasopharyngeal and oropharyngeal swab samples are used for Covid-19 viral detection. Sample collection procedure was tedious and uncomfortable and unsuitable for biochemical and CBC analysis in swab samples. Biochemistry and CBC tests are key determinant in management of Covid-19 patients. We developed a LAMP test to detect viral RNA in blood samples. LAMP is required four specific primers targeting the internal transcribed S-region and loop primers for viral RNA amplification. RNA was extracted from blood samples by TRIzol method. LAMP reaction was performed at 60 °C for 1 hour and amplicons were visualized in HNB dye. No cross-reactivity was seen with HBV, HCV, and HIV infected sample. Out of 40 blood samples, 33 samples were positive for LAMP and Q-PCR analysis, one sample was positive for LAMP and negative for Q-PCR, two samples were negative for LAMP but positive for Q-PCR, and four blood samples were negative for LAMP and Q-PCR. LAMP method has an accuracy of 92.50%, with sensitivity and specificity of 94.28% and 80%, respectively. Thus, LAMP diagnostic test has proved reliable, fast, inexpensive and can be useful for detection where the limited resources available.•LAMP method is a potential tool for detection of SARS-CoV-2.•Blood samples are the key determinant for routine diagnostics as well as molecular diagnostics.•LAMP assay is an appropriate diagnostics method which offers greater simplicity, low cost, sensitivity, and specificity than other methods in molecular diagnostics.

Profile of Candidemia in a national level HAI Surveillance Network of India

Background: Candida is responsible for roughly 96% of all opportunistic mycoses and is a major cause of bloodstream infections (BSIs). The potential for nosocomial spread of Candidemia infections is a new concern concurrent with the rapid expansion of intensive care facilities for COVID-19 patients. With the pandemic of COVID-19 now moving into 2022, it is understood that critically ill COVID-infected patients in the ICUs are commonly infected with highly resistant bacterial and fungal infections. Objective(s): To estimate the incidence rates and compare the epidemiology of candidemia in COVID infected and non-infected patients requiring ICU care. Methodology: In this 2-year retrospective multicentric study, we present the findings on candidemia from the Healthcare-Associated Infections (HAI) surveillance network which includes 40 hospitals across India and with special emphasis on differ-ences in the epidemiology of Candidemia in COVID infected and non-infected patients in the pre-COVID (April 2019 to April 2020) and COVID times (April 2020 to April 2021) across this network. We compared the incidence of candidemia between COVID infected and non-infected patients using Poisson regression analysis. Chi-squared (<=2) test was used to test for differ-ences in variables such as gender and 14-day mortality between the patients and Wilcoxon rank-sum (Mann-Whitney) test was used to compare median between the patients. Result(s): A total of 628 patients with candidemia were screened from HAI Surveillance Database where 68 patients are COVID infected and 560 non-infected patients from both pre-COVID and COVID periods. Incidence of Candida-associated BSI increased significantly from 1.47 (95% CI, 1.35-1.60) to 3.08 (95% CI, 2.38-3.92) in non-infected and COVID-infected patients respectively, while in CLABSI the rates increased from 2.62 (95% CI, 2.34-2.92) in non-infected to 5.99 (95% CI, 4.30-8.12) in COVID-infected patients.COVID infected patients in the age group (> 60 years) were significantly more prone to candidemia compared to non-infected patients.During the COVID period, the maximum time for candidemia to develop (from the time of ICU admission) in COVID-infected patients was shorter (< 65 days) than in non-infected patients (> 90 days). Conclusion(s): We observed an increased incidence of candidemia in hospitalized patients during the COVIDperiod compared with the same during the pre-COVID period.

Maternal and neonatal characteristics, operative details and outcomes in COVID-19 positive parturients undergoing cesarean sections: A retrospective observational study.

Background and Aims: There is a marked inclination towards cesarean sections as the preferred mode of delivery in parturients with COVID-19 disease. However, the challenges associated with planning and performing a surgery in the COVID-19 setup are considerable. These factors may lead to widespread changes in obstetric decision-making, operative planning, and perioperative outcomes. Thus, our study aimed to study the clinical and logistical factors involved in cesarean sections in COVID-19 parturients. Material and Methods: This was a retrospective observational study performed at a dedicated COVID-19 tertiary care center in India. All women undergoing cesarean section in the specially earmarked operating room between 1st May 2020 and 31st December 2020 were included in the study. The clinical characteristics, operative details, and neonatal details, along with maternal and fetal outcomes were noted and analyzed. Results: A total of 44 women underwent cesarean section during the study period, with elective and emergency surgeries numbering 22 each. No indication, apart from COVID-19 status, was listed in over one-fourth of the women (13/44). The most common preoperative comorbidity was hypothyroidism (12/44). Median surgical duration was 117.5 min (IQR 100-133), with a median of 7.5 (IQR 6-8.25) healthcare personnel in the OT. Over one-fourth (12/44) of the delivered babies had low birth weight, while 4.5% (2/44) tested positive for SARS-CoV-2. Conclusion: COVID-19 status alone continues to be a common indication for cesarean section. Operative time is increased, but the number of healthcare personnel involved can be trimmed with proper planning. Maternal and fetal outcomes are largely positive, with low transmission rates, but a considerable proportion of low-birth-weight neonates.

A Novel Work on Analyzing STRESS and Depression level of Indian Population During COVID-19

Objective: The world is facing the pandemic of COVID-19, which has led to a considerable level of stress and depression in mankind as well as in society. Statistical measurements can be made for early identification of the stress and depression level and prevention of the pre-vailing stressful conditions. Several studies have been carried out in this regard. The Machine learning model is the best way to predict the level of stress and depression in humankind by statistically analyzing the behavior of humans which helps in the early detection of stress and de-pression. This helps to prevent society from psychological pressures from any disaster like COVID-19. COVID-19 pandemic is one of the public health emergencies that are of great international concern. It imposes a great physiological burden and challenges on the population of the country facing the calamity caused by this disease. Methods: In this paper, the authors conducted a survey based on some questionnaires related to depression and stress and used the machine learning approach to predict the stress and depression level of humankind in the pandemic of COVID-19. The data sets were analyzed using the Multiple Linear Regression Model. The predicted score of stress and depression was mapped into DASS-21. The predictions have been made over different age groups, gender, and categories. The machine learning model is the best way to predict the level of stress and depression in humans by statistically analyzing their behavior which helps in the early detection of stress and depression. Results: Women, in general, were more stressed and depressed than men. Moreover, the people who are 45+ years of age were found to be more stressed and depressed, including male and fe-male students. The overall analysis showed that the people of India were stressed and depressed at “Serve” level due to COVID-19. It may be because students were more depressed about their study and career, women were stressed about their business as well as their salary and aged people were depressed due to their health concerns in COVID-19 disaster. Conclusion: The researchers conducted an analysis of data based on DASS-21 parameters defined for anxiety, depression, and stress at the global level. By the analysis defined in section 5, researchers concluded that the people of India are more stressed and depressed at "Serve" level due to COVID-19. © 2022 Bentham Science Publishers.

Food safety knowledge and changes in practices and concerns of Indian families during the COVID-19 pandemic

Purpose: Unsafe food can lead to various foodborne diseases and even death, especially among children. This paper aims to assess food safety knowledge and changes in practices and concerns among adults ≥ 18 years during the coronavirus disease 2019 (COVID-19) pandemic. Design/methodology/approach: A cross-sectional, web-based survey was conducted among 325 adults living in Northern India. Demographic data and information regarding their knowledge, practices and concerns about various food safety issues were collected to see if there were any changes due to the COVID-19 pandemic. Findings: The results showed that the participants had slightly higher than average knowledge and good food safety practices with mean scores of 9.75 ± 2.23 and 24.87 ± 2.28, respectively. Contracting COVID-19 from food and food packaging materials was of high concern for more than 70% of the participants. Majority (> 80%) of them reported an increase in the frequency of handwashing. About 16% of the participants used chemical disinfectants for washing fruits and vegetables. An increase (57.5%) in the frequency of food label reading was also noted during the pandemic. Freshness and the general quality of food items (49.5%), safety of food (30.8%) and cost (18.2%) were the top drivers that influenced the purchase decision. Originality/value: This study highlighted the need to send out clear messages on safe food handling practices and keeping the tempo up for sustaining good hygienic practices. This will help in reducing the risk of foodborne diseases. © 2022, Emerald Publishing Limited.

Detection of COVID-19 from X-Ray Images using Deep Neural Networks

In Dec 2019, Coronavirus has first shown in China and since then there is a big rise in the number of these cases. On March 28, 2020, WHO (World Health Organization) tweeted and proclaimed it's an epidemic. The test kits are relatively less likely to be checked by collecting blood samples because they are easily infectious, and collecting blood samples are very time taking. But it's important to get a quick and simpler way to validate the covid-19. Lungs are getting very badly affected by the Coronavirus and it increases in lungs gradually so we have to come up with the Convolutional Neural Network (CNN). This detects Corona virus utilising X-Rays of the Chest within about few seconds. To diagnose Coronavirus from X-ray Image dataset using different Convolutional Neural Network methodologies like Mobile Net, Inception, Exception, VGG. However, the findings obtained are based on the VGG16, VGG19 model. Apply the models to the X-ray dataset this was obtained from the Kaggle source. This dataset included 100 X-ray images of the lungs(chest) of the Patients with CORONA VIRUS, and 100 X-ray images of the lungs(chest) of People who are healthy. Python language is being used to execute the COVID19 dataset and Google Collaboratory is used for coding purposes. The focus of this research is to see how successful automatically detecting COVID-19 from chest X-rays using Convolutional Neural Networks. This study shows that to detecting COVID-19, VGG16 performs better than other method. The accuracy is 96.15% using VGG16 method. The excellent achievement of these models has the potential to rapidly better the COVID-19 diagnosis performance and speed. Although, A bigger dataset of chest X-ray pictures (COVID-19 positive) are necessary while using deep transfer learning to achieve consistent, accurate and better results to detecting COVID-19 diseases. © 2021 IEEE.

Statistical analysis on the COVID-19 infection spread in United State of America: A Prophet Forecasting Model

In the current scenario, the pandemic COVID-19 spread globally starting from the end of 2019, in Wuhan, a city of China. As per the current data taken up to 26th of May 2020, globally there are a huge number of people are affected (Approximately 3 billions) by the pandemic. Though the entire data varies depending upon the several parameters like, population size, congestion of area, climate condition, awareness of peoples etc. we have only analyzes on the data of the country USA. The entire data is partitioned into various categories such as: infected rate, mortality rate. A statistical analysis is prepared to analyze or predict the future strategies of the infected rate as well as the removal (Death/cured) rate. The growth of both the infected and the removed can be predicted with the same observed data taken on daily basis from 15th February 2020. We retrieved these data from an authenticate source provided by Worldometer (http://www.worldometers.info). However, Prophet Forecasting Model (PMF) is used to simulate and discussed for the prediction of the mortality rate, active rate due to pandemic COVID-19. The proposed method is also tested for accuracy of model via cross validation method. © 2021 IEEE.

A Straight to Test Pathway in Colorectal Cancer Referrals Tailor Made for an Elderly and Frail Population

Purpose: The number of target colorectal cancer [TCC] referrals continues to rise, particularly in the elderly and frail population. Here we evaluate the outcome of patients over 80 seen in clinic not qualifying for our Straight To Test (STT) scheme. We aim to propose an alternative, cost-effective pathway for these vulnerable patients in a Covid-19 era. Method: A retrospective observational study of 267 >80-year- old patients referred to the STT service over a 3 month period. Data on demographics, investigations and outcome was taken from the electronic patient record system and analysed. Results: Of the 171 patients having a face-to- face appointment;90% had a CT (including virtual colonoscopy) requested as deemed unfit for bowel prep/endoscopy. Comparing patients only having a CT with those having more invasive investigations, they were older and frailer (WHO performance score of >2) (P = 0.0017∗ and 0.005∗ ∗) but there was no difference in discharge frequency (P = 0.45∗ ∗) and identification of Lower GI or other malignancy (P = 0.48 and 0.63∗ ∗). Conclusion: Selection of patients based on their age and frailty to go directly to CT is a safe and effective strategy in TCC referrals processed by a STT service without comprising outcome. This process may lead to higher patient satisfaction as well as fulfilling current Clinical Practice Groups' key performance indicators.

New normality among orthodontists amid pandemic COVID-19

The most recently found illness COVID-19 is a viral disease that started in Wuhan, China, in December 2019. The dental procedures consist of the use of aerosols when treating patients. Orthodontists, hence, must be aware of the available evidence and guidelines to create a safe environment for themselves, their patients, and the entire orthodontic team. The following literature includes various protocols, ways out to deal with the pandemic situation, as well as describes the new normality to be accepted post pandemic in clinics as well as college departments.

Perceived stress, resilience, well-being, and COVID 19 response in Isha yoga practitioners compared to matched controls: A research protocol.

OBJECTIVES: The COVID-19 pandemic has been a significant stressor worldwide and reports of psychological distress, depression, sedentary lifestyles, and overall decreased wellbeing are increasing. Yoga practices have been found to improve mental and physical health. The purpose of this randomized controlled trial is to compare Isha yoga practitioners to controls on perceived stress, resilience, wellbeing, and protection and recovery from COVID-19. Trial Design. In this prospective randomized control trial, the effects of yoga practices are being compared between seasoned yoga practitioners with two controls who are age (±3 years), gender matched, and living in the same neighborhood. METHODS: Participants will be asked to complete a series of web-based surveys at baseline, six weeks, and 12 weeks. These surveys include validated scales and objective questions on COVID-19 infection and medical history. The validated questionnaires assess stress, mood states, resilience, and overall wellbeing. Questionnaires, weekly activity diaries, and medical history, will be collected using REDCap. RESULTS: We hypothesize that routine yoga practice during the COVID-19 pandemic will reduce stress, enhance well-being, and provide protective effects against COVID-19. CONCLUSION: With the growing concern about the physical and mental impacts of COVID-19 and increased interest in alternative practices such as yogic practices, this study will contribute to the growing body of evidence about the safety and efficacy of yoga for emotional, mental, and physical health conditions.

Personality matters during a pandemic: Implicit theory beliefs influence preparedness and prevention behaviors

We show that individuals’ implicit theory orientation serves as an important determinant of the extent and timing of their COVID-19 preparedness behaviors (e.g., buying masks) and prevention behaviors (e.g., wearing masks), as well as their pandemic-related beliefs (e.g., perceptions of self-efficacy, vulnerability, severity). Across two studies, we show that those who endorse an incremental theory report better and earlier pandemic-related preparedness, greater compliance with pandemic-preventive behaviors, and believe in the severity of the disease, and yet those who endorse an entity theory report feeling more vulnerable to the disease. We suggest that by documenting the connection between implicit theory orientation and COVID-19 beliefs and behaviors, we provide a foundation for better understanding how to motivate individual responses to public health crises. © 2021 the Association for Consumer Research. All rights reserved.