Abstract The role of gamma‐glutamyl–carboxylated growth arrest–specific 6 (cGas6) in mediating the beneficial effect of vitamin K (VK) on regulating glucose metabolism remains elusive. We took a three‐pronged approach—evaluating human type 2 diabetes (T2D), high‐fat diet (HFD)–fed mice, and in vitro cultured myotubes—to address this. Blood samples were collected from both T2D patients and control subjects; skeletal muscle and blood samples were collected from HFD–fed mice with or without VK supplementation (1, 3, and 5 µg/kg BW, 8 weeks); and the molecular mechanism of cGas6 was dissected using GGCX, Gas6, AXL, or IR siRNA–transfected cultured myotubes. Plasma cGas6 and VK were significantly lower in T2D patients compared with control; and cGas6 and the cGas6/Gas6 ratio were positively correlated with VK and inversely correlated with fasting glucose in T2D patients, suggesting an important role for plasma VK and cGas6 in maintaining glucose homeostasis in T2D. Animal studies revealed that VK supplementation dose‐dependently upregulated plasma cGas6; stimulated the protein expression of cGas6, PI3K, pAKT, and GLUT4 in skeletal muscle; and reduced hyperglycemia in HFD‐fed T2D mice. And in vitro mRNA knockdown studies demonstrated the requirement of cGas6 in mediating the positive effect of VK on glucose metabolism via stimulating the PI3K/pAKT/GLUT4 signaling pathway in high glucose–treated myotubes. These results demonstrate a significant involvement of cGas6 in mediating the beneficial effect of VK on regulating glucose homeostasis in T2D.
Summary To understand the spread of SARS-CoV2, in August and September 2020, the Council of Scientific and Industrial Research (India), conducted a sero-survey across its constituent laboratories and centers across India. Of 10,427 volunteers, 1058 (10.14%) tested positive for SARS CoV2 anti-nucleocapsid (anti-NC) antibodies; 95% with surrogate neutralization activity. Three-fourth recalled no symptoms. Repeat serology tests at 3 (n=346) and 6 (n=35) months confirmed stability of antibody response and neutralization potential. Local sero-positivity was higher in densely populated cities and was inversely correlated with a 30 day change in regional test positivity rates (TPR). Regional seropositivity above 10% was associated with declining TPR. Personal factors associated with higher odds of sero-positivity were high-exposure work (Odds Ratio, 95% CI, p value; 2·23, 1·92–2·59, 6·5E-26), use of public transport (1·79, 1·43–2·24, 2·8E-06), not smoking (1·52, 1·16–1·99, 0·02), non-vegetarian diet (1·67, 1·41–1·99, 3·0E-08), and B blood group (1·36,1·15-1·61, 0·001). Impact Statement Widespread asymptomatic and undetected SARS-CoV2 infection affected more than a 100 million Indians by September 2020. Declining new cases thereafter may be due to persisting humoral immunity amongst sub-communities with high exposure. Funding Council of Scientific and Industrial Research, India (CSIR)
COPD may develop due to variation in the functioning of antioxidants along with smoking and environmental factors in genetically susceptible individuals. Since there are different views about the antioxidants responsible for detoxifying xenobiotic compound in the human body whose functional variation may lead to obstructive disease, this associative study has been taken up between GST gene polymorphism and COPD in populations exposed to coal dusts.Genotypes of the 70 COPD patients and 85 non COPD patients were determined by PCR based methods followed by multiplex PCR of GSTT1 and GSTM1 genes taking albumin gene as a control. Suspended particulate analyses were determined through the Respirable Dust sampler along with the FTIR analysis of the dust samples from the glass microfiber filters.Dust sampling analysis reveals higher level of respirable suspended particulate matter, non respirable particulate matter, SO2 and NO2 present in air of the study site. FTIR analysis also suggests a higher concentration of organic silicone and aliphatic C-F compounds present in air of the study site and when spirometry was done, low lung function was observed among most of the subjects. GSTM1 null type was significantly associated with low lung function in smoker groups and the presence of at least one active allele (either GSTM1/GSTT1) seemed to have a protective role in the development of COPD.GSTM1 (null genotype) appeared to be a risk factor for lower lung function in smokers living in the vicinity of coal mines. Apart from polluted environment and genetic susceptibility, mixed coal dust exposure rich in organic silicone and aliphatic C-F compounds also appears to be a factor for the low lung function.
Article Figures and data Abstract Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract To understand the spread of SARS-CoV2, in August and September 2020, the Council of Scientific and Industrial Research (India) conducted a serosurvey across its constituent laboratories and centers across India. Of 10,427 volunteers, 1058 (10.14%) tested positive for SARS-CoV2 anti-nucleocapsid (anti-NC) antibodies, 95% of which had surrogate neutralization activity. Three-fourth of these recalled no symptoms. Repeat serology tests at 3 (n = 607) and 6 (n = 175) months showed stable anti-NC antibodies but declining neutralization activity. Local seropositivity was higher in densely populated cities and was inversely correlated with a 30-day change in regional test positivity rates (TPRs). Regional seropositivity above 10% was associated with declining TPR. Personal factors associated with higher odds of seropositivity were high-exposure work (odds ratio, 95% confidence interval, p value: 2.23, 1.92–2.59, <0.0001), use of public transport (1.79, 1.43–2.24, <0.0001), not smoking (1.52, 1.16–1.99, 0.0257), non-vegetarian diet (1.67, 1.41–1.99, <0.0001), and B blood group (1.36, 1.15–1.61, 0.001). Introduction The World Health Organization declared SARS-CoV-2 infection as a pandemic on March 11, 2020 (WHO, 2020). Within 2 weeks, India announced a lockdown strategy that severely influenced the growth of the pandemic, which was initially very focal in the large cities, gathering pace and spreading to smaller cities and towns as the nation unlocked for societal and economic considerations. Early literature pointed towards asymptomatic transmission of SARS-CoV-2 and raised the need for extended testing (Nishiura et al., 2020; Bai et al., 2020). While RT-PCR was an undisputed choice for establishing a positive infection, serosurveillance revealed that many more were probably getting infected without manifesting symptoms (Brown and Walensky, 2020; Oran and Topol, 2020). Initial estimates of asymptomatic infection rate from the West were around 40–45% (Oran and Topol, 2020). In India, the first case of Covid was reported on January 30, 2020 (Andrews et al., 2020). Serological surveys have confirmed that spread beyond the Indian megacities was minimal in early May–June, with less than 1% seropositivity outside the designated containment zones, suggesting that the lockdown had been effective in limiting the spread (Murhekar et al., 2020a). This was not without human and economic costs. By the end of June, migrant workforces caught in the cities during the lockdown were sent to their rural homes, which may have led to subsequent rapid, multifocal rise in cases in July 2020. By October 2020, total new cases began to decline with further outbreaks limited to a few geographies. Thus, the period of August–September 2020 is an important transition point. The present study is one of two studies at a national scale that was designed to assess the spread of infection. A national serosurvey in 70 districts of India, conducted by the Indian Council of Medical Research (ICMR), had a reported crude positivity rate of about 10% (Murhekar et al., 2020b). Since only a few districts of India account for the majority of urban areas, the ICMR survey is not representative of Indian cities (Murhekar et al., 2020a), where a combination of higher population density and indoor lives has led to greater spread of disease than rural areas. Existing city-wise serosurveys are variable in target population as well as methodology and cannot be easily compared to determine the relative course of the pandemic (Ray et al., 2020; Satpati et al., 2020; Babu et al., 2020; Sharma et al., 2020). The current study was launched by the Council of Scientific and Industrial Research (CSIR) in its more than 40 constituent laboratories and centers spread all over the country, representing a wide range of ethnicities, geosocial habitats, and occupational exposures in the form of a longitudinal cohort (Phenome-India Cohort). Though limited by not being a population-denominated study, the cohort includes permanent staff, families of staff members, students, and temporary employees proving support services such as security, sanitation, housekeeping, etc. This is a diverse microcosm of India encompassing multiple socioeconomic groups and has the advantage of permitting deeper assessments such as questionnaire surveys and periodic reassessment of humoral antibody response in those found to be seropositive. Our data is thus the first that permits valid comparisons between many important urban regions of India. Here, we report results from phase 1 of this study covering the critical period of August–September 2020. Results Seropositivity varied widely across India In 10,427 subjects from over 17 states and 2 union territories, the average seropositivity was 10.14% (95% confidence interval [CI] 9.6–10.7), but varied widely across locations (Figure 1). We found that 95% of the seropositive subjects also had significant neutralizing activity, suggesting at least partial immunity (Figure 1—source data 1). Figure 1 Download asset Open asset Demographics of serosurvey (India map may not be to scale and is for representation purposes only and seropositivity is rounded off). Figure 1—source data 1 Data for all labs/centers utilized for Figure 2A,B. Lab, district, and date of collection (DOC) (columns A–C), total samples collected, number of seropositive samples, seropositivity in percentage (rounded off), and number of samples tested and found positive for neutralizing antibody (NAB). Columns (E–I) Number of confirmed cases and tests done for respective states 15 days and prior and after the DOC. Columns (L–N) Data obtained from http://www.covid19India.org. State data has been utilized as a surrogate to city/district data for city/district data was not available for number of cases/tests done for many. https://cdn.elifesciences.org/articles/66537/elife-66537-fig1-data1-v2.docx Download elife-66537-fig1-data1-v2.docx Seropositivity, population density, and trajectory of new infections As expected from the known outward spread of infection from large Indian cities, seropositivity was greater in regions with higher population density (Figure 2A). Changes in test positivity rate (TPR) are a relatively robust marker of the local level of transmission and are preferred when absolute number of tests or test rates are variable, as was the case here. Lab-wise seropositivity was correlated with the regional change in TPR. By this measure, regional transmission of SARS-CoV2 was inversely correlated to local seropositivity (Figure 2B). Seropositivity of 10% or more was associated with reductions in TPR, which may mean declining transmission (Figure 2—source data 1). Figure 2 Download asset Open asset Seropositivity and test positivity rate. (A) Population density-based seropositivity, an overall p value of <0.0001 was obtained on one-way ANOVA. (B) Change in COVID19 test positivity rate (%) for states (y-axis) against observed seropositivity of labs/centers in the state (x-axis). A negative slope reflects declining test positivity rate with increase in seropositivity. Figure 2—source data 1 Data for all labs/centers utilized for Figure 2A, B. Lab, district, and date of collection (DOC) (columns A–C), total samples collected, number of seropositive samples, seropositivity in percentage (rounded off), number of samples tested and found positive for neutralizing antibody (NAB). Columns (E–I) Number of confirmed cases and tests done for respective states 15 days and prior and after the DOC. Columns (L–N) Data obtained from http://www.covid19India.org. State data has been utilized as a surrogate to city/district data for city/district data was not available for number of cases/tests done for many. https://cdn.elifesciences.org/articles/66537/elife-66537-fig2-data1-v2.docx Download elife-66537-fig2-data1-v2.docx Survey-based correlates of seropositivity Out of 861 seropositive subjects who also provided data on symptomatology, 647 subjects (75.3%) did not recall any of the nine symptoms asked for (two of these did not provide gender data) (Supplementary file 1). Among the minority of subjects with symptoms, the most reported symptom constellation was those of a mild flu-like disease with fever (~50%) as the most frequent symptom. Loss of smell or taste was uncommonly reported (~25% of symptomatic subjects) (Supplementary file 2). We further examined associations of other available variables with seropositivity to explore potential factors that modulate risk of infection in India. Apart from gender and age, distribution of the other variables recorded in CSIR-cohort (prevalence of smoking, diet, physiological parameters like ABO blood group type) was similar to the national averages and the sample can be considered representative (Patidar and Dhiman, 2021; Mohan et al., 2018; Government of India, 2014). The univariate associations are shown in Figure 3, separately for each gender. Due to gender imbalance and possible confounding between various parameters, significance of associations was further tested in a balanced iterative logistic regression (Figure 3—figure supplement 1). The strongest gender-independent associations were with occupation and mode of transport. Outsourced staff performing support services such as security, housekeeping, etc., and subjects using public transport were more likely to be seropositive. In males, smoking and vegetarian diet was associated with lower seropositivity. Figure 3 with 1 supplement see all Download asset Open asset Demographics of data available for different variables. Figure 3—source data 1 Raw data for Figure 3. https://cdn.elifesciences.org/articles/66537/elife-66537-fig3-data1-v2.xlsx Download elife-66537-fig3-data1-v2.xlsx Figure 3—source data 2 Source file for tables show in Figure 3. https://cdn.elifesciences.org/articles/66537/elife-66537-fig3-data2-v2.docx Download elife-66537-fig3-data2-v2.docx Blood group type was reported for 7496 subjects. Blood group distribution of subjects in our study was similar to national reference based on a recent systematic review (Patidar and Dhiman, 2021). Seropositivity was significantly different between different groups, being highest for blood group type AB (10.19%) followed by B (9.94%), O (7.09%), and A (6.97%). Blood group O was found to be associated with a lower seropositivity rate, with an odds ratio (OR) of 0.76 (95% CI 0.64–0.91, p=0.018) vs. non-O blood group types, while B appeared to be at high risk with an OR of 1.36 (95% CI 1.15–1.61, p=0∙001). While blood group A had an OR of 0.78, the association was not found to be significant (p=0.10) and a similar observation was made with blood group AB (p=0.35), it had an OR of 1.27 (Supplementary file 3). Rh factor was not found to have a significant association with seropositivity (p=0.35). Stability of humoral response to SARS-CoV-2 Of 607 subjects whose samples were collected again at 3 months, anti-nucleocapsid (anti-NC) antibody levels were similar or higher for most, with 17 (2.8%) becoming seronegative (Figure 4A). In contrast, 34 subjects (5.6%) did not have neutralizing activity based on a surrogate measure (>20% inhibition of receptor-spike protein binding; Figure 4B). Of 175 subjects whose samples were collected again at 5–6 months, 8 (4.6%) became seronegative (Figure 4C). In contrast, neutralizing activity was not present in 31 (17.7%) subjects (Figure 4D, Figure 4—source data 1). Figure 4 Download asset Open asset Antibody levels (A) and neutralizing activity (B) level at baseline (x-axis) and after 3 months (y-axis). Antibody levels (C) and neutralizing activity (D) level at baseline (x-axis) and after 5–6 months (y-axis). Figure 4—source data 1 Raw data for antibody and neutralizing antibody levels at baseline, 3 months and 5-6 months. https://cdn.elifesciences.org/articles/66537/elife-66537-fig4-data1-v2.xlsx Download elife-66537-fig4-data1-v2.xlsx Discussion This study, which recruited subjects from 24 cities in India, provides an important and timely snapshot of the spread of SARS-CoV2 pandemic across India shortly before the peak of new cases. It confirms that by September 2020 a large pool of recovered Indians with at least partial immunity existed. Between our study and the other national serosurvey at the same time, more than a hundred million Indians were likely to belong to this category. The subsequent nationwide decline of new cases starting in October 2020 can be well understood through these observations. There is some evidence of declining transmission in high-seropositivity regions within September, based on falling TPRs, but due to changes in tests and expansion of testing, this can only be indirectly inferred. As shown in our study, the fraction of such recovered subjects with resistance to reinfection was more than double among people performing high-contact jobs and using public transport. Thus, it is not surprising that in combination with a strong emphasis on masking and distancing, new cases started declining soon after this serosurvey. As of January 2021, despite the onset of winter and new year festivities, India has not seen major outbreaks. It is important to note that the crude seropositivity rate reported by us needs adjustments for demographics, fraction of infected subjects who may not develop antibodies, test characteristics, among others, to be a true population seropositivity rate. Here, we intentionally avoid such adjustments since it provides a false sense of precision with too many unknowns and unmeasurables. We focus on the more meaningful variation of the crude rate, its clinical correlates, and public health implications. We are confident from the data that a very large pool of recovered and immune subjects existed by September 2020, as stated, and expect that downward adjustments for national demographics will be counterbalanced by upward adjustments for almost 20% of RT-PCR-proven asymptomatic infections who develop transient antibody responses (Koopmans and Haagmans, 2020). Apart from the seropositivity rate, our data also reveals important associations between demographic, physiological, lifestyle-related, and occupational attributes with susceptibility to infection. The workforce in our cohort comprised adult population, and no major difference was observed in seropositivity among different age groups or those with comorbidities. Males were found to be more susceptible, in agreement with other published reports (Ortolan et al., 2020). However, there were fewer females in our study and many of the occupational responsibilities with higher chances of exposure, like that of security personnel, were skewed towards males. On iteratively ran regression models, we did not find gender to be a predictor for seropositivity. ABO blood group type has been shown to be associated with SARS-CoV-2 infection, but the results are variable in different studies. Most studies found that the O group is associated with a lower risk of infection or severity and blood group A was reported to be high risk in some studies (Zhao et al., 2020; Wu et al., 2020; Göker et al., 2020; Ellinghaus et al., 2020). In a meta-analysis authored by Golinelli et al., 2020, they observed positive association with A blood group, while blood group O was to be associated with lesser positivity using a random effects model. Another study from India observed blood group O to be associated with less mortality while blood group B with higher mortality when they analyzed the national data available (Padhi et al., 2020). A complex molecular interaction is said to play a significant role, and the molecular pathways for the same need to be elucidated for the effects observed especially with blood group O, which was also confirmed by our study. The associations with diet and smoking are intriguing but preliminary. It has been proposed that a fiber-rich diet may play an important role in COVID-19 through anti-inflammatory properties by modification of gut microbiota (Conte and Toraldo, 2020). A recent review has highlighted the role of trace elements, nutraceuticals, and probiotics in COVID-19 (Jayawardena et al., 2020). The negative association with smoking has been reported elsewhere, but not shown to be causal (Fontanet et al., 2020; Makoto Miyara et al., 2020; Rossato et al., 2020; Petrilli et al., 2020; Farsalinos et al., 2020). Further exploration is necessary before reaching any conclusions, especially since seropositivity is an imperfect marker of infection risk and may equally well be explained by altered antibody response and dynamics. The stability of antibody response is poorly understood, especially in India (Figueiredo-Campos et al., 2020; Seow et al., 2020; Deshpande et al., 2020). Our data shows that while anti-NC antibodies provide long-lasting evidence of viral exposure or infection, about 20% of seropositive individuals lack meaningful neutralization activity after 5–6 months. Using more stringent measures (more than 30% inhibition of surrogate receptor-spike protein binding), the loss of neutralization may be even higher. We speculate that this may be related to recurrence of outbreaks in March 2021, after the peak in September 2020. The aggregate seropositivity of 10.14% in our multicentric study suggests that India had a large pool of recovered immune subjects by September 2020, especially among its high-contact workers and people using public transport, leading to a decline in new infections. However, the duration of such immunity may not be sufficient to prevent future outbreaks, even in highly affected regions. Materials and methods Study design, sampling, and data collection Request a detailed protocol The longitudinal cohort study was approved by the Institutional Human Ethics Committee of CSIR-IGIB vide approval CSIR-IGIB/IHEC/2019–20. The participation was voluntary, and participants had to fill an online informed consent with a consent to publish and share findings and deidentified data. Online statistical tool was utilized to calculate the minimum sample size for estimating seropositivity of about 5% with 10% precision (0.005) with 95% confidence to be 7300 (Dhand and Khatkar, 2014). In this study, we enrolled >10,000 subjects. 10,427 adult subjects working in the CSIR laboratories and their family members enrolled for the study based on voluntary participation. Informed consent was obtained from all the participants, and the samples were collected maintaining all recommended precautions. Blood samples (6 ml) were collected in EDTA vials from each participant, and antibodies to SARS-CoV-2 NC antigen were measured using an Electro-chemiluminescence Immunoassay (ECLIA)-Elecsys Anti-SARS-CoV-2 kit (Roche Diagnostics) as per the manufacturer's protocol. This approved assay is considered a method of choice when a single test is to be deployed (Krüttgen et al., 2021). A Cut-off index COI >1 was considered seropositive. Positive samples were further tested for neutralizing antibody (NAB) response directed against the spike protein using GENScript cPass SARS-CoV-2 Neutralization Antibody Detection Kit (GenScript, USA), according to the manufacturer's protocol. This is a blocking ELISA used for qualitative detection of total neutralizing antibodies against SARS-CoV-2 virus in plasma. A value of 20% or above was considered to have neutralizing ability. All the participants were requested to fill an online questionnaire, which included information on date of birth, gender, blood group, type of occupation, history of diabetes, hypertension, cardiovascular disease, liver and kidney disease, diet preferences, mode of travel, contact history, and hospital visits. These forms were then downloaded in MS Excel data format and merged with registration forms filled at the time of sample collection based on unique IDs. Data and statistical analysis Request a detailed protocol Region-wise and total seropositivity was calculated from the fraction of samples positive for antibodies to SARS-CoV-2 NC antigen. Data regarding RT-PCR/rapid antigen testing and positive cases was gathered from http://www.covid19India.org. Change in TPR, a robust parameter for estimating the level of infection transmission when the level of testing is variable, was calculated as per the following equation: ChangeinTPR=(TPR15DaysafterDOC−TPR15DaysbeforeDOC)MeanofTPR15DaysPriorand15DaysAfter∗100 *DOC = date of collection. IGIB, New Delhi, and NAL, Bengaluru, were removed from this change in TPR analysis for the sample collection was spread over 2–3 weeks in these labs. Questionnaire-based variables were assessed for response types and blank fields, that is, responses that were not provided by the participants of the survey. Based on multiple response types for each variable, categories were made to assign the response to either of the categories. For visualization, ggpubr (v0.4.0), ggrepel (v0.8.2), and ggplot2 (v3.3.2) packages were used in R. No data imputation was carried out. Chi-square test was performed to evaluate variables that had a significant association with outcome of being tested positive (p<0.05) along with OR with 95% CI. An adjusted p value was obtained through Bonferroni correction method for multiple comparison testing. Following the chi-square test, an iterative logistic regression was carried out on a balanced dataset. Variance inflation factor (VIF) was separately evaluated to assess multicollinearity. Statistical analysis and model development were carried out with visualization in R programming environment version 3.6.1, MS Excel 2016, and OriginPro V2021; faraway (v1.0.7) package was utilized for estimation of VIF. Role of the funding source Request a detailed protocol The sponsor of this study had no role in the study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding authors had full access to all the data in the study and had final responsibility for the decision to submit for publication. Add a comment + Open annotations. The current annotation count on this page is being calculated. Data availability Source data files have been provided for Figures 1, 2,3 and 4. For Figure 2B, data is available in source data file and the district wise data listed in the source file on the number of confirmed cases and tests was taken from https://www.covid19india.org/. From this site, the data has to be manually extracted for desired time points by manually hovering over the number of cases and tests done graph when a district is selected after selection of the state. Code of Data Analysis and Model Definition Files- is available on Github at https://github.com/rintukutum/pan-india-csir-sero-epi (copy archived at https://archive.softwareheritage.org/swh:1:rev:7b789acbdb09c6842ab208c0ea84b52e88be43d7). References Andrews MA Areekal B Rajesh KR Krishnan J Suryakala R Krishnan B Muraly CP Santhosh PV (2020) First confirmed case of COVID-19 infection in India: a case report Indian Journal of Medical Research 151:490–492. https://doi.org/10.4103/ijmr.IJMR_2131_20 Google Scholar Preprint Babu GR Sundaresan R Athreya S (2020) The burden of active infection and anti-SARS-CoV-2 IgG antibodies in the general population: results from a statewide survey in Karnataka, India medRxiv. https://doi.org/10.1101/2020.12.04.20243949 Google Scholar Bai Y Yao L Wei T Tian F Jin DY Chen L Wang M (2020) Presumed asymptomatic carrier transmission of COVID-19 Jama 323:1406–1407. https://doi.org/10.1001/jama.2020.2565 PubMed Google Scholar Brown TS Walensky RP (2020) Serosurveillance and the COVID-19 epidemic in the US: undetected, uncertain, and out of control Jama 324:749–751. https://doi.org/10.1001/jama.2020.14017 PubMed Google Scholar Conte L Toraldo DM (2020) Targeting the gut–lung microbiota axis by means of a high-fibre diet and probiotics may have anti-inflammatory effects in COVID-19 infection Therapeutic Advances in Respiratory Disease 14:175346662093717. https://doi.org/10.1177/1753466620937170 Google Scholar Deshpande G Sapkal G Tilekar B (2020) Neutralizing antibody responses to SARS-CoV-2 in COVID-19 patients Indian Journal of Medical Research 152:82–87. https://doi.org/10.4103/ijmr.IJMR_2382_20 Google Scholar Website Dhand NK Khatkar MS (2014) Statulator: an online statistical calculator Sample Size Calculator for Estimating a Single Proportion. Accessed June 17, 2020. http://statulator.com/SampleSize/ss1P.html Ellinghaus D Degenhardt F Bujanda L Buti M Albillos A Invernizzi P Fernández J Prati D Baselli G Asselta R Grimsrud MM Milani C Aziz F Kässens J May S Wendorff M Wienbrandt L Uellendahl-Werth F Zheng T Yi X de Pablo R Chercoles AG Palom A Garcia-Fernandez AE Rodriguez-Frias F Zanella A Bandera A Protti A Aghemo A Lleo A Biondi A Caballero-Garralda A Gori A Tanck A Carreras Nolla A Latiano A Fracanzani AL Peschuck A Julià A Pesenti A Voza A Jiménez D Mateos B Nafria Jimenez B Quereda C Paccapelo C Gassner C Angelini C Cea C Solier A Pestaña D Muñiz-Diaz E Sandoval E Paraboschi EM Navas E García Sánchez F Ceriotti F Martinelli-Boneschi F Peyvandi F Blasi F Téllez L Blanco-Grau A Hemmrich-Stanisak G Grasselli G Costantino G Cardamone G Foti G Aneli S Kurihara H ElAbd H My I Galván-Femenia I Martín J Erdmann J Ferrusquía-Acosta J Garcia-Etxebarria K Izquierdo-Sanchez L Bettini LR Sumoy L Terranova L Moreira L Santoro L Scudeller L Mesonero F Roade L Rühlemann MC Schaefer M Carrabba M Riveiro-Barciela M Figuera Basso ME Valsecchi MG Hernandez-Tejero M Acosta-Herrera M D'Angiò M Baldini M Cazzaniga M Schulzky M Cecconi M Wittig M Ciccarelli M Rodríguez-Gandía M Bocciolone M Miozzo M Montano N Braun N Sacchi N Martínez N Özer O Palmieri O Faverio P Preatoni P Bonfanti P Omodei P Tentorio P Castro P Rodrigues PM Blandino Ortiz A de Cid R Ferrer R Gualtierotti R Nieto R Goerg S Badalamenti S Marsal S Matullo G Pelusi S Juzenas S Aliberti S Monzani V Moreno V Wesse T Lenz TL Pumarola T Rimoldi V Bosari S Albrecht W Peter W Romero-Gómez M D'Amato M Duga S Banales JM Hov JR Folseraas T Valenti L Franke A Karlsen TH Severe Covid-19 GWAS Group (2020) Genomewide association study of severe Covid-19 with respiratory failure The New England Journal of Medicine 383:1522–1534. https://doi.org/10.1056/NEJMoa2020283 PubMed Google Scholar Farsalinos K Barbouni A Niaura R (2020) Systematic review of the prevalence of current smoking among hospitalized COVID-19 patients in China: could nicotine be a therapeutic option? Internal and Emergency Medicine 15:845–852. https://doi.org/10.1007/s11739-020-02355-7 Google Scholar Figueiredo-Campos P Blankenhaus B Mota C Gomes A Serrano M Ariotti S Costa C Nunes-Cabaço H Mendes AM Gaspar P Pereira-Santos MC Rodrigues F Condeço J Escoval MA Santos M Ramirez M Melo-Cristino J Simas JP Vasconcelos E Afonso  Veldhoen M (2020) Seroprevalence of anti-SARS-CoV-2 antibodies in COVID-19 patients and healthy volunteers up to 6 months post disease onset European Journal of Immunology 50:2025–2040. https://doi.org/10.1002/eji.202048970 PubMed Google Scholar Preprint Fontanet A Tondeur L Madec Y (2020) Cluster of COVID-19 in northern France: a retrospective closed cohort study medRxiv. https://doi.org/10.1101/2020.04.18.20071134 Google Scholar Göker H Aladağ Karakulak E Demiroğlu H Ayaz Ceylan ÇM Büyükaşik Y Inkaya AÇ Aksu S Sayinalp N Haznedaroğlu IC Uzun Ö Akova M Özcebe OI Ünal S (2020) The effects of blood group types on the risk of COVID-19 infection and its clinical outcome Turkish Journal of Medical Sciences 50:679–683. https://doi.org/10.3906/sag-2005-395 PubMed Google Scholar Golinelli D Boetto E Maietti E Fantini MP (2020) The association between ABO blood group and SARS-CoV-2 infection: a meta-analysis PLOS ONE 15:e0239508. https://doi.org/10.1371/journal.pone.0239508 PubMed Google Scholar Website Government of India (2014) Sample registration system Accessed February 26, 2021. https://censusindia.gov.in/2011-Common/Sample_Registration_System.html2020 Jayawardena R Sooriyaarachchi P Chourdakis M Jeewandara C Ranasinghe P (2020) Enhancing immunity in viral infections, with special emphasis on COVID-19: a review Diabetes & Metabolic Syndrome: Clinical Research & Reviews 14:367–382. https://doi.org/10.1016/j.dsx.2020.04.015 PubMed Google Scholar Koopmans M Haagmans B (2020) Assessing the extent of SARS-CoV-2 circulation through serological studies Nature Medicine 26:1171–1172. https://doi.org/10.1038/s41591-020-1018-x PubMed Google Scholar Krüttgen A Cornelissen CG Dreher M Hornef MW Imöhl M Kleines M (2021) Determination of SARS-CoV-2 antibodies with assays from diasorin, Roche and IDvet Journal of Virological Methods 287:113978. https://doi.org/10.1016
To understand the spread of SARS-CoV2, in August and September 2020, the Council of Scientific and Industrial Research (India) conducted a serosurvey across its constituent laboratories and centers across India. Of 10,427 volunteers, 1058 (10.14%) tested positive for SARS-CoV2 anti-nucleocapsid (anti-NC) antibodies, 95% of which had surrogate neutralization activity. Three-fourth of these recalled no symptoms. Repeat serology tests at 3 (n = 607) and 6 (n = 175) months showed stable anti-NC antibodies but declining neutralization activity. Local seropositivity was higher in densely populated cities and was inversely correlated with a 30-day change in regional test positivity rates (TPRs). Regional seropositivity above 10% was associated with declining TPR. Personal factors associated with higher odds of seropositivity were high-exposure work (odds ratio, 95% confidence interval, p value: 2.23, 1.92–2.59, <0.0001), use of public transport (1.79, 1.43–2.24, <0.0001), not smoking (1.52, 1.16–1.99, 0.0257), non-vegetarian diet (1.67, 1.41–1.99, <0.0001), and B blood group (1.36, 1.15–1.61, 0.001).
The populations residing near polluted sites are more prone to various types of diseases. The important causes of air pollution are the suspended particulate matter, respirable suspended particulate matter, sulfur dioxide and nitrogen dioxide. As limited information is available enumerating the effect of these pollutants on liver physiology of the population living near the polluted sites; in the present study, we tried to investigate their effect on liver of the population residing near the oil drilling sites since birth. In this study, a randomly selected 105 subjects (46 subjects from oil drilling site and 61 subjects from control site) aged above 30 years were taken under consideration. The particulate matter as well as the gaseous pollutants, sulfur dioxide and nitrogen dioxide, were analyzed through a respirable dust sampler. The level of alkaline phosphatase, alanine transaminase and aspartate transaminase enzymes in serum were measured by spectrophotometer. The generalized regression model studies suggests a higher concentration of respirable suspended particulate matter, suspended particulate matter and nitrogen dioxide lowers the alkaline phosphatase level (p<0.0001) by 3.5 times (95% CI 3.1-3.9), 1.5 times (95% CI 1.4 - 1.6) and 12 times (95% CI 10.74 -13.804), respectively in the exposed group. The higher concentration of respirable suspended particulate matter and nitrogen dioxide in air was associated with increase in alanine transaminase level (p<0.0001) by 0.8 times (95% CI 0.589-1.049) and by 2.8 times (95% CI 2.067-3.681) respectively in the exposed group. The increase in nitrogen dioxide level was also associated with increase in aspartate transaminase level (p<0.0001) by 2.5 times (95% CI 1.862 – 3.313) in the exposed group as compared to control group. Thus, the study reveals that long-term exposure to the environmental pollutants may lead to liver abnormality or injury of populations living in polluted sites.
'%3e%3ccircle r='20' fill='%23008'/%3e%3ccircle r='17.5' fill='%23fff'/%3e%3ccircle r='3.5' fill='%23008'/%3e%3cg id='d'%3e%3cg id='c'%3e%3cg id='b'%3e%3cg id='a' fill='%23008'%3e%3ccircle r='.875' transform='rotate(7.5 -8.75 133.5)'/%3e%3cpath d='M0 17.5.6 7 0 2l-.6 5L0 17.5z'/%3e%3c/g%3e%3cuse xlink:href='%23a' transform='rotate(15)'/%3e%3c/g%3e%3cuse xlink:href='%23b' transform='rotate(30)'/%3e%3c/g%3e%3cuse xlink:href='%23c' transform='rotate(60)'/%3e%3c/g%3e%3cuse xlink:href='%23d' transform='rotate(120)'/%3e%3cuse xlink:href='%23d' transform='rotate(-120)'/%3e%3c/g%3e%3c/svg%3e)