COVID-19 vaccine uptake and vaccine hesitancy in Indian patients with cancer: A questionnaire-based survey

Background: Patients with cancer are at a higher risk of severe forms of coronavirus disease 2019 (COVID-19) and mortality. Therefore, widespread COVID-19 vaccination is required to attain herd immunity. Objective(s): We aimed to evaluate the uptake of the COVID-19 vaccine in Indian patients with cancer and to collect information regarding vaccine hesitancy and factors that contributed to vaccine hesitancy. Material(s) and Method(s): This was a questionnaire-based survey conducted between May 7, 2021 and June 10, 2021 in patients aged 45 years and over, with solid tumors. The primary end points of the study were the proportion of Indian patients with cancer aged 45 years and older who had not received the COVID-19 vaccine, and the reasons why these patients had not received the COVID-19 vaccine. Our secondary end points were the proportion of patients with a history of COVID-19 infection, and the proportion of the patients who had vaccine hesitancy. Additionally, we attempted to assess the factors that could impact vaccine hesitancy. Result(s): A total of 435 patients were included in the study. Of these, 348 (80%) patients had not received even a single dose of the COVID-19 vaccine;66 (15.2%) patients had received the first dose, and 21 (4.8%) had received both the doses. Approximately half (47.1%) of the patients reported that they took the COVID-19 vaccine based on the advice from a doctor. The reasons for not taking the COVID-19 vaccine could be considered as vaccine hesitancy in 259 (77%) patients. The two most common reasons were fear in 124 (38%) patients (fear of side-effects and of the impact of the vaccine on the cancer/therapy) and lack of information in 87 (26.7%) patients. On the multivariate analysis, the two factors found to be significantly associated with vaccine hesitancy were a lower educational level (OR, 1.78;95% CI, 1-3.17;P = 0.048) and a lack of prior advice regarding the COVID-19 vaccine (OR, 2.80;95% CI, 1.73-4.53;P < 0.001). Conclusion(s): Vaccine hesitancy is present in over half of our patients, and the most common reasons are a fear of the vaccine impacting the cancer therapy, fear of side-effects, and lack of information. Widespread vaccination can only be attained if systematic programs for education and dissemination of information regarding the safety and efficacy of the COVID-19 vaccine are given as much importance as fortification of the vaccination supply and distribution system.Copyright © 2021 Cancer Research, Statistics, and Treatment Published by Wolters Kluwer - Medknow.

Comparison of Self-Reported to Automated General Surgery Case Logging by a Secure Web-Based Data Management Platform throughout the COVID-19 Pandemic

Introduction: A decrease in operative volume of general surgery chiefs graduating the first year of the COVID-19 pandemic (2020) was previously noted using self-reported Accreditation Council for Graduate Medical Education (ACGME) case logs. The purpose of this study is to examine if this trend was captured in self-reported case logs and if those trends were similarly captured in an automated multi-institution artificial intelligence-based case log. Method(s): The ACGME National Data Report of general surgery cases was queried for mean cases performed as surgeon chief for the pre-pandemic (2018-2019) and pandemic (2019-2020 and 2020- 2021) period. A 24-institute HIPAA-compliant, web-based, surgical education management platform using an embedded artificial intelligence algorithm to generate case logs from electronic operative schedules was also queried. Percent change was calculated and statistical significance was calculated with unpaired T-Test. Result(s): Fifty-three ACGME categories were reviewed. A significant (p<0.05) decrease occurred in 19 categories (35.8%) the first pandemic year compared with pre-pandemic. The second pandemic year (2020-2021) 10 categories (18.8%) had a significant increase (p<0.05). The automated case log system did not see the same trend with only 2.7% of categories (9/324) with a significant decrease the first pandemic year. No subsequent significant increases occurred the second pandemic year. Conclusion(s): ACGME case logs reveal a recovery of operative volume for general surgery chiefs during the second year of the pandemic. However, the 24-institution, automatically logged system did not see the same trend. Regional variation or improved accuracy of automated case logs may explain the discrepancy.

Satisfaction of Patients Other Than COVID-19 During the Pandemic at a Multidisciplinary Centre with COVID-19 Services

Background: The services rendered by hospitals during the pandemic may not be efficient. This might impact the satisfaction of patients seeking healthcare. The aim of this study is to assess the satisfaction level of patients other than those with COVID-19 during the pandemic with different services provided by the hospital. Method: A quantitative, analytical and cross-sectional study was carried out in a multidisciplinary hospital. Valid questionnaire, derived from PSQ III and PSQ 18, was used for data collection from 250 outpatients. Ethical approval was obtained. Systematic random sampling was done to enrol patients into the study after taking their consent. Descriptive analysis was performed using frequency, proportion, median and inter-quartile range. Mann–Whitney U test and Kruskal–Wallis test were carried out to find the association between overall satisfaction and different socio-demographic and other variables. Statistical significance was set at p-value < 0.05. Result: Almost two-thirds of the respondents visiting the hospital during the pandemic were female (male: 35.6% and female: 64.4%). More than half (50.4%) of the patients reported that access to the hospital was feasible. Of the patients reporting dissatisfaction, most of them (86.4%) considered the establishment of separate COVID-19 hospitals as the best option. The median satisfaction score for the overall satisfaction of patients towards different service domains was 54.0 (45–60). Almost all respondents (95.6%) found that services were easily available. Patient satisfaction score was significantly associated with expenditure (p < 0.001). Satisfaction score was also significantly associated with the time spent in the hospital by the patients (p < 0.001). Conclusion: Majority of the patients reporting to the multidisciplinary hospital were satisfied with the provisioning of treatment and different services during the COVID-19 pandemic. Relatively lesser satisfaction was reported for the provision of maintenance of social distance, availability of hand washing/sanitisation, overall hospital cleanliness and cost of treatment. Moreover, satisfaction among patients was associated with their perceived fear of the pandemic. © 2023 Indian Institute of Health Management Research.

Forecasting COVID-19 Cases Using n-SARS-CoV-2 Variants

This work proposes a novel Deep Learning-based model to forecast the total number of confirmed COVID-19 cases in four of the worst-hit states of India. Along with statewide restrictions and public holidays, a novel parameter is introduced for training the proposed model, which considers the Alpha, Beta, Delta, and Omicron variants and the degree of their prevalence in each of the four states. Recurrent Neural Network-based Long-Short Term Memory is applied to the custom dataset, with the lowest Mean Absolute Percentage Error being 0.77% for the state of Maharashtra. SHapley Additive exPlanations values are used to examine the significance of the various parameters. The proposed model can be applied to other countries and can include newer variants of the novel coronavirus discovered in the future. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Significance of Face Mask detection

The new coronavirus, initially detected in Wuhan, China, has spread worldwide, wreaking devastation. The speed with which it spreads and the severity of the epidemic have made this a global emergency. COVID-19 is a very concerning disease from a public health perspective, making it critical to take precautions against its transmission, such as limiting close personal contact and using protective gear. The primary objective of this research is to create a system that can recognise human face masks and determine whether individuals are attempting to maintain social distance. Alterations to everyone's daily routines. In such stages, everyone must always keep their identity concealed. Because the massive number of populations has changed since the Outbreak of the Coronavirus pandemic, finding people who are not wearing veils is a challenge. The global spread of COVID-19 has altered society. Many of us are staying in our homes, avoiding contact with city dwellers, and adjusting our routines-such as when and where we go to work or school-in ways we never would have imagined. We need updated timetables as we transition away from outmoded procedures. What stands out the most is the widespread practise of hiding one's face behind a veil or other kind of covering whenever we enter a public building. Wearing a veil or covering one's face may provide some comfort while also preventing the spread of the COVID-19 virus. By preventing anybody, even the unwitting carriers, from spreading the virus, widespread usage of protective clothing has the potential to significantly reduce the rate of disease spread in a given area. Thus, the importance of the veil and its identification are made very plain. There has been a rise in the importance of face recognition frameworks, which are especially useful in hospitals and medical clinics where privacy of patients is a concern. They're also vital in places like airports, sports stadiums, warehouses, and other such crowded areas where heavy foot traffic necessitates strict security measures to ensure everyone's safety. The framework of face veil recognition can ensure our safety and the safety of those around us. This assignment may serve as a digitally administered test anywhere from a classroom to a hospital to a bank to an airport terminal. Through the use of photo processing and extensive learning, we are able to recognise human faces and separate them into two groups, those with and without head coverings. The assignment will let a person who is responsible for screening people to do so even if they are located at a remote location, while still being able to effectively screen and provide guidance. Open CV, Tensor Flow, and Keras are some of the Python libraries used. With Deep Learning, As part of their model preparation, these activities make use of Convolution Neural Networks, a subset of Deep Neural Networks. © 2022 IEEE.

Chemi-Informatic Approach to Investigate Putative Pharmacoactive Agents of Plant Origin to Eradicate COVID-19

Background: The scientific community has supported the medicinal flora of ancient as well as modern times in extracting chemicals, which holds therapeutic potential. In many previous studies, Amentoflavone discovered as an anti-viral agent, and it is present as a bioactive constituent in many plants of different families like Selaginellaceae, Euphorbiaceae, and Calophyllaceae. Withania somnifera (Ashwagandha) is already considered a significant anti-viral agent in traditional medicine, and it is the main source of Somniferine-A and Withanolide-B. Objective(s): In this study, phytochemicals such as withanolide-b, somniferine-a, stigmasterol, amentoflavone, and chavicine were analyzed to screen protein inhibitors, out of them;such proteins are involved in the internalization and interaction of SARS-CoV-2 with human cytological domains. This will help in developing a checkpoint for SARS-CoV-2 internalization. Method(s): Chemi-informatic tools like basic local alignment search tool (BLAST), AutoDock-vina, SwissADME, MDWeb, Molsoft, ProTox-II, and LigPlot were used to examine the action of pharmacoactive agents against SARS-CoV-2. The tools used in the study were based on the finest algorithms like artificial neural networking, machine learning, and artificial intelligence. Result(s): On the basis of binding energies less than equal to-8.5 kcal/mol, amentoflavone, stigmasterol, and somniferine-A were found to be the most effective against COVID-19 disease as these chemical agents exhibit hydrogen bond interactions and competitively inhibit major proteins (SARS-CoV-2 Spike, Human ACE-2 receptor, Human Furin protease, SARS-CoV-2 RNA binding protein) that are involved in its infection and pathogenesis. Simulation analysis provides more validity to the selection of the drug candidate Amentoflavone. ADMET properties were found to be in the feasible range for putative drug candidates. Conclusion(s): Computational analysis was successfully used for searching pharmacoactive phytochemicals like Amentoflavone, Somniferine-A, and Stigmasterol that can bring control over COVID-19 expansion. This new methodology was found to be efficient, as it reduces monetary expenditures and time consumption. Molecular wet-lab validations will provide approval for finalizing our selected drug model for controlling the COVID-19 pandemic.Copyright © 2022 Bentham Science Publishers.

Post-COVID pulmonary rehabilitation at primary hospital in Nepal: Preliminary experience from LMIC

Introduction: Nepal is low and middle income country (LMIC) in South Asia with over 11 million SARS-CoV-2 infections reported till February 2022. Decline in lung function with poor functional status have been observed in substantial proportion among post-COVID patients in Nepal. Aims and objectives: The aim of this study was to assess benefits of pulmonary rehabilitation in post-COVID patients at Methinkot hospital, a primary level hospital in Nepal where specialized health services and rehabilitation facilities are not available. Method(s): A cross-sectional study was conducted in December 2021 at Methinkot hospital. Post-COVID patients with persistent symptoms were included. A structured pulmonary rehabilitation program comprising of breathing exercise, airway clearance technique, flexibility and endurance exercise was designed and was delivered by a visiting pulmonary rehabilitation specialist from tertiary hospital. Patients were instructed for thrice weekly home based rehabilitation for four weeks and were assessed at the end of four weeks. Result(s): Forty-two patients were included. Mean age of patients was 38.33+/-10.7 years. There were 17 (40.5 %) male. Shortness of breath on exertion (71.4%), fatigue (52.4%) and chest discomfort (35.7%) were the most common reported symptoms. Upon completion of four-week pulmonary rehabilitation, 34(81%) patients reported symptomatic improvement while 30(71.4%) reported rehabilitation measure to be beneficial. Conclusion(s): Early pulmonary rehabilitation service could be highly beneficial in reducing post-COVID morbidities. Establishment and continuation of sustainable and affordable rehabilitation service is challenging in resource limited setting.

LiMS-Net: A Lightweight Multi-Scale CNN for COVID-19 Detection from Chest CT Scans

Recent years have witnessed a rise in employing deep learning methods, especially convolutional neural networks (CNNs) for detection of COVID-19 cases using chest CT scans. Most of the state-of-the-art models demand a huge amount of parameters which often suffer from overfitting in the presence of limited training samples such as chest CT data and thereby, reducing the detection performance. To handle these issues, in this paper, a lightweight multi-scale CNN called LiMS-Net is proposed. The LiMS-Net contains two feature learning blocks where, in each block, filters of different sizes are applied in parallel to derive multi-scale features from the suspicious regions and an additional filter is subsequently employed to capture discriminant features. The model has only 2.53M parameters and therefore, requires low computational cost and memory space when compared to pretrained CNN architectures. Comprehensive experiments are carried out using a publicly available COVID-19 CT dataset and the results demonstrate that the proposed model achieves higher performance than many pretrained CNN models and state-of-the-art methods even in the presence of limited CT data. Our model achieves an accuracy of 92.11% and an F1-score of 92.59% for detection of COVID-19 from CT scans. Further, the results on a relatively larger CT dataset indicate the effectiveness of the proposed model. © 2023 Association for Computing Machinery.

Decoding employee experiences during pandemic through online employee reviews: insights to organizations

Purpose: The purpose of this paper is to unearth various dimensions of employee experience (EX) and explore how pandemic impacted various EX factors using online employee reviews. The authors identify employee-discussed EX-factors and quantify the associated sentiments and importance. Design/methodology/approach: This paper employs Latent Dirichlet Allocation on the online employee reviews to identify the key EX-factors. The authors probe sentiments and importance associated with key EX-factors using sentiment analysis, importance analysis, regression analysis and dominance analysis. Findings: The result of topic modeling identifies 20 EX-factors that shape overall EX. While skill development plays a major role in shaping overall EX, employees perceived Salary and Growth as the most important EX-factor and expressed negative sentiments during the pandemic. Employee sentiments significantly influence overall EX. Practical implications: When employees have extensive change experience, managers should consider various facets of EX to manage the smooth change and deliver a better EX. This research offers key EX-factors to be considered by managers while dealing with employees. Online employee reviews websites are recommended to include the identified key EX-factors to comprehend the holistic EX. Originality/value: This study contributes to the growing literature on the employee experience as a concept by identifying various EX-factors. The authors expand the extant EX scales by identifying an inclusive and updated set of EX-factors. © 2023, Emerald Publishing Limited.

Online learning in the face of covid-19 pandemic: Assessment of students' satisfaction at chitwan medical college of nepal

Background Online learning can play a vital role in the process of teaching and learning during Corona Virus Disease 2019 (COVID-19) pandemic. However, learners' satisfaction is extremely important in effective implementation of the online learning, especially at institutions where it is newly adopted. Objective To assess satisfaction towards online learning and its predictors among students at Chitwan Medical College, Bharatpur. Method A web-based cross-sectional survey was undertaken among 434 undergraduate and postgraduate students from various academic programs who had participated in the online classes started during this COVID-19 pandemic. A structured questionnaire consisting of 31 items (5-point Likert scale) covering four major student satisfaction domains (learners' dimensions, technological characteristics, instructors' characteristics and course management and coordination) was distributed to the students using Google Form. Result More than half (53.5%) of the students were satisfied with the online learning, while 29.7% gave neutral views. Bivariate analyses found that all four domains scores were positively correlated with each other as well as with the students' overall satisfaction towards learning. In multivariate analysis, female gender [aOR: 2.72, p = 0.013], WiFi as internet modality for learning [aOR: 3.36, p = 0.001) and learners' dimension score [aOR: 1.27, p<0.001] were the significant predictors of students' satisfaction. Conclusion Although recently adopted, the satisfaction of the students towards online classes appears good, and prioritizing the identified predictors and working on the weak links could assist in enhancing students' satisfaction and better outcomes. Copyright © 2020, Kathmandu University. All rights reserved.

Case report: Fatal COVID-19 in a neonate after probable late intrauterine transmission.

We report the case of a 35-week gestation infant girl born by emergent cesarean section for fetal distress in a woman with recent coronavirus disease 2019 (COVID-19). Tests for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using polymerase chain reaction (PCR) on the infant at 24 and 48 hours of life were negative. However, at 72 hours of life, the infant's respiratory status worsened, and a repeat SARS-CoV-2 PCR was positive. The infant developed leukopenia, thrombocytopenia, and progressive respiratory failure, and died on the ninth day of life. Pathologic examination of the placenta revealed findings consistent with COVID-19 placentitis, and SARS-CoV-2 RNA staining was positive, suggesting intrauterine transmission of the infection.

Caller Concerns and Mental Distress during COVID-19

The COVID-19 pandemic unleashed a range of mental health and psychosocial concerns across the globe, necessitating a response. Given the mobility restrictions during the pandemic, helplines emerged as an accessible and effective measure to address these concerns. The present paper analyses call documentation of the first 500 calls received during the pandemic by iCALL, a national level helpline from India. Findings revealed that 70.4 percent of callers were male and 36 percent were in the age group of 18-30 years. A significantly higher number of calls were made by men for practical concerns, and by women for concerns related to prior history of psychiatric diagnosis, suicidality, and relationship issues. Case narratives highlighted the psychosocial stressors and the resulting mental distress experienced by communities in general and vulnerable groups such as migrants, daily wage earners, women survivors of violence, LGBTQ individuals, and individuals with disability in particular. The study findings provide useful insights into the mental health impact of COVID-19 pandemic, psychosocial needs of the communities, and in turn, informing strategies and interventions to address the same. © 2022 Tata Institute of Social Sciences. All rights reserved.

Lab-on-PCB-Based Electrical Immunosensing Platform for Point-of-Care Detection of SARS-CoV-2

The present work shows a multiplexed lab-on-printed circuit board (PCB) platform for label-free immunosensing of SARS-CoV-2 nucleocapsid and spike antigens based on electrical impedance measurements. The sensor consists of an interdigitated electrode of soft gold integrated on a PCB with microwells for sample loading. A mercaptoundecanoic acid-protein-G-based site-specific biofunctionalization strategy is employed to efficiently immobilize dual antibodies on the device surface toward the sensitive and rapid antigen test of SARS-CoV-2. Electrical impedance measurements carried out in a point-of-care setting using the PalmSens Sensit Smart system that could detect nucleocapsid and spike proteins with a detection limit of 40 and 20 pg each. Experiments with nasopharyngeal swab samples from N = 5 healthy and N = 14 SARS-CoV-2 positive subjects showed significantly different electrical responses for subjects with high viral load (Ct < 25) compared with healthy subjects and control.

Online based Viva Voce Control System

Viva voce is an assessment method carried out by academic staff to assess the knowledge capacity of candidates. The assessment is usually held physically. With the Covid-19 pandemic, the whole process has been shifted onto an online context. There are several difficulties that come across when conducting online, such as marking of answers, guaranteeing the honesty of the candidate, and the manageability of the whole viva session. This research paper discusses the solution to the problem of conducting and managing online viva voce assessments. The proposed solution consists of mechanisms such as, a sandboxed environment to isolate the application, an advanced authenticating system to identify the intended candidate, a comprehensive monitoring system to monitor the candidate during the assessment, an answer validating system to provide a percentage mark to the answers provided by the candidate against a set marking scheme and finally a process to coordinate the viva voce session. © 2022 IEEE.

Negative Urine Legionella Antigen Does Not Rule out Legionella Pneumonia: A Case Report

INTRODUCTION: Legionella is an important cause of community acquired pneumonia. Here we describe a case with strong clinical suspicion of Legionella pneumonia despite negative urine antigen but confirmed by polymerase chain reaction (PCR) test on a lower respiratory sample. DESCRIPTION: A 52-year-old male, nonsmoker with unremarkable past medical history presented with a 4-day history of fever, nonproductive cough, and malaise, after returning from a trip to Italy. He underwent Computed Tomography Angiography (CTA) chest which revealed bilateral lung infiltrates at Urgent Care. Both Rapid Ag and PCR test for SARS CoV-2 were negative. He was vaccinated against SARS-CoV2. On hospital admission his oxygen saturation was 85% on room air. Lab work revealed white blood cell (WBC) 22.5, Hemoglobin 12.8, Platelets 397 with 96% neutrophils. His Sodium was 135 mmol/L (135- 146), CRP 433.2 mg/L (normal < 5). Respiratory PCR was negative for Influenza A, B, RSV. Urine Pneumococcal and Legionella Ag were negative. He was started on Ceftriaxone and Azithromycin. He developed rapidly progressing respiratory failure leading to intubation, prone positioning, inhaled Prostacyclin due to significant hypoxia (P/F ratio 57). His antibiotics were changed to high dose Levofloxacin (750 mg IV Q Day) because of strong suspicion of Legionella. He underwent bronchoscopy with BAL and the PCR came back positive for Legionella. The patient was extubated in 48 hours and discharged home after a 10-day course of Levaquin. The BAL sample was sent to Centers for Disease Control and Prevention (CDC) which identified Legionella species as serotype 1. DISCUSSION: Legionella are gram negative facultative intracellular bacteria with soil and water as reservoirs. Legionella grows poorly on routine culture media. Urine antigen (Ag) testing has a sensitivity of 75% and detects Legionella pneumophilia serotype 1, the dominant cause (80% of cases) but does not detect the other 30 Legionella species that have been isolated from humans. A lower respiratory PCR detects other serotypes and perhaps is more sensitive than urine Ag in detecting serotype 1. If clinical suspicion of Legionella is high PCR must be performed on a lower respiratory sample and one must not solely rely on a negative Urinary Ag test.

Osmotic Demyelinating Syndrome and COVID-19

Introduction: Osmotic demyelination syndrome (ODS) is a dreaded complication of rapid sodium correction in high-risk hyponatremic patients. Predisposing factors include chronic alcoholism, malnourishment, severe hyponatremia. SARS-Cov2 infection may also be a risk factor as it is linked with multiple patterns of brain injury, renal damage and hyponatremia. Case Description: Patient is a 48-year-old female with history of alcohol use disorder who presented with malaise, vomiting, diarrhea for 3 days. On admission, the patient was stuporous and confused. She was clinically hypovolemic. Initial labs demonstrated severe hyponatremia (102 mmol/L), hypokalemia (2.2 mmol/L), HCO3 of 35mmol/L, lactic acid of 4 mmol/L, no EtOH, preserved GFR. SARS-CoV2 PCR was positive. She was not hypoxic, her chest X-ray was clear. The patient was resuscitated with 1L of isotonic saline, potassium correction was attempted. Her bloodwork 4 hours later showed Na of 113 mmol/L and K of 2.4 mmol/L. At this point patient had prominent diuresis, UNa was 13mmol/L, Uosm 175mOsm/kg and U spec gravity 1.006. Immediately DDAVP and D5W were started. She had a poor response to this therapy and her sodium continued raising even at maximal doses. At 24h her sodium was 118 mmol/L and at 48h it was 125mmol/L with stabilization at this level. She had clinical improvement and was more responsive on day 3. On the following days, sodium gradually drifted toward 132 mmol/L. On day 5 she developing worsening mental status. She was found poorly responsive with fixed gaze, aphasia, minimally removing extremities from pain, able to blink when asked. Brain MRI revealed signal abnormalities in the central pons, bilateral thalami, caudate, basal ganglia, subinsular regions consistent with ODS. Intensive treatment was restarted with D5W and DDAVP. Na of 124mmol/L was achieved at 24h. Over the course of the following days, she had partial recovery. She was discharged to rehab, able to smile, move her head and partially move her extremities. Discussion(s): SARS-Cov2 causes hyponatremia through several mechanisms. Poor oral intake, gastrointestinal loses, kidney injury and SIADH have been described. All of them may occur at the same time and cause hypovolemic/euvolemic states with high ADH. Volume replacement rapidly shuts off the ADH drive predisposing patients to get sodium overcorrection.

The Mosaic of Autoimmunity

Introduction: Lupus nephritis (LN) is one of the most common manifestations affecting 45% of patients with systemic lupus erythematosus (SLE). Here we present a case of rapid progression of LN in the setting of recent COVID-19 infection, suggesting a possible synergistic cascade of cytokines contributing to rapid disease flare up. Case Description: 52-year-old hispanic lady with past medical history of hypertension and newly diagnosed SLE presented to the clinic with chief complaint of generalized anasarca, fatigue and low back ache. She was found to have a hemoglobin of 8.8 along with severe leukopenia. Urinalysis was positive for large amount of blood, protein with a protein: creatinine ratio of 9 gm. ANA titer was positive along with low levels of C3 complement and normal levels of C4 complement. Creatinine of 2.3 which was 4 times higher than her baseline. Labs from 2 months ago showed creatinine of 0.57. Of note, the patient was diagnosed with COVID-19 a month ago. She had a renal biopsy and was diagnosed with stage IV LN and was started on dialysis. Discussion(s): LN usually has an indolent course with people developing ESRD within 5 years of diagnosis of lupus. This case strikes out as a rapid progression of LN with progression to ESRD within less than 3 months of diagnosis of SLE. COVID-19 a few months before she was diagnosed with Lupus is a possible source of a cytokine storm. Suggested mechanisms of induction of autoimmunity include both molecular mimicry as well as bystander activation whereby the infection may lead to activation of antigen presenting cells that may in turn activate pre-primed auto-reactive T-cells, thus leading to pro-inflammatory mediators, which in turn may lead to tissue damage. Strategies to prevent rapid progression to ESRD in these patients needs to be studied and better understood. Perhaps patients with autoimmune conditions like SLE need more robust management of diseases like COVID-19 which is known to alter and activate the immunological cascade. As per recent literature exaggerated extrafollicular B cell response characteristic of active SLE also characterizes the B cell response to COVID-19. Understanding and targeting the B cell pathway could potentially help dampen a severe response and disease progression. Overlap including racial preponderance of disease severity also needs to be studied further.

A comparative cross-sectional survey among medical and engineering students about awareness and preventive practices during lockdown for COVID-19 pandemic

Background: The WHO declared COVID-19 infection as a pandemic in March 2020 due to high infectivity and wide information is freely available regarding precautionary measures. Every sector of the society must follow the preventive steps in the absence of proper drugs or vaccines. There is a need to assess the awareness level and precautionary measures adopted by the different sectors of the society. Hence, we planned this comparative study among students of health and engineering professionals. Methodology: This cross-sectional survey was conducted among medical and engineering students from one of the respective college of Wardha district (July-September 2020). Second class randomly selected and all students, i.e., 200 from each faculty were involved. Questionnaire was prepared by referring guidelines of the World Health Organization and Ministry of Health and Family Welfare for preventive measures toward COVID-19 pandemic, shared by internet and Chi-square test applied. Results: One hundred and seventy-eight medical and 176 engineering students responded (response rate-89%). Significantly high numbers of medical students (84%) were well aware with various modes of infection spread than engineering (40%-68%) (P = 0.0001). Seventy-four percent engineering as compared to 53% medical students recognized >3 feet as safe social distance (P = 0.01) and sincerely maintained by engineering students (69.32%) more often than medical (55.6%) (P = 0.01);whereas wearing mask for outdoor activity was practiced more frequently by medical (76.97%) than engineering (52.84%) (P = 0.0001). About 29% both group students never moved out from home. 50% medical and 37% engineering students were moved out occasionally before survey and mostly for purchasing daily needs. Conclusion: Overall, there is gap in complete chain of knowledge about novel coronavirus among both the groups. There is a significant difference in awareness and practices adopted by both professional students toward COVID-19 pandemic. High awareness and safe practices may be expected for medical background, but contrast findings for some markers were observed. The mode of IEC should be robust, more modified, or innovative. © 2022 Journal of Datta Meghe Institute of Medical Sciences University ;Published by Wolters Kluwer-Medknow.

GDenseMNet: Global Dense Multiscale Feature Learning Network for Efficient COVID-19 Detection in CT Images

Accurate and rapid diagnosis of COVID-19 is crucial for curbing its fast spread across the globe, with constant mutations leading to newer variants. Recent studies have exhibited that chest CT scans manifest clear radiological findings for the COVID-19 infected patients. Convolutional neural networks (CNN) have been used considerably for COVID-19 diagnosis;however, most CNN architectures demand a huge amount of parameters, resulting in overfitting on limited training data and a slower inference. Further, residual and densely connected neural networks such as ResNet and DenseNet have been proven to strengthen feature extraction and feature propagation but fail to fully discover both local and global representations. Moreover, few linearly stacked networks fall short in capturing and preserving multiscaled features from various receptive fields. This paper proposes a new CNN architecture called global dense multiscale feature learning network (GDenseMNet) for COVID-19 detection from CT images that effectively incorporates global dense connections while capturing multiscaled features. The GDenseMNet model comprises multiscale local feature extraction (MLF) blocks that capture local features of various size receptive fields using multiple filters and residual skip connections. The global dense connections between these blocks further enable global feature learning capability. The proposed architecture is lightweight, end-to-end learnable, and validated using the SARS-CoV-2 CT-Scan dataset. Experimental results demonstrate that the GDenseMNet model achieves promising detection performance compared to state-of-the-art CNN approaches and hence, it can be utilized as an effective tool real-time COVID-19 diagnosis. © 2022 IEEE.