Endogenous fungal endophthalmitis following COVID-19 infection with microbiological and molecular biological correlation - A report of two cases.

This case report describes three eyes of two patients, who were diagnosed to have endogenous fungal endophthalmitis post coronavirus disease 2019 (COVID-19) infection. Both patients underwent vitrectomy with intravitreal anti-fungal injection. Intra-ocular samples confirmed the fungal etiology by conventional microbiological investigations and polymerase chain reaction in both cases. The patients were treated with multiple intravitreal and oral anti-fungal agents; however, vision could not be salvaged.

The effect of job security, insecurity, and burnout on employee organizational commitment

The current research aims to understand the detrimental effects of job security (i.e., remaining in the same job), insecurity (i.e., concerns about continuing in the existing job), and burnout on employee organizational commitment. By investigating this context, the study also looks into how benevolent leadership may mitigate these negative effects. Employing event system theory, we conceptualize job insecurity, job security, and burnout effects as Covid-19-related events and how they may impact employee organizational commitment. Based to the findings, despite the mediation effects of benevolent leadership, job insecurity, and job burnout negatively impacted employee organizational commitment. Furthermore, job security was lowered by the mediating influence of benevolent leadership. The study findings have important implications for managers and organizations in dealing with future crises. © 2023 Elsevier Inc.

Application of Deep Learning in Healthcare

Automated Patient monitoring is rising to importance in the mobile healthcare services as it makes day-to-day activities risk-free, by continuously monitoring their vital signs. Clinical solutions are being provided to patients in no time, which is made possible due to the latest improvements in the "Internet of Things (IoT), cloud computing, and fog computing”. "Machine learning and Deep learning” are now being extensively used for various applications in healthcare such as extracting relations from vast amounts of patient data, analyzing patterns to predict the propagation of diseases, classify reports and X-rays to detect diseases, to name a few. In this paper, a deep learning-based model is proposed to monitor Covid-affected patients within hospitals. Our model can provide an online link between a patient and medical facility while also collecting patient data. This will enhance the care taken for patients. At the hospital end, we present a deep learning model using ResNet-50 that could classify chest X-rays as Covid positive or No Covid. Through this model we expect to quicken the process of COVID-19 detection while lowering the healthcare expenses. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Dual Watermarking for Security of COVID-19 Patient Record

In recent years, smart healthcare systems have gained popularity due to the ease of sharing e-patient records over the open network. The issue of maintaining the security of these records has attracted many researchers. Thus, robust and dual watermarking based on redundant discrete wavelet transform (RDWT), Hessenberg Decomposition (HD), and randomized singular value decomposition (RSVD) are put forward for CT scan images of COVID-19 patients. To ensure a high level of authentication, multiple watermarks in form of Electronic Patient Record (EPR) text and medical image are embedded in the cover. The EPR is encoded via turbo code to reduce /eliminate the channel noise if any. Further, both imperceptibility and robustness are achieved by a fuzzy inference system, and the marked image is encrypted using a lightweight encryption technique. Moreover, the extracted watermark is denoised using the concept of deep neural network (DNN) to improve its robustness. Experiment results and performance analyses verify the proposed dual watermarking scheme. © 2004-2012 IEEE.

Use cases for rapid antigen-detecting tests for COVID-19 screening and surveillance: a systematic review

Background. Testing remains critical to controlling the COVID-19 pandemic. Antigen-detecting rapid diagnostic tests (Ag-RDTs), which can be used at the point of care, have the potential to increase access to COVID-19 testing, particularly in settings with limited laboratory capacity. This systematic review synthesized literature on specific use cases and performance of Ag-RDTs for detecting SARS-CoV-2, for the first comprehensive assessment of Ag-RDT use in real-world settings. Methods. We searched three databases (PubMed, EMBASE and medRxiv) up to 12 April 2021 for publications on Ag-RDT use for large-scale screening and surveillance of COVID-19, excluding studies of only presumptive COVID-19 patients. We tabulated data on the study setting, populations, type of test, diagnostic performance, and operational findings. We assessed risk of bias using QUADAS-2 and an adapted tool for prevalence studies. Results. From 4313 citations, 39 studies conducted in asymptomatic and symptomatic individuals were included. Of 39 studies, 37 (94.9%) investigated lateral flow Ag-RDTs and 2 (5.1%) investigated multiplex sandwich chemiluminescent enzyme immunoassay Ag-RDTs. Six categories of testing initiatives were identified: mass screening (n=13), targeted screening (n=11), healthcare entry testing (n=6), at-home testing (n=4), surveillance (n=4) and prevalence survey (n=1). Sensitivity and specificity values by testing category are shown in the table. Ag-RDTs were noted as convenient, easy-to-use, and low cost, with a rapid turnaround time and high user acceptability. Risk of bias was generally low or unclear across studies. Conclusion. During the first year of the COVID-19 pandemic, Ag-RDTs were used across a wide range of real-world settings for screening and surveillance of COVID-19 in both symptomatic and asymptomatic individuals. Ag-RDTs were fast and simple to run, but due to their often low sensitivity, careful consideration must be given to their implementation and interpretation. Ag-RDTs have subsequently been rolled out more broadly and recommended for COVID-19 self-testing.

Low Serum Albumin at Admission Is Associated with Mortality among Inpatients with Cirrhosis: A Multi-National Experience

Background: MELD and Child-Pugh scores have traditionally been used as prognostic indicators in patients with cirrhosis. Albumin infusions in outpatients have been associated with improved outcomes, but not in transplant waitlisted patients or inpatients. This aim of this study was to assess whether low serum albumin (sAlb) on admission alone is a poor prognostic indicator among cirrhotic inpatients from a new multi-national cohort. Method(s): The CLEARED study is a global study that enrolled consecutive non-electively admitted inpatients without organ transplant or COVID-19 from 6 continents. Admission demographics, medical history, laboratory data, inpatient course, death/hospice transfer and mortality at 30 days post-discharge were recorded. Patients were divided into 3 groups: sAlb <28gm/L(A), sAlb >=28 but <35gm/L (B), and sAlb>=35gm/L (C) were compared. Multi-variable logistic regression was performed using inpatient mortality and overall 30-day mortality as outcomes. Result(s): 2429 patients were enrolled from 21 countries worldwide. The distribution was A:49%, B:39%, C:12%. Gp A patients were significantly younger (54yrs vs. 57yrs vs 58yrs p<0.0001) but with similar gender distribution, and higher MELD-Na score of 25 vs. 20 vs. 17 (p<0.0001). Gp A patients were more likely to have alcohol as etiology of cirrhosis (49% vs. 45% vs 38%, p=0.004), and were more likely to have either infection (27% vs. 18% vs. 13%, p<0.0001), HE (39% vs. 33% vs. 23%, p=0.005) or fluid related issues as a reason for admission (p<0.0001). More patients in Gp A received albumin infusion during their hospital stay (120gm vs. 100gm vs. 100gm p=0.0004), mostly for the indications of AKI (47% vs. 49% vs. 47%, p=0.79) and performance of large volume paracentesis (44% vs. 42% vs. 41%, p=0.80), followed by bacterial peritonitis indication (22% vs. 17% vs. 11%, p=0.01). Group A patients had longer hospital stays (9 days vs. 8 days vs. 7 days (p<0.001), but similar ICU transfer (23% vs. 22% vs. 20%, p=0.55). group A patients were more likely to die while inpatients (19% vs. 11% vs. 5%, p<0.0001), or by 30 days post-discharge (29% vs. 20% vs. 9%, p<0.0001). Table shows the admission variables associated with a poor outcome. Conclusion(s): Hypoalbuminemia is extremely common among admitted cirrhotic patients, with sAlb of <28gm/L occurring in almost half. Together with MELD-Na score and infection at admission, a low sAlb is associated with a poor outcome in these patients. Future studies will need to validate these findings and to assess whether albumin infusions will improve the outcome of these patients. (Figure Presented).

Differences in Mortality Rates in Inpatients with Cirrhosis Depends on the Low, Middle or High Income Country Location: Multi-National Cleared Consortium Experience

Background: Although cirrhosis is a major cause of mortality worldwide, there could be disparities in outcomes. This needs a global consortium to study disparities in inpatient cirrhosis care Aim: Define the impact of location in prediction of outcomes in inpts with cirrhosis. Method(s): CLEARED prospectively enrolled non-electively admitted cirrhosis pts without COVID from all continents. To ensure equity, we allowed only 50 pts/site. Admission details, cirrhosis history, inpatient & 30-day course were recorded. World bank classification of low/low middle income (LMI), upper middle (UMI) & High income (HI) were used. Cirrhosis details, inpatient & 30-day outcomes were compared between groups. Multi-variable regression was performed using inpatient & 30-day mortality as outcomes. Result(s): 2758 pts from 21 countries from all continents, including Africa & Australia, were included.727 were L/LMI, 1050 UMI & 981 pts were from HICs. More men & younger pts were in LMI. Cirrhosis details: More pts in HI gp had 6M hospitalizations & infections, HE & ascites while prior variceal bleeding was higher in LMI . Prior HCC & transplant listings were lower in LMI but similar in UMI/HI. Alcohol & NASH was highest in HI. Viral hepatitis & cryptogenic were highest in UMI.Admissions: Admission MELD was highest in LMI. LMI pts were admitted more for GI Bleed, HE, & DILI, while anasarca & HBV flares were higher in UMI. Higher SBP (36% vs 24% vs 21% p<0.0001) & lowest skin/soft-tissue infections were in LMI (5% vs 5% vs 10% p=0.008);rest were similar. Nosocomial infections, driven by UTI were highest in LMI & HI pts (15% vs 14% vs 11% UMI, p=0.03). Admission diuretics, PPIs, Lactulose & statins were highest & antivirals lower in HI. SBP prophylaxis & rifaximin were highest in LMI pts. Outcome(s): More LMI pts needed ICU & had more organ failures (Fig B). Discharge MELD was highest in LMI. In-hospital mortality was highest & transplant lowest in LMI. This extended to 30-day mortality & transplant in LMI patients vs HI pts.Regression: In-hospital mortality was linked with age, infections, MELD & being in a LMI/UMI vs HIC while being on a transplant list, diabetes, & SBP prophylaxis were protective (Fig C). 30-day mortality predicted by age, ascites, HCC, discharge MELD, organ failures, LMI/UMI vs HIC but rifaximin was protective(Fig D). In-hospital transplant was higher with high MELD, admission rifaximin & listed pts &lower in LMI (OR 0.26) & UMI (OR 0.22) & age. 30-day transplant was higher in those with hyponatremia, ascites & HRS, on the list & on rifaximin and lower in LMI (OR 0.24) & UMI (OR 0.59) vs HI. Conclusion(s): In a global study of inpatients with cirrhosis, there were major differences in outcomes. Not being in a high-income country significantly increased the risk of inpatient and 30-day mortality independent of demographics, medications, in-hospital course, and cirrhosis severity likely due to disparities in access to transplant, which should be accounted for in global models. (Figure Presented).

Application of Deep Learning in Healthcare

Automated Patient monitoring is rising to importance in the mobile healthcare services as it makes day-to-day activities risk-free, by continuously monitoring their vital signs. Clinical solutions are being provided to patients in no time, which is made possible due to the latest improvements in the “Internet of Things (IoT), cloud computing, and fog computing”. “Machine learning and Deep learning” are now being extensively used for various applications in healthcare such as extracting relations from vast amounts of patient data, analyzing patterns to predict the propagation of diseases, classify reports and X-rays to detect diseases, to name a few. In this paper, a deep learning-based model is proposed to monitor Covid-affected patients within hospitals. Our model can provide an online link between a patient and medical facility while also collecting patient data. This will enhance the care taken for patients. At the hospital end, we present a deep learning model using ResNet-50 that could classify chest X-rays as Covid positive or No Covid. Through this model we expect to quicken the process of COVID-19 detection while lowering the healthcare expenses. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Energy Audit in an Urban Indian Residential Community-A Case Study

This paper presents an energy audit study conducted for an urban residential community in Mumbai. The consumers are categorized using a k-means clustering algorithm based on their electricity consumption. The energy-efficient appliance selection is undertaken by a benchmarking study based on the appliance energy labeling and star rating initiated by the Bureau of Energy Efficiency(BEE) in India. The study establishes the techno-economic feasibility of energy savings in Indian urban households with an average payback period of 3.3 years. The energy-saving opportunities are selected based on each cluster's capital cost and payback period. Sensitivity analysis of electricity tariff of a region on payback period is undertaken. The covid impact analysis on the residential energy consumption is conducted by comparing energy consumption before and after the covid. The benefits are replicable in most Indian households, especially the urban residential consumers with high consumption in regions with high electricity tariffs. © 2022 IEEE.

Modelling the impact of lock-downs in top-4 COVID-19 spreading states of India

A pandemic like COVID-19 being a highly infectious disease has severely affected mankind and business activities. Seeing the critical situation, the honourable Prime Minister of India has called for a lockdown in the entire country in order to suppress the spread of this pandemic. While there are many debates about the spread of disease and lockdown in the entire country, we wish to mathematically understand the diffusion of this pandemic in the context of four highly infected states of India. Moreover, through this paper, we wish to examine the impact of these lockdown periods in order to understand the spread of COVID-19. Copyright © 2022 Inderscience Enterprises Ltd.

Variants of the COVID Virus before the Onset of Omicron

COVID-19 belongs to a group of Coronavirus diseases that SARS-CoV-2 causes. The virus spreads from one person to another via the respiratory droplets from an infected individual produced when such an individual coughs, talks, or sneezes. The symptoms of the diseases range from mild to severe, and individuals at age extremities, that are, very old (from 65 years of age), are highly exposed to severe complications. The symptoms manifest from the second day, fourteen days after exposure to the virus. COVID strains keep on changing as a result of mutations in the viral genomic composition. Different variants of COVID-19 exist;these variants vary in severity, as reported by the World Health Organization. There are thousands of variants of COVID in the world;the virus mutates all the time, making the changes inconsequential. Some of the mutations make the virus more infectious, and some mutated viral strains tend to be dominant. Variants of concern include those that have the most potentially concerning changes. India is among the nations where the virus strains have been reported to dominate and spread to other nations. The virus is claiming the lives of many individuals, with every strain spreading from one country to another. The article will address the research review on types of COVID variants and COVID-19 epidemiology in the world. Copyright © 2022, Institute of Medico-legal Publication. All rights reserved.

Modelling the impact of lock-downs in top-4 COVID-19 spreading states of India

A pandemic like COVID-19 being a highly infectious disease has severely affected mankind and business activities. Seeing the critical situation, the honourable Prime Minister of India has called for a lockdown in the entire country in order to suppress the spread of this pandemic. While there are many debates about the spread of disease and lockdown in the entire country, we wish to mathematically understand the diffusion of this pandemic in the context of four highly infected states of India. Moreover, through this paper, we wish to examine the impact of these lockdown periods in order to understand the spread of COVID-19.

EFFICACY OF YOGA-BASED BREATHING INTERVENTION IN COVID-19 POSITIVE, POST-COVID AND HEALTH CARE WORKERS: A RANDOMIZED CLINICAL TRIAL

SARS-CoV-2 is highly infectious and has ability to mutate into newer, more contagious, and lethal strains. Moreover, presence of comorbidities and low immunity increases the COVID-19 susceptibility and severity. Thus, COVID-19 is challenging to treat and eradicate globally. This increase stress and anxiety among the patients, worsening their condition. Even health care workers (HCWs) are distressed and anxious while managing the COVID-19. Mental stress and depression increases risk of COVID-19. Yogic breathing techniques may be beneficial in improving immunity and reducing stress and anxiety. The present study investigated the effectiveness of short and controlled Yoga-based breathing protocols in COVID-positive, COVID-recovered and HCWs. Study subjects were recruited from Postgraduate Institute of Medical Education and Research, Chandigarh, India from 13th October, 2020 to 7th January 2021. Each group was randomly divided into intervention or yoga group and non-intervention or control group. COVID-positive practiced a 5-min routine and COVID-recovered and HCW practiced 5-min and 18-min routines for 15 days. Pre-post estimation of neuropsychological parameters and heart rate variability and baseline, 7th and 15th day estimation of biochemical parameters, 6-minute walk and 1-minute sit-stand tests were conducted. Based on Ayurveda, Prakriti-type was assessed. WBC count was elevated in COVID-positive intervention (p<0.001) and control groups (p=0.003). WBC count (p=0.002) and D-dimer (p=0.002) was decreased in COVID-recovered intervention. A non-significant reduction in perceived stress and tension was noted in COVID-positive intervention. Tension was reduced and quality of life improved in HCW intervention (p>0.05). The Kapha Prakriti (48.9 %) was dominant among COVID-19 infected (positive and recovered) subjects. Distance covered in 6-min increased after intervention in COVID-positive (p=0.01) and HCW (p=0.002). The covered distance was more after intervention in all groups than control sub-group. COVID-positive intervention group shows reduced heart rate (p>0.05) and high-frequency power (p=0.01). The interventions were capable of improving exercise capacity in patients and HCW and reduced cardiovascular risk in COVID-19. The studied breathing protocol can be integrated for the management of COVID-19 and is beneficial to HCWs.

Structural and Functional Insights of Thiazole Derivatives as Potential Anti-inflammatory Candidate: A New Contender on Chronic and Acute SARS-CoV-2 Inflammation and Inhibition of SARS-CoV-2 Proteins

Thiazoles are notable five-membered heterocyclic rings and their moieties can be found in several biologically active compounds of natural origin, as well as synthetic molecules that possess a wide range of pharmacological activities. Inflammation is the common cause that is associated with different disorders and diseases such as psoriasis, arthritis, infections, asthma, cancer, etc. In this article, the synthesis pattern of these novel molecules are discussed and their anti-inflammatory activities against cyclooxygenase-1 (COX-1), cyclooxygenase-2 (COX-2) and lipoxygenase (LOX) were reviewed and documented. The potent 26 thiazole analogs were validated with molecular docking against main protease (6LU7) and spike binding domain ACE2 receptor (6M0J) to defeat from the COVID-19 infections. Among this, THI9a showed excellent binding energy and affinity against deadly SAR CoV-2. The reviewed and theoretical study information strongly suggested that thiazole derivatives can be used for the development of futuristic target drugs against death-causing diseases like SAR-CoV-2. © 2022 Chemical Publishing Co.. All rights reserved.

SARS-CoV-2 RNA and N Antigen Quantification via Wastewater at the Campus Level, Building Cluster Level, and Individual-Building Level

Monitoring wastewater for SARS-CoV-2 from populations smaller than those served by wastewater treatment plants may help identify small spatial areas (subsewersheds) where COVID-19 infections are present. We sampled wastewater from three nested locations with different sized populations within the same sewer network at a university campus and quantified SARS-CoV-2 RNA using reverse transcriptase droplet digital polymerase chain reaction (PCR). SARS-CoV-2 RNA concentrations and/or concentrations normalized by PMMoV were positively associated with laboratory-confirmed COVID-19 cases for both the sewershed level and the subsewershed level. We also used an antigen-based assay to detect the nucleocapsid (N) antigen from SARS-CoV-2 in wastewater samples at the sewershed level. The N antigen was regularly detected at the sewershed level, but the results were not associated with either laboratory-confirmed COVID-19 cases or SARS-CoV-2 RNA concentrations. The results of this study indicate that wastewater monitoring based on quantification of SARS-CoV-2 RNA using PCR-based methods is associated with COVID-19 cases at multiple geographic scales within the subsewershed level and can serve to aid the public health response.

Reduce Academic Fatigue and Enhance Retention for The Determined Ones (TDOs) in Online Learning

This article emanates from the pilot phase of a qualitative study of the impact on academic fatigue and retention for The Determined Ones (TDO) students, studying at the Higher Colleges of Technology Campuses, UAE. The purpose of the study was to identify effective strategies for online learning that will be enhanced for the TDO students, thereby reduce academic fatigue and increase retention. The COVID-19 pandemic has had a remarkable influence on approaches to the day to day activities around the world, an influence which had led to a 'new normal'. In the spring of 2020, with the abrupt and essential transition from on-campus learning to distance learning, students and educators had limited time to prepare for such a massive shift in teaching and learning. Not many could have been prepared for such a shift, and new approach in teaching and learning. The impact has been felt more by students with disabilities, because their normal routines have been abandoned, leading to anxiety and stress resulting from the unknown. The move to online learning was a reactive than a proactive approach because no one apparently saw the COVID-19 pandemic and its subsequent impact on lifestyles coming. By summer 2020, as a result of online learning, key issues relating to academic fatigue and retention in students were widely identified through surveys and other data. This study emanated from these concerns to provide the opportunity to address the issues from a reactive approach into a proactive one, including the use of methods that will enhance student retention. Although digital technologies are a regular part of learning in the 21st Century, it cannot be denied that the sudden change to online learning platforms has affected both students and educators. Institutions went digital, relying on video conferencing programs like Zoom and Microsoft Teams for individuals to carry on working in isolation from their homes. Therefore, as the majority of interactions moved to this virtual realm, with the most widely used software being Zoom, it has come to be commonly referred to as 'zoom academic fatigue' as stated by [1]. Consequently, combating this new form of exhaustion has directly impacted on students' learning, especially for students with disabilities in higher education institutions. In the United Arab Emirates (UAE), The Determined Ones (TDOs) is the official appellation given to people with disabilities. This study will therefore be referring to students with disabilities at The Higher Colleges of Technology (HCT), in Abu Dhabi, the higher education institution where this study is taking place. Reports from surveys conducted at HCT in the summer of 2020 when the Covid 19 pandemic was raging revealed that the transition to an online learning platform left students feeling tired, anxious and stressed out as they waited for the next video call lesson. Their normal routines no longer applied. The effects of this academic fatigue within online classrooms requires attention and solutions to combat it. © 2021 IEEE.

A System for Monitoring of COVID-19 patients at Home based on Internet of things and Fog Computing

The number of elderly persons, who generally have chronic illnesses and have seen a substantial spread of the novel coronavirus in recent years, has grown in recent years (COVID-19). The majority of older persons suffering from various chronic diseases have died as a result of infection with this virus. Thousands of people have died as a result of this. Coronavirus has also caused several problems in hospitals, and people are not being treated as a result of the large number of patients who require medical care. Medical and paramedic personnel have also been infected, and there is a risk that the virus will spread to the patient and his attendants via medical and paramedical workers. You may avoid this problem by staying at home, where you can monitor your whereabouts and warn in an emergency. The Internet of Things was critical in real-time monitoring of COVID-19-infected patients. Many cloud-based IoT applications are available;nevertheless, high latency, bandwidth, and energy consumption are significant concerns with cloud computing. Fog computing attempts to address cloud computing issues such as bandwidth, network latency, and energy consumption. Similarly, one of the primary fog computing applications is data handling generated by healthcare IoT devices. Healthcare IoT devices generate a large amount of data, which must be managed effectively with low latency, no failures, low energy consumption, low cost, and high accuracy. © The Electrochemical Society