EXERCISE CAPACITY IS REDUCED IN HIV INDEPENDENT OF SARS-CoV-2 INFECTION

Background: Reduced exercise capacity occurs as a post-acute sequela of COVID-19 ("PASC" or "Long COVID"). Cardiopulmonary exercise testing (CPET) is the gold standard for measuring exercise capacity and identifying reasons for exercise limitations. Only one prior study used CPET to examine exercise limitations among people living with HIV (PLWH). Extending our prior findings in PASC, we hypothesized that PLWH would have a greater reduction in exercise capacity after SARS-CoV-2 co-infection due to chronotropic incompetence (inability to increase heart rate). Method(s): We performed CPET within a COVID recovery cohort that included PLWH (NCT04362150). We evaluated associations of HIV and prior SARS-CoV- 2 infection with or without PASC with: (1) exercise capacity (peak oxygen consumption, VO2) and (2) adjusted heart rate reserve (AHRR, marker of chronotropic incompetence) using linear regression with adjustment for age, sex, and body mass index. Result(s): We included 83 participants (median age 54, 35% female, 10% hospitalized, 37 (45%) PLWH) who underwent CPET at 16 months (IQR 14-17) after SARS-CoV-2 infection. Among PLWH (median duration living with diagnosed HIV 21 years (IQR 15-28), all virally suppressed on antiretroviral therapy), 14 (39%) had not had SARS-CoV-2 infection, 12 (32%) had prior SARSCoV- 2 infection without PASC, and 11 (30%) had PASC (Long COVID symptoms at CPET). Median CD4 count was 608 (370-736) and CD4/CD8 ratio 0.92 (0.56-1.27). Peak VO2 was reduced among PLWH compared to individuals without HIV with an achieved exercise capacity only 80% vs 99% (p=0.005, Fig.), a difference in peak VO2 of 5.5 ml/kg/min (95%CI 2.7-8.2, p< 0.001). Exercise capacity did not vary by SARS-CoV-2 infection among PLWH (p=0.48 for uninfected vs infected;p=0.25 for uninfected vs no PASC;p=0.32 no PASC vs PASC). Chronotropic incompetence was present in 38% of PLWH vs 11% without HIV (p=0.002), and AHRR (normal >80%) was significantly reduced among PLWH vs individuals without HIV (60% vs 83%, p< 0.0001, Fig.). Heart rate response varied by SARSCoV- 2 status among those with HIV: namely, 3/14 (21%) without SARS-CoV-2, 4/12 (25%) with SARS-CoV-2 without PASC, and 7/11 (64%) with PASC (p=0.04 PASC vs no PASC). Among PLWH, CD4 count, CD4/CD8 ratio, and hsCRP were not associated with peak VO2 or AHRR. Conclusion(s): Exercise capacity is reduced among PLWH, with no differences by SARS-CoV-2 infection or PASC. Chronotropic incompetence may be a mechanism of reduced exercise capacity among PLWH. (Figure Presented).

Candidemia in a tertiary care hospital: Epidemiology, speciation and antifungal susceptibility pattern

Objective: The incidence of bloodstream fungal infection is on the rise and Candida species remains responsible for the majority of the cases. Candidemia is frequently associated with a high rate of mortality and morbidity. The purpose of this study was to characterize Candidemia, its epidemiology, species distribution, and antifungal susceptibility pattern in a tertiary care hospital. Methods and Material: Candida species isolated from the blood culture of 51 patients in a tertiary care hospital during the period from 2016 to 2021 were included in the study. The growth on SDA was confirmed by Gram staining and speciation and antifungal susceptibility were performed with Automated system VITEK 2.0. Result(s): Out of51 isolates, Candida auris wasthe most common speciesaccounting for about 37.2% followedby C.albicans 19.7%, C. tropicalis 17.6%, and C. famata 9.8%. Candida auris has emerged as the predominant species during severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2) pandemic.The incidence has risen from 22% to 60% during the pandemi C. Candida specieswere foundto be96.08% sensitiveto flucytosine, 94.12% tovoricanazole, 90.19%to casp ofungin/micafungin, 60.78% to amphoterecin B, and 56.86% to fluconazole. Conclusion(s): Candida auris has emerged as the predominant species in ICU setup and during SARS-CoV-2 pandemi C. Empirical treatment with echinocandines would be appropriate in high-risk patients with suspected Candidemia.

INDIA'S PROGRESS TOWARDS SUSTAINABLE DEVELOPMENT THROUGH ICT ACCESS AND QUALITY EDUCATION FOR COVID-19 RECOVERY

It was just as we were about to enter the fifth year of the SDGs that we were made hostages to the virus that was finagling its way into our life. Many scholarly papers, reports, and investigations have highlighted the impact of Covid-19 on global governance. When the 2030 Agenda for Sustainable Development is fully implemented, this article aims to investigate the post-Covid world's potential for SDGs as a catalyst for minimising trade-off and maximising synergies. As part of the Indian government's effort to navigate this difficult time, the author uses a multidisciplinary approach to try and map the impact of Covid-19 on SDGs. Following a review of global economic trends, the article focuses on the impact of the crisis on India's economy, specifically. An effort has been made to highlight the importance of SDGs as a post-Covid recovery and development path in India.

INFLAMMATION, EXERCISE CAPACITY, CHRONOTROPY, and SYMPTOMS in POST-ACUTE COVID-19

Background: Cardiopulmonary symptoms and reduced exercise capacity can persist after SARS-CoV-2 infection. Mechanisms of post-acute sequelae of COVID-19 ("PASC" or "Long COVID") remain poorly understood. We hypothesized that systemic inflammation would be associated with reduced exercise capacity and pericardial/myocardial inflammation. Methods: As part of a COVID recovery cohort (NCT04362150) we assessed symptoms, biomarkers, and echocardiograms in adults >2 months after PCR-confirmed SARS-CoV-2 infection. In a subset, we performed cardiac magnetic resonance imaging (CMR), ambulatory rhythm monitoring (RM), and cardiopulmonary exercise testing (CPET) >12 months after acute infection. Associations between symptoms and oxygen consumption (VO2), cardiopulmonary parameters and biomarkers were evaluated using linear and logistic regression with adjustment for age, sex, BMI, and time since infection. Results: We studied 120 participants (median age 51, 42% female, and 47% had cardiopulmonary symptoms at median 7 months after acute infection). Elevated hsCRP was associated with symptoms (OR 1.32 per doubling, 95%CI 1.01-1.73, p=0.04). No differences in echocardiographic indices were found except for presence of pericardial effusions among those with symptoms (p=0.04). Of the subset (n=33) who underwent CMR at a median 17 months, all had normal cardiac function (LVEF 53-76%), 9 (27%) had pericardial effusions and none had findings suggestive of prior myocarditis. There were no differences on RM by symptoms. On CPET, 33% had reduced exercise capacity (peak VO2 <85% predicted). Individuals with symptoms had lower peak VO2 compared to those reporting recovery (28.4 vs 21.4 ml/kg/min, p=0.04, Figure). Elevated hsCRP was independently associated with lower peak VO2 after adjustment (-9.8 ml/kg/min per doubling, 95%CI-17.0 to-2.5;p=0.01, Figure). The predominant mechanism of reduced peak VO2 was chronotropic incompetence (HR 19% lower than predicted, 95%CI 11-26%;p<0.0001, Figure). Chronotropic incompetence on CPET correlated with lower peak HR during ambulatory RM (p<0.001). Conclusion: Persistent systemic inflammation (hsCRP) is associated with pericardial effusions and reduced exercise capacity > 1 year after acute SARS-CoV-2 infection. This finding appears to be driven mainly by chronotropic incompetence rather than respiratory compromise, cardiac pump dysfunction, or deconditioning. Evaluation of therapeutic strategies to target inflammation and/or chronotropy to alleviate PASC is urgently needed.

Intracellular Cytokine Profiling by High-Dimensional Flow Cytometry Demonstrates Highly Augmented Cytotoxicity and Overly Activated NK cells in COVID-19 Patients

Background: COVID-19 pandemic has caused more than 4.7 million deaths worldwide to date and still continues globally unabated. Numerous studies have linked the mortality in COVID-19 to aggressive immune response and cytokine storm. However, little is known about the cytokine profiles of individual immune cells that are directly involved in tissue damage. Here we investigate intracellular cytokines in individual T and NK cells of COVID-19 patients. Design: We studied 50 blood samples from 22 COVID-19 patients, 4 with mild, 6 moderate and 12 severe disease. There were 6 healthy controls. We performed high-dimensional 30-color spectral flow cytometry to characterize the immune cell subsets. For cytokine study, cells were stimulated for 6 hours, and stained for surface antigens and intracellular cytokines (IL1b, IL2, IL4, IL6, IL8, IL10, IL12, IL17a, IL21, INFg, GnzB, TNFa, and GMCSF). Data ware acquired on FACSymphony 50-parameter analyzer and analysis performed using FlowJo. Results: Our studies revealed significant differences in lymphocyte cytokine profiles between COVID+ and healthy controls (Fig 1). CD4+ and CD8+ T-cells exhibited increased percentages of IL2+ and IFNg+ cells, indicating a shift towards Th1 reaction. Granzyme B is highly upregulated in all T and NK cell subsets, demonstrating highly armed cytotoxic cells in COVID patients. The most prominent changes were noted in NK cells, 7 cytokines were highly expressed, most are proinflammatory cytokines. Of particular interest are IL-21 and GMCSF, both are known to play important roles in inflammatory cell recruitment, activation and renewal, which can lead to augmented tissue inflammation and injury. These changes were already evident in patients with mild disease, but there is heightened cytokine production in severe cases. Conclusions: Using high-dimensional flow cytometry we demonstrated for the first time significantly increased production of multiple proinflammatory cytokines and cytotoxic molecules in individual T and NK cells of COVID-19 patients. NK cells are most drastically activated. It is conceivable that when recruited to the target tissue such as lung, these highly primed cells will play a major role in tissue injury and ultimately organ failure via their direct cytotoxicity and cytokine secretion. This is consistent with previous reports of increased NK cells in the COVID lungs. Analysis of NK cell cytokine profiles may serve to predict disease progression, and reveal new targets for immune-therapy for severe COVID patients. (Table Presented).

Environmental Effects of Covid-19 in India: A Qualitative Analysis

The Corona Virus Disease 2019 (COVID-19) is an acute virus creating respiratory disease and gastro intestine disease in humans. The outbreak of novel corona virus (COVID-19) has brought serious impact on all counties around the world. Spread of COVID-19 was controlled by countries through restricted movement, self-hygiene practices and social distancing. Despite all the efforts made by the governments, this pandemic brought serious effect on economy and environment. The impacts of COVID-19 on air, water and waste management were assessed and were observed that air and water quality has improved due to lockdown but the management of waste is a serious issue. This article describes the results of study performed on the environmental effects particularly in air and water by assessing the environmental conditions before and after the outbreak of pandemic COVID-19. The study results yields that the purity of air and water has been improved during the pandemic period when compared with the period before the outbreak of COVID-19 virus. Waste generated from self-quarantine houses, hospitals and self-hygiene practices followed by people has posed an enormous effect on waste management sector. Disposal of infectious waste along with municipal solid waste has created threat to people handling the waste and the environment. Based on the environmental analysis performed on air, water and waste management, solid guidelines has been provided in treating the waste management effectively. This article recommends the need for improving the waste treatment methodology and the significances of policy framework to face pandemic situation in future. This study improves the hope that, implementation of proposed guidelines will improve the purity level of environment and management of biomedical wastes effectively.

VIRONIX: REMOTE SCREENING, MONITORING, AND TRIAGE OF VIRAL RESPIRATORY ILLNESS

TOPIC: Chest Infections TYPE: Original Investigations PURPOSE: Viral Respiratory illnesses such as Covid-19 and Influenza pose significant health challenges worldwide. There are more than 150M confirmed cases of Covid-19 with a reported 3.15M deaths (as of April, 2021). The WHO reports there to be ~ 1 billion influenza cases and 290-650K influenza-associated deaths annually. A signature feature of these illnesses is an early infection period that, if insufficiently recognized and controlled early, can lead to viral spread and avoidable morbidity/mortality. The need for personalized, remote care tools that facilitate early detection and triage of viral illness has never been greater. To address this gap, we developed an institutional software, Vironix, that uses machine-learned (ML) prediction models to enable real-time risk stratification and decision support for global organizations. METHODS: ML models were trained on clinical characteristic data from East and South Asia, Western Europe, and USA. Algorithms take an input of symptom, profile, biometric, and exposure data and return an assessment of disease severity. Covid-19 algorithms were validated on computer generated patient vigenttes and deployed in the Vironix web app among 22 participants in a small business commercial pilot for member self-screening. Members conducted daily health assessments and received personalized decision support while organization managers received work-from-home recommendations and compliant symptom monitoring without seeing member health data. For influenza, Vironix ML algorithms were tested on a dataset (with a 90/10 train test split) collected from one academic and two community emergency rooms from March 2014 to July 2017 (Hong et al.). RESULTS: ML-predictions showed 87.6% accuracy, 85.5% sensitivity, and 87.8% specificity in identifying severe Covid-19 presentations in an out-of-sample validation set of 5,000 patient cases. After 4-months pilot use, Vironix issued 14 stay-at-home and 10 healthcare escalation recommendations while maintaining 30-day and 7-day user retention of 66% and 72%, greatly exceeding common app adoption rates. ML predictions for the Influenza data set showed 67.8% accuracy, 71.7% sensitivity, and 65.4% specificity in identifying admissible or dischargeable presentations of influenza in an out-of-sample validation set of 56,000 patient cases. CONCLUSIONS: Covid-19 ML-severity assessments showed strong accuracy, sensitivity, and specificity in identifying severe clinical presentations. The deployed web-app showed high adoption with members receiving relevant decision support. Flu algorithm performance could be bolstered by inclusion of biometric features. Additional controlled trials could be conducted to establish validated markers of health improvement and early illness detection resulting from Vironix use. The overall methodology for mapping clinical characteristic data into patient scenarios for training ML classifiers of health deterioration is generalizable for a variety of potential software and hardware deployments across disease spaces. CLINICAL IMPLICATIONS: The technology detailed in this study represents a potential low cost, scalable, hardware/software agnostic, global solution for early detection and intervention on infectious respiratory illness. These solutions can be integrated into remote care and institutional wellness workflows to support public health initiatives. DISCLOSURES: No relevant relationships by Anna Berryman, source=Web Response No relevant relationships by Shreyas Iyer, source=Web Response No relevant relationships by Vinay Konda, source=Web Response Advisory Committee Member relationship with ABMRCC Please note: $1-$1000 by Chris Landon, source=Web Response, value=Consulting fee Removed 04/28/2021 by Chris Landon, source=Web Response Consultant relationship with ABM Respiratory Please note: 11/20 - date Added 04/30/2021 by Chris Landon, source=Web Response, value=Consulting fee no disclosure on file for Nicholas Mark;No relevant relationships by James Morrill, source=Web R sponse No relevant relationships by Sriram Ramanathan, source=Web Response Owner/Founder relationship with Vironix Health, Inc Please note: 05/2020 - Present Added 04/28/2021 by Sumanth Swaminathan, source=Web Response, value=Ownership interest Owner/Founder relationship with Vironix Health Please note: 04/2020-Now Added 05/10/2021 by Botros Toro, source=Web Response, value=Ownership interest Consultant relationship with Vironix Please note: 2019-present Added 04/28/2021 by Nicholas Wysham, source=Web Response, value=Ownership interest

Vironix: A machine-learned approach to remote screening, surveillance, and triage of viral respiratory illness

RATIONALE The Covid-19 pandemic has posed a serious, ongoing global health challenge. The United States has been the worst affected, with more than 11M confirmed cases and 246K deaths (as of November 2020). Two primary and persisting concerns are the continued necessity for shutdown/isolation and the possibility of singular waves of rapid virus spread that could overwhelm global healthcare systems, resulting in preventable mortality and substantial economic burden. While vaccines are being developed and disseminated, the need for remote patient care has never been more critical. To that end, we developed a Covid-19 remote triage software, Vironix, which uses machine-learning algorithms to enable real-time risk stratification and decision support for users. This remote management approach has significant potential to increase safety, improve health outcomes, and stem virus spread as organizations reopen. METHODS Vironix uses personalized machine-learning algorithms trained off clinical characteristic data from the EU, East Asia, and the USA in tandem with prescribed guidelines from the CDC, WHO, and Zhejiang University's handbook on Covid-19 prevention. Clinical characteristics of thousands of patients in the literature were mapped into patient vignettes using Bayesian inference. Subsequent stacked, ensemble decision tree classifiers were trained on these vignettes to classify severity of presenting symptoms and signs. Crucially, the algorithm continuously learns from ongoing use of the application, strengthening decisions, and adapting decision boundaries based on inputted information. Vironix was deployed using a user-friendly API, allowing users to easily screen themselves and obtain remote decision support through a variety of devices (mobile apps, computers, health monitors, etc).RESULTS Algorithm performance was assessed based on its binary classification performance in an out-of-sample test set including severe and nonsevere labels. Vironix correctly assigned the severity classes with an accuracy of 87.6%. Vironix further demonstrated superior specificity (87.8%) and sensitivity (85.5%) in identifying positive (severe) presentations of Covid-19. The algorithms, deployed behind the Vironix Web Application, have been invoked by tens of thousands of users around the world. CONCLUSION 1. The Vironix approach is a highly novel, generalizable methodology for mapping clinical characteristic data into patient scenarios for the purpose of training machine-learning prediction models to detect health deterioration due to viral illness. 2. Vironix exhibits excellent accuracy, sensitivity, and specificity in identifying and triaging clinical presentations of Covid-19 and the most appropriate level of medical urgency. 3. Algorithms continuously learn and improve decision boundaries as individual user input increases. .

Impact of COVID-19 pandemic on HIV and tuberculosis: Who perspective

The COVID-19 pandemic has infected more than 100 million people, killed more than 2.4 million, and had a major impact on the health system's ability to deliver essential health services. The impact of COVID-19 on other infectious diseases such as HIV and tuberculosis (TB) has been immense, particularly in low-resource settings with high HIV and TB burden. Ongoing TB data collection and analysis from 200 countries have shown reduced access to care in outpatient and inpatient facilities, impacting the entire care cascade, including prevention, with case detection rates dropping by over 50% in some endemic countries in 2020. By its negative impact on poverty and malnutrition, it is possible that TB incidence could actually increase, strengthening the argument for robust prevention measures. The pandemic has caused significant disruption to HIV programs by limiting access to life-saving antiretrovirals due to movement restrictions, local stockouts, and decrease in uptake of facility-based services. These disruptions are also expected to have reverted some of the progress made in preventing vertical transmission of HIV, resulting in increased numbers of paediatric HIV infections. Therefore, strengthening systems for the maintenance of HIV, TB/HIV, and TB services is an urgent need in many high-burden countries. Although COVID-19 has challenged TB and HIV programmes, it has also offered several lessons, including how we join forces, innovate, and accelerate research and development. Some examples are the use of digital tools for contact tracing, use of AI-based diagnostic algorithms, widespread sharing of genomic sequence data to track virus evolution and emergence of new variants, and large multisite clinical trials to test new therapeutics and vaccines. The development and evaluation of new TB and HIV treatments and vaccines should learn from the past year of accelerated development and explore new models of public-private partnership for the development of global public goods. Despite progress, vulnerable populations such as children and pregnant women continue to lag behind innovations for TB and HIV, and these groups need to be included in clinical trials much sooner. Finally, we need to expand and strengthen the integration of services within the primary healthcare platform, optimizing differentiated service delivery, community engagement and the use of digital technologies to reach those most at risk of TB and HIV with screening, prevention, diagnosis, and treatment.