Multi-operator Variant of Differential Evolution and its Application in Classification of COVID-19 CT-scan Images

Novel coronavirus disease (COVID-19), caused by the virus (SARS-CoV-2), has drastically impacted human beings' lives since early 2020. The virus is constantly changing, and with mutations, it becomes diverse and spreads more easily. Several automatic COVID-19 diag-nostic tools are proposed that emphasize feature extraction mechanism from radiographical images using modern deep learning technology. The general idea is to leverage smart solutions of pre-trained networks for deep-feature processing. However, all the extracted features may not essentially contribute to the performance of the COVID-19 diag-nostic model, and hence an optimal subset of features must be discov-ered. Motivated by this, we propose a novel feature selection method based on multi-operator differential evolution (MODE), which helps to acquire optimal feature-subset. To show the efficacy of the proposed algorithm, we focus on applying the COVID-19 classification model through medical imaging. Eight advanced pre-trained architectures have been selected for COVID-19 feature extraction from CT-scan medi-cal imaging. After that, the proposed feature selection technique based on MODE is applied. A customized SVM kernel is implemented that supports feature classification. The result analysis shows the perfor-mance of the existing COVID-19 designs with the proposed feature selection technique, MODE, integrated with a customized SVM kernel. It even beats the existing state-of-the-art frameworks carried forward for COVID-19 diagnosis. We have observed that MODE feature selec-tion is suitable for fast COVID-19 detection, having overall accuracy of 85.27%.

Virtual consulting in the times of COVID-19

Background: Confinement and economic uncertainty, along with disease-specific issues such as pain and suffering may uniquely impact rheumatology patients, putting non-COVID morbidity at par with COVID-related losses.Methods: We conducted a review of records to explore the clinical profile and interventions in rheumatology patients at a tertiary care center in Northern India to identify the potential problems and propose direction for the future.Results: In our experience spanning 5 weeks, among 508 rheumatology patients who sought teleconsultations, rheumatoid arthritis was the most common diagnosis (35.2'), and 43' were asked to continue the same treatment over tele consults, whereas investigations were awaited for another 16'. Nearly one-third (29.9') required an intervention, though this largely consisted of dose titrations (104 of 152, 68.4'). Certain diseases such as myositis were under-represented among consults (1.2') but over-represented (33.3') among admissions. Among the 54 patients who were asked to rate their teleconsultation experience, the voice and consultation quality were rated and average of 9.9 each, and 88.9' (48/54) said they would prefer teleconsultations until the pandemic is over.Conclusion: A sizeable proportion of rheumatology patients can be managed with teleconsultations, with some requiring greater assistance, calling for a triage protocol for the times ahead. With propositions for a deliberate transition into the realm of virtual consulting, chronic caregivers can hope to reach an optimal balance between e-consults and in-person visits.

Predicting the Pre-release Success of a Web Series' Season

With the onset of COVID-19, OTT platforms have become popular. With this added popularity, many production companies tend to release their content on platforms like Netflix, Amazon Prime, Disney + Hotstar, etc. Through this research work, we tend to check the impact of different classical factors like genre, age certification, time of release, the platform of release, etc. as well as various social factors like the sentiment of the audience around the trailer, songs, and success of the previous season in predicting the success of the pre-release season of an English web series by creating our dataset. This will enhance the business strategies that production houses can use to improve their profits. We have trained different classification models like Decision Tree, Support Vector Machine, Multinomial Naive Bayes, and hyper tuned the parameters of Random Forest and K-Nearest Neighbours. We have also created a Multi-Layer Perceptron model and an ensemble classifier and trained them on our dataset. The best accuracy of 76.66% was achieved by the Hard Voting type ensemble classifier. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

How does a Rational Agent Act in an Epidemic?

The evolution of a disease in a large population is a function of the top-down policy measures from a centralized planner and the self-interested decisions (to be socially active) of individual agents in a large heterogeneous population. This paper is concerned with understanding the latter based on a mean-field type optimal control model. Specifically, the model is used to investigate the role of partial information on an agent's decision-making and study the impact of such decisions by a large number of agents on the spread of the virus in the population. The motivation comes from the presymptomatic and asymptomatic spread of the COVID-19 virus, where an agent unwittingly spreads the virus. We show that even in a setting with fully rational agents, limited information on the viral state can result in epidemic growth. © 2022 IEEE.

Virtual consulting in the times of COVID-19

Background: Confinement and economic uncertainty, along with disease-specific issues such as pain and suffering may uniquely impact rheumatology patients, putting non-COVID morbidity at par with COVID-related losses. Method(s): We conducted a review of records to explore the clinical profile and interventions in rheumatology patients at a tertiary care center in Northern India to identify the potential problems and propose direction for the future. Result(s): In our experience spanning 5 weeks, among 508 rheumatology patients who sought teleconsultations, rheumatoid arthritis was the most common diagnosis (35.2'), and 43' were asked to continue the same treatment over tele consults, whereas investigations were awaited for another 16'. Nearly one-third (29.9') required an intervention, though this largely consisted of dose titrations (104 of 152, 68.4'). Certain diseases such as myositis were under-represented among consults (1.2') but over-represented (33.3') among admissions. Among the 54 patients who were asked to rate their teleconsultation experience, the voice and consultation quality were rated and average of 9.9 each, and 88.9' (48/54) said they would prefer teleconsultations until the pandemic is over. Conclusion(s): A sizeable proportion of rheumatology patients can be managed with teleconsultations, with some requiring greater assistance, calling for a triage protocol for the times ahead. With propositions for a deliberate transition into the realm of virtual consulting, chronic caregivers can hope to reach an optimal balance between e-consults and in-person visits. Copyright © 2022 Wolters Kluwer Medknow Publications. All rights reserved.

Predicting the Pre-release Success of a Web Series' Season

With the onset of COVID-19, OTT platforms have become popular. With this added popularity, many production companies tend to release their content on platforms like Netflix, Amazon Prime, Disney + Hotstar, etc. Through this research work, we tend to check the impact of different classical factors like genre, age certification, time of release, the platform of release, etc. as well as various social factors like the sentiment of the audience around the trailer, songs, and success of the previous season in predicting the success of the pre-release season of an English web series by creating our dataset. This will enhance the business strategies that production houses can use to improve their profits. We have trained different classification models like Decision Tree, Support Vector Machine, Multinomial Naive Bayes, and hyper tuned the parameters of Random Forest and K-Nearest Neighbours. We have also created a Multi-Layer Perceptron model and an ensemble classifier and trained them on our dataset. The best accuracy of 76.66% was achieved by the Hard Voting type ensemble classifier. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Renal IgA Deposits in Cirrhosis, Innocent Bystander to Rapidly Progressive GN: A Case Report

Introduction: Cirrhosis is associated with renal mesangial IgA deposits thought to be due to reduced Immune complex hepatic clearance but associated rapidly proliferative Glomerulonephritis(RPGN) is uncommon. We present a case of IgA predominant RPGN in a cirrhotic patient. Case Description: 36 y/o M with PMHx of psoriasis, ETOH cirrhosis, strong family h/o FSGS presented with AKI along with nephrotic syndrome & microscopic hematuria. Labs: Serum cr: 4.3 mg/dl, UPCR -13 gm/gm, Una <10, Ucr: 153, CK - 409, INR - 1.2, WBC - 6200/ cucm, hb - 6.8 gm/dl, Sr albumin -1.7 gm/dl Urine microscopy: Multiple dysmorphic rbcs & RBC casts Imaging : Renal US without hydronephrosis, TTE revealed no obvious valvular abnormalities or no intra-cardiac vegetations or thrombus. Significant serologies : negative for HIV, viral hepatitis panel, SPEP with immunofixation, Anti-gbm ANA, Anti-ds dna, anca, ASLO and RF, qunatiferon gold, covid-19, INR -1.2 cryocrit negative. Low complent c4 (17mg/dl) with c3 (94mg/dl). Clinical course : Given Cirrhosis & low urine Na;albumin based volume resusictation & HRS protocol was attempted with no improvement. Urine microscopy findings prompted us to do renal biopsy. Biopsy : LM, 12 glomeruli, 3 globally sclerosed: Diffuse, global endocapillary and mesangial hypercellularity. No crescents or microthrombi Glomerular capillary walls with double contours and fuchsinophilic material within mesangial, subendothelial and epimembranous areas. IF: capillary wall and mesangial immunostaining: Ig-A 3+, IgG: 1+, IgM: 1+, C3: 3+,C1q: trace, kappa: 2+, lambda: 3 + EM: 1 glomerulus : Extensive subendothelial expansion + subendothelial/mesangial immune-type deposits & associated cellular interposition. Intramembranous/subepithelial hump-like deposits. No tubuloreticular inclusions.70% podocyte foot process effacement. Patient diagnosed with Ig A predominant MPGN & was treated with solumedrol x 3 and started on steroid taper along with cellcept and discharged home with improvement in creatinine and proteinuria. Discussion(s): Immune complex glomerular disease in cirrhotic patients can be of varied etiology. Although common, renal IgA deposition in cirrhotic patients are of unclear clinical significance. Larger scale clinicopathological studies are needed to understand the risk factors for development of clinically significant glomerulonephritis in cirrhosis.

Postbiotics: From emerging concept to application

The microbiome innovation has resulted in an umbrella term, postbiotics, which refers to non-viable microbial cells, metabolic byproducts and their microbial components released after lysis. Postbiotics, modulate immune response, gene expression, inhibit pathogen binding, maintain intestinal barriers, help in controlling carcinogenesis and pathogen infections. Postbiotics have antimicrobial, antioxidant, and immunomodulatory properties with favorable physiological, immunological, neuro-hormonal, regulatory and metabolic reactions. Consumption of postbiotics relieves symptoms of various diseases and viral infections such as SARS-CoV-2. Postbiotics can act as alternatives for pre-probiotic specially in immunosuppressed patients, children and premature neonates. Postbiotics are used to preserve and enhance nutritional properties of food, elimination of biofilms and skin conditioning in cosmetics. Postbiotics have numerous advantages over live bacteria with no risk of bacterial translocation from the gut to blood, acquisition of antibiotic resistance genes. The process of extraction, standardization, transport, and storage of postbiotic is more natural. Bioengineering techniques such as fermentation technology, high pressure etc., may be used for the synthesis of different postbiotics. Safety assessment and quality assurance of postbiotic is important as they may induce stomach discomfort, sepsis and/or toxic shock. Postbiotics are still in their infancy compared to pre- and pro- biotics but future research in this field may contribute to improved physiological functions and host health. The current review comprehensively summarizes new frontiers of research in postbiotics. Copyright © 2022 Aggarwal, Sabharwal, Kaushik, Joshi, Aayushi and Suri.

A qualitative analysis of perspectives of district level frontline managers in Karnataka during first wave of COVID 19

With the emergence of pandemic, health sector has been facing innumerable challenges right from prevention to management of COVID-19. This study attempts to understand these factors from a local perspective that will help in better planning and management of situation for the future. Karnataka was one of the 8 states selected as part of a multisectoral qualitative study. Perspectives of frontline managers regarding the management of COVID 19 pandemic situation were taken by in depth interviews. Deductive thematic analysis from the audio recordings and transcripts showed public apprehensions, gaps in decentralized approach and specific guidelines, monitoring difficulties and workforce related issues as factors of concern. © 2022, Indian Association of Preventive and Social Medicine. All rights reserved.

Antibiotic point prevalence survey at a tertiary healthcare hospital in India: Identifying strategies to improve the antibiotic stewardship program immediately after a COVID-19 wave

Background The COVID-19 pandemic has substantially affected the antibiotic stewardship activities in most hospitals of India. Aims We conducted an antibiotic point-prevalence survey (PPS) immediately after the decline of a major COVID-19 wave at a dedicated COVID-19 hospital. By doing so we aimed to identify the antibiotic prescription patterns, identify factors influencing the choice of antibiotics, and identify/develop strategies to improve the antibiotic stewardship program in such setups. Methods The PPS was single-centred, cross-sectional, and retrospective in nature. Patients admitted in various wards and intensive care units (ICUs) between September 2021 to October 2021 were included in our PPS. Results Of the included 460 patients, 192 were prescribed antibiotics. Of these 192 patients, ICU-admitted patients had the highest number of antibiotics prescribed i.e. 2.09 ± 0.92. Only a minor fraction (7.92 %) of antibiotics prescriptions were on the basis of culture reports. Most of the antibiotics were prescribed empirically by the parenteral route. The most common group of antibiotics prescribed were third-generation cephalosporins. Carbapenems were the most common designated antibiotics prescribed. A large number of patients (22.40 %) were prescribed double anaerobic cover. Conclusion The strategies that we identified to improve the antibiotic stewardship program at our institute included reviving the culture of sending culture reports to prescribe antibiotics, improving surgical prophylaxis guidelines, training resident doctors to categorize antibiotic prescriptions appropriately, closely monitoring prescriptions providing double anaerobic cover, and improving the electronic medical record system for improving prescription auditing.

Correlation between NEWS, CT Severity Score and RT-PCR Cycle Threshold Value among Mild and Moderate COVID-19 Patients

Introduction: The clinical diagnosis of COVID-19 is supplemented by clinical severity indices. These indices are the National Early Warning Score (NEWS, which aids in risk stratification), CT severity score (radiological severity score), and Reverse Transcription-Polymerase Chain Reaction (RT-PCR) cycle threshold (Ct value, which provides a semi-quantitative measure of viral load). Aim(s): To assess the correlation between NEWS at admission, RT-PCR Ct value and CT severity score in mild and moderate COVID-19 patients. Methods and Materials: This prospective cohort study was conducted in Maulana Azad Medical College and Lok Nayak hospital, New Delhi, from January to June 2021. The study included 50 subjects (25 with mild COVID-19 and 25 with moderate COVID-19). NEWS was calculated at admission and Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Ct value was estimated using real-time RT-PCR. CT severity score was calculated based on High Resolution Computed Tomography (HRCT) chest findings. The correlation among the parameters was determined using Pearson correlation formula. Result(s): The mean age of subjects in the mild and moderate COVID-19 groups were 49.52 years and 51.84 years, respectively. The mean RT-PCR Ct value of E gene was 24.48 and Rdrp gene was 24.56 in the mild COVID-19 group;while in the moderate group it was 23.72 for both E gene and Rdrp genes. The correlation between NEWS and Ct value of E gene (r-value=-0.06, p-value=0.68), Ct value of Rdrp gene (r-value=-0.03, p-value=0.79) and the correlation between CT severity score and Ct value of E gene (r-value=-0.05, p-value=0.73), Ct value of Rdrp gene (r-value=-0.06, p-value=0.68) was negative and insignificant. The mean CT severity score in mild COVID-19 group was 3.92, and in moderate COVID-19 group was 9.88. A significant positive correlation was found between the CT severity score and NEWS at admission. Conclusion(s): The clinical severity of COVID-19 as estimated by NEWS corroborates with CT severity score while the relationship between RT-PCR Ct value and clinicoradiological severity needs to be ascertained by further research. Copyright © 2022 Journal of Clinical and Diagnostic Research. All rights reserved.

Analyzing the Quantitative and Domino impact of Selected Financial Crises on the Brazil’s Banking and Economic parameters-A Diverse and Inclusive Challenge

Financial crises impact the economy as well as the banking sector of the country in a systemic risk form. It’s a contagion impact that rolls from one nation to the another, one bank to the another, one crisis to the another and one factor to the another. This paper studies and analyses this domino impact of the 4 selected financial crises (US subprime crises, Russian financial crises, Eurozone crises and Covid 19 crises) on the Brazil’s economy and banking sector, along with the quantitative impact on various key variables. The researcher analysed the financial position of top 2 banks of Brazil from 2002 to 2021 along with the country’s macroeconomic position in the same period. The researcher has done an in-depth analysis of each bank with financial factors as dependent variables, macroeconomic factors as independent variables and selected financial crises as fixed and latent variables. MANOVA is used to analyse the mean difference of each parameter during each selected financial crises for both the banks. The groups of key factors are formed based upon the results, which showed almost no discrepancy in the results of both the banks. ANOVA pairwise comparisons is used to understand the domino impact of each variable and the corresponding pairs of each selected crises. The first bank showed much dominance of US subprime crises on Russian, Eurozone and Covid 19 crises for many factors, and only one factor (ROE) dominance for Russian on eurozone and covid 19. The second bank showed all the selected financial crises dominance on each other for many factors.

Measurement of Black Carbon in Delhi: Evidences of Regional Transport, Meteorology and Local Sources for Pollution Episodes

Measurement of particulate matter (PM) constituent such as black carbon (BC) over urban sites is critically important owing to its adverse health and climate impacts. However, the impacts associated with BC are poorly understood primarily because of the scarcity and uncertainties of measurements of BC. Here, we present BC measurement at an urban site of Delhi using a characterized continuous soot monitoring system (COSMOS) for a year-long period, i.e., from September, 2019 to August, 2020. This measurement period covers events, i.e., period of crop residue burnings from nearby states, festive events, e.g., Diwali and New Year, and first COVID-19 lockdown period. Effects of these events combining with local emissions and meteorological conditions on BC mass concentration (MBC) are investigated to find the possible cause of severe pollution levels in Delhi. Mean MBC for the complete observation period was found to be 5.02 ?? 4.40 ??g m???3. MBC showed significant seasonal as well diurnal variations. Winter season (December to February) is observed to be the most polluted season owing to increased local emissions and non-favorable meteorological conditions. Regional emission from crop burning in nearby states during October and November is the main contributing factor for increased pollution in this postmonsoon season. Furthermore, analysis reveals that cracker burning during festivals can also be considered as contributing factor to high MBC for a short period in post-monsoon and winter seasons. Significant decrease in MBC due to COVID-19 lockdown is also observed. MBC in summer and monsoon are lower as compared to other seasons but are still higher than mean MBC levels in several other urban cities of different countries. Also, the BC data obtained from nearby sites surface black carbon (SBC) are compared against the MBC to evaluate coherency among the different datasets, and discussed in detail.

Lending ears to unheard voices: An empirical analysis of user-generated content on social media

Governments and healthcare organizations increasingly pay attention to social media for handling a disease outbreak. The institutions and organizations need information support to gain insights into the situation and act accordingly. Currently, they primarily rely on ground-level data, collecting which is a long and cumbersome process. Social media data present immense opportunities to use ground data quickly and effectively. Governments and HOs can use these data in launching rapid and speedy remedial actions. Social media data contain rich content in the form of people's reactions, calls-for-help, and feedback. However, in healthcare operations, the research on social media for providing information support is limited. Our study attempts to fill the gap mentioned above by investigating the relationship between the activity on social media and the quantum of the outbreak and further using content analytics to construct a model for segregating tweets. We use the case example of the COVID-19 outbreak. The pandemic has advantages in contributing to the generalizability of results and facilitating the model's validation through data from multiple waves. The findings show that social media activity reflects the outbreak situation on the ground. In particular, we find that negative tweets posted by people during a crisis outbreak concur with the quantum of a disease outbreak. Further, we find a positive association between this relationship and increased information sharing through retweets. Building further on this insight, we propose a model using advanced analytical methods to reduce a large amount of unstructured data into four key categories-irrelevant posts, emotional outbursts, distress alarm, and relief measures. The supply-side stakeholders (such as policy makers and humanitarian organizations) could use this information on time and optimize resources and relief packages in the right direction proactively.

Diabetic ketoacidosis presentation during the early part of the covid-19 pandemic: What was different?

Background and Aim: Covid-19 has been associated with erratic glycaemic control, sometimes leading to diabetic emergencies, including diabetic ketoacidosis (DKA). We reviewed DKA admissions to the Sandwell and West Birmingham Hospitals to assess whether being admitted with DKA during the early phase of the covid-19 pandemic affected the clinical presentation and outcomes of DKA locally. Methods: This retrospective study included all patients with DKA in the pre-covid period (31 patients): 16th January-15th March 2020, versus those with DKA in the initial covid period (29 patients): 16th March-15th May 2020. Data collected included demographics, comorbidities, length of hospital stay (LOS), and diabetes-specific parameters, including type and duration of diabetes, last HbA1c, diabetes medications, DKA parameters leading to Critical Care admission, and time to DKA resolution. Results: Those presenting with DKA during the covid period were more likely to be male, of ethnic background, had longer duration of diabetes, better-controlled diabetes, and there were three times more patients with type 2 diabetes. Prescription of long-acting insulin alongside DKA protocol was significantly better during the covid period. Mean time to DKA resolution was significantly longer and mean LOS was 24 hours longer in the covid period. Conclusions: Diagnosis and management of DKA was objectively much better during the covid period but DKA occurred in more unexpected groups and recovery time was longer. Further study of larger samples with a multi-centred approach to see how DKA presentation has evolved during the pandemic would benefit our understanding of the impact of covid-19 on diabetes.

Psychological impact of COVID-19 on female health professionals

Background: The World Health Organization in January 2020 declared an outbreak of novel coronavirus disease, COVID-19, an international public health emergency. The suddenness of this calamity with no end in sight caused a great deal of stress, anxiety, and depression throughout the world. Aim: The aim of this study was to assess the psychological impact during the developing COVID-19 situation among female health professionals. Methods: This was an observation-based cross-sectional study conducted for a total of 3-month duration between August and October 2020. A structured questionnaire to assess the psychological impact was sent via the (email) electronic mail system, and responses were obtained from 343 female doctors. Results: The mean & PLUSMN;standard deviation values for age were found to be 39 & PLUSMN;8 years. Insomnia, stress, and anxiety were seen in a majority of the respondents. Conclusions: The present study has shown severe psychological impact associated with the pandemic on female health-care workers and highlights the major factors associated.

A Cross-Sectional Study to Estimate the Severity of Anxiety in Professionals Remotely Working from Home during COVID-19 Pandemic

Background: The COVID-19 pandemic had a significant impact on public mental health besides playing havoc with one's physical health. The study aims to fill the existing gap in the research concerning the impact of COVID-19 on professionals working from homes (WFH). Aims: To estimate the severity of anxiety in WFH Professionals during COVID-19 and to assess its impact on their financial, personal and professional lives. Material and Methods: It was an online questionnaire designed to profile remotely working professionals to assess the anxiety levels using Becks anxiety inventory (BAI) scale and the impact of Covid-19 on the personal, professional and financial status on 255 qualified respondents (123 women & 135 men). Statistical analysis: Chi-square test was done by using Statistical Package for the Social Sciences (SPSS) version 20 software. P-value P<0.05 was considered significant. Results: WFH during the COVID-19 restrictions increased moderate to severe anxiety levels (32.09%) with females (51.02%) suffering at higher rates than their male counterparts (15.09%). In these remotely working women, being married (64%) staying in a joint family (90.9%), having children (90.9%) heightened this anxiety. Results suggest that remotely working has adversely impacted their personal lives with females suffering at higher levels than men. It affected their financial lives adversely with females suffering at higher rates. Professional situation got severely impacted by this WFH however;women and men were similarly affected. Conclusion: Worsening anxiety levels and adverse impact on the personal, financial and professional lives in these remotely working Professionals especially women necessitates finding solutions by employers, psychologists and psychiatrists to alleviate this indirect impact of Covid 19.

COVID'19 in India: Emotion of the Nation A novel Spatio-Temporal Unsupervised Sentiment Analysis

The coronavirus was originated in Wuhan City in China in 2019, and it led to something for which the world was not prepared. In the world where the extent of COVID'19 was ubiquitous, India was one of the countries that witnessed multiple phases of its spread. Moreover, since India has one of the largest populations, this made analyzing the sentiment of people during this time a task that held significance. COVID'19 brought a mix of emotions across the different periods in its first 18 months. During this time, social media was flooded with tweets and hashtags expressing both favorable and negative opinions about COVID'19, pandemic, lockdown, and vaccines. © 2021 IEEE.