Parenchymal lung abnormalities, clinical severity and outcomes in patients with COVID 19 pneumonia: a prospective longitudinal observational study

Background: Clinical outcome and parenchymal lung abnormalities (PLA) data from hospitalized patients with COVID 19 pneumonia are limited. Objective(s): (1) Understand and compare the patterns of PLA on high resolution computed tomographic (HRCT) at admission, 4-8 weeks post-admission in all patients and 10-12 weeks post-admission in a subgroup of patients (2) follow up their general health status on phone 6 months post admissionMethods: Prospective, observational study of consecutive adult patients hospitalized with RT-PCR confirmed COVID-19 pneumonia in a tertiary centre, India. Clinical data and HRCT image patterns and distribution of PLA at admission, 4-8 weeks in all patients and at 10-12 weeks in a subgroup of patients were analysed using a novel, composite radiological score (CRS). Surviving patients were followed up telephonically 6 months later. Finding(s): Of 179 patients, HRCT features were ground glass opacity (144, 80.4%), consolidation (23, 12.8%) and reticulation (7, 4%) at admission. 74% demonstrated resolving PLA with 14% showing complete resolution at 4-8 weeks. Fine reticulations were seen in 12% at 8 weeks and 20% in a subgroup of 44 patients who had persisting symptoms at 10-12 weeks. CRS correlated well with clinical severity and recovery (p=0.003). At 6 months, 144 responded to the phone follow up, reported no functional impairment and had returned to their pre-COVID health status. Conclusion(s): PLA resolved in 88% at 8 weeks and all the 144 patients who were followed up at 6 months reported return to pre-COVID 19 health status. This is quite reassuring amidst concerns of 'long COVID'.

Diaphragm dysfunctions among patients recovered from COVID

Introduction: Due to the presence of ACE 2 receptors in the diaphragm, its involvement is likely among patients with Covid 19. This study aimed to describe the presence of diaphragm dysfunction among patients who have recovered from Covid 19. Method(s): The thickness fraction (TF) of the diaphragm was assessed via ultrasonography in supine or semirecumbent posture in the right hemidiaphragm in the zone of apposition. A TF of less than 20% was classified as diaphragm dysfunction. Result(s): A total of 50 (males 34) with a mean age of 49.36 years and recovered from acute Covid were enrolled in the study. Among these 50 participants, 11, 12, and 27 had recovered from mild, moderate, severe Covid, respectively. Overall, 37(74%) subjects were hospitalized, 13 (26%) cases were home quarantined;30 (60%) subjects required oxygen during the course of illness. The average weeks from the onset of symptoms to enrolment was 7.1 weeks. Among the study participants, 9 (18%) subjects (8 males and 1 female) had diaphragm dysfunction (thickness fraction less than 20%). Among subjects who were managed at the hospital (n=37), diaphragm dysfunction was observed in 8 (21.6%). Of patients who had diaphragm dysfunction, 3 out of 9 had mild disease, whereas 6 of 9 (66.6%) subjects had severe illness. sub7 out of 9 (77.7%) subjects were hospitalized and 6 required oxygen during the illness. Conclusion. A significant percentage of COVID-19 survivors had diaphragm dysfunction which may also attribute to the persistent shortness of breath. There is an urgent need for large-scale (nationwide) assessment of diaphragm function among the survivors.

Persistent symptoms and spirometric abnormalities after COVID-19: A prospective cohort study

Introduction: The persistence of symptoms beyond 4 weeks of SARS-CoV-2 infection is referred to as long COVID. There is lack of data about the clinical determinants and natural history of this condition. Aims & objectives: We aimed to determine the risk factors, symptomatology, spirometric abnormalities and evolution of long COVID over a 1-year period. Method(s): We enrolled adult patients at 1-4 months after diagnosis of COVID-19. The demographics, COVID-19 history, clinical symptoms and spirometric results were recorded. Follow-up assessments were done in-person or telephonically at 4-8 months and 8-12 months, respectively. Result(s): We enrolled 128 patients (69% male) with median (IQR) age of 49 (37-56) years. Among these, 99 (77%) needed hospitalization, and 47 (37%) received oxygen for COVID-19. At the 1st assessment at median (IQR) of 64.5 (39.5-90) days after COVID-19 onset, 86 (67%) patients had symptoms, most commonly dyspnea (34%), fatigue (19%) and cough (19%). Reduced FVC (<80% of predicted) was found in 61% subjects. On multivariate analysis, the predictors of symptomatic long COVID were female gender (OR, 4.1;95% CI: 1.5-11;p=0.006) and dyspnea during acute COVID-19 (OR, 3.6;95% CI:1.1-11.3;p=0.03). The predictors of reduced FVC were dyspnea (OR, 4.4;95% CI: 1.6-12;p=0.004) and oxygen therapy (OR, 5.6;95% CI: 1.5-21;p=0.01) during acute COVID-19. The proportion of symptomatic patients reduced between 1st and 2nd assessment (67% vs 37%, p<0.001) and then plateaued at 3rd assessment (42%). Conclusion(s): Persistent symptoms are common over a 1-year follow-up among survivors of COVID-19. Female gender and dyspnea during acute COVID-19 may predict development of long COVID.

Covid and Kidney-Oea Friend or Foe

BACKGROUND: Covid-19 has been associated with worsened prognosis in patients with kidney involvement. The incidence of acute kidney injury (AKI) in Coronavirus-19 disease (COVID-19) patients ranges from 0.5% to 35%. AIM OF THE STUDY: We evaluated the prevalence severity risk factors and prognosis in patients with COVID-19 having AKI or CKD. METHOD(S): We conducted a retrospective analysis of 70 patients with Covid-19 presenting to nephrology department. Outcome of patients with CKD stage 1-2 was compared with that of patients with AKI kidney transplant and CKD stage G3a-G5D. RESULT(S): In this study, 15 (21.4%) patients had CKD stage G1-2, 18 (25.71%) had CKD stage G3a-5c and 11 (15.7%) had CKD stage G5d. Eight patients (11.4%) were with functioning renal allograft (CKD-T). Four (5.71%) developed AKI and 14 patients (20%) had acute on CKD. Overall;in-hospital mortality was 27.14% (n = 19). Of these, 3 patients (15.78%) had CKD stage G1-G2, 7 (36.84%) had CKD stage G3a-5c, 3 had CKD G5D, 2 (10.55%) had acute on CKD, one had AKI and 3 patients had a functioning kidney allograft. Baseline & nadir serum creatinine & eGFR of CKD stage 1-2, CKD Stage 3a-5c and stage CKD-t was 0.87 (eGFR 82), 7.34 (eGFR 11.61), 3.24 (eGFR-24.86);and 0.74 (eGFR 93.55), 5.37 (eGFR 16.55) and 1.85 (eGFR 42.28) respectively. CONCLUSION(S): A rather low prevalence of AKI in our Covid-19 patients, lower mortality in acute on CKD patients & improvement in eGFR in CKD & transplant patient in our study suggest that coronavirus has minimal, if any direct toxic effect on kidney. But presence of renal failure worsens the outcome of Covid-19 disease.

Impact of Government reforms on Indian MSMEs during COVID 19: An analytical study

The whole world is going through a revolutionary change but this time it is not because of changes in technology or government policies. The entire world has changed in order to adapt to the unwarranted pandemic situation created by COVID-19 virus. The economic growth is negative across the world and the Indian economy is no exception. The worst affected is the micro, small and medium scale sector of India, which faced some major challenges. The paper explores the problems these MSMEs (Micro, Small & Medium scale Enterprises) faced during this pandemic and the crisis management that the government can do for the revival of this sector. The paper analyses the government reforms and the changes that Indian MSMEs need to bring to survive such hard times.

Erratum: 422 Patient satisfaction with telehealth in cardiology: Lessons and caution from the response to COVID-19 restrictions (European Heart Journal Supplements DOI: 10.109310.1093/eurheartj/suaa208)

In the originally published version of 422, there was an error in an author s surname. The incorrect spelling wasNicholas Coffee. The correct spelling is Nicholas Coffey. This error has been corrected online. Copyright © 2020 Oxford University Press. All rights reserved.

Online Educating System and Musculoskeletal Issues in Students: A Concern

Sudden change in education system from offline to online educating system due to pandemic (Covid 19) had a drastically impact on way of teaching methodologies in school and colleges. Black board teaching is replaced by a online teaching by using of various app or software program like-blackboard, Google meet and zoom etc. It was a new experience for teachers and as well as for student. Instant transformation of in-person teaching to e-learning has increased the academic demand of school student's entails rigorous work (assignments, tests and writing examinations). These academic tasks involve the constant use of the computers;as such students spend most of their times in computer laboratories. Several reasons could be advanced to explain the prevalence of musculoskeletal problems among the school students. Copyright © 2022, Anka Publishers. All rights reserved.

Volatility Dynamics of BITCOIN amidst COVID-19 through GARCH Modelling

Bitcoin becoming more popular among investors as it provides high returns but due to unregulated property, it is highly volatile in nature. Amidst pandemic spread across world, anyone who hold bitcoin would have keenly watched market with alarming fluctuations recently. Investors are looking for assets that are not impacted by slowdown triggered by lockdown. The study aims to analyze volatility dynamics of Bitcoin from FY2015 to FY2020 by performing general GARCH analysis for modelling by extracting Daily price data from coinmarketcap.com. The study incorporates Augmented Dickey Fuller test for checking stationarity of the series, ARCH LM test for heteroskedasticity and Ljung Box test for determining the mean equation and estimating the variance equation with GARCH (1,1) model in EVIEWS. The results approve that GARCH model is better model works better in period of the high volatility. © Indian Institute of Finance.

Neonatal Fever in the COVID-19 Era:Odds of a Serious Bacterial Infection

Background: Pre-COVID literature suggests that viral infections account for about 90% of cases of fever in infants ≤56 days of age. Given the reduction in non-SARS-CoV-2 viral infections observed during the COVID-19 pandemic, we sought to determine if SBI accounted for a higher than usual proportion of fever cases in this age group during this period. Methods: We performed a multi-center, retrospective chart review of infants age ≤56 days presenting with fever to emergency departments (EDs) of six community hospitals affiliated with the same academic children's hospital. We compared the incidence of SBIs, viral meningitis, and viral bronchiolitis during March 2020 - February 2021 (pandemic year) with the same calendar months in the two preceding years (pre-pandemic years). Results: From March 2018 to February 2021, 543 febrile infants presented to the EDs, 95 during the pandemic year (Mar 2020-Feb 2021) compared to 231 and 217 in the pre-pandemic years (Mar 2018- Feb 2019 and Mar 2019-Feb 2020, respectively). The incidence of SBI was 28.4% (27/95) during the pandemic year compared to 11.6% (27/231) and 6.9% (15/217) in the pre-pandemic years (p<0.001);bacteremia 13.7% (13/95) during the pandemic year compared to 2.2% (5/231) and 1.4% (3/217) in the pre-pandemic years;and UTI 19% (18/95) during the pandemic compared to 11.3% (26/231) and 6.5% (14/217) in the pre-pandemic years (TABLE 1). Five patients were diagnosed with bacterial meningitis over the three-year period, four of them during the pandemic year, a rate of 4.2% (4/95). Rate of positivity for viral CSF PCR during the pandemic year was 6.4% (3/47) compared to 20.8% (25/120) and 20.4% (23/113) in the pre-pandemic years (Mar 2018-Feb 2019 and Mar 2019-Feb 2020 respectively;p=0.070). 2.1% (2/95) febrile young infants were admitted with a co-morbid diagnosis of bronchiolitis during the pandemic year compared to 4.3% (10/231) and 6.0% (13/217) in the pre-pandemic years (Mar 2018-Feb 2019 and Mar 2019-Feb 2020 respectively;p=0.310). The risk ratio for SBI (FIGURE 1) for pre-pandemic year 1 (referent;Mar 2018-Feb 2019) compared to the pandemic year (Mar 2020-Feb 2021) was 2.43 (95% CI 1.51-3.92;Bonferroni adjusted p=0.001);and the risk ratio for pre-pandemic year 2 (referent;Mar 2019-Feb 2020) compared to the pandemic year was 4.11 (95%CI 2.29-7.37;Bonferroni adjusted p<0.001). Conclusion: The COVID-19 pandemic led to an increase in the proportion of SBIs among febrile infants ≤56 days of age. This is likely a result of reduction in non-SARS-CoV-2 viral infections. Greater vigilance is thus warranted in the evaluation of febrile infants during the COVID-19 pandemic. (Table Presented).

THE RELATIONS BETWEEN NEUTROPHIL-LYMPHOCYTIC RATIO AND DIFFERENT COMORBIDITIES IN CORONAVIRUS-INFECTED PATIENTS

Objective: The study's aim was to determine the neutrophil-to-lymphocyte ratio (NLR) is most helpful predictor factor for COVID-19-related serious illness. Methods: A total of 51 patients with COVID-19 infection with laboratory-confirmed reports were enrolled in this study: Age, neutrophil-to-lymphocyte (LYMLYM) ratio (NLR), an examination, and comparison. Data analysis, compilation, and report writing were completed based on the acquired data. Using SPSS.ver-23, standard statistical procedures were used to analyze the mean and standard deviation, as well as the Pearson correlation. If p<0.05, it is deemed significant. Results: The mean hemoglobin level was 12.44±3.55%, the mean platelet count was 1.95±0.65 cumm, the mean white blood cell count was 17400 ±6455.22 cumm, and the mean NLR was 5.72±1.24. When we looked at people who had hypertension, diabetes mellitus, and high cholesterol, we found that the NLR value was significantly higher in people with these diseases (p=0.05). Conclusion: We found that NLR is an excellent way to predict COVID-19-infected patients who are likely to get a lot of other illnesses and have a lot of problems early on.

Impact of Air Quality Variations on COVID-19 Determinants: A Study of Four Selected Cities of India

Governments across the world are making considerable efforts in confronting COVID-19, from nationwide lockdowns to hygiene measures and maintaining social distancing. But at the same time, role of aerosols or/and the high concentrations of fine particulate matter or/and AQI levels in infection transmission and increasing the prevalence, morbidity and mortality of pandemic has been largely unexplored specifically in India where pollution attains peak in October and November every year. In the present study, we collected data regarding air quality index and COVID-19 determinants of four Indian cities : Bangalore, Delhi, Mumbai and Shillong from 1 October 2020 to 16 November 2020. We performed an analysis of variance on the regression model to estimate and quantify the strength of relationship between COVID-19 determinants and air pollution index (AQI). Results show that AQI has a significant impact on both response variables, that is COVID-19 cases as well as mortality (p < 0.05 at 95% confidence level) in Delhi, Mumbai and Bangalore (p < 0.05) but in Shillong no impact of AQI on COVID-19 cases and AQI (p = 0.343), as well as deaths (p = 0.664), was observed. We conclude that it is both conceivable and reasonable to suspect the role of increased AQI levels in aggravating COVID-19 morbidity and mortality. Thus, we recommend that critical meteorological conditions, like haze/smog caused by factors, like stubble burning or firing crackers should be predicted and monitored more systematically as they may lead to deterioration of respiratory problems. As the whole world is striving to fight against the deadly pandemic, it is extremely imperative to focus not only on human health as a part of response but also on global planetary health. Short term measures that can minimize supplementary risks, like adverse weather situations including pollution, poor air quality should be considered more meticulously and judiciously so that new flares of COVID-19 morbidity and mortality can be restricted. © 2022 - Kalpana Corporation.

The silver lining behind the dark cloud: Exploring the psycho-social factors impacting successful adaptation during the COVID-19 pandemic

While most studies have been reporting the psychological issues being faced by the public due to the global spread of coronavirus and sudden restrictions and changes accompanying it, the present study attempted to explore dynamic human experiences during the COVID-19 pandemic and resultant lockdown, so as to understand the psycho-social factors that acted as adaptive resources or as buffers to maintain a stable mental state amidst this crisis. In-depth telephonic interviews with 30 participants were conducted to explore their experiences in dealing with the COVID-19 pandemic and the lockdown. Thematic analysis performed to identify the positive and protective factors that helped people adapt in a healthy way revealed that although the initial response of the participants to the pandemic was "optimistic bias" followed by downplaying the seriousness of the issue, later they demonstrated increased realization and acceptance to the seriousness of the situation. Upon realizing the situation, their positive psychological resources acted as a buffer against the ill effects of the pandemic, and they used both cognitive and behavioral coping. The study clearly demonstrates that crisis in life is not just a source of stress, anxiety, and uncertainty but also an opportunity to test one's psychological resources to learn and grow.

Financing for SDGs in India in Post Pandemic era - Challenges & Way forward

In 2015, a resolution known as Agenda 2030 was passed by United Nations General Assembly in which seventeen goals for Sustainable Development were laid down for global dignity, peace and prosperity. The post- pandemic era became full of uncertainties in pursuing those Sustainable Development Goals (SDGs) and its implementation became a challenge especially for the developing economies like India. The country is facing a tremendous gap in arranging for resources to meet the climatic changes and attaining the SDGs. India requires 170 billion dollars per year from 2015-2030 to fulfill the Sustainable Development Goals as per the estimation done by National Determined Contribution, a body setup after Paris agreement 2015 to monitor the efforts of the country towards reducing national emissions and adapting to climate change. There is a huge concern amongst the various agencies on exploring the ways to fill this financing gap especially after the economic slowdown seen in the post pandemic era. This research paper analyses the challenges imposed by the COVID 19 pandemic on financing for SDGs and also explores the options to mitigate them. The articles and research papers related to SDG financing are reviewed by the researchers to arrive at the above mentioned statements. This paper is an attempt to draw the attention of worldwide authorities towards this grim situation as sustainable finance is far from reality in India and requires immediate up scaling. © The Electrochemical Society

AECNet: Attentive EfficientNet For Crowd Counting

In the COVID pandemic situation, crowd counting became one of the tools to monitor if the social-distancing norms are being followed or not. However, in designing crowd counting algorithm, there are several challenges such as background noise, camera-to-objects distance, occlusion, and variations due to illumination, scale, and viewpoint. In this research, we propose a novel pipeline for density estimation in crowd counting. The proposed pipeline makes use of an encoder-decoder-based architecture in which we explore the family of EfficientNets for the encoder architecture. For the decoder, we propose a deeper attention network to assist the model in a better distinction between foreground and background pixels. We empirically show that for a crowd counting dataset, the use of average pooling operation for any backbone architecture of encoder gives a significant improvement in performance. In terms of Mean Absolute Error, the proposed pipeline outperforms existing state-of-the-art techniques by a large margin on large-scale and small-scale counting datasets, UCF-QNRF and UCF_CC_50 dataset. We also achieve state-of-the-art results on the ShanghaiTech and Mall datasets. We additionally propose a crowd counting dataset captured using drones. We perform benchmark experiments on this dataset with existing and the proposed methods.The proposed dataset can be found at http://www.iab-rubric.org/resources/CrowdUAV.html.

A Lightweight COVID-19 predictive model with Synthetic CT images using Conditional GAN Knowledge Distillation

With the increase in the cases of COVID-19, the necessity of improving testing and treatment is increasing rapidly. Many techniques are currently being used by the medical fraternity for detection of COVID-19 in a patient such as RT-PCR, Chest CT Scan Images, Chest X-Ray scans, etc. Among these techniques, a Chest CT scan has proven to be highly accurate for screening of the novel coronavirus. But a trained professional like a radiologist is needed to analyze the CT scan and determine whether the patient is positive or not. Due to the sudden spike in the number of infections, there is a shortage of such professionals. A machine learning based system can be highly effective in assisting the doctors if it can accurately predict COVID-19 from a chest CT scan. However, the number of chest CT scan images available are very less in order to build an accurate machine learning based predictive model. We present a generative model for data augmentation of COVID-19 positive and negative Chest CT images. We use Conditional DCGAN for generating nearly 1502 COVID-19 positive and 1510 negative images thus extending a publicly available dataset. We also build predictive models using pre-trained models like VGG and ResNet to detect COVID-19, achieving an accuracy upto 87.7%. We also apply the technique of knowledge distillation to build a lightweight and computationally cheap predictive model that has an accuracy of 86.2% and is nearly 11 times smaller than the best model available on the dataset. © 2022 IEEE.

KDV-Explorer: A Near Real-Time Kernel Density Visualization System for Spatial Analysis

Kernel density visualization (KDV) is a commonly used visualization tool for many spatial analysis tasks, including disease outbreak detection, crime hotspot detection, and traffic accident hotspot detection. Although the most popular geographical information systems, e.g., QGIS, and ArcGIS, can also support this operation, these solutions are not scalable to generate a single KDV for datasets with million-scale data points, let alone to support exploratory operations (e.g., zoom in, zoom out, and panning operations) with KDV in near real-time (< 5 sec). In this demonstration, we develop a near real-time visualization system, called KDV-Explorer, that is built on top of our prior study on the efficient kernel density computation. Participants will be invited to conduct some kernel density analysis on three large-scale datasets (up to 1.3 million data points), including the traffic accident dataset, crime dataset and COVID-19 dataset. We will also compare the performance of our solution and the solutions in QGIS and ArcGIS.

Identification of patients who can safely undergo same day discharge following atrial fibrillation ablation: Lessons learned in the COVID-19 era

Background: Historically, pts have been admitted for overnight observation following atrial fibrillation (AF) ablation. The COVID-19 pandemic ushered the need to consider same day discharge (SDD). It remains unclear how to identify pts who can safely undergo SDD. Objective: To evaluate acute (within 4 hrs) and subacute (within 24 hrs) safety of SDD post AF ablation;we also sought to identify predictors of safe discharge. Methods: All pts undergoing AF ablation at our center following the end of the COVID imposed lockdown were enrolled. In each pt, ICE guided single transseptal puncture using the VersaCross® (Baylis) system was performed. Following ablation, protamine was not administered;all femoral venous access sites were closed with Vascade™ (Cardiva Medical) closure devices. Pts ambulated after 2 hrs of bedrest. Pts who had SDD were compared to those who stayed for overnight observation. Results: The cohort included 226 pts (65 ± 10 yrs, 157 [69%] male, 118 [52%] PAF, CHA2DS2 -VASc 2.4 ± 1.7). Cryo PVI was performed in 193 (85%) pts;34 (15%) pts had a redo procedure. SDD was attempted in 126 pts and successfully accomplished in 115 (91%) pts at 251 + 72 minutes from procedure end. The most common reason for failed SDD attempt was access site oozing necessitating additional bedrest. No SDD pt had a major complication. Overnight observation was performed in 100 (44%) pts, most commonly due to physician/pt preference. Compared to pts who had SDD, these pts were older and more likely to have heart failure and history of TIA/stroke. Oozing within the first 4 hrs was observed at a similar rate to SDD pts. A minor complication was seen in 1 pt each in SDD and overnight stay group between 4 and 24 hrs of ablation (Figure). Conclusions: Our study shows that when attempted, SDD after AF ablation can be accomplished in >90% of pts. Venous access site oozing was the greatest hinderance to pts going home. However, if pts had no issue 4 hrs after AF ablation, they had an uneventful course over the next 24 hrs. (Figure Presented).

Impact of coronavirus disease-19 lockdown on psychological and mental health status among residents of delhi-national capital region

Background: The second largest populous country India underwent a complete lockdown to curb the rampant spread and to prepare the health sector of our country to face the coronavirus disease (COVID)-19 pandemic. The curtailed access to family, friends, and other social support systems caused loneliness and mental issues such as anxiety and depression. Aim and Objectives: The present study was taken to find the impact of social lockdown on the psychological and mental health of the participants and to find out the differences between different sex, age groups, and occupations. Materials and Methods: An online cross-sectional study was administered to 1047 participants in the age group of 16–70 years through Google forms to assess the psychological and mental status of the residents of Delhi-NCR, India. The psychological status was judged by IES(R), while mental health status was checked by Depression, Anxiety and Stress Scale-21 (DASS-21). Results: The mean age of the participants was 48.29 ± 26.16 years. About 75.4% of participants had a moderate psychological impact of the lockdown as calculated by the IES(R) scale. DASS-21 scale revealed that the participants suffered from mild to moderate depression, anxiety and stress. Females had higher IES(R) but males scored higher DASS-21 scores. Healthcare workers along with the auxiliary staff suffered maximal mental agony. Conclusion: In the post-COVID-19 days, all efforts should be directed to minimize the toxic emotional and psychological impact of social lockdown by having tele psychological counseling programs with special emphasis to older age groups and medical fraternity.