Some mechanistic underpinnings of molecular adaptations of SARS-COV-2 spike protein by integrating candidate adaptive polymorphisms with protein dynamics (preprint)

We integrate evolutionary predictions based on the neutral theory of molecular evolution with protein dynamics to generate mechanistic insight into the molecular adaptations of the SARS-COV-2 Spike (S) protein. With this approach, we first identified Candidate Adaptive Polymorphisms (CAPs) of the SARS-CoV-2 Spike protein and assessed the impact of these CAPs through dynamics analysis. Not only have we found that CAPs frequently overlap with well-known functional sites, but also, using several different dynamics-based metrics, we reveal the critical allosteric interplay between SARS-CoV-2 CAPs and the S protein binding sites with the human ACE2 (hACE2) protein. CAPs interact far differently with the hACE2 binding site residues in the open conformation of S protein compared to the closed form. In particular, the CAP sites control the dynamics binding residues in the open state, suggesting an allosteric control of hACE2 binding. We also explored the characteristic mutations of different SARS-CoV-2 strains to find dynamic hallmarks and potential effects of future mutations. Our analyses reveal that Delta strain-specific variants have non-additive (i.e., epistatic) interactions with CAP sites, whereas the less pathogenic Omicron strains have mostly compensatory variants. Finally, our dynamics-based analysis suggests that the novel mutations observed in the Omicron strain epistatically interact with the CAP sites to help escape antibody binding.

Effect of lockdown on activities of daily living in the built environment and wellbeing.

In an effort to arrest the spread of coronavirus (COVID-19) infection, a nationwide lockdown was declared in India in March 2020. To assess how personal built environments affected the citizens in the first few weeks, an explorative online survey was conducted, eliciting responses about work habits before the lockdown, psychological wellbeing, time spent in various activities, characteristics of those who worked from home, and food and sleep patterns. We received 121 (76 male and 45 female) responses with an average age of 35.5 years [max: 70 years, min: 18 years, standard deviation (SD): 12.9 years]. The major difference caused by the lockdown was a reduction in the time taken and distance travelled of the commute to workplaces, which was an average of 30 minutes and 9.5 km, respectively. In terms of diet, subjects who were vegetarian did not experience any difference, unlike those who were non-vegetarians (p < 0.05). The results show an association of the dependent variable of 'feeling in general' with predictor variables of 'energy, pep, vitality' and 'feel healthy to work' during the pandemic, whereas the predictor variables of 'energy, pep, vitality', 'happy and satisfied personal life', 'feel healthy to work' show an association with the dependent variable of 'feeling in general' before the lockdown with a significance of p < 0.02 and R2 = 0.51 and R2 = 0.60, respectively. Among those who worked from home in constrained environments, people found spaces and seemed to adapt reasonably well to the built environment with employees showing a preference for working from bedrooms and students for working from 'sit-out' (outside) spaces (p < 0.05). There was no change in the quality or quantity of sleep during the lockdown. This study in the early weeks of the lockdown documents the way in which individuals lived through it in terms of the built environment at home.

Role of phytocompounds as the potential anti-viral agent: an overview.

Viral diseases are the most notorious infective agent(s) causing morbidity and mortality in every nook and corner for ages; viruses are active in host cells, and specific anti-virus medicines' developments remain uncanny. In this century of the biological era, human viruses act predominantly as versatile spreaders. The infection of the present COVID-19 virus is up in the air; blithely, the integument of medicinal chemistry approaches, particularly bioactive derived phytocompounds could be helpful to control those human viruses, recognized in the last 100 years. Indeed, natural products are being used for various therapeutic purposes. The major bioactive phytocompounds are chemically containing coumarin, thiosulfonate, steroid, polysaccharide, tannin, lignin, proanthocyanidin, terpene, quinone, saponin, flavonoid, alkaloid, and polyphenol, that are documented for inhibitory action against several viral infections. Mostly, about 20-30% of plants from tropical or temperate regions are known to have some antiviral activity. This comprehensive analysis of bioactive-derived phytocompounds would represent a significant impact and might be helpful for antiviral research and the current state of viral treatments.

Carcinoembryonic antigen (CEA) and other surrogate inflammatory biomarkers in COVID-19

It has been two years since the global outbreak of the highly contagious and deadly corona virus disease (COVID-19) caused by SARS-CoV-2 first emerged in China. Since then, various diagnostic, prognostic and treatment strategies undertaken to address the pandemic have been dynamically evolving. Predictive and prognostic role of various biomarkers in COVID-19 has been a subject of intense exploration. We aimed to determine the association of Carcinoembryonic antigen (CEA) and various surrogate inflammatory biomarkers with the severity of COVID-19 disease. This retrospective cohort study was carried out on 98 patients admitted in Jaypee Hospital, Noida with COVID-19 disease. Information regarding demographics, laboratory parameters and clinical history was collected from Hospital Information System. Serum levels of CEA and other biomarkers such as Neutrophil-lymphocyte ratio (NLR), C-reactive protein (CRP), Interleukin-6 (IL-6), Ferritin, and Procalcitonin (PCT) were assessed. Correlation analyses were performed between the parameters and acute respiratory distress syndrome (ARDS) stages. Logistic regression and ROC curve analysis were performed to assess the various parameters for distinguishing COVID-19 patients requiring ICU admission. Mean hospital stay, NLR, CEA, IL-6, CRP, Ferritin (P < 0.0001) and PCT (P = 0.01) were significantly higher in ICU patients when compared to general ward patients. NLR, median serum CEA, IL-6, and CRP levels were significantly higher in non-survivor compared to the survivors (P < 0.0001, 0.0341 and 0.0092). CEA correlated well with disease severity based upon ARDS classification and was a better marker to differentiate patient according to ARDS stages (ARDS 0 vs 2 P = 0.0006;0 vs 3 P < 0.0001;ARDS 1 vs 2 P = 0.0183;1 vs 3 P = 0.0006). The area under the Receiver operating characteristic (ROC) curve for CEA was 0.7467 (95% CI- 0.64885- 0.84459) which revealed the potential of CEA as a biomarker to distinguish COVID-19 patients requiring ICU admission. CEA can be used to predict the severity of COVID-19 associated ARDS as well as patients requiring ICU admission. Along with routine inflammatory biomarkers (NLR, CRP, IL-6, PCT, and ferritin), CEA should be used for early identification of critical COVID-19 positive patients and for assessing prognosis.

COVID-19 Spreading Prediction and Impact Analysis by Using Artificial Intelligence for Sustainable Global Health Assessment (preprint)

The COVID-19 pandemic is considered as the most alarming global health calamity of this century. COVID-19 has been confirmed to be mutated from coronavirus family. As stated by the records of The World Health Organization (WHO at April 18 2020), the present epidemic of COVID-19, has influenced more than 2,164,111 persons and killed more than 146,198 folks in over 200 countries across the globe and billions had confronted impacts in lifestyle because of this virus outbreak. The ongoing overall outbreak of the COVID-19 opened up new difficulties to the research sectors. Artificial intelligence (AI) driven strategies can be valuable to predict the parameters, hazards, and impacts of such an epidemic in a cost-efficient manner. The fundamental difficulties of AI in this situation is the limited availability of information and the uncertain nature of the disease. Here in this article, we have tried to integrate AI to predict the infection outbreak and along with this, we have also tried to test whether AI with help deep learning can recognize COVID-19 infected chest X-Rays or not. The global outbreak of the virus posed enormous economic, ecological and societal challenges into the human population and with help of this paper, we have tried to give a message that AI can help us to identify certain features of the disease outbreak that could prove to be essential to protect the humanity from this deadly disease.

Health Risk Appraisal Associated with Air Quality over Coal-Fired Thermal Power Plants and Coalmine Complex Belts of Urban-Rural Agglomeration in the Eastern Coastal State of Odisha, India

Manufacturing and mining sectors are serious pollution sources and risk factors that threaten air quality and human health. We analyzed pollutants at two study sites (Talcher and Brajrajnagar) in Odisha, an area exposed to industrial emissions, in the pre-COVID-19 year (2019) and consecutive pandemic years, including lockdowns (2020 and 2021). We observed that the annual data for pollutant concentration increased at Talcher: PM2.5 (7-10%), CO (29-35%), NO2 and NOx (8-57% at Talcher and 14-19% at Brajrajnagar);while there was slight to substantial increase in PM10 (up to 11%) and a significant increase in O3 (41-88%) at both sites. At Brajrajnagar, there was a decrease in PM2.5 (up to 15%) and CO (around half of pre-lockdown), and a decrease in SO2 concentration was observed (30-86%) at both sites. Substantial premature mortality was recorded, which can be attributed to PM2.5 (16-26%), PM10 (31-43%), NO2 (15-21%), SO2 (4-7%), and O3 (3-6%). This premature mortality caused an economic loss between 86-36 million USD to society. We found that although lockdown periods mitigated the losses, the balance of rest of the year was worse than in 2019. These findings are benchmarks to manage air quality over Asia's largest coalmine fields and similar landscapes.

Suicide numbers during the first 9-15 months of the COVID-19 pandemic compared with pre-existing trends: An interrupted time series analysis in 33 countries.

Background: Predicted increases in suicide were not generally observed in the early months of the COVID-19 pandemic. However, the picture may be changing and patterns might vary across demographic groups. We aimed to provide a timely, granular picture of the pandemic's impact on suicides globally. Methods: We identified suicide data from official public-sector sources for countries/areas-within-countries, searching websites and academic literature and contacting data custodians and authors as necessary. We sent our first data request on 22nd June 2021 and stopped collecting data on 31st October 2021. We used interrupted time series (ITS) analyses to model the association between the pandemic's emergence and total suicides and suicides by sex-, age- and sex-by-age in each country/area-within-country. We compared the observed and expected numbers of suicides in the pandemic's first nine and first 10-15 months and used meta-regression to explore sources of variation. Findings: We sourced data from 33 countries (24 high-income, six upper-middle-income, three lower-middle-income; 25 with whole-country data, 12 with data for area(s)-within-the-country, four with both). There was no evidence of greater-than-expected numbers of suicides in the majority of countries/areas-within-countries in any analysis; more commonly, there was evidence of lower-than-expected numbers. Certain sex, age and sex-by-age groups stood out as potentially concerning, but these were not consistent across countries/areas-within-countries. In the meta-regression, different patterns were not explained by countries' COVID-19 mortality rate, stringency of public health response, economic support level, or presence of a national suicide prevention strategy. Nor were they explained by countries' income level, although the meta-regression only included data from high-income and upper-middle-income countries, and there were suggestions from the ITS analyses that lower-middle-income countries fared less well. Interpretation: Although there are some countries/areas-within-countries where overall suicide numbers and numbers for certain sex- and age-based groups are greater-than-expected, these countries/areas-within-countries are in the minority. Any upward movement in suicide numbers in any place or group is concerning, and we need to remain alert to and respond to changes as the pandemic and its mental health and economic consequences continue. Funding: None.

Trends in diabetes care with special emphasis to medicinal plants: Advancement and treatment

Diabetic mellitus (DM) is a common metabolic disorder prevailing throughout the world. It may affect a child to an older person depending upon the physiology and the factors influencing the internal metabolic system of the body. Several treatments are available in the market ranges from synthetic drugs, insulin therapy, herbal drugs, and transdermal patches. Interestingly, the development of technologies and digital health have proving very helpful in improving the lifestyle of diabetic patients. All treatment approaches have their own advantages and disadvantages in the form of effectiveness and side effects. Medicinal plants have a long history of traditional application in the treatment of diabetes and even the use of plants are growing day-by-day due to the significant results against diseases and fewer side effects as compared to other treatment therapies. The intention behind writing this review is to gather all information and discussed them exhaustively in an article. The novel Coronavirus 2019 (COVID-19) pandemic has affected my lives including diabetic patients. The antidiabetic treatment strategies during this period has also discussed. In this article, we highlighted the molecular mechanism and herbal phytoconstituents that are responsible for lowering blood glucose level. The factors responsible for the progression of metabolic disorders can be controlled with the use of phytoconstituents present in herbal plants to maintain β-cells performance and restore blood glucose level. It can be concluded that medicinal plants are effective and affordable with lesser side effects for treating DM.

Surgical Diseases Management during COVID-19 Crisis at a Tertiary Care Hospital of India: Our Institutional Strategy.

Introduction In response to the national coronavirus disease 2019 (COVID-19) pandemic, all hospitals and medical institutes gave priority to COVID-19 screening and to the management of patients who required hospitalization for COVID-19 infection. Surgical departments postponed all elective operative procedures and provided only essential surgical care to patients who presented with acute surgical conditions or suspected malignancy. Ample literature has emerged during this pandemic regarding the guidelines for safe surgical care. We report our experience during the lockdown period including the surgical procedures performed, the perioperative care provided, and the specific precautions implemented in response to the COVID-19 crisis. Materials and Methods We extracted patient clinical data from the medical records of all surgical patients admitted to our tertiary care hospital between the March 24th, 2020 and May 31st, 2020. Data collected included: patient demographics, surgical diagnoses, surgical procedures, nonoperative management, and patient outcomes. Results Seventy-seven patients were included in this report: 23 patients were managed medically, 28 patients underwent a radiologic intervention, and 23 patients required an operative procedure. In total eight of the 77 patients died due to ongoing sepsis, multiorgan failure, or advanced malignancy. Conclusion During the COVID-19 lockdown period, our surgical team performed many lifesaving surgical procedures and appropriately selected cancer operations. We implemented and standardized essential perioperative measures to reduce the spread of COVID-19 infection. When the lockdown measures were phased out a large number of patients remained in need of delayed elective and semi-elective operative treatment. Hospitals, medical institutes, and surgical leadership must adjust their priorities, foster stewardship of limited surgical care resources, and rapidly implement effective strategies to assure perioperative safety for both patients and operating room staff during periods of crisis.

Corrigendum to "PRAK-03202: A triple antigen virus-like particle vaccine candidate against SARS CoV-2"[Heliyon 7 (10) (October 2021) e08124].

[This corrects the article DOI: 10.1016/j.heliyon.2021.e08124.].

TopHap: Rapid inference of key phylogenetic structures from common haplotypes in large genome collections with limited diversity (preprint)

Motivation: Building reliable phylogenies from very large collections of sequences with a limited number of phylogenetically informative sites is challenging because sequencing errors and recurrent/backward mutations interfere with the phylogenetic signal, confounding true evolutionary relationships. Massive global efforts of sequencing genomes and reconstructing the phylogeny of SARS-CoV-2 strains exemplify these difficulties since there are only hundreds of phylogenetically informative sites and millions of genomes. For such datasets, we set out to develop a method for building the phylogenetic tree of genomic haplotypes consisting of positions harboring common variants to improve the signal-to-noise ratio for more accurate phylogenetic inference of resolvable phylogenetic features. Results: We present the TopHap approach that determines spatiotemporally common haplotypes of common variants and builds their phylogeny at a fraction of the computational time of traditional methods. To assess topological robustness, we develop a bootstrap resampling strategy that resamples genomes spatiotemporally. The application of TopHap to build a phylogeny of 68,057 genomes (68KG) produced an evolutionary tree of major SARS-CoV-2 haplotypes. This phylogeny is concordant with the mutation tree inferred using the co-occurrence pattern of mutations and recovers key phylogenetic relationships from more traditional analyses. We also evaluated alternative roots of the SARS-CoV-2 phylogeny and found that the earliest sampled genomes in 2019 likely evolved by four mutations of the most recent common ancestor of all SARS-CoV-2 genomes. An application of TopHap to more than 1 million genomes reconstructed the most comprehensive evolutionary relationships of major variants, which confirmed the 68KG phylogeny and provided evolutionary origins of major variants of concern. Availability: TopHap is available on the web at https://github.com/SayakaMiura/TopHap.

Frequency distribution of pollutant concentrations over Indian megacities impacted by the COVID-19 lockdown.

The megacities experience poor air quality frequently due to stronger anthropogenic emissions. India had one of the longest lockdowns in 2020 to curb the spread of COVID-19, leading to reductions in the emissions from anthropogenic activities. In this article, the frequency distributions of different pollutants have been analysed over two densely populated megacities: Delhi (28.70° N; 77.10° E) and Kolkata (22.57° N; 88.36° E). In Delhi, the percentage of days with PM2.5 levels exceeding the National Ambient Air Quality Standards (NAAQS) between 25 March and 17 June dropped from 98% in 2019 to 61% in 2020. The lockdown phase 1 brought down the PM10 (particulate matter having an aerodynamic diameter ≤ 10 µm) levels below the daily NAAQS limit over Delhi and Kolkata. However, PM10 exceeded the limit of 100 µgm-3 during phases 2-5 of lockdown over Delhi due to lower temperature, weaker winds, increased relative humidity and commencement of limited traffic movement. The PM2.5 levels exhibit a regressive trend in the highest range from the year 2019 to 2020 in Delhi. The daily mean value for PM2.5 concentrations dropped from 85-90 µgm-3 to 40-45 µgm-3 bin, whereas the PM10 levels witnessed a reduction from 160-180 µgm-3 to 100-120 µgm-3 bin due to the lockdown. Kolkata also experienced a shift in the peak of PM10 distribution from 80-100 µgm-3 in 2019 to 20-40 µgm-3 during the lockdown. The PM2.5 levels in peak frequency distribution were recorded in the 35-40 µgm-3 bin in 2019 which dropped to 15-20 µgm-3 in 2020. In line with particulate matter, other primary gaseous pollutants (NOx, CO, SO2, NH3) also showed decline. However, changes in O3 showed mixed trends with enhancements in some of the phases and reductions in other phases. In contrast to daily mean O3, 8-h maximum O3 showed a reduction over Delhi during lockdown phases except for phase 3. Interestingly, the time of daily maximum was observed to be delayed by ~ 2 h over Delhi (from 1300 to 1500 h) and ~ 1 h over Kolkata (from 1300 to 1400 h) almost coinciding with the time of maximum temperature, highlighting the role of meteorology versus precursors. Emission reductions weakened the chemical sink of O3 leading to enhancement (120%; 11 ppbv) in night-time O3 over Delhi during phases 1-3.