Predictors of Mortality among COVID-19 Patients - A Case Control Study at Tertiary Care Center, Nanded (Maharashtra).

Background: The novel Coronavirus is belonging to the family of SARS & MERS-CoV, the impact of the earlier is more dreadful as demonstrated by the steady increase in morbid cases. The average incubation period of COVID-19 is 1-14 days with a mean of 6 days. Aim - To evaluate predictors of mortality among COVID-19 patients. Objectives - 1. To assess risk predictors associated with mortality among COVID-19 patients 2. To a suggest prediction model for preventing mortality in future outbreaks. Materials and Methods: Study design - A case-control study. Study place -Tertiary care center, Nanded, Maharashtra. The present study included 400 cases that died off due to Covid-19 and 400 controls survived COVID-19 disease in a 1:1 proportion. Results: On admission, a significant difference was observed among cases and controls with reference to the percentage of SpO2 (p < 0.05). The proportion of associated co-morbidities among cases was very high i.e., 75.75% as compared to controls with a proportion of 29.25% co-morbidities. The median days of hospital stay were significantly lower in cases compared to controls (3 days vs 12 days, P < 0.001). Conclusion: Length of hospital stay (in days) was showing a significant difference among cases and control (3 days Vs 12 days); hospital stay was less (median 3 days) for cases, as they reported late and thus died earlier; hence concluded that early hospital admission will decrease chances of death due to COVID-19.

COVID-19 Mimics Pulmonary Dysfunction in Muscular Dystrophy as a Post-Acute Syndrome in Patients.

Although progressive wasting and weakness of respiratory muscles are the prominent hallmarks of Duchenne muscular dystrophy (DMD) and long-COVID (also referred as the post-acute sequelae of COVID-19 syndrome); however, the underlying mechanism(s) leading to respiratory failure in both conditions remain unclear. We put together the latest relevant literature to further understand the plausible mechanism(s) behind diaphragm malfunctioning in COVID-19 and DMD conditions. Previously, we have shown the role of matrix metalloproteinase-9 (MMP9) in skeletal muscle fibrosis via a substantial increase in the levels of tumor necrosis factor-α (TNF-α) employing a DMD mouse model that was crossed-bred with MMP9-knockout (MMP9-KO or MMP9-/-) strain. Interestingly, recent observations from clinical studies show a robust increase in neopterin (NPT) levels during COVID-19 which is often observed in patients having DMD. What seems to be common in both (DMD and COVID-19) is the involvement of neopterin (NPT). We know that NPT is generated by activated white blood cells (WBCs) especially the M1 macrophages in response to inducible nitric oxide synthase (iNOS), tetrahydrobiopterin (BH4), and tetrahydrofolate (FH4) pathways, i.e., folate one-carbon metabolism (FOCM) in conjunction with epigenetics underpinning as an immune surveillance protection. Studies from our laboratory, and others researching DMD and the genetically engineered humanized (hACE2) mice that were administered with the spike protein (SP) of SARS-CoV-2 revealed an increase in the levels of NPT, TNF-α, HDAC, IL-1ß, CD147, and MMP9 in the lung tissue of the animals that were subsequently accompanied by fibrosis of the diaphragm depicting a decreased oscillation phenotype. Therefore, it is of interest to understand how regulatory processes such as epigenetics involvement affect DNMT, HDAC, MTHFS, and iNOS that help generate NPT in the long-COVID patients.

Simulation of COVID-19 symptoms in a genetically engineered mouse model: implications for the long haulers.

The ongoing pandemic (also known as coronavirus disease-19; COVID-19) by a constantly emerging viral agent commonly referred as the severe acute respiratory syndrome corona virus 2 or SARS-CoV-2 has revealed unique pathological findings from infected human beings, and the postmortem observations. The list of disease symptoms, and postmortem observations is too long to mention; however, SARS-CoV-2 has brought with it a whole new clinical syndrome in "long haulers" including dyspnea, chest pain, tachycardia, brain fog, exercise intolerance, and extreme fatigue. We opine that further improvement in delivering effective treatment, and preventive strategies would be benefited from validated animal disease models. In this context, we designed a study, and show that a genetically engineered mouse expressing the human angiotensin converting enzyme 2; ACE-2 (the receptor used by SARS-CoV-2 agent to enter host cells) represents an excellent investigative resource in simulating important clinical features of the COVID-19. The ACE-2 mouse model (which is susceptible to SARS-CoV-2) when administered with a recombinant SARS-CoV-2 spike protein (SP) intranasally exhibited a profound cytokine storm capable of altering the physiological parameters including significant changes in cardiac function along with multi-organ damage that was further confirmed via histological findings. More importantly, visceral organs from SP treated mice revealed thrombotic blood clots as seen during postmortem examination. Thus, the ACE-2 engineered mouse appears to be a suitable model for studying intimate viral pathogenesis thus paving the way for identification, and characterization of appropriate prophylactics as well as therapeutics for COVID-19 management.

Lung involvement in COVID-19 positive pregnant women and their outcomes - A clinical and imaging based retrospective case study

Background and Aims: COVID-19 has been a globally concerning pandemic affecting more than 20 million people worldwide. Due to physiological and anatomical changes, pregnant women are more susceptible to viral respiratory infections. Although the clinical and radiological features of COVID positive pregnant and non-pregnant women are comparable, literature pertaining to the clinical presentation and the outcomes in COVID positive pregnant women are being researched upon. Aims and Objectives: The main objective is to assess the lung involvement in COVID-19 positive pregnant women based on their clinical presentation and CT imaging features. The secondary aim is to study their clinical outcomes based on the above findings. Methods: This was a retrospective study carried out on COVID-19 positive pregnant women admitted to our hospital over 6 months (from May 2020 to October 2020). The collected data were analyzed with IBM.SPSS statistics software 23.0 Version. Results: There were a total of 480 COVID positive antenatal women detected Out of 480 patients 75.8% (364) were asymptomatic, one hundred and two patients (21.3%) presented with mild symptoms such as fever, dry cough, runny nose, loss of taste/smell without any breathing difficulty. Fourteen patients (2.9%) were identified in the moderate to severe symptomatic category with lung involvement with a 95% Confidence Intervals between 1.41 and 4.42. Three patients sustained mortality, the overall Mortality rate being 0.6%. Conclusion: The majority of the COVID positive antenatal women are asymptomatic or present with mild symptoms as detected from this study. Only a small proportion (2.9%) were identified with respiratory compromise. Although their infectivity rate is quite high, 99.4% of the population were cured and discharged.

A machine learning-based approach to determine infection status in recipients of BBV152 whole virion inactivated SARS-CoV-2 vaccine for serological surveys (preprint)

Data science has been an invaluable part of the COVID-19 pandemic response with multiple applications, ranging from tracking viral evolution to understanding the effectiveness of interventions. Asymptomatic breakthrough infections have been a major problem during the ongoing surge of Delta variant globally. Serological discrimination of vaccine response from infection has so far been limited to Spike protein vaccines used in the higher-income regions. Here, we show for the first time how statistical and machine learning (ML) approaches can discriminate SARS-CoV-2 infection from immune response to an inactivated whole virion vaccine (BBV152, Covaxin, India), thereby permitting real-world vaccine effectiveness assessments from cohort-based serosurveys in Asia and Africa where such vaccines are commonly used. Briefly, we accessed serial data on Anti-S and Anti-NC antibody concentration values, along with age, sex, number of doses, and number of days since the last vaccine dose for 1823 Covaxin recipients. An ensemble ML model, incorporating a consensus clustering approach alongside the support vector machine (SVM) model, was built on 1063 samples where reliable qualifying data existed, and then applied to the entire dataset. Of 1448 self-reported negative subjects, 724 were classified as infected. Since the vaccine contains wild-type virus and the antibodies induced will neutralize wild type much better than Delta variant, we determined the relative ability of a random subset of such samples to neutralize Delta versus wild type strain. In 100 of 156 samples, where ML prediction differed from self-reported uninfected status, Delta variant, was neutralized more effectively than the wild type, which cannot happen without infection. The fraction rose to 71.8% (28 of 39) in subjects predicted to be infected during the surge, which is concordant with the percentage of sequences classified as Delta (75.6%-80.2%) over the same period.