Development and Validation of an Acute Respiratory Distress Syndrome Prediction Model in Coronavirus Disease 2019: Updated Lung Injury Prediction Score.

OBJECTIVE: To develop and validate an updated lung injury prediction score for coronavirus disease 2019 (COVID-19) (c-LIPS) tailored for predicting acute respiratory distress syndrome (ARDS) in COVID-19. PATIENTS AND METHODS: This was a registry-based cohort study using the Viral Infection and Respiratory Illness Universal Study. Hospitalized adult patients between January 2020 and January 2022 were screened. Patients who qualified for ARDS within the first day of admission were excluded. Development cohort consisted of patients enrolled from participating Mayo Clinic sites. The validation analyses were performed on remaining patients enrolled from more than 120 hospitals in 15 countries. The original lung injury prediction score (LIPS) was calculated and enhanced using reported COVID-19-specific laboratory risk factors, constituting c-LIPS. The main outcome was ARDS development and secondary outcomes included hospital mortality, invasive mechanical ventilation, and progression in WHO ordinal scale. RESULTS: The derivation cohort consisted of 3710 patients, of whom 1041 (28.1%) developed ARDS. The c-LIPS discriminated COVID-19 patients who developed ARDS with an area under the curve (AUC) of 0.79 compared with original LIPS (AUC, 0.74; P<.001) with good calibration accuracy (Hosmer-Lemeshow P=.50). Despite different characteristics of the two cohorts, the c-LIPS's performance was comparable in the validation cohort of 5426 patients (15.9% ARDS), with an AUC of 0.74; and its discriminatory performance was significantly higher than the LIPS (AUC, 0.68; P<.001). The c-LIPS's performance in predicting the requirement for invasive mechanical ventilation in derivation and validation cohorts had an AUC of 0.74 and 0.72, respectively. CONCLUSION: In this large patient sample c-LIPS was successfully tailored to predict ARDS in COVID-19 patients.

Twelve tips for optimising learning for postgraduate doctors in the operating theatre.

Learning in the operating theatre forms a critical part of postgraduate medical education. Postgraduate doctors present a diverse cohort of learners with a wide range of learning needs that will vary by their level of experience and curriculum requirements. With evidence of both trainee dissatisfaction with the theatre learning experience and reduced time spent in the operating theatre, which has been exacerbated by the effects of the Covid-19 pandemic, it is vital that every visit to the operating theatre is used as a learning opportunity. We have devised 12 tips aimed at both learners and surgeons to optimise learning in the operating theatre, set out into four domains: educational context, preparation, learning in theatre, feedback and reflection. These tips have been created by a process of literature review and acknowledgment of established learning theory, with further discussion amongst surgical trainees, senior surgical faculty, surgical educators and medical education faculty.

Covid-19 vaccination program for children: An electronic assessment of symptoms.

BACKGROUND: The evolution of COVID-19 vaccinations, which are mostly seen as crucial to curb the epidemic, is a result of remarkable and ground-breaking researches by scientists around the world. The main goal of this study was to identify the significant adverse reactions of these vaccines, specifically in Homo sapiens. METHODS: In this research, a trial version of Qualtrics CoreXM software was used, and 18 questionnaires were prototyped as part of an online survey that was done in the northern part of India. RESULTS: The dataset included survey responses from 286 vaccinated (Corbevax) respondents' samples detailing their demographics, daily activities, type of gastronomic preferences, and any prior illnesses. The data were collected between March 24, 2022, and April 26, 2022. After analysis, 70.98% of respondents who took the first dose of the medication experienced side effects, while 50.62% of respondents who took the second dose of the medication stated the same. The major side effects reported were injection site pain, fever, tiredness, body ache, headache, etc. Conclusion: After conducting a poll on children (aged 12-18) who had received the COVID-19 vaccination, we concluded that immunizations rarely cause moderate side effects that are manageable.

When months matter; modelling the impact of the COVID-19 pandemic on the diagnostic pathway of Motor Neurone Disease (MND).

BACKGROUND: A diagnosis of MND takes an average 10-16 months from symptom onset. Early diagnosis is important to access supportive measures to maximise quality of life. The COVID-19 pandemic has caused significant delays in NHS pathways; the majority of GP appointments now occur online with subsequent delays in secondary care assessment. Given the rapid progression of MND, patients may be disproportionately affected resulting in late stage new presentations. We used Monte Carlo simulation to model the pre-COVID-19 diagnostic pathway and then introduced plausible COVID-19 delays. METHODS: The diagnostic pathway was modelled using gamma distributions of time taken: 1) from symptom onset to GP presentation, 2) for specialist referral, and 3) for diagnosis reached after neurology appointment. We incorporated branches to simulate delays: when patients did not attend their GP and when the GP consultation did not result in referral. An emergency presentation was triggered when diagnostic pathway time was within 30 days of projected median survival. Total time-to-diagnosis was calculated over 100,000 iterations. The pre-COVID-19 model was estimated using published data and the Improving MND Care Survey 2019. We estimated COVID-19 delays using published statistics. RESULTS: The pre-COVID model reproduced known features of the MND diagnostic pathway, with a median time to diagnosis of 399 days and predicting 5.2% of MND patients present as undiagnosed emergencies. COVID-19 resulted in diagnostic delays from 558 days when only primary care was 25% delayed, to 915 days when both primary and secondary care were 75%. The model predicted an increase in emergency presentations ranging from 15.4%-44.5%. INTERPRETATIONS: The model suggests the COVID-19 pandemic will result in later-stage diagnoses and more emergency presentations of undiagnosed MND. Late-stage presentations may require rapid escalation to multidisciplinary care. Proactive recognition of acute and late-stage disease with altered service provision will optimise care for people with MND.

Analysis and forecasting of air quality index based on satellite data.

OBJECTIVE: The air quality index (AQI) forecasts are one of the most important aspects of improving urban public health and enabling society to remain sustainable despite the effects of air pollution. Pollution control organizations deploy ground stations to collect information about air pollutants. Establishing a ground station all-around is not feasible due to the cost involved. As an alternative, satellite-captured data can be utilized for AQI assessment. This study explores the changes in AQI during various COVID-19 lockdowns in India utilizing satellite data. Furthermore, it addresses the effectiveness of state-of-the-art deep learning and statistical approaches for forecasting short-term AQI. MATERIALS AND METHODS: Google Earth Engine (GEE) has been utilized to capture the data for the study. The satellite data has been authenticated against ground station data utilizing the beta distribution test before being incorporated into the study. The AQI forecasting has been explored using state-of-the-art statistical and deep learning approaches like VAR, Holt-Winter, and LSTM variants (stacked, bi-directional, and vanilla). RESULTS: AQI ranged from 100 to 300, from moderately polluted to very poor during the study period. The maximum reduction was recorded during the complete lockdown period in the year 2020. Short-term AQI forecasting with Holt-Winter was more accurate than other models with the lowest MAPE scores. CONCLUSIONS: Based on our findings, air pollution is clearly a threat in the studied locations, and it is important for all stakeholders to work together to reduce it. The level of air pollutants dropped substantially during the different lockdowns.

Bioinformatics pipeline unveils genetic variability to synthetic vaccine design for Indian SARS-CoV-2 genomes.

In the worrisome scenarios of various waves of SARS-CoV-2 pandemic, a comprehensive bioinformatics pipeline is essential to analyse the virus genomes in order to understand its evolution, thereby identifying mutations as signature SNPs, conserved regions and subsequently to design epitope based synthetic vaccine. We have thus performed multiple sequence alignment of 4996 Indian SARS-CoV-2 genomes as a case study using MAFFT followed by phylogenetic analysis using Nextstrain to identify virus clades. Furthermore, based on the entropy of each genomic coordinate of the aligned sequences, conserved regions are identified. After refinement of the conserved regions, based on its length, one conserved region is identified for which the primers and probes are reported for virus detection. The refined conserved regions are also used to identify T-cell and B-cell epitopes along with their immunogenic and antigenic scores. Such scores are used for selecting the most immunogenic and antigenic epitopes. By executing this pipeline, 40 unique signature SNPs are identified resulting in 23 non-synonymous signature SNPs which provide 28 amino acid changes in protein. On the other hand, 12 conserved regions are selected based on refinement criteria out of which one is selected as the potential target for virus detection. Additionally, 22 MHC-I and 21 MHC-II restricted T-cell epitopes with 10 unique HLA alleles each and 17 B-cell epitopes are obtained for 12 conserved regions. All the results are validated both quantitatively and qualitatively which show that from genetic variability to synthetic vaccine design, the proposed pipeline can be used effectively to combat SARS-CoV-2.

COVID-19 severity is associated with population-level gut microbiome variations.

The human gut microbiome interacts with many diseases, with recent small studies suggesting a link with COVID-19 severity. Exploring this association at the population-level may provide novel insights and help to explain differences in COVID-19 severity between countries. We explore whether there is an association between the gut microbiome of people within different countries and the severity of COVID-19, measured as hospitalisation rate. We use data from the large (n = 3,055) open-access gut microbiome repository curatedMetagenomicData, as well as demographic and country-level metadata. Twelve countries were placed into two groups (high/low) according to COVID-19 hospitalisation rate before December 2020 (ourworldindata.com). We use an unsupervised machine learning method, Topological Data Analysis (TDA). This method analyses both the local geometry and global topology of a high-dimensional dataset, making it particularly suitable for population-level microbiome data. We report an association of distinct baseline population-level gut microbiome signatures with COVID-19 severity. This was found both with a PERMANOVA, as well as with TDA. Specifically, it suggests an association of anti-inflammatory bacteria, including Bifidobacteria species and Eubacterium rectale, with lower severity, and pro-inflammatory bacteria such as Prevotella copri with higher severity. This study also reports a significant impact of country-level confounders, specifically of the proportion of over 70-year-olds in the population, GDP, and human development index. Further interventional studies should examine whether these relationships are causal, as well as considering the contribution of other variables such as genetics, lifestyle, policy, and healthcare system. The results of this study support the value of a population-level association design in microbiome research in general and for the microbiome-COVID-19 relationship, in particular. Finally, this research underscores the potential of TDA for microbiome studies, and in identifying novel associations.

Human miRNAs to Identify Potential Regions of SARS-CoV-2

It is two years now but the world is still struggling against COVID-19 due to the havoc created by the SARS-CoV-2 virus and its multiple variants. Considering this perspective, in this work, we have hypothesized a new approach in order to identify potential regions in SARS-CoV-2 similar to the human miRNAs. Thus, they may have similar consequences as caused by the human miRNAs in human body. Therefore, the same way by which human miRNAs are inhibited can be applied for such potential regions of virus as well by administering drugs to the interacting human proteins. In this regard, the multiple sequence alignment technique Clustal Omega is used to align 2656 human miRNAs with the SARS-CoV-2 reference genome to identify the potential regions within the virus reference genome which have high similarities with the human miRNAs. The potential regions in virus genome are identified based on the highest number of nucleotide match, greater than or equal to 5 at a genomic position, for the aligned miRNAs. As a result, 38 potential SARS-CoV-2 regions are identified consisting of 249 human miRNAs. Among these 38 potential regions, some top regions belong to nucleocapsid, RdRp, helicase, and ORF8. To understand the biological significance of these potential regions, the targets of the human miRNAs are considered for KEGG pathways and protein–protein and drug–protein interaction analysis as the human miRNAs are similar to the potential regions of SARS-CoV-2. Significant pathways are found which lead to comorbidities. Subsequently, drugs like emodin, bicalutamide, vorinostat, etc. are identified that may be used for clinical trials.

Association of Obesity With COVID-19 Severity and Mortality: An Updated Systemic Review, Meta-Analysis, and Meta-Regression.

Background: Obesity affects the course of critical illnesses. We aimed to estimate the association of obesity with the severity and mortality in coronavirus disease 2019 (COVID-19) patients. Data Sources: A systematic search was conducted from the inception of the COVID-19 pandemic through to 13 October 2021, on databases including Medline (PubMed), Embase, Science Web, and Cochrane Central Controlled Trials Registry. Preprint servers such as BioRxiv, MedRxiv, ChemRxiv, and SSRN were also scanned. Study Selection and Data Extraction: Full-length articles focusing on the association of obesity and outcome in COVID-19 patients were included. Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines were used for study selection and data extraction. Our Population of interest were COVID-19 positive patients, obesity is our Intervention/Exposure point, Comparators are Non-obese vs obese patients The chief outcome of the study was the severity of the confirmed COVID-19 positive hospitalized patients in terms of admission to the intensive care unit (ICU) or the requirement of invasive mechanical ventilation/intubation with obesity. All-cause mortality in COVID-19 positive hospitalized patients with obesity was the secondary outcome of the study. Results: In total, 3,140,413 patients from 167 studies were included in the study. Obesity was associated with an increased risk of severe disease (RR=1.52, 95% CI 1.41-1.63, p<0.001, I2 = 97%). Similarly, high mortality was observed in obese patients (RR=1.09, 95% CI 1.02-1.16, p=0.006, I2 = 97%). In multivariate meta-regression on severity, the covariate of the female gender, pulmonary disease, diabetes, older age, cardiovascular diseases, and hypertension was found to be significant and explained R2 = 40% of the between-study heterogeneity for severity. The aforementioned covariates were found to be significant for mortality as well, and these covariates collectively explained R2 = 50% of the between-study variability for mortality. Conclusions: Our findings suggest that obesity is significantly associated with increased severity and higher mortality among COVID-19 patients. Therefore, the inclusion of obesity or its surrogate body mass index in prognostic scores and improvement of guidelines for patient care management is recommended.

Delayed cardiac repolarisation as a predictor of in-hospital mortality in patients with COVID-19.

OBJECTIVE: With the rapid influx of COVID-19 admissions during the first wave of the pandemic, there was an obvious need for an efficient and streamlined risk stratification tool to aid in triaging. To this date, no clinical prediction tool exists for patients presenting to the hospital with COVID-19 infection. METHODS: This is a retrospective cohort study of patients admitted in one of 13 Northwell Health Hospitals, located in the wider New York Metropolitan area between 1 March 2020 and 27 April 2020. Inclusion criteria were a positive SARS-CoV-2 nasal swab, a 12-lead ECG within 48 hours, and a complete basic metabolic panel within 96 hours of presentation. RESULTS: All-cause, in-hospital mortality was 27.1% among 7098 patients. Independent predictors of mortality included demographic characteristics (male gender, race and increased age), presenting vitals (oxygen saturation <92% and heart rate >120 bpm), metabolic panel values (serum lactate >2.0 mmol/L, sodium >145, mmol/L, blood urea nitrogen >40 mmol/L, aspartate aminotransferase >40 U/L, Creatinine >1.3 mg/dL and glycose >100 mg/L) and comorbidities (congestive heart failure, chronic obstructive pulmonary disease and coronary artery disease). In addition to those, our analysis showed that delayed cardiac repolarisation (QT corrected for heart rate (QTc) >500 ms) was independently associated with mortality (OR 1.41, 95% CI 1.05 to 1.90). Previously mentioned parameters were incorporated into a risk score that accurately predicted in-hospital mortality (AUC 0.78). CONCLUSION: In the largest cohort of COVID-19 patients with complete ECG data on presentation, we found that in addition to demographics, presenting vitals, clinical history and basic metabolic panel values, QTc >500 ms is an independent risk factor for in-hospital mortality.

Palindromic target site identification in SARS-CoV-2, MERS-CoV and SARS-CoV-1 by adopting CRISPR-Cas technique.

Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) associated Cas protein (CRISPR-Cas) has turned out to be a very important tool for the rapid detection of viruses. This can be used for the identification of the target site in a virus by identifying a 3-6 nt length Protospacer Adjacent Motif (PAM) adjacent to the potential target site, thus motivating us to adopt CRISPR-Cas technique to identify SARS-CoV-2 as well as other members of Coronaviridae family. In this regard, we have developed a fast and effective method using k-mer technique in order to identify the PAM by scanning the whole genome of the respective virus. Subsequently, palindromic sequences adjacent to the PAM locations are identified as the potential target sites. Palindromes are considered in this work as they are known to identify viruses. Once all the palindrome-PAM combinations are identified, PAMs specific for the RNA-guided DNA Cas9/Cas12 endonuclease are identified to bind and cut the target sites. In this regard, PAMs such as 5'-TGG-3' and 5'-TTTA-3' in NSP3 and Exon for SARS-CoV-2, 5'-GGG-3' and 5'-TGG-3' in Exon and NSP2 for MERS-CoV and 5'-AGG-3' and 5'-TTTG-3' in Helicase and NSP3 respectively for SARS-CoV-1 are identified corresponding to SpCas9 and FnCas12a endonucleases. Finally, to recognise the target sites of Coronaviridae family as cleaved by SpCas9 and FnCas12a, complements of the palindromic target regions are designed as primers or guide RNA (gRNA). Therefore, such complementary gRNAs along with respective Cas proteins can be considered in assays for the identification of SARS-CoV-2, MERS-CoV and SARS-CoV-1.

Hotspot Mutations in SARS-CoV-2.

Since its emergence in Wuhan, China, severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has spread very rapidly around the world, resulting in a global pandemic. Though the vaccination process has started, the number of COVID-affected patients is still quite large. Hence, an analysis of hotspot mutations of the different evolving virus strains needs to be carried out. In this regard, multiple sequence alignment of 71,038 SARS-CoV-2 genomes of 98 countries over the period from January 2020 to June 2021 is performed using MAFFT followed by phylogenetic analysis in order to visualize the virus evolution. These steps resulted in the identification of hotspot mutations as deletions and substitutions in the coding regions based on entropy greater than or equal to 0.3, leading to a total of 45 unique hotspot mutations. Moreover, 10,286 Indian sequences are considered from 71,038 global SARS-CoV-2 sequences as a demonstrative example that gives 52 unique hotspot mutations. Furthermore, the evolution of the hotspot mutations along with the mutations in variants of concern is visualized, and their characteristics are discussed as well. Also, for all the non-synonymous substitutions (missense mutations), the functional consequences of amino acid changes in the respective protein structures are calculated using PolyPhen-2 and I-Mutant 2.0. In addition to this, SSIPe is used to report the binding affinity between the receptor-binding domain of Spike protein and human ACE2 protein by considering L452R, T478K, E484Q, and N501Y hotspot mutations in that region.

Interactome of Human and SARS-CoV-2 Proteins to Identify Human Hub Proteins Associated with Comorbidities

SARS-CoV-2 has a higher chance of progression in adults of any age with certain underlying health conditions or comorbidities like cancer, neurological diseases and in certain cases may even lead to death. Like other viruses, SARS-CoV-2 also interacts with host proteins to pave its entry into host cells. Therefore, to understand the behaviour of SARS-CoV-2 and design of effective antiviral drugs, host-virus protein-protein interactions (PPIs) can be very useful. In this regard, we have initially created a human-SARS-CoV-2 PPI database from existing works in the literature which has resulted in 7085 unique PPIs. Subsequently, we have identified at most 10 proteins with highest degrees viz. hub proteins from interacting human proteins for individual virus protein. The identification of these hub proteins is important as they are connected to most of the other human proteins. Consequently, when they get affected, the potential diseases are triggered in the corresponding pathways, thereby leading to comorbidities. Furthermore, the biological significance of the identified hub proteins is shown using KEGG pathway and GO enrichment analysis. KEGG pathway analysis is also essential for identifying the pathways leading to comorbidities. Among others, SARS-CoV-2 proteins viz. NSP2, NSP5, Envelope and ORF10 interacting with human hub proteins like COX4I1, COX5A, COX5B, NDUFS1, CANX, HSP90AA1 and TP53 lead to comorbidities. Such comorbidities are Alzheimer, Parkinson, Huntington, HTLV-1 infection, prostate cancer and viral carcinogenesis. Subsequently, using Enrichr tool possible repurposable drugs which target the human hub proteins are reported in this paper as well. Therefore, this work provides a consolidated study for human-SARS-CoV-2 protein interactions to understand the relationship between comorbidity and hub proteins so that it may pave the way for the development of anti-viral drugs.

Characterisation of SARS-CoV-2 clades based on signature SNPs unveils continuous evolution.

Since the emergence of SARS-CoV-2 in Wuhan, China more than a year ago, it has spread across the world in a very short span of time. Although, different forms of vaccines are being rolled out for vaccination programs around the globe, the mutation of the virus is still a cause of concern among the research communities. Hence, it is important to study the constantly evolving virus and its strains in order to provide a much more stable form of cure. This fact motivated us to conduct this research where we have initially carried out multiple sequence alignment of 15359 and 3033 global dataset without Indian and the dataset of exclusive Indian SARS-CoV-2 genomes respectively, using MAFFT. Subsequently, phylogenetic analyses are performed using Nextstrain to identify virus clades. Consequently, the virus strains are found to be distributed among 5 major clades or clusters viz. 19A, 19B, 20A, 20B and 20C. Thereafter, mutation points as SNPs are identified in each clade. Henceforth, from each clade top 10 signature SNPs are identified based on their frequency i.e. number of occurrences in the virus genome. As a result, 50 such signature SNPs are individually identified for global dataset without Indian and dataset of exclusive Indian SARS-CoV-2 genomes respectively. Out of each 50 signature SNPs, 39 and 41 unique SNPs are identified among which 25 non-synonymous signature SNPs (out of 39) resulted in 30 amino acid changes in protein while 27 changes in amino acid are identified from 22 non-synonymous signature SNPs (out of 41). These 30 and 27 amino acid changes for the non-synonymous signature SNPs are visualised in their respective protein structure as well. Finally, in order to judge the characteristics of the identified clades, the non-synonymous signature SNPs are considered to evaluate the changes in proteins as biological functions with the sequences using PROVEAN and PolyPhen-2 while I-Mutant 2.0 is used to evaluate their structural stability. As a consequence, for global dataset without Indian sequences, G251V in ORF3a in clade 19A, F308Y and G196V in NSP4 and ORF3a in 19B are the unique amino acid changes which are responsible for defining each clade as they are all deleterious and unstable. Such changes which are common for both global dataset without Indian and dataset of exclusive Indian sequences are R203M in Nucleocapsid for 20B, T85I and Q57H in NSP2 and ORF3a respectively for 20C while for exclusive Indian sequences such unique changes are A97V in RdRp, G339S and G339C in NSP2 in 19A and Q57H in ORF3a in 20A.

Whole genome analysis of more than 10 000 SARS-CoV-2 virus unveils global genetic diversity and target region of NSP6.

Whole genome analysis of SARS-CoV-2 is important to identify its genetic diversity. Moreover, accurate detection of SARS-CoV-2 is required for its correct diagnosis. To address these, first we have analysed publicly available 10 664 complete or near-complete SARS-CoV-2 genomes of 73 countries globally to find mutation points in the coding regions as substitution, deletion, insertion and single nucleotide polymorphism (SNP) globally and country wise. In this regard, multiple sequence alignment is performed in the presence of reference sequence from NCBI. Once the alignment is done, a consensus sequence is build to analyse each genomic sequence to identify the unique mutation points as substitutions, deletions, insertions and SNPs globally, thereby resulting in 7209, 11700, 119 and 53 such mutation points respectively. Second, in such categories, unique mutations for individual countries are determined with respect to other 72 countries. In case of India, unique 385, 867, 1 and 11 substitutions, deletions, insertions and SNPs are present in 566 SARS-CoV-2 genomes while 458, 1343, 8 and 52 mutation points in such categories are common with other countries. In majority (above 10%) of virus population, the most frequent and common mutation points between global excluding India and India are L37F, P323L, F506L, S507G, D614G and Q57H in NSP6, RdRp, Exon, Spike and ORF3a respectively. While for India, the other most frequent mutation points are T1198K, A97V, T315N and P13L in NSP3, RdRp, Spike and ORF8 respectively. These mutations are further visualised in protein structures and phylogenetic analysis has been done to show the diversity in virus genomes. Third, a web application is provided for searching mutation points globally and country wise. Finally, we have identified the potential conserved region as target that belongs to the coding region of ORF1ab, specifically to the NSP6 gene. Subsequently, we have provided the primers and probes using that conserved region so that it can be used for detecting SARS-CoV-2. Contact:indrajit@nitttrkol.ac.inSupplementary information: Supplementary data are available at http://www.nitttrkol.ac.in/indrajit/projects/COVID-Mutation-10K.

Concurrent variation in oil and gas methane emissions and oil price during the COVID-19 pandemic

Methane emissions associated with the production, transport, and use of oil and natural gas increase the climatic impacts of energy use;however, little is known about how emissions vary temporally and with commodity prices. We present airborne and ground-based data, supported by satellite observations, to measure weekly to monthly changes in total methane emissions in the United States' Permian Basin during a period of volatile oil prices associated with the COVID-19 pandemic. As oil prices declined from ∼ USD 60 to USD 20 per barrel, emissions changed concurrently from 3.3 % to 1.9 % of natural gas production;as prices partially recovered, emissions increased back to near initial values. Concurrently, total oil and natural gas production only declined by∼ 10 % from the peak values seen in the months prior to the crash. Activity data indicate that a rapid decline in well development and subsequent effects on associated gas flaring and midstream infrastructure throughput are the likely drivers of temporary emission reductions. Our results, along with past satellite observations, suggest that under more typical price conditions, the Permian Basin is in a state of overcapacity in which rapidly growing associated gas production exceeds midstream capacity and leads to high methane emissions.

The Unique Impact of COVID-19 on Human Gut Microbiome Research.

The coronavirus (COVID-19) pandemic has disrupted clinical trials globally, with unique implications for research into the human gut microbiome. In this mini-review, we explore the direct and indirect influences of the pandemic on the gut microbiome and how these can affect research and clinical trials. We explore the direct bidirectional relationships between the COVID-19 virus and the gut and lung microbiomes. We then consider the significant indirect effects of the pandemic, such as repeated lockdowns, increased hand hygiene, and changes to mood and diet, that could all lead to longstanding changes to the gut microbiome at an individual and a population level. Together, these changes may affect long term microbiome research, both in observational as well as in population studies, requiring urgent attention. Finally, we explore the unique implications for clinical trials using faecal microbiota transplants (FMT), which are increasingly investigated as potential treatments for a range of diseases. The pandemic introduces new barriers to participation in trials, while the direct and indirect effects laid out above can present a confounding factor. This affects recruitment and sample size, as well as study design and statistical analyses. Therefore, the potential impact of the pandemic on gut microbiome research is significant and needs to be specifically addressed by the research community and funders.

Immunogenicity and antigenicity based T-cell and B-cell epitopes identification from conserved regions of 10664 SARS-CoV-2 genomes.

The surge of SARS-CoV-2 has created a wave of pandemic around the globe due to its high transmission rate. To contain this virus, researchers are working around the clock for a solution in the form of vaccine. Due to the impact of this pandemic, the economy and healthcare have immensely suffered around the globe. Thus, an efficient vaccine design is the need of the hour. Moreover, to have a generalised vaccine for heterogeneous human population, the virus genomes from different countries should be considered. Thus, in this work, we have performed genome-wide analysis of 10,664 SARS-CoV-2 genomes of 73 countries around the globe in order to identify the potential conserved regions for the development of peptide based synthetic vaccine viz. epitopes with high immunogenic and antigenic scores. In this regard, multiple sequence alignment technique viz. Clustal Omega is used to align the 10,664 SARS-CoV-2 virus genomes. Thereafter, entropy is computed for each genomic coordinate of the aligned genomes. The entropy values are then used to find the conserved regions. These conserved regions are refined based on the criteria that their lengths should be greater than or equal to 60 nt and their corresponding protein sequences are without any stop codons. Furthermore, Nucleotide BLAST is used to verify the specificity of the conserved regions. As a result, we have obtained 17 conserved regions that belong to NSP3, NSP4, NSP6, NSP8, RdRp, Helicase, endoRNAse, 2'-O-RMT, Spike glycoprotein, ORF3a protein, Membrane glycoprotein and Nucleocapsid protein. Finally, these conserved regions are used to identify the T-cell and B-cell epitopes with their corresponding immunogenic and antigenic scores. Based on these scores, the most immunogenic and antigenic epitopes are then selected for each of these 17 conserved regions. Hence, we have obtained 30 MHC-I and 24 MHC-II restricted T-cell epitopes with 14 and 13 unique HLA alleles and 21 B-cell epitopes for the 17 conserved regions. Moreover, for validating the relevance of these epitopes, the binding conformation of the MHC-I and MHC-II restricted T-cell epitopes are shown with respect to HLA alleles. Also, the physico-chemical properties of the epitopes are reported along with Ramchandran plots and Z-Scores and the population coverage is shown as well. Overall, the analysis shows that the identified epitopes can be considered as potential candidates for vaccine design.

Genome-wide analysis of 10664 SARS-CoV-2 genomes to identify virus strains in 73 countries based on single nucleotide polymorphism.

Since the onslaught of SARS-CoV-2, the research community has been searching for a vaccine to fight against this virus. However, during this period, the virus has mutated to adapt to the different environmental conditions in the world and made the task of vaccine design more challenging. In this situation, the identification of virus strains is very much timely and important task. We have performed genome-wide analysis of 10664 SARS-CoV-2 genomes of 73 countries to identify and prepare a Single Nucleotide Polymorphism (SNP) dataset of SARS-CoV-2. Thereafter, with the use of this SNP data, the advantage of hierarchical clustering is taken care of in such a way so that Average Linkage and Complete Linkage with Jaccard and Hamming distance functions are applied separately in order to identify the virus strains as clusters present in the SNP data. In this regard, the consensus of both the clustering results are also considered while Silhouette index is used as a cluster validity index to measure the goodness of the clusters as well to determine the number of clusters or virus strains. As a result, we have identified five major clusters or virus strains present worldwide. Apart from quantitative measures, these clusters are also visualized using Visual Assessment of Tendency (VAT) plot. The evolution of these clusters are also shown. Furthermore, top 10 signature SNPs are identified in each cluster and the non-synonymous signature SNPs are visualised in the respective protein structures. Also, the sequence and structural homology-based prediction along with the protein structural stability of these non-synonymous signature SNPs are reported in order to judge the characteristics of the identified clusters. As a consequence, T85I, Q57H and R203M in NSP2, ORF3a and Nucleocapsid respectively are found to be responsible for Cluster 1 as they are damaging and unstable non-synonymous signature SNPs. Similarly, F506L and S507C in Exon are responsible for both Clusters 3 and 4 while Clusters 2 and 5 do not exhibit such behaviour due to the absence of any non-synonymous signature SNPs. In addition to all these, the code, SNP dataset, 10664 labelled SARS-CoV-2 strains and additional results as supplementary are provided through our website for further use.