Cardiovascular risk factors and outcomes in COVID-19: A hospital-based study in India

Background & objectives Presence of cardiovascular (CV) risk factors enhance adverse outcomes in COVID-19. To determine association of risk factors with clinical outcomes in India we performed a study. Methods Successive virologically confirmed adult patients of COVID-19 at a government hospital were recruited at admission and data on clinical presentation and in-hospital outcomes were obtained. The cohort was classified according to age, sex, hypertension, diabetes and tobacco use. In-hospital death was the primary outcome. Logistic regression was performed to compared outcomes in different groups. Results From April to September 2020 we recruited 4645 (men 3386, women 1259) out of 5103 virologically confirmed COVID-19 patients (91.0%). Mean age was 46±18y, hypertension was in 17.8%, diabetes in 16.6% and any tobacco-use in 29.5%. Duration of hospital stay was 6.8±3.7 days, supplemental oxygen was in 18.4%, non-invasive ventilation in 7.1%, mechanical ventilation in 3.6% and 7.3% died. Unadjusted and age-sex adjusted odds ratio(OR) and 95% confidence intervals(CI) for in-hospital mortality, respectively, were: age ≥60y vs <40y, OR 8.47(95% CI 5.87–12.21) and 8.49(5.88–12.25), age 40-59y vs <40y 3.69(2.53–5.38) and 3.66(2.50–5.33), men vs women 1.88(1.41–2.51) and 1.26(0.91–1.48);hypertension 2.22(1.74–2.83) and 1.32(1.02–1.70), diabetes 1.88(1.46–2.43) and 1.16(0.89–1.52);and tobacco 1.29(1.02–1.63) and 1.28(1.00–1.63). Need for invasive and non-invasive ventilation was greater among patients in age-groups 40–49 and ≥60y and hypertension. Multivariate adjustment for social factors, clinical features and biochemical tests attenuated significance of all risk factors. Conclusion Cardiovascular risk factors, age, male sex, hypertension, diabetes and tobacco-use, are associated with greater risk of in-hospital death among COVID-19 patients.

Biomarkers and outcomes in hospitalised patients with COVID-19: a prospective registry.

OBJECTIVES: To determine association of biomarkers-high-sensitivity C reactive protein (hsCRP), D-dimer, interleukin-6 (IL-6), lactic dehydrogenase (LDH), ferritin and neutrophil-lymphocyte ratio (NLR)-at hospitalisation with outcomes in COVID-19. DESIGN AND SETTING: Tertiary-care hospital based prospective registry. PARTICIPANTS: Successive virologically confirmed patients with COVID-19 hospitalised from April 2020 to July 2021 were prospectively recruited. Details of clinical presentation, investigations, management and outcomes were obtained. PRIMARY AND SECONDARY OUTCOME MEASURES: All biomarkers were divided into tertiles to determine associations with clinical features and outcomes. Primary outcome was all-cause deaths and secondary outcome was oxygen requirement, non-invasive and invasive ventilation, dialysis, duration of stay in ICU and hospital. Numerical data are presented in median and interquartile range (IQR 25-75). Univariate and multivariate (age, sex, risk factors, comorbidities, treatments) ORs and 95% CIs were calculated. RESULTS: 3036 virologically confirmed patients with COVID-19 were detected and 1251 hospitalised. Men were 70.0%, aged >60 years 44.8%, hypertension 44.1%, diabetes 39.6% and cardiovascular disease 18.9%. Median symptom duration was 5 days (IQR 4-7) and oxygen saturation 95% (90%-97%). Total white cell count was 6.9×109/L (5.0-9.8), neutrophils 79.2% (68.1%-88.2%), lymphocytes 15.8% (8.7%-25.5%) and creatinine 0.93 mg/dL (0.78-1.22). Median (IQR) for biomarkers were hsCRP 6.9 mg/dL (2.2-18.9), D-dimer 464 ng/dL (201-982), IL-6 20.1 ng/dL (6.5-60.4), LDH 284 mg/dL (220-396) and ferritin 351 mg/dL (159-676). Oxygen support at admission was in 38.6%, subsequent non-invasive or invasive ventilatory support in 11.0% and 11.6%, and haemodialysis in 38 (3.1%). 173 (13.9%) patients died and 15 (1.2%) transferred to hospice care. For each biomarker, compared with the first, those in the second and third tertiles had more clinical and laboratory abnormalities, and oxygen, ventilatory and dialysis support. Multivariate-adjusted ORs (95% CI) for deaths in second and third versus first tertiles, respectively, were hsCRP 2.24 (1.11 to 4.50) and 12.56 (6.76 to 23.35); D-dimer 3.44 (1.59 to 7.44) and 14.42 (7.09 to 29.30); IL-6 2.56 (1.13 to 5.10) and 10.85 (5.82 to 20.22); ferritin 2.88 (1.49 to 5.58) and 8.19 (4.41 to 15.20); LDH 1.75 (0.81 to 3.75) and 9.29 (4.75 to 18.14); and NLR 3.47 (1.68 to 7.14) and 17.71 (9.12 to 34.39) (p<0.001). CONCLUSION: High levels of biomarkers-hsCRP, D-dimer, IL-6, LDH, ferritin and NLR-in COVID-19 are associated with more severe illness and higher in-hospital mortality. NLR, a widely available investigation, provides information similar to more expensive biomarkers.

An AI-enabled pre-trained model-based Covid detection model using chest X-ray images.

The year 2020 and 2021 was the witness of Covid 19 and it was the leading cause of death throughout the world during this time period. It has an impact on a large geographic area, particularly in countries with a large population. Due to the fact that this novel coronavirus has been detected in all countries around the world, the World Health Organization (WHO) has declared Covid-19 to be a pandemic. This novel coronavirus spread quickly from person to person through the saliva droplets and direct or indirect contact with an infected person. The tests carried out to detect the Covid-19 are time-consuming and the primary cause of rapid growth in Covid19 cases. Early detection of Covid patient can play a significant role in controlling the Covid chain by isolation the patient and proper treatment at the right time. Recent research on Covid-19 claim that Chest CT and X-ray images can be used as the preliminary screening for Covid-19 detection. This paper suggested an Artificial Intelligence (AI) based approach for detecting Covid-19 by using X-ray and CT scan images. Due to the availability of the small Covid dataset, we are using a pre-trained model. In this paper, four pre-trained models named VGGNet-19, ResNet50, InceptionResNetV2 and MobileNet are trained to classify the X-ray images into the Covid and Normal classes. A model is tuned in such a way that a smaller percentage of Covid cases will be classified as Normal cases by employing normalization and regularization techniques. The updated binary cross entropy loss (BCEL) function imposes a large penalty for classifying any Covid class to Normal class. The experimental results reveal that the proposed InceptionResNetV2 model outperforms the other pre-trained model with training, validation and test accuracy of 99.2%, 98% and 97% respectively.

COVID-19 Lesion Segmentation and Classification of Lung CTs Using GMM-Based Hidden Markov Random Field and ResNet 18

COVID-19 has been circulating around the world for over a year, causing a severe pandemic in every country, affecting billions of people. One of the most extensively utilized diagnostic methodologies for diagnosing and detecting the presence of the COVID-19 virus is reverse transcription-polymerase chain reaction (RT-PCR). Various ideas have been proposed for the detection of COVID-19 using medical imaging. CT or computed tomography is one of the beneficial technologies for diagnosing COVID-19 patients, the need for screening of positive patients is an essential task to prevent the spread of the disease. Segmentation of Lung CT is the initial step to segment the infection caused by the virus in the lungs and to analyze the lungs CT. This article introduces a novel Hidden Markov Random Field based on Gaussian Mix Model (GMM-HMRF) method ensembled with the modified ResNet18 deep architecture for binary classification. The proposed architecture performed well in terms of accuracy, sensitivity, and specificity and achieved 86.1%, 86.77%, and 85.45%, respectively.

Cardiovascular risk factors and outcomes in COVID-19: A hospital-based study in India.

BACKGROUND & OBJECTIVES: Presence of cardiovascular (CV) risk factors enhance adverse outcomes in COVID-19. To determine association of risk factors with clinical outcomes in India we performed a study. METHODS: Successive virologically confirmed adult patients of COVID-19 at a government hospital were recruited at admission and data on clinical presentation and in-hospital outcomes were obtained. The cohort was classified according to age, sex, hypertension, diabetes and tobacco use. In-hospital death was the primary outcome. Logistic regression was performed to compared outcomes in different groups. RESULTS: From April to September 2020 we recruited 4645 (men 3386, women 1259) out of 5103 virologically confirmed COVID-19 patients (91.0%). Mean age was 46±18y, hypertension was in 17.8%, diabetes in 16.6% and any tobacco-use in 29.5%. Duration of hospital stay was 6.8±3.7 days, supplemental oxygen was in 18.4%, non-invasive ventilation in 7.1%, mechanical ventilation in 3.6% and 7.3% died. Unadjusted and age-sex adjusted odds ratio(OR) and 95% confidence intervals(CI) for in-hospital mortality, respectively, were: age ≥60y vs <40y, OR 8.47(95% CI 5.87-12.21) and 8.49(5.88-12.25), age 40-59y vs <40y 3.69(2.53-5.38) and 3.66(2.50-5.33), men vs women 1.88(1.41-2.51) and 1.26(0.91-1.48); hypertension 2.22(1.74-2.83) and 1.32(1.02-1.70), diabetes 1.88(1.46-2.43) and 1.16(0.89-1.52); and tobacco 1.29(1.02-1.63) and 1.28(1.00-1.63). Need for invasive and non-invasive ventilation was greater among patients in age-groups 40-49 and ≥60y and hypertension. Multivariate adjustment for social factors, clinical features and biochemical tests attenuated significance of all risk factors. CONCLUSION: Cardiovascular risk factors, age, male sex, hypertension, diabetes and tobacco-use, are associated with greater risk of in-hospital death among COVID-19 patients.

Educational status and COVID-19 related outcomes in India: hospital-based cross-sectional study.

OBJECTIVE: Association of educational status, as marker of socioeconomic status, with COVID-19 outcomes has not been well studied. We performed a hospital-based cross-sectional study to determine its association with outcomes. METHODS: Successive patients of COVID-19 presenting at government hospital were recruited. Demographic and clinical details were obtained at admission, and in-hospital outcomes were assessed. Cohort was classified according to self-reported educational status into group 1: illiterate or ≤primary; group 2: higher secondary; and group 3: some college. To compare intergroup outcomes, we performed logistic regression. RESULTS: 4645 patients (men 3386, women 1259) with confirmed COVID-19 were recruited. Mean age was 46±18 years, most lived in large households and 30.5% had low educational status. Smoking or tobacco use was in 29.5%, comorbidities in 28.6% and low oxygen concentration (SpO2 <95%) at admission in 30%. Average length of hospital stay was 6.8±3.7 days, supplemental oxygen was provided in 18.4%, high flow oxygen or non-invasive ventilation 7.1% and mechanical ventilation 3.6%, 340 patients (7.3%) died. Group 1 patients had more tobacco use, hypoxia at admission, lymphocytopaenia, and liver and kidney dysfunction. In group 1 versus groups 2 and 3, requirement of oxygen (21.6% vs 16.7% and 17.0%), non-invasive ventilation (8.0% vs 5.9% and 7.1%), invasive ventilation (4.6% vs 3.5% and 3.1%) and deaths (10.0% vs 6.8% and 5.5%) were significantly greater (p<0.05). OR for deaths were higher in group 1 (1.91, 95% CI 1.46 to 2.51) and group 2 (1.24, 95% CI 0.93 to 1.66) compared with group 3. Adjustment for demographic and comorbidities led to some attenuation in groups 1 (1.44, 95% CI 1.07 to 1.93) and 2 (1.38, 95% CI 1.02 to 1.85); this persisted with adjustments for clinical parameters and oxygen support in groups 1 (1.38, 95% CI 0.99 to 1.93) and 2 (1.52, 95% CI 1.01 to 2.11). CONCLUSION: Low educational status patients with COVID-19 in India have significantly greater adverse in-hospital outcomes and mortality. TRIAL REGISTRATION NUMBER: REF/2020/06/034036.

Gilteritinib monotherapy as a transplant bridging option for high risk FLT3-mutated AML with t(6;9)(p23;q34.1);DEK-NUP214 in morphological but not cytogenetic or molecular remission following standard induction chemotherapy.

We report a case of FLT3-mutated AML with t(6;9) in which induction chemotherapy with DA and midostaurin failed to achieve complete cytogenetic or molecular remission. Due to the COVID-19 pandemic and co-existing cellulitis, monotherapy with the selective FLT3-inhibitor gilteritinib was used as an alternative consolidation treatment option rather than further intensive chemotherapy. Gilteritinib was able to achieve complete molecular and cytogenetic remission despite the additional cytogenetic abnormality. This case provides supporting evidence for the use of single agent gilteritinib in high-risk primary refractory FLT3-mutated AML with t(6;9) prior to transplantation.

Variations in the financial impact of the COVID-19 pandemic across 5 continents: A cross-sectional, individual level analysis.

BACKGROUND: COVID-19 has caused profound socio-economic changes worldwide. However, internationally comparative data regarding the financial impact on individuals is sparse. Therefore, we conducted a survey of the financial impact of the pandemic on individuals, using an international cohort that has been well-characterized prior to the pandemic. METHODS: Between August 2020 and September 2021, we surveyed 24,506 community-dwelling participants from the Prospective Urban-Rural Epidemiology (PURE) study across high (HIC), upper middle (UMIC)-and lower middle (LMIC)-income countries. We collected information regarding the impact of the pandemic on their self-reported personal finances and sources of income. FINDINGS: Overall, 32.4% of participants had suffered an adverse financial impact, defined as job loss, inability to meet financial obligations or essential needs, or using savings to meet financial obligations. 8.4% of participants had lost a job (temporarily or permanently); 14.6% of participants were unable to meet financial obligations or essential needs at the time of the survey and 16.3% were using their savings to meet financial obligations. Participants with a post-secondary education were least likely to be adversely impacted (19.6%), compared with 33.4% of those with secondary education and 33.5% of those with pre-secondary education. Similarly, those in the highest wealth tertile were least likely to be financially impacted (26.7%), compared with 32.5% in the middle tertile and 30.4% in the bottom tertile participants. Compared with HICs, financial impact was greater in UMIC [odds ratio of 2.09 (1.88-2.33)] and greatest in LMIC [odds ratio of 16.88 (14.69-19.39)]. HIC participants with the lowest educational attainment suffered less financial impact (15.1% of participants affected) than those with the highest education in UMIC (22.0% of participants affected). Similarly, participants with the lowest education in UMIC experienced less financial impact (28.3%) than those with the highest education in LMIC (45.9%). A similar gradient was seen across country income categories when compared by pre-pandemic wealth status. INTERPRETATION: The financial impact of the pandemic differs more between HIC, UMIC, and LMIC than between socio-economic categories within a country income level. The most disadvantaged socio-economic subgroups in HIC had a lower financial impact from the pandemic than the most advantaged subgroup in UMIC, with a similar disparity seen between UMIC and LMIC. Continued high levels of infection will exacerbate financial inequity between countries and hinder progress towards the sustainable development goals, emphasising the importance of effective measures to control COVID-19 and, especially, ensuring high vaccine coverage in all countries. FUNDING: Funding for this study was provided by the Canadian Institutes of Health Research and the International Development Research Centre.

Progressive left and right heart dysfunction in coronavirus disease-19: Prospective echocardiographic evaluation.

AIMS: Cardiac dysfunction in coronavirus disease-19 (COVID-19) has been reported during acute phase but serial changes have not been well studied. To determine serial changes in type and severity of echocardiographic left and right heart functions we performed a prospective study. METHODS AND RESULTS: Successive COVID-19 patients at discharge from the hospital from June to December 2020 were enrolled. Clinical details were obtained and echocardiography was performed using Philips IE33X-Matrix. Follow-up evaluation was performed after 3 months. In total, 1789 COVID-19 patients were evaluated. Baseline echocardiography was performed in 1000 eligible patients (men 611, women 389). Mean age was 50.2 ± 15 years, hypertension was in 44.0%, diabetes in 49.4%, and coronary disease in 10.8%. COVID-19 was mild in 47.0%, moderate in 39.5%, and severe in 13.5%. Baseline cardiac parameters were more impaired in severe vs. moderate or mild COVID-19. At 3 months, in 632 patients where baseline and follow-up data were available, decline was observed in select left [left ventricular internal diameter in diastole +0.9 ± 0.2 mm, left atrial volume +7.6 ± 0.1 mL/m2, mitral E/e' +4.8 ± 0.1, and left ventricular ejection fraction (LVEF) -3.7 ± 0.2%] and right [right ventricular internal diameter in diastole +2.1 ± 0.1 mm, right atrial internal dimension +1.6 ± 0.1 mm, tricuspid Vmax +1.0 ± 0.1 cm, and tricuspid annulus plane systolic excursion (TAPSE) -2.7 ± 0.2 mm] heart variables (P < 0.001). Compared to mild COVID-19, decline was significantly greater in moderate/severe disease, LVEF -1.1 ± 0.3 vs. -3.8 ± 0.3%; mitral E/e' +3.2 ± 0.1 vs. +4.8 ± 0.1, tricuspid Vmax +0.3 ± 0.1 vs. +1.0 ± 0.1 cm, and TAPSE -0.7 ± 0.2 vs. -2.7 ± 0.2 mm (P < 0.001). CONCLUSION: This study shows impaired cardiac functions in severe and moderate COVID-19 compared to mild at hospital discharge and progressive decline in left and right heart functions at 3 months. Impairment is significantly greater in patients with moderate to severe disease.

A Deep Neural Network for Detecting Coronavirus Disease Using Chest X-Ray Images

The novel Covid-19 is one of the leading cause of death worldwide in the year 2020 and declared as a pandemic by world health organization (WHO). This virus affecting all countries across the world and 5 lakh people die as of June 2020 due to Covid-19. Due to the highly contagious nature, early detection of this virus plays a vital role to break Covid chain. Recent studies done by China says that chest CT and X-Ray image may be used as a preliminary test for Covid detection. Deep learning-based CNN model can use to detect Coronavirus automatically from the chest X-rays images. This paper proposed a transfer learning-based approach to detect Covid disease. Due to the less number of Covid chest images, we are using a pre-trained model to classify X-ray images into Covid and Normal class. This paper presents the comparative study of a various pre-trained model like VGGNet-19, ResNet50 and Inception_ResNet_V2. Experiment results show that Inception_ResNet_V2 gives the better result as compare to VGGNet and ResNet model with training and test accuracy of 99.26 and 94, respectively.

Mild cognitive impairment in COVID-19 survivors: Measuring the brain fog

Background Objective Methods Results/Conclusions The coronavirus disease 2019 (COVID-19) pandemic has been impacting individuals throughout the world. Millions have been affected, and while many have recovered, a growing number of recovered COVID-19 patients are reportedly facing neurological symptoms, described as “slow thinking,” “difficulty in focusing,” “confusion,” “lack of concentration,” “forgetfulness,” or “haziness in thought process.” These experiences of mental fatigue, associated with and related to mild cognitive impairments, may be conceptually defined as “brain fog.”To study the prevalence and severity of these brain fog symptoms in COVID-19 recovered patients, and examining their association with age, gender, and COVID-19 symptom severity.A total of 300 patients who tested positive for Real-Time Reverse Transcriptase–Polymerase Chain Reaction (RT-PCR) for SARSCoV-2 during April–August 2020 were included in our study after complete recovery from their acute illness. They were assessed for brain fog symptoms using the 9-item validated Wood’s mental fatigue inventory.The overall cumulative prevalence of any components of brain fog was 34%, with a mean score of 6.11 ± 1.7 in those who experienced it. Males were more affected than females (42.3% vs. 29.1%) with males scoring higher than females. The mean score was higher in severe ill and Intensive Care Unit (ICU) patients and those who required oxygen or were on a ventilator. [ABSTRACT FROM AUTHOR] Copyright of International Journal of Mental Health is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Long COVID following mild SARS-CoV-2 infection: characteristic T cell alterations and response to antihistamines.

Long COVID is characterized by the emergence of multiple debilitating symptoms following SARS-CoV-2 infection. Its etiology is unclear and it often follows a mild acute illness. Anecdotal reports of gradual clinical responses to histamine receptor antagonists (HRAs) suggest a histamine-dependent mechanism that is distinct from anaphylaxis, possibly mediated by T cells, which are also regulated by histamine. T cell perturbations have been previously reported in post-viral syndromes, but the T cell landscape in patients who have recovered from mild COVID-19 and its relationship to both long COVID symptoms and any symptomatic response to HRA remain underexplored. We addressed these questions in an observational study of 65 individuals who had recovered from mild COVID-19. Participants were surveyed between 87 and 408 days after the onset of acute symptoms; none had required hospitalization, 16 had recovered uneventfully, and 49 had developed long COVID. Symptoms were quantified using a structured questionnaire and T cell subsets enumerated in a standard diagnostic assay. Patients with long-COVID had reduced CD4+ and CD8+ effector memory (EM) cell numbers and increased PD-1 (programmed cell death protein 1) expression on central memory (CM) cells, whereas the asymptomatic participants had reduced CD8+ EM cells only and increased CD28 expression on CM cells. 72% of patients with long COVID who received HRA reported clinical improvement, although T cell profiling did not clearly distinguish those who responded to HRA. This study demonstrates that T cell perturbations persist for several months after mild COVID-19 and are associated with long COVID symptoms.

SOCIAL ENTERPRISE AND ENTREPRENEURSHIP: LESSON LEARNT FROM PANDEMIC CRISIS

Under current economical water transport, Indian government liberalized flexible policies for boosting exports, encountering dragonmodel, encouragement to individual women entrepreneurs belongs to unorganized sector through ease of doing business with close coordination of respective State Government as a ray of hope of generating more employment after Post Covid-19 pandemic counter measures in central India region 1.1NEED FOR IMPLEMENTING EASE OF DOING BUSINESS In order to revive Indian Economy & boost Indian Exports, Post Covid-19, by exploring & honing the skills of unorganized sectors from vocal to global emergence through e-Markets. Recently Chief Minister Shivraj Singh Chauhan called in Rewa , Madhya Pradesh, the second largest state in India ,does not having lacking of skills or talent under self-reliant M.P., by forwarding rural authentic crafts , toys after conducting field survey of traditional toy manufacturing units .Realizing the potential for new segment for traditional zardozi work in Bhopal, skilled unorganized women youth sparsely distributed n the state along with consultancy support from IIM Indore & MPAVNfor a period of 3 years along with other high potential engineering clusters, Health care .Heavy Engineering , food processing, water treatment plants etc. leads to innovative start up for unemployed youth from 224 Engineering Colleges, 114 Polytechnic, 415 ITI'S , 135 Skill Development centers (SDC) along with Vocational Development centers make perfect place for startup hubs like Detroit in USA. [...]in order to encounter Chinese toy competition Indian government succeeded in creating congenial atmosphere for preserving art, culture of skilled Wo(man) unorganized crafts Wo(man) ecosystem EXIM Policy 2020-2025 to nurture & promote start up culture within the Madhya Pradesh state. 1.2OBJECTIVE OF COMPLIANCE OF EASE OF DOING BUSINESS * To strengthen the unorganized women startup culture by providing E-markets with close coordination from FIEO, World Bank, IMF Etc. * To nurture unemployed technical high educate youths by motivating them through innovates ideas and * EOI under public private partnership mode. 1.4STARTUP-HAND HOLDING WITH RESPECTIVE DEMOGRAPHIC STATE GOVERNMENT (a) The eligible start up should not have received more than 10 Lakhs of monetary support under central or start Government Scheme. (b) The eligible share holding Indian promoters in the startup should be minimum 51% at the time of application submission for the incubator under the scheme. (c) The incubator must also have a full time Chief Executive Officer (CEO) 1.5 NODAL AGENCIES For all registered MSME'Sfalls under the respective state, concerned District Industries Centre (DIC) act as nodal agency to promote organizational ecosystem along with registered unorganized women crafted entrepreneurs. 2.0 VISION FOR EASE OF DOING BUSINESS * To establish, India as preferred destination for FDI& FII by providing an conducive ecosystem * To support MSME'S Entrepreneurial culture in the South Asia Pacific Region Mission * To align all Indian states with the 'Self-Reliant India, Flagship Initiative of the Government of India * To develop a sustainable ecosystem of innovation, Research & Development, Manufacturing in the Central India region. * To eradicate and minimizing corruption for granting permission through single window Scheme within 30 days for new start up. 3.0 (A) STRATEGIES FOR INNOVATIVE INCUBATION NETWORK * The Individual state Government would foster on constructing Incubation centers with in Engineering Colleges, Management Institutes & Professional Colleges fall across selected state to provide innovative ideas along with plug and innovative road map facilities to young entrepreneurs, budding students. * These incubation centers transform into innovative network to build a collaborative platform among Host institutes/Incubators which can be used to learn the best practices in order to understand the requirements of startup's/ entrepreneurs. * The Network shall also provide a platform to identify National /International mentors from institutes or corporate industries. * The incubation network shall play a play a vital role in providing handholding to startup's in funding support assistance in legal compliance & regulatory support from respective state Government. 3.0 (B) VENTURE CAPITALIST NETWORK / CALIPHS INVESTORS * The identification & selection of an caliphs investors /Venture capitalist plays a crucial role in the journey of any new start up. * It bridges the gap between start up and their initial stage required capital support . * It helps in building sustainable eco-system within demographic region. 3.1POLICIES FOR EASE OF DOING BUSINESS (AVAILABILITY OF INCUBATION FACILITY) * Ease in approvals & Registrations-A single window clearance. * Single window service should be establish in order to give clearance to recognized 41,061 start up's as on December2020, along with flexible labor laws & reforms like E-land records, in a demographic region. 3.2 INCENTIVES TO START UP'S & EXISTING ENTREPRENEURS According to MSME Act 2020, for availing incentives the incubators fall under state demographic region ,eligible to register under MSMEUAN ,irrespective of rural, urban, semi urban landscape etc. 3.2.1 INTEREST SUBSIDY Eligible new start up's shall be provided with extended tax holiday for a period of 3 years on the rate of interest paid on loans obtained from Nationalized banks /financial institutions subjected to the maximum limit of INR 25 lakhs per year linked to turn over per year . 3.2.2 LEASE RENTAL SUBSIDY Reimbursement of 25% lease rental subsidy to start up's units established in the state operating from incubators shall be eligible for a period of 3 years subject to a ceiling of 5 lakhs per year from the date of rent payment to incubator. 3.2.3 PATENT/QUALITY PROMOTION SUBSIDY Reimbursement for patent & quality certification per unit up to a limit of INR 5 lakhs for Domestic and INR 25 lakhs for International Quality Certification after securing Certification from country's Certification Boards. 3.2.4START UP MARKETING ASSISTANCE Under membership of Engineering and Exports Promotion council (EEPC), Federation of exports organization (FI O), eligible start up's for participation in national & International Trade fairs &conferences anywhere in the globe. 4.0 EASE OF DOING POLICY APPLICABILITY The state incubation & start up policy shall be applicable to mentioned below thrust areas as per 2021-2022 new policies. * E-Commerce Marketing, Digital Marketing * Textile, Space Technology, Information Technology & Other Allied Areas * Healthcare, Pharmaceuticals

Novel deep neural network technique for detecting environmental effect of COVID-19

The studies claim that COVID-19 has positive impacts on the environment because it minimizes air pollution, water pollution, and noise pollution due to lockdown. On the contrary, COVID-19 is harming the environment due to increased medical wastage. COVID-19 has been declared a pandemic by the World Health Organization (WHO). Due to the exponential growth of COVID-19 cases people using large quantities of medical accessories to shield themselves from coronavirus, a large amount of medical wastage is produced per day. This medical wastage is a major concern for the expert because this medical waste is not adequately handled. The early detection of the COVID patient is the only solution to control this coronavirus. Several COVID detection models have been proposed in the last few months. Most of the existing models have a high false-positive rate where COVID patients are classified as healthy. To address this problem, this paper explores the positive and negative environmental consequences of COVID-19 and suggests a novel method based on artificial intelligence (AI) to identify COVID-19 disease. A comparative analysis of different previously trained models such as Visual Geometry Group Network (VGGNet-19), Residual Network (ResNet50), and Inception ResNet V2 is presented in this paper. Experimental results show that Inception_ResNet_V2 is a better choice for COVID detection. It has a minimal false-positive rate and offers 99.26% and 94% higher training and test accuracy compared to VGGNet and ResNet, respectively. [ABSTRACT FROM AUTHOR] Copyright of Energy Sources Part A: Recovery, Utilization & Environmental Effects is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Macrolevel association of COVID-19 with non-communicable disease risk factors in India.

BACKGROUND & AIMS: Greater COVID-19 related mortality has been reported among persons with various non-communicable diseases (NCDs). We performed an ecological study to determine the association of state-level cases and deaths with NCD risk factors and healthcare and social indices. METHODS: We obtained cumulative national and state-level data on COVID-19 cases and deaths from publicly available database www.covid19india.org from February to end November 2020. To identify association with major NCD risk factors, NCDs, healthcare related and social variables we obtained data from public sources. Association was determined using univariate and multivariate statistics. RESULTS: More than 9.5 million COVID-19 cases and 135,000 deaths have been reported in India towards end of November 2020. There is significant positive correlation (Pearson r) of state-level COVID-19 cases and deaths per million, respectively, with NCD risk factors- obesity (0.64, 0.52), hypertension (0.28, 0.16), diabetes (0.66, 0.46), NCD epidemiological transition index (0.58, 0.54) and ischemic heart disease mortality (0.22, 0.33). Correlation is also observed with indices of healthcare access and quality (0.71, 0.61), urbanization (0.75, 0.73) and human (0.61, 0.56) and sociodemographic (0.70, 0.69) development. Multivariate adjusted analyses shows strong correlation of COVID-19 burden and deaths with NCD risk factors (r2 = 0.51, 0.43), NCDs (r2 = 0.32, 0.16) and healthcare (r2 = 0.52, 0.38). CONCLUSIONS: COVID-19 disease burden and mortality in India is ecologically associated with greater state-level burden of NCDs and risk factors, especially obesity and diabetes.

Increased Complement Receptor-3 levels in monocytes and granulocytes distinguish COVID-19 patients with pneumonia from those with mild symptoms.

BACKGROUND: The reasons why some patients with COVID-19 develop pneumonia and others do not are unclear. To better understand this, we used multiparameter flow cytometry to profile circulating leukocytes from non-immunocompromised adult patients with PCR-proven COVID-19 and specifically compared those with mild symptoms with those who had developed pneumonia. METHODS: Using clinically validated antibody panels we studied leukocytes from 29 patients with PCR-proven COVID-19. Ten were hypoxic requiring ventilatory support, eleven were febrile but otherwise well, and eight were convalescing having previously required ventilatory support. Additionally, we analysed patients who did not have COVID-19 but received ventilatory support for other reasons. We examined routine Full Blood Count (FBC) specimens that were surplus to routine diagnostic requirements; normal ranges were established in a historic group of healthy volunteers. FINDINGS: We observed striking and unexpected differences in cells of the innate immune system. Levels of CD11b and CD18, which together comprise Complement Receptor 3 (CR3), were increased in granulocytes and monocytes from hypoxic COVID-19 patients, but not in those with COVID-19 who remained well, or in those without COVID-19 but ventilated for other reasons. Granulocyte and monocyte numbers were unchanged, however Natural Killer (NK) cell numbers were two-fold higher than normal in COVID-19 patients who remained well. INTERPRETATION: CR3 is central to leukocyte activation and subsequent cytokine release in response to infection. It is also a fibrinogen receptor, and its over-expression in granulocytes and monocytes of patients with respiratory failure tables it as a candidate effector of both the thrombotic and inflammatory features of COVID-19 pneumonia, and both a biomarker of impending respiratory failure and potential therapeutic target. NK cells are innate immune cells that retain immunological memory. Rapid expansion of memory NK cells targeting common antigens shared with other Coronaviruses may explain why most patients with COVID-19 do not develop respiratory complications. Understanding the innate immune response to SARS-CoV-may uncover why most infected individuals experience mild symptoms, and inform a preventive approach to COVID-19 pneumonia in the future.

Characteristics and Outcomes of Hospitalized Young Adults with Mild Covid -19

BACKGROUND: Stormy course has been reported among hospitalized adults with COVID-19 in high- and middle-income countries. To assess clinical outcomes in consecutively hospitalized patients with mild covid-19 in India we performed a study. METHODS: We developed a case registry of successive patients admitted with suspected covid-19 infection to our hospital (n=501). Covid-19 was diagnosed using reverse transcriptase polymerase chain reaction (RT-PCR). Demographic, clinical, investigations details and outcomes were recorded. Descriptive statistics are presented. RESULTS: Covid-19 was diagnosed in 234 (46.7%) and data compared with 267 (53.3%) negative controls. Mean age of covid-19 patients was 35.1±16.6y, 59.4% were <40y and 64% men. Symptoms were in less than 10% and comorbidities were in 4-8%. History of BCG vaccination was in 49% cases vs 10% controls. Cases compared to controls had significantly greater white cell (6.96+1.89 vs 6.12+1.69x109 cells/L) and lower lymphocyte count (1.98+0.79 vs 2.32+0.91x109 cells/L). No radiological and electrocardiographic abnormality was observed. All these were isolated or quarantined in the hospital and observed. Covid-19 patients received hydroxychloroquine and azithromycin according to prevalent guidelines. One patient needed oxygen support while hospital course was uncomplicated in the rest. All were discharged alive. Conversion to virus negative status was in 10.2±6.4 days and was significantly lower in age >40y (9.1±5.2) compared to 40-59y (11.3±6.1) and ≥60y (16.4±13.3) (p=0.001). CONCLUSIONS: This hospital-based registry shows that mildly symptomatic or asymptomatic young covid-19 patients have excellent prognosis.