Analysing the distribution of SARS-CoV-2 infections in schools: integrating model predictions with real world observations (preprint)

School closures were used as strategies to mitigate transmission in the COVID-19 pandemic. Understanding the nature of SARS-CoV-2 outbreaks and the distribution of infections in classrooms could help inform targeted or precision preventive measures and outbreak management in schools, in response to future pandemics. In this work, we derive an analytical model of Probability Density Function (PDF) of SARS-CoV-2 secondary infections and compare the model with infection data from all public schools in Ontario, Canada between September-December, 2021. The model accounts for major sources of variability in airborne transmission like viral load and dose-response (i.e., the human bodys response to pathogen exposure), air change rate, room dimension, and classroom occupancy. Comparisons between reported cases and the modeled PDF demonstrated the intrinsic overdispersed nature of the real-world and modeled distributions, but uncovered deviations stemming from an assumption of homogeneous spread within a classroom. The inclusion of near-field transmission effects resolved the discrepancy with improved quantitative agreement between the data and modeled distributions. This study provides a practical tool for predicting the size of outbreaks from one index infection, in closed spaces such as schools, and could be applied to inform more focused mitigation measures. Author summaryAt the start of the COVID-19 pandemic, there was huge uncertainty around the risks of SARS-CoV-2 spread in classrooms. In the absence of early predictions surrounding classroom risks, many jurisdictions across countries closed in-person education. There is great interest in adopting a more precision approach to better inform future interventions in the context of airborne virus risks. For this purpose, we need tools that can predict the probability of the size of outbreaks within classrooms along with the impact of interventions including masks, better ventilation, and physical distancing by limiting the number of students per classroom. To this end, we have developed a robust but practical model that yields the probability of secondary infections stemming from index cases occurring within schools on a given day. During model development, the major underlying physical and biological factors that dictate the disease transmission process, both at long-range and close-range, have been accounted for. This enables our model to modify its predictions for different scenarios - and possibly allows its use beyond schools. Finally, the models predictive capability has been verified by comparing its outputs with publicly available data on SARS-CoV-2 diagnoses in Ontario public schools. To our knowledge, this is the first time an analytical model derived from mostly first principles describes real-world infection distributions, satisfactorily. The quantitative match between the theoretical prediction and real-world data offers the proposed model as a possible powerful tool for better-informed precision pandemic mitigation strategies in indoor environments like schools.

COVID-19 Associated Mucormycosis.

Mucormycosis is a life-threatening opportunistic fungal infection seen in immunocompromised states. Rising incidence of mucormycosis among Coronavirus Disease-2019 infected individuals is an increasing concern in India. The disease which was endemic has blown out to become an epidemic. The purpose of this research is to study the epidemiology, management and outcome of Coronavirus Disease-2019 Associated Mucormycosis (CAM) cases. Additionally, the role of diabetes and steroids in the causation of CAM was determined. A hospital-based observational study was conducted at a tertiary care centre involving cases with rhino-orbital mucormycosis with recent history of COVID-19 infection. Out of 205,166(81%) cases had Diabetes Mellitus as a comorbid condition. Among them, 75(36.6%) cases were diagnosed with diabetes during COVID-19 treatment. 161/205(78.5%) cases received corticosteroids during COVID-19 treatment. Corticosteroids were notindicated in 43(26.7%) cases. 177/205(85.4%) cases were alive at the end of 12 weeks. 8 out of 10 deaths were seen in cases having diabetes. As the incidence of mucormycosis is increasing, better awareness among general population about the disease, early diagnosis and multidisciplinary approach is required to improve prognosis.

Pandemic within a pandemic! Policy Implications of community-based Interventions to mitigate violence against women during COVID-19 in Urban Slums of Lucknow, India.

Background: The COVID-19 pandemic has brought an unprecedented adverse impact on women's health. Evidence from the literature suggests that violence against women has increased multifold. Gender-based violence in urban slums has worsened due to a lack of water and sanitation services, overcrowding, deteriorating conditions and a lack of institutional frameworks to address gender inequities. Methods: The SAMBHAV (Synchronized Action for Marginalized to Improve Behaviors and Vulnerabilities) initiative was launched between June 2020 to December 2020 by collaborating with the Uttar Pradesh state government, UNICEF and UNDP. The program intended to reach 6000 families in 30 UPS (Urban Poor settlements) of 13 city wards. These 30 UPS were divided into 5 clusters. The survey was conducted in 760 households, 397 taken from randomly selected 15 interventions and 363 households from 15 control UPS. This paper utilized data from a baseline assessment of gender and decision-making from a household survey conducted in the selected UPS during July 03-15, 2020. A sample size of 360 completed interviews was calculated for intervention and control areas to measure changes attributable to the SAMBHAV intervention in the behaviours and service utilization (pre- and post-intervention). Results: The data analysis showed a significant difference (p-value < 0.001) between respondents regarding women's freedom to move alone in the control and intervention area. It also reflected a significant difference between control and intervention areas as the respondents in the intervention area chose to work for the cause of gender-based violence. Conclusion: The SAMBHAV initiative brought an intersectional lens to gender issues. The community volunteers were trained to approach issues based on gender-based violence with the local public, and various conferences and meetings were organized to sensitize the community. The initiative's overall impact was that it built momentum around the issue of applying the concept of intersectionality for gender issues and building resilience in the community. There is still a need to bring multi-layered and more aggressive approaches to reduce the prevalence of gender-based violence in the community.

Significance of 3D printing for a sustainable environment

The additive manufacturing, also known as 3D-printing, allows for a complete control over the entire manufacturing process that can be tuned to any application. In recent years, it has been tested for its role in various areas like environmental contaminant monitoring, providing solutions for the energy generation and storage and healthcare. To encourage sustainable detection platforms for the pollutants, the sensing electrodes have been reported to be 3D printed because of enhanced electrochemical properties coming from the high surface area of the printed materials. In general, the conventional methods of electrode preparation are time consuming, expensive, and mostly not adaptable. 3D printing however, negates all these challenges. Similarly, in the energy generation and storage field, the rapid and lightweight materials used during 3D printing make them viable and suitable alternative. Since 3D printing is a bottom-up method for the fabrication, the amount of raw material consumed can be tuned and the by-products or wastage minimized. Apart from these demanding areas, additive manufacturing, in last 2 years which witnessed epidemic outspread, has supported health sector immensely in fight against COVID. 3D printing allowed the rapid manufacturing of COVID-19 detection kits and helped maintain the COVID-19 safeguards in place. Sample collection swabs, respirators and other components of the PPE kits were among many products developed using 3D printing during the pandemic. Keeping these things in mind, this review encapsulates the use of 3 D printing for energy application, detection of water and biological contaminants and as safeguard tool during covid pandemic.

Prevalence of coronary artery disease among COVID-19 patients: a systematic review and meta-analysis (preprint)

Background Throughout the surge of the COVID-19 pandemic high rate of chronic diseases have been reported, including respiratory diseases and cardiovascular diseases. The prevalence of coronary artery disease has remained high throughout the COVID-19 pandemic, which also draws great concern towards it. This study seeks to provide a pooled estimate of the burden of coronary artery disease in COVID-19. Objective To estimate the overall prevalence of coronary artery disease among COVID-19 patients. Data Sources In this systematic review and meta-analysis, an extensive literature search was conducted in PubMed, Scopus, Embase, EBSCO ,Web of Science, Cochrane,Proquest and preprint servers (medRxiv, arXiv, bioRxiv, BioRN, ChiRxiv, ChiRN, and SSRN). References fo eligible articles, forward citation tracking, and expert opinion were used to identify other relevant articles. All published articles until 13 April 2023 were assessed as per the PROSPERO registration protocol (CRD42022367501). Study Selection, Data Extraction, and Synthesis Primary studies that reported coronary artery disease among COVID-19 patients were included. The characteristics of the study and information on the number of cases of coronary artery disease were extracted from the included studies. Individual study estimates were pooled using the random intercept logistic regression model. The heterogeneity between the selected studies was assessed using the I2 statistic, tau, tau-squared, Cochrans Q. Prediction interval was used to identify the range into which future studies are expected to fall. Subgroup analysis based on geography (continent) was done to reduce heterogeneity. Publication bias was analyzed using doi plot and LFK index. The risk of bias in the studies was assessed as per the tools proposed by the National Institute of Health. Main outcomes The primary outcome was the pooled prevalence of coronary artery disease among COVID-19 patients within the examined population. {-} Results 510 records were initially retrieved from electronic databases in addition to other sources like reference screening. 33 studies with 40,064 COVID-19 patients were included for quantitative synthesis. The prevalence of coronary artery disease among COVID-19 patients was 15.24% (95% CI: 11.41% - 20.06%). The prediction interval ranged from 2.49% to 55.90%. The studies were highly heterogeneous (tau-sqaured of 0.89), and subgroup analysis significantly reduced it (test of moderators: Q = 14.77, df=2, P=.002). Europe reported the highest prevalence [21.70% (14.80% - 30.65%)], and Asia has the least prevalence [10.07% (6.55% - 15.19%)]. Meta-regression for sample size was not significant (P=.11). A symmetric doi plot and an LFK index of 0.57 revealed no evidence of publication bias or small-study effects. Conclusion The burden of coronary artery disease has been considerable, varying with geography. and further research in this area is needed. Routine cardiac screening and assessment of COVID-19 patients can help uncover undiagnosed cases, and better optimise the management of all COVID-19 patients.

Exploring the role of secretory proteins in the human infectious diseases diagnosis and therapeutics.

Secretory proteins are playing important role during the host-pathogen interaction to develop the infection or protection into the cell. Pathogens developing infectious disease to human being are taken up by host macrophages or number of immune cells, play an important role in physiological, developmental and immunological function. At the same time, infectious agents are also secreting various proteins to neutralize the resistance caused by host cells and also helping the pathogens to develop the infection. Secretory proteins (secretome) are only developed at the time of host-pathogen interaction, therefore they become very important to develop the targeted and potential therapeutic strategies. Pathogen specific secretory proteins released during interaction with host cell provide opportunity to develop point of care and rapid diagnostic kits. Proteins secreted by pathogens at the time of interaction with host cell have also been found as immunogenic in nature and numbers of vaccines have been developed to control the spread of human infectious diseases. This chapter highlights the importance of secretory proteins in the development of diagnostic and therapeutic strategies to fight against human infectious diseases.

In-silico molecular modelling studies of some camphor imine based compounds as anti-influenza A (H1N1) pdm09 virus agents.

The advent of influenza A (H1N1) drug-resistant strains led to the search quest for more potent inhibitors of the influenza A virus, especially in this devastating COVID-19 pandemic era. Hence, the present research utilized some molecular modelling strategies to unveil new camphor imine-based compounds as anti-influenza A (H1N1) pdm09 agents. The 2D-QSAR results revealed GFA-MLR (R2train = 0.9158, Q2=0.8475) and GFA-ANN (R2train = 0.9264, Q2=0.9238) models for the anti-influenza A (H1N1) pdm09 activity prediction which have passed the QSAR model acceptability thresholds. The results from the 3D-QSAR studies also revealed CoMFA (R2train =0.977, Q2=0.509) and CoMSIA_S (R2train =0.976, Q2=0.527) models for activity predictions. Based on the notable information derived from the 2D-QSAR, 3D-QSAR, and docking analysis, ten (10) new camphor imine-based compounds (22a-22j) were designed using the most active compound 22 as the template. Furthermore, the high predicted activity and binding scores of compound 22j were further justified by the high reactive sites shown in the electrostatic potential maps and other quantum chemical calculations. The MD simulation of 22j in the active site of the influenza hemagglutinin (HA) receptor confirmed the dynamic stability of the complex. Moreover, the appraisals of drug-likeness and ADMET properties of the proposed compounds showed zero violation of Lipinski's criteria with good pharmacokinetic profiles. Hence, the outcomes in this work recommend further in-depth in vivo and in-vitro investigations to validate these theoretical findings.Communicated by Ramaswamy H. Sarma.

Multi-pathogen based chimeric vaccine to fight against COVID-19 and concomitant coinfections.

BACKGROUND: COVID-19 has proved to be a fatal disease of the year 2020, due to which thousands of people globally have lost their lives, and still, the infection cases are at a high rate. Experimental studies suggested that SARS-CoV-2 interacts with various microorganisms, and this coinfection is accountable for the augmentation of infection severity. METHODS AND RESULTS: In this study, we have designed a multi-pathogen vaccine by involving the immunogenic proteins from S. pneumonia, H. influenza, and M. tuberculosis, as they are dominantly associated with SARS-CoV-2. A total of 8 antigenic protein sequences were selected to predict B-cell, HTL, and CTL epitopes restricted to the most prevalent HLA alleles. The selected epitopes were antigenic, non-allergenic, and non-toxic and were linked with adjuvant and linkers to make the vaccine protein more immunogenic, stable, and flexible. The tertiary structure, Ramachandran plot, and discontinuous B-cell epitopes were predicted. Docking and MD simulation study has shown efficient binding of the chimeric vaccine with the TLR4 receptor. CONCLUSION: The in silico immune simulation analysis has shown a high level of cytokines and IgG after a three-dose injection. Hence, this strategy could be a better way to decrease the disease's severity and could be used as a weapon to prevent this pandemic.

Enhancing Security of Medical Image Data in the Cloud Using Machine Learning Technique

To prevent medical data leakage to third parties, algorithm developers have enhanced and modified existing models and tightened the cloud security through complex processes. This research utilizes PlayFair and K-Means clustering algorithm as double-level encryption/ decryption technique with ArnoldCat maps towards securing the medical images in cloud. K-Means is used for segmenting images into pixels and auto-encoders to remove noise (de-noising);the Random Forest regressor, tree-method based ensemble model is used for classification. The study obtained CT scan-images as datasets from ‘Kaggle' and classifies the images into ‘Non-Covid' and ‘Covid' categories. The software utilized is Jupyter-Notebook, in Python. PSNR with MSE evaluation metrics is done using Python. Through testing-and-training datasets, lower MSE score (‘0') and higher PSNR score (60%) were obtained, stating that, the developed decryption/ encryption model is a good fit that enhances cloud security to preserve digital medical images.

Template Entrance Channel as Possible Allosteric Inhibition and Resistance Site for Quinolines Tricyclic Derivatives in RNA Dependent RNA Polymerase of Bovine Viral Diarrhea Virus.

The development of potent non-nucleoside inhibitors (NNIs) could be an alternate strategy to combating infectious bovine viral diarrhea virus (BVDV), other than the traditional vaccination. RNA-dependent RNA polymerase (RdRp) is an essential enzyme for viral replication; therefore, it is one of the primary targets for countermeasures against infectious diseases. The reported NNIs, belonging to the classes of quinolines (2h: imidazo[4,5-g]quinolines and 5m: pyrido[2,3-g] quinoxalines), displayed activity in cell-based and enzyme-based assays. Nevertheless, the RdRp binding site and microscopic mechanistic action are still elusive, and can be explored at a molecular level. Here, we employed a varied computational arsenal, including conventional and accelerated methods, to identify quinoline compounds' most likely binding sites. Our study revealed A392 and I261 as the mutations that can render RdRp resistant against quinoline compounds. In particular, for ligand 2h, mutation of A392E is the most probable mutation. The loop L1 and linker of the fingertip is recognized as a pivotal structural determinant for the stability and escape of quinoline compounds. Overall, this work demonstrates that the quinoline inhibitors bind at the template entrance channel, which is governed by conformational dynamics of interactions with loops and linker residues, and reveals structural and mechanistic insights into inhibition phenomena, for the discovery of improved antivirals.

Effect of an educational intervention on diet and physical activity among school-aged adolescents in Delhi -The i-PROMISe (PROMoting health literacy in Schools) Plus Study

Purpose Emerging lifestyle changes due to rapid urbanization have led to an epidemiological transition and the rising prevalence of obesity is responsible for major non-communicable diseases (NCDs) which have further aggravated due to the COVID-19 pandemic. This study aims to assess the effectiveness of a comprehensive school-based intervention on diet and physical activity-related behavior of adolescents. Methods In 2019, a cluster-randomized controlled trial was conducted in randomly selected (n = 8) private schools. A 2-year intervention program was implemented over consecutive academic years (2019–2020 and 2020–2021) with students who were in the 6th and 7th grades when the study began. Four schools were randomly assigned to the intervention (n = 794) and four schools to the control group (n = 774). Results The difference in changes in diet and physical-activity-related behaviors of the students between the intervention and control schools were not significant in the intention to treat analysis probably due to the large drop-out due to COVID-19 measures: 304 students were available for follow-up in the intervention group and 122 in the control group (391 cases were excluded to make data comparable with baseline survey). The intake of vegetables (once a day) [β = 0.35, OR = 1.42, 95% CI (1.03, 1.95)] in the per-protocol analysis has increased among adolescents in the intervention group as compared to the control group. Conclusion The findings of this study indicated a positive effect of the intervention on diet and physical-activity-related changes in the expected direction and highlights the importance of addressing such behavior to prevent obesity among adolescents and thus NCDs in the later stage of life.

COVID-19 and overconfidence bias: the case of developed, emerging and frontier markets

PurposeThe main purpose of the present study is to delve into the overconfidence bias in global stock markets during both pre COVID-19 and COVID-19 phases.Design/methodology/approachThe present study makes use of daily adjusted closing prices and volume of the broad market indices of 46 global stock markets over a period ranging from July 2015 till June 2020. The sample period is split into pre COVID-19 and COVID-19 phases. In order to test the overconfidence fallacy in the chosen stock markets, bivariate market-wide vector auto regression (VAR) models and impulse response functions (IRFs) have been employed in both phases.FindingsA highly significant contemporaneous relationship between market return and volume appears to be more pronounced in the Japanese, US, Chinese and Vietnamese stock markets in the pre COVID-19 era for the relevant coefficients are positive and highly significant for most lags. Coming to the period of turbulence, the present study discovers strong overconfident behavior in the Chinese, Taiwanese, Turkish, Jordanian and Vietnamese stock markets during COVID-19 phase.Practical implicationsA stark finding is that none of the developed stock markets reveal strong overconfidence bias during pandemic, suggesting a loss or decline in the investors' confidence. Therefore, the regulators should try to regain the investors' trust and confidence in the markets by ensuring honest, fair and transparent practices. The money managers should reduce the transaction cost to encourage trading and educate investors to hold a well-diversified portfolio to mitigate risk in the long run. The governments may launch recovery packages focusing on sustaining and improving economic activities. Finally, a better investment culture may be built by the corporate houses through good corporate governance practices to regain lost trust.Originality/valueThe present study appears to be the very first attempt to gauge overconfidence bias in the wake of a recent COVID-19 pandemic.

Engaging Employers in Apprentice Training: Focus Group Insights from Small-to-Medium-Sized Employers in Ontario, Canada.

Several factors have been identified to influence the registration and retention of apprentices in the construction trades. Employer engagement is a key factor to promote growth in apprenticeships in the construction trades as participation rates continue to be low among small-to-medium-sized employers. In this study, we evaluated the effectiveness of the Ontario Electrical League's (OEL) employer mentorship program through the perspectives of small-to-medium-sized employers using a qualitative approach. Two focus groups were conducted virtually with 11 employers. Focus group audio transcripts were recorded and transcribed for thematic analysis. Themes were generated using a data-driven approach to examine the relationships between mentorship program outcomes and perspectives on industry-related recruitment and retention barriers. Three themes were identified: (a) long-term apprentice recruitment and retention challenges; (b) equity and mental health in the workplace; and (c) industry challenges and mentorship program outcomes. Generally, this sample of employers appreciated the value of the OEL mentorship program through praise of the continued educational support, employer management expertise, hiring resources, and apprentice onboarding tools despite industry barriers in trade stigma, equity and mental health in the workplace, and recruitment and retention challenges. Industry partners should work with these small-to-medium-sized employers to develop workplace initiatives and engage external partners to provide ongoing apprenticeship mentorship support to address the recruitment and retention barriers identified in this study.

Effect of an educational intervention on diet and physical activity among school-aged adolescents in Delhi -The i-PROMISe (PROMoting health literacy in Schools) Plus Study.

Purpose: Emerging lifestyle changes due to rapid urbanization have led to an epidemiological transition and the rising prevalence of obesity is responsible for major non-communicable diseases (NCDs) which have further aggravated due to the COVID-19 pandemic. This study aims to assess the effectiveness of a comprehensive school-based intervention on diet and physical activity-related behavior of adolescents. Methods: In 2019, a cluster-randomized controlled trial was conducted in randomly selected (n = 8) private schools. A 2-year intervention program was implemented over consecutive academic years (2019-2020 and 2020-2021) with students who were in the 6th and 7th grades when the study began. Four schools were randomly assigned to the intervention (n = 794) and four schools to the control group (n = 774). Results: The difference in changes in diet and physical-activity-related behaviors of the students between the intervention and control schools were not significant in the intention to treat analysis probably due to the large drop-out due to COVID-19 measures: 304 students were available for follow-up in the intervention group and 122 in the control group (391 cases were excluded to make data comparable with baseline survey). The intake of vegetables (once a day) [ß = 0.35, OR = 1.42, 95% CI (1.03, 1.95)] in the per-protocol analysis has increased among adolescents in the intervention group as compared to the control group. Conclusion: The findings of this study indicated a positive effect of the intervention on diet and physical-activity-related changes in the expected direction and highlights the importance of addressing such behavior to prevent obesity among adolescents and thus NCDs in the later stage of life.

Global vaccine inequities and multilateralism amid COVID-19: Reconnaissance of Global Health Diplomacy as a panacea?

Background: The ongoing COVID-19 pandemic has shown a crystal-clear warning that nobody will be safe until everybody is safe against the pandemic. However, how everyone is safe when the pandemic's fat tail risks have broken every nerve of the global economy and healthcare facilities, including vaccine equity. Vaccine inequity has become one of the critical factors for millions of new infections and deaths during this pandemic. Against the backdrop of exponentially growing infected cases of COVID-19 along with vaccine in-equity, this paper will examine how multilateralism could play its role in mitigating vaccine equity through Global Health Diplomacy (GHD). Second, given the most affected developing countries' lack of participation in multilateralism, could GHD be left as an option in the worst-case scenario?. Methods: In this narrative review, a literature search was conducted in all the popular databases, such as Scopus, Web of Science, PubMed and Google search engines for the keywords in the context of developing countries and the findings are discussed in detail. Results: In this multilateral world, the global governance institutions in health have been monopolized by the global North, leading to COVID-19 vaccine inequities. GHD aids health protection and public health and improves international relations. Besides, GHD facilitates a broad range of stakeholders' commitment to collaborate in improving healthcare, achieving fair outcomes, achieving equity, and reducing poverty. Conclusion: Vaccine inequity is a major challenge of the present scenario, and GHD has been partly successful in being a panacea for many countries in the global south.

COVID-19 vaccine acceptance and associated factors in 13 African countries: A systematic review and meta-analysis.

Background: The COVID-19 pandemic has severely affected the entire world, especially sub-Saharan Africa. As a result, researchers and government agencies are working to create effective COVID-19 vaccinations. While vaccination campaigns are moving rapidly in high-income nations, COVID-19 is still ruthlessly affecting people in low-income nations. However, this difference in the spread of the disease is not because of a lack of a COVID-19 vaccine but mainly due to people's reluctance. As a result, this review summarized the data on COVID-19 vaccination adoption and factors related among nations in sub-Saharan Africa. Method: Comprehensive searches were conducted using PubMed, Embase, Medline, Web of Science, Google Scholar, and the Cochrane Library databases. The risk of bias and methodological quality of each published article that fit the selection criteria were evaluated using Critical Appraisal Checklist tools. All statistical analysis was done by STATA 16. Results: This review was based on 29 studies with 26,255 participants from sub-Saharan Africa. Using a random-effects model, the pooled prevalence of COVID-19 vaccine acceptance among study participants was 55.04% (95 % CI: 47.80-62.27 %), I2 = 99.55%. Being male [POR = 1.88 (95% CI: 1.45, 2.44)], having a positive attitude toward the COVID-19 vaccine [POR = 5.56 (95% CI: 3.63, 8.51)], having good knowledge in the COVID-19 vaccine [POR = 4.61 (95% CI: 1.24, 8.75)], having government trust [POR = 7.10 (95% CI: 2.37, 21.32)], and having undergone COVID-19 testing in the past [POR = 4.41 (95%CI: (2.51, 7.75)] were significant predictor variables. Conclusion: This analysis showed that respondents had a decreased pooled prevalence of COVID-19 vaccination acceptance. Sex, attitude, knowledge, government trust, and COVID-19 testing were statistically significantly correlated characteristics that affected the acceptability of the COVID-19 vaccine. All stakeholders should be actively involved in increasing the uptake of the COVID-19 vaccine and thereby reducing the consequences of COVID-19. The acceptance of the COVID-19 vaccination can be increased by using this conclusion as an indicator for governments, healthcare professionals, and health policymakers in their work on attitude, knowledge, government trust, and COVID-19 testing.

Machine Learning-Based 30-Day Hospital Readmission Predictions for COPD Patients Using Physical Activity Data of Daily Living with Accelerometer-Based Device.

Chronic obstructive pulmonary disease (COPD) is a significantly concerning disease, and is ranked highest in terms of 30-day hospital readmission. Generally, physical activity (PA) of daily living reflects the health status and is proposed as a strong indicator of 30-day hospital readmission for patients with COPD. This study attempted to predict 30-day hospital readmission by analyzing continuous PA data using machine learning (ML) methods. Data were collected from 16 patients with COPD over 3877 days, and clinical information extracted from the patients' hospital records. Activity-based parameters were conceptualized and evaluated, and ML models were trained and validated to retrospectively analyze the PA data, identify the nonlinear classification characteristics of different risk factors, and predict hospital readmissions. Overall, this study predicted 30-day hospital readmission and prediction performance is summarized as two distinct approaches: prediction-based performance and event-based performance. In a prediction-based performance analysis, readmissions predicted with 70.35% accuracy; and in an event-based performance analysis, the total 30-day readmissions were predicted with a precision of 72.73%. PA data reflect the health status; thus, PA data can be used to predict hospital readmissions. Predicting readmissions will improve patient care, reduce the burden of medical costs burden, and can assist in staging suitable interventions, such as promoting PA, alternate treatment plans, or changes in lifestyle to prevent readmissions.

COVID-19 associated multisystemic mucormycosis from India: a multicentric retrospective study on clinical profile, predisposing factors, cumulative mortality and factors affecting outcome.

PURPOSE: The clinical course of COVID-19 has been complicated by secondary infections, including bacterial and fungal infections. The rapid rise in the incidence of invasive mucormycosis in these patients is very much concerning. COVID-19-associated mucormycosis was detected in huge numbers during the second wave of the COVID-19 pandemic in India, with several predisposing factors indicated in its pathogenesis. This study aimed to evaluate the epidemiology, predisposing factor, cumulative mortality and factors affecting outcomes among the coronavirus disease COVID-19-associated mucormycosis (CAM). METHODS: A multicenter retrospective study across three tertiary health care centers in Southern part of India was conducted during April-June 2021. RESULTS: Among the 217 cases of CAM, mucormycosis affecting the nasal sinuses was the commonest, affecting 95 (44%) of the patients, orbital extension seen in 84 (38%), pulmonary (n = 25, 12%), gastrointestinal (n = 6, 3%), isolated cerebral (n = 2) and disseminated mucormycosis (n = 2). Diabetes mellitus, high-dose systemic steroids were the most common underlying disease among CAM patients. The mucormycosis-associated case-fatality at 6 weeks was 14%, cerebral or GI or disseminated mucormycosis had 9 times higher risk of death compared to other locations. Extensive surgical debridement along with sequential antifungal drug treatment improved the survival in mucormycosis patients. CONCLUSION: Judicious and appropriate management of the predisposing factor and factors affecting mortality associated with CAM with multi-disciplinary approach and timely surgical and medical management can be much helpful in achieving a successful outcome.