Challenges to research implementation during public health emergencies: anecdote of insights and lessons learned during the COVID-19 pandemic in Gujarat, India (preprint)

Background: Health emergencies, including pandemics, are not entirely new occurrences; some notable ones occurred in the past. However, the scale of the COVID-19 pandemic is unprecedented. The COVID-19 pandemic exposed the unpreparedness of national health systems in effectively managing health emergencies. During the pandemic, controlling the spread of the virus and hopes of exiting into a post-pandemic era were reliant on research to improve patient care and inform government policies. Nonetheless, research implementation during health emergencies can be challenging in low-resourced settings. This paper presents anecdotes of experiences encountered in implementing a research project during the COVID-19 pandemic in Gujarat, India, and offers insights into ways research can be supported during health emergencies. Methods: We implemented a longitudinal study to investigate the impact of the COVID-19 pandemic, including SARS-CoV-2 infection, during pregnancy on birth outcomes, growth, and development in early childhood. The study utilises hospital records and databases to recruit women who were infected with SARS-CoV-2 during pregnancy and those with no known infection. Mother-infant pairs in the infected and uninfected group were then followed longitudinally for over 3 years. Results: Observations of challenges encountered during implementation of the research, including those related to planning, retrieval of hospital records, tracking, recruitment, and follow-up of eligible women were recorded by study staff, and the observations produced as anecdotes. The challenges observed are group into three overarching themes: a) individual factors, b) health system challenges, and c) research operational challenges. Some notable observations include misinformation, misconception, mistrust, underdeveloped health record systems, stigma, and hesitance. Conclusion: We believe early planning, effective communication, and community awareness are likely to be helpful in implementing a successful research project. Additionally, efforts at improving collaboration and co-creation between health practitioners, researchers and the public may benefit the implementation of research projects during a health emergency.

SARS-CoV-2 antibodies cross-react and enhance dengue infection (preprint)

Dengue disease is highly prevalent in tropical and subtropical regions worldwide. However, its pathogenesis is still incompletely understood, particularly in comparison to other endemic viruses. Antibody-dependent enhancement (ADE) is a well-known phenomenon for dengue viruses. Given the recent surge in dengue cases and potential cross-reactivity with SARS-CoV-2 antibodies, this study explores the impact of anti-SARS-CoV-2 antibodies on DENV-2 infection. The study assessed the cross-reactivity of SARS-CoV-2 antibodies with the DENV-2 Virus. Human convalescent plasma samples collected during different waves of COVID-19 and monoclonal and polyclonal antibodies raised against SARS-CoV-2 were examined for their potential to cause ADE of DENV-2 infection using cell-based assays. The study found that anti-SARS-CoV-2 antibodies acquired from natural infection in humans or through experimental immunization in animals were cross-reactive with DENV-2 and had the potential to enhance DENV-2 infection in K562 and U937 cells. In-silico and in-vitro studies indicated a strong interaction between SARS-CoV-2 antibodies and DENV-2 E-protein, providing a molecular basis for these findings. This study is the first to demonstrate that anti-SARS-CoV-2 antibodies can cross-react with DENV-2 and can enhance its infection through ADE. These findings have implications for SARS-CoV-2 vaccine development and deployment strategies in regions where dengue is endemic.

Interaction of SCoV-2 NSP7 or NSP8 alone may cause constriction of the RNA entry channel in NSP12: Implications for novel RdRp inhibitor drug discovery (preprint)

RNA-dependent RNA polymerase (RdRP) is a critical component of the RNA virus life cycle, including SCoV-2. Among the Coronavirus-encoded proteins, non-structural protein 12 (NSP12) exhibits polymerase activity in collaboration with one unit of NSP7 and two units of NSP8, constituting the RdRp holoenzyme. While there is abundant information on SCoV-2 RdRp-mediated RNA replication, the influence of interplay among NSP12, NSP7, and NSP8 on template RNA binding and primer extension activity remains relatively unexplored and poorly understood. Here, we recreated a functional RdRp holoenzyme in vitro using recombinant SCoV-2 NSP12, NSP7, and NSP8, and established its functional activity. Subsequently, molecular interactions among the NSPs in the presence of a variety of templates and their effects on polymerase activity were studied, wherein we found that NSP12 alone exhibited notable polymerase activity that increased significantly in the presence of NSP7 and NSP8. However, this activity was completely shut down, and the template RNA primer complex was detached from NSP12 when one of the to cofactors was present. Through computational analysis, we found that the template RNA entry channel was more constricted in the presence of one of the two cofactors, which was relatively more constricted in the presence of NSP8 compared to that in the presence of NSP7. In conclusion, we report that NSP7 and NSP8 together synergise to enhance the activity of NSP12, but antagonise when present alone. Our findings have implications for novel drug development, and compounds that obstruct the binding of NSP7 or NSP8 to NSP12 can have lethal effects on viral RNA replication.

COVID-19: From Emergence to Endemicity - A Comprehensive Review (preprint)

Severe Acute Respiratory Syndrome Coronavirus - 2 (SARS-CoV-2), later renamed Coronavirus Disease 2019 (COVID-19) was first identified in Wuhan, China, in early December 2019. Initially, the China office of the World Health Organization was informed of numerous cases of pneumonia of unidentified etiology in Wuhan, Hubei Province on December 31, 2019. This would subsequently result in a global pandemic with over 76 million confirmed cases of COVID-19 and 6.9 million deaths reported to the WHO. We have analyzed most of the data published since the beginning of the pandemic to compile this comprehensive review of SARS-CoV-2. We look at the core ideas, such as the etiology, epidemiology, pathogenesis, clinical symptoms, diagnostics, histopathologic findings, consequences, therapies, and vaccines. We have also included the long-term effects and myths associated with some therapeutics of COVID-19. This study comprehensively assesses of the SARS-CoV-2 virology, vaccines, medicines, and significant variants identified during the pandemic. Our review article is intended to provide medical practitioners with a better understanding of the fundamental sciences, clinical treatment, and prevention of COVID-19. As of June 2023, this paper contains the most recent data made accessible.

Characterizing the Emotion Carriers of COVID-19 Misinformation and Their Impact on Vaccination Outcomes in India and the United States (preprint)

The COVID-19 Infodemic had an unprecedented impact on health behaviors and outcomes at a global scale. While many studies have focused on a qualitative and quantitative understanding of misinformation, including sentiment analysis, there is a gap in understanding the emotion-carriers of misinformation and their differences across geographies. In this study, we characterized emotion carriers and their impact on vaccination rates in India and the United States. A manually labelled dataset was created from 2.3 million tweets and collated with three publicly available datasets (CoAID, AntiVax, CMU) to train deep learning models for misinformation classification. Misinformation labelled tweets were further analyzed for behavioral aspects by leveraging Plutchik Transformers to determine the emotion for each tweet. Time series analysis was conducted to study the impact of misinformation on spatial and temporal characteristics. Further, categorical classification was performed using transformer models to assign categories for the misinformation tweets. Word2Vec+BiLSTM was the best model for misinformation classification, with an F1-score of 0.92. The US had the highest proportion of misinformation tweets (58.02%), followed by the UK (10.38%) and India (7.33%). Disgust, anticipation, and anger were associated with an increased prevalence of misinformation tweets. Disgust was the predominant emotion associated with misinformation tweets in the US, while anticipation was the predominant emotion in India. For India, the misinformation rate exhibited a lead relationship with vaccination, while in the US it lagged behind vaccination. Our study deciphered that emotions acted as differential carriers of misinformation across geography and time. These carriers can be monitored to develop strategic interventions for countering misinformation, leading to improved public health.

An Efficient Artificial Intelligence based Human-Machine Interaction System

In today's computer systems, the mouse is an essential input device. Touch interfaces are high-contact planes that we use on a regular basis and frequently throughout the period. As a result, the input device gets infested with bacteria and pathogens. Despite the fact that wireless mouse have eliminated the bunch of tangled wires, there is still a desire to tap the gadget. In light of the epidemic, this proposed method employs a outlying webcam or an in-built image sensor to capture arm gestures and identify fingertip detection, allowing users to execute standard mouse activities such as left click, scrolling and other mouse activities. The algorithm is trained using machine learning with the use of image sensor and the fingers are identified efficiently. As a result, this reliance on corporeal devices to manage the computational system cancels out the requirement of man-machine interface. Thus the suggested approach will prevent the proliferation of Covid-19. © 2023 IEEE.

Self-reported Medication Adherence in Schizophrenia and Bipolar Disorder Patients during COVID-19 Pandemic in a COVID Care Hospital: A Cross-sectional Study

Introduction: The Coronavirus Disease 2019 (COVID-19) pandemic has made it difficult for patients with Schizophrenia and Bipolar Affective Disorder (BPAD) to receive ongoing care, which has led to non adherence to medication and undesirable health outcomes. Lower treatment adherence in severe mental illness might lead to symptom exacerbation and relapses and might cause a strain on the health system during COVID-19 pandemic. Aim: To assess the treatment adherence in Schizophrenia and BPAD patients during prevailing COVID-19 situation in India. Materials and Methods: This cross-sectional study was conducted among 92 patients with Schizophrenia and BPAD through a questionnaire between January to September 2021 in a COVID-19 care hospital Government Medical College Palakkad/District hospital Palakkad, Kerala, India. Demographic and clinical data, adherence to treatment, along with Brief Psychiatric Rating Scale (BPRS), Young Mania Rating Scale (YMRS), Hamilton Rating Scale for Depression (HAM-D). Clinical Global impression (CGI S&I), Work and Social Adjustment Scale (WSAS), Modified COVID Threat Scale (CTS) and Medication Adherence Rating Scale (MARS) were collected. The outcomes included adherence to medication, deterioration of the psychopathology, improvement in severity, social functioning. Descriptive statistics was used to define the sample characteristics and presented as mean and standard deviation and frequency and percentages. Spearman's Correlation coefficient was used to find the correlation between MARS and other variables: CTS, BPRS, YMRS, HAM-D, CGI and WSAS. Results: A total of 92 patients were interviewed which comprised of 43 patients with Schizophrenia and 49 patients with Bipolar disorder. As assessed by MARS rating scale 19 patients (20.65%) had a MARS score less than six suggesting poor adherence and 73 (79.35%) had MARS score 6 and greater suggesting better adherence to the treatment. This was supported by negative correlation with BPRS, YMRS, HAM-D and CGI-S and CGI-I scales which implies that the COVID-19 pandemic did not hinder the patients to take the medication. The mean MARS score was 7.31±2.11. A total of 37 (40.2%) patients had acute exacerbation of the illness during the COVID-19 related lockdown and 37 (40.2%) had exacerbation during the six months prior to COVID-19 lockdown. There was negative correlation between MARS scores and the CTS but was not statistically significant. Conclusion: Despite the COVID-19 pandemic related restriction, patients with Schizophrenia and BPAD were adherent to the medication and the number of relapses during COVID-19 pandemic was similar to the period before the pandemic. COVID-19 related anxiety didn't have an impact on medication adherence and relapse in this study. [ FROM AUTHOR] Copyright of Journal of Clinical & Diagnostic Research is the property of JCDR Research & Publications Private Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Social distancing causing crises: learning from workforce adaptation by e-tailers

PurposeThis study explores workforce related adaptation by e-tailers during social distancing causing crises (SDCC) and provides a conceptual framework.Design/methodology/approachA grounded theory based approach has been used wherein about 120 news articles were analyzed to understand the real-world measures taken. 50 journal papers were also referred to. A typical qualitative methodology, including open, and axial was used.FindingsIn the early stages, panic buying emerged as the key disrupting factor which necessitated staff shortage management. In the long term, e-tailers can adapt to prevent reverse worker migration and modify their hiring and training processes.Research limitations/implicationsThis study synthesizes knowledge on workforce-related adaptation by e-tailers and offers considerable potential for future research as well as the development of case studies and consulting services for the industry. Two research propositions are offered that can guide hypothesis generation and further studies can be conducted in sectors other than retail also.Practical implicationsThis study puts forward propositions based on theoretical dimensions for managers adapting to workforce-related problems during SDCC. The pandemic has led to vast unemployment and the shutting down of a number of businesses across the globe due to economic downfall. Hence, this study has economic and social implications.Originality/valueThis study is unique as it is one of the few that delves into e-tailers' workforce- related adaptation as SDCC evolves and contributes to a body of literature which is scarce.

Treatment of Novel Coronavirus (2019-nCoV) Using Hinokitiol (β-thujaplicin) Copper Chelate

Human Coronavirus (HCoV) or Novel Coronavirus (2019-nCoV) is probably a brand new version of coronavirus that belongs to Betacoronaviruses kind Human Coronaviruses, similar to the Severe Acute Respiratory Syndrome (SARS) coronavirus and Middle-East Respiratory Syndrome (MERS) coronavirus. China recorded the number one case of this virus in December 2019 at Wuhan, the capital town of Hubei province. By 27 March 2020, 10:00 CET, nearly 23,335 humans died out of 509,164 showed instances recorded throughout the world. By the give up of January 2020, China showed that the Novel Coronavirus (2019-nCoV) transmitted from one human to another. This studies pursuits to research a completely specific medicament called "Hinokitiol Copper Chelate” towards the large quantity 2019-nCoV Spike Glycoprotein with a unmarried receptor binding domain. This take a look at gives a super version for Hinokitiol Copper Chelate to be examined in silico towards 2019-nCoV Main Protease. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

Res-CovNet: an internet of medical health things driven COVID-19 framework using transfer learning.

Major countries are globally facing difficult situations due to this pandemic disease, COVID-19. There are high chances of getting false positives and false negatives identifying the COVID-19 symptoms through existing medical practices such as PCR (polymerase chain reaction) and RT-PCR (reverse transcription-polymerase chain reaction). It might lead to a community spread of the disease. The alternative of these tests can be CT (Computer Tomography) imaging or X-rays of the lungs to identify the patient with COVID-19 symptoms more accurately. Furthermore, by using feasible and usable technology to automate the identification of COVID-19, the facilities can be improved. This notion became the basic framework, Res-CovNet, of the implemented methodology, a hybrid methodology to bring different platforms into a single platform. This basic framework is incorporated into IoMT based framework, a web-based service to identify and classify various forms of pneumonia or COVID-19 utilizing chest X-ray images. For the front end, the.NET framework along with C# language was utilized, MongoDB was utilized for the storage aspect, Res-CovNet was utilized for the processing aspect. Deep learning combined with the notion forms a comprehensive implementation of the framework, Res-CovNet, to classify the COVID-19 affected patients from pneumonia-affected patients as both lung imaging looks similar to the naked eye. The implemented framework, Res-CovNet, developed with the technique, transfer learning in which ResNet-50 used as a pre-trained model and then extended with classification layers. The work implemented using the data of X-ray images collected from the various trustable sources that include cases such as normal, bacterial pneumonia, viral pneumonia, and COVID-19, with the overall size of the data is about 5856. The accuracy of the model implemented is about 98.4% in identifying COVID-19 against the normal cases. The accuracy of the model is about 96.2% in the case of identifying COVID-19 against all other cases, as mentioned.

Novel risk patterns of vasovagal reactions in NZ blood donations complicated by COVID-19 restrictions.

Introduction: Vasovagal reactions (VVRs) are common but complex donor adverse reactions (DAEs) in blood donations. VVRs have been extensively studied with a multitude of risk factors identified including young age, female gender and first-time donor status. How they may interplay remains obscure. Methods: A total of 1,984,116 blood donations and 27,952 immediate VVRs (iVVRs) and 1,365 delayed VVRs (dVVRs) reported between 2011 and 2021 in NZ were used in multivariate logistic regression analyses each concerning donations with iVVRs as cases and those free of DAEs as controls. For each analysis stepwise selection was used to identify the best model and risk factors carrying significant main effects and/or interactions. Identified interactions informed further in-depth regression analyses to dissect iVVR risk patterns. Results: Over 95% of VVRs were iVVRs that had lower female preponderance and deferrals than dVVRs. iVVRs had a school seasonal pattern in whole blood donations driven by first-time donors from schools/colleges, and interactions between gender and age group differentiating the first-time from repeat donations. Subsequent regression analyses identified the known and novel risk factors of year and mobile collection sites and their interactions. iVVR rates were roundly elevated in 2020 and 2021 probably because of COVID-19 restrictions like facemask wearing. Exclusion of the 2020 and 2021 data removed the interactions with year, but confirmed interactions of gender with mobile collection sites (p = 6.2e-07) in first-time donations only and with age group in repeat donations only (p < 2.2e-16), together indicating young female donors at the highest risk of iVVRs. Our results also revealed that donation policy changes contributed to the year effects; donors had a lower iVVR risk at mobile sites than well-medicalized donation centers probably because of under-reporting. Conclusion: Modeling statistical interactions is valuable in identifying odds and revealing novel iVVR risk patterns and insights into blood donations.

Cutaneous side effects of hydroxychloroquine in health care workers in a COVID referral hospital - implications for clinical practice.

The utility of hydroxychloroquine for the prophylaxis and treatment of alarmingly rising COVID-19 infection has been widely explored in several studies. However, its cutaneous adverse effects among health care workers and COVID patients taking prophylactic doses has not been reported. We report cases of palmoplantar among health care workers who were on prophylactic doses of hydroxychloroquine and their management with cetirizine and methylprednisolone.

Approaches to engage an aging, rural cohort in southern India during the COVID-19 crisis and the psychological impact of COVID-19 in this cohort.

INTRODUCTION: The COVID-19 pandemic produced an unprecedented crisis across the world. Long-term cohort studies were stalled, including our longitudinal aging cohort study in rural India. METHODS: We describe approaches undertaken to engage with our cohort (n = 1830) through multiple rounds of calls and how we provided useful services to our subjects during the lockdown period. Consenting subjects also underwent telephonic assessments for depression and anxiety using validated, self-report questionnaires. RESULTS: Subjects reported benefitting from our telephonic engagement strategies, including the COVID-related safety awareness and counselling service. The proportion of subjects with depression increased from 7.42% pre-COVID to 28.97% post-COVID. DISCUSSION: We envisage that such engagement strategies would improve subject rapport and cohort retention, and thus, could be adopted by similar cohort studies across the world. This marginalized, rural Indian community had severe, adverse psychological impact in this pandemic. Urgent public health measures are needed to mitigate this impact and develop appropriate preventive strategies. This article is protected by copyright. All rights reserved.

SwasthGarbh: A smartphone App for improving the quality of antenatal care and ameliorating maternal-fetal health.

The utility of telemedicine in healthcare has been brought to the forefront by the COVID-19 pandemic. 'SwasthGarbh' (Healthy Pregnancy) is a multi-functional, interactive smartphone application for providing antenatal care and real-time medical support to all pregnant women (especially those in rural areas and/or do not have easy access to doctors). A randomized controlled trial (n = 150) demonstrates its utility in improving the quality of antenatal care, reducing obstetric/medical complications and achieving a positive pregnancy experience. The test group (patients registered on the App) showed a significantly higher number of mean (± SD) antenatal visits (7.0 ± 1.5 vs. 5.7 ± 1.8; P<0.001) as well as better compliance with the WHO visit protocol (87.2% vs. 69.8%, P<0.001) and antenatal investigations (73.2% vs. 41.7%, P<0.001) in comparison to the control group (followed-up conventionally), respectively. Furthermore, substantial reduction in medical (38.0% vs. 55.5%, P = 0.04) and obstetric (52.1% vs. 59.7%, P = 0.36) complications during pregnancy as well as significant improvement in mean (± SD) maternal systolic BP (118.9 ± 11.8 vs. 123.4 ± 14.2 mmHg; P = 0.046), diastolic BP (76.0 ± 8.4 vs. 80.0 ± 10.9 mmHg; P = 0.02) and hemoglobin (11.5 ± 1.4 vs. 10.9 ± 1.4 g/dL; P = 0.03) parameters at delivery was observed in the test group compared to the controls, respectively. All the above mentioned positive clinical outcomes were the result of the provision of high quality antenatal care, timely detection of complications, prompt medical assistance and improved medication adherence. This is first pregnancy App that provides instantaneous access to doctor's advice and is clinically endorsed as well as credible.

Th17 overexpression in severe COVID-19: a prospective observational study.

INTRODUCTION: The objective of the study was to determine T-cell subtypes, Natural Killer cell activity and cytokines in COVID-19 patients with mild to moderate disease and compare them between patients who had recovered and those who had progressed to severe disease. METHODS: Peripheral blood samples of COVID-19 patients were collected at the time of hospital admission and after one week. These samples were analysed for interleukins (IL-6, IL-17a) using chemiluminescence ELISA. The T-cell subsets (T naïve, T regulatory, Th17, Th1, Th2, CD8+ T cells] were studied using flow cytometry. Mild, moderate and severe COVID-19 are defined as per CDC guidelines. RESULTS: Nineteen COVID-19-positive patients were enrolled between June 2020 to December 2021. Nine had mild COVID-19 and 10 had moderate COVID-19 at recruitment. All mild cases recovered without progression to severe disease, while five patients from the moderate group progressed to severe disease. Overall, there is a decrease in lymphocyte count in patients with moderate-severe disease, but the ratio of Th17 [5.91 (2.69-12.01)] was higher compared to Th1 [1.12 (0.27-3.13)] and Th2[2.34 (2-3.5)]. The high baseline level of IL-6 observed in patients with moderate disease leads to the proliferation of more Th17 type of CD4+ T-cells(p=0.002) and suppression of Treg cells. A higher Th17 subset leads to neutrophilic inflammation in patients with severe COVID-19. CONCLUSION: Interpretation conclusions: Higher baseline IL-6 leads to depletion of regulatory T-cells, Th1 Th2 CD4 cells. IL-6 leads to the proliferation of Th17 type of CD4+ subsets in moderate COVID-19. Higher Th17 cells in moderate COVID-19 patients lead to the production of IL-17a, which may result in intense neutrophilic inflammatory response and cytokine storm.