Molekularni dokaz toksinskih tipova bakterije Clostridium perfringens, enteropatogenih sojeva Escherichia coli te rotavirusa i koronavirusa u uzorcima proljeva kod neonatalne jaradi

In the present study, out of 1156 neonatal goat kids, 238 showing clinical diarrhea were used for detection of toxinotypes of Clostridium perfringens, Enteropathogenic E. coli (EPEC), Group A rotavirus (GARV) and Bovine coronavirus (BCV). Isolation and toxinotyping of isolates were done by multiplex Polymerase chain reaction (PCR) using primers for cpa, cpb, cpb2, etx and iap genes. For EPEC, isolation and identification were done using bfpA gene and SYBR green based real time PCR (qPCR). GARV and BCV were detected, by one-step RT-PCR (osRT-PCR). The incidence of C. perfringens was 15.13% with 75% isolates toxinotype A, 25% type D and 61.11% of isolates carrying the β2-toxin gene. The incidence of EPEC was 68.07% based on qPCR, whereas 21.85% were positive for GARV and 15.97% for BCV by osRT-PCR. There was mixed infection of C. perfringens and EPEC in 11.76% and 3.78% for C. perfringens and GARV and 2.1% of C. perfringens and BCV. EPEC and GARV was 19.74% and EPEC plus BCV positivity was 11.34%. GARV and BCV was 5.88%, and 4.20% had mixed infection of EPEC, GARV and BCV. Of the total diarrheic kids sampled, 0.84% had mixed infection of C. perfringens, GARV, BCV and EPEC. On the basis of the above findings, it may be concluded that isolation, multiplex PCR and real time PCR facilitated the characterization of circulating C. perfringens toxinotypes and EPEC in goats reared under semi-arid conditions. The importance of enteritis caused by GARV and BCV and their role in mixed infection in goats requires extensive screening and pathogenicity studies to associate the symptoms with disease.U populaciji od 1156 neonatalnih jarića 238 je pokazivalo kliničke znakove proljeva. Od njih su uzeti uzorci izmeta za dokazivanje toksinskih tipova bakterije Clostridium perfringens, enteropatogenih sojeva bakterije E. coli (engl. enteropathogenic E. coli, EPEC), rotavirusa skupine A (engl. group A rotavirus, GARV) i goveđeg koronavirusa (engl. bovine coronavirus, BCV). Izdvajanje i toksinska tipizacija izolata provedeni su višestrukom lančanom reakcijom polimerazom (PCR) upotrebom početnica za gene cpa, cpb, cpb2, etx i iap. Izdvajanje i identifikacija EPEC-a provedeni su pretragom na gen bfpA i PCR-om u stvarnom vremenu, uz upotrebu SYBR zelenila (qPCR). Za dokaz rotavirusa skupine A i goveđeg koronavirusa upotrijebljena je RT-PCR (osRT-PCR). Incidencija bakterije C. perfringens iznosila je 15,13 %. Od toga je 75 % izolata pripadalo toksinskom tipu A, 25 % tipu D, dok je 61,11 % izolata imalo gen za toksin β2. Incidencija EPEC-a iznosila je 68,07 %, a 21,85 % pretraženih uzoraka bilo je pozitivno na GARV te 15,97 % na BCV. Mješovita infekcija bakterijom C. perfringens i EPEC-om utvrđena je u 11,76 % uzoraka, C. perfringens i GARV u 3,78 % te C. perfringens i BCV u 2,1 % pretraženih uzoraka. Mješovita infekcija EPEC-om i GARV-om utvrđena je u 19,74 %, a EPEC-om i BCV-om u 11,34 %. Mješovita infekcija rotavirusom i koronavirusom bila je ustanovljena u 5,88 %, a mješovita infekcija EPEC-om, GARV-om i BCV-om u 4,20 % uzoraka. Od ukupnog broja pretražene jaradi s proljevom u njih 0,84 % dokazana je mješovita infekcija bakterijom C. perfringens, rotavirusom skupine A, goveđim koronavirusom i EPEC-om. Na osnovi prikazanih rezultata može se zaključiti da izdvajanje, višestruki PCR te PCR u stvarnom vremenu omogućuju karakterizaciju i praćenje kolanja toksinskih tipova bakterija C. perfringens i EPEC-a u koza uzgajanih u sušnim uvjetima. Važnost enteritisa uzrokovanog rotavirusom skupine A i goveđim koronavirusom te njihova uloga kod mješovitih infekcija u koza zahtijevaju pojačan nadzor i istraživanje patogenosti radi povezivanja kliničkih znakova s ustanovljenom bolešću

Development of a model webserver for breed identification using microsatellite DNA marker

BACKGROUND: Identification of true to breed type animal for conservation purpose is imperative. Breed dilution is one of the major problems in sustainability except cases of commercial crossbreeding under controlled condition. Breed descriptor has been developed to identify breed but such descriptors cover only “pure breed” or true to the breed type animals excluding undefined or admixture population. Moreover, in case of semen, ova, embryo and breed product, the breed cannot be identified due to lack of visible phenotypic descriptors. Advent of molecular markers like microsatellite and SNP have revolutionized breed identification from even small biological tissue or germplasm. Microsatellite DNA marker based breed assignments has been reported in various domestic animals. Such methods have limitations viz. non availability of allele data in public domain, thus each time all reference breed has to be genotyped which is neither logical nor economical. Even if such data is available but computational methods needs expertise of data analysis and interpretation. RESULTS: We found Bayesian Networks as best classifier with highest accuracy of 98.7% using 51850 reference allele data generated by 25 microsatellite loci on 22 goat breed population of India. The F(ST) values in the study were seen to be low ranging from 0.051 to 0.297 and overall genetic differentiation of 13.8%, suggesting more number of loci needed for higher accuracy. We report here world’s first model webserver for breed identification using microsatellite DNA markers freely accessible at http://cabin.iasri.res.in/gomi/. CONCLUSION: Higher number of loci is required due to less differentiable population and large number of breeds taken in this study. This server will reduce the cost with computational ease. This methodology can be a model for various other domestic animal species as a valuable tool for conservation and breed improvement programmes

A first update on mapping the human genetic architecture of COVID-19

peer reviewe

Global estimates on the number of people blind or visually impaired by age-related macular degeneration: a meta-analysis from 2000 to 2020

Background: We aimed to update estimates of global vision loss due to age-related macular degeneration (AMD). Methods: We did a systematic review and meta-analysis of population-based surveys of eye diseases from January, 1980, to October, 2018. We fitted hierarchical models to estimate the prevalence of moderate and severe vision impairment (MSVI; presenting visual acuity from <6/18 to 3/60) and blindness ( < 3/60) caused by AMD, stratified by age, region, and year. Results: In 2020, 1.85 million (95%UI: 1.35 to 2.43 million) people were estimated to be blind due to AMD, and another 6.23 million (95%UI: 5.04 to 7.58) with MSVI globally. High-income countries had the highest number of individuals with AMD-related blindness (0.60 million people; 0.46 to 0.77). The crude prevalence of AMD-related blindness in 2020 (among those aged ≥ 50 years) was 0.10% (0.07 to 0.12) globally, and the region with the highest prevalence of AMD-related blindness was North Africa/Middle East (0.22%; 0.16 to 0.30). Age-standardized prevalence (using the GBD 2019 data) of AMD-related MSVI in people aged ≥ 50 years in 2020 was 0.34% (0.27 to 0.41) globally, and the region with the highest prevalence of AMD-related MSVI was also North Africa/Middle East (0.55%; 0.44 to 0.68). From 2000 to 2020, the estimated crude prevalence of AMD-related blindness decreased globally by 19.29%, while the prevalence of MSVI increased by 10.08%. Conclusions: The estimated increase in the number of individuals with AMD-related blindness and MSVI globally urges the creation of novel treatment modalities and the expansion of rehabilitation services

Population-level risks of alcohol consumption by amount, geography, age, sex, and year: a systematic analysis for the Global Burden of Disease Study 2020

BACKGROUND: The health risks associated with moderate alcohol consumption continue to be debated. Small amounts of alcohol might lower the risk of some health outcomes but increase the risk of others, suggesting that the overall risk depends, in part, on background disease rates, which vary by region, age, sex, and year. METHODS: For this analysis, we constructed burden-weighted dose-response relative risk curves across 22 health outcomes to estimate the theoretical minimum risk exposure level (TMREL) and non-drinker equivalence (NDE), the consumption level at which the health risk is equivalent to that of a non-drinker, using disease rates from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2020 for 21 regions, including 204 countries and territories, by 5-year age group, sex, and year for individuals aged 15-95 years and older from 1990 to 2020. Based on the NDE, we quantified the population consuming harmful amounts of alcohol. FINDINGS: The burden-weighted relative risk curves for alcohol use varied by region and age. Among individuals aged 15-39 years in 2020, the TMREL varied between 0 (95% uncertainty interval 0-0) and 0·603 (0·400-1·00) standard drinks per day, and the NDE varied between 0·002 (0-0) and 1·75 (0·698-4·30) standard drinks per day. Among individuals aged 40 years and older, the burden-weighted relative risk curve was J-shaped for all regions, with a 2020 TMREL that ranged from 0·114 (0-0·403) to 1·87 (0·500-3·30) standard drinks per day and an NDE that ranged between 0·193 (0-0·900) and 6·94 (3·40-8·30) standard drinks per day. Among individuals consuming harmful amounts of alcohol in 2020, 59·1% (54·3-65·4) were aged 15-39 years and 76·9% (73·0-81·3) were male. INTERPRETATION: There is strong evidence to support recommendations on alcohol consumption varying by age and location. Stronger interventions, particularly those tailored towards younger individuals, are needed to reduce the substantial global health loss attributable to alcohol. FUNDING: Bill & Melinda Gates Foundation

Prevalence of chronic cough, its risk factors and population attributable risk in the Burden of Obstructive Lung Disease (BOLD) study: a multinational cross-sectional study

© 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)Background: Chronic cough is a common respiratory symptom with an impact on daily activities and quality of life. Global prevalence data are scarce and derive mainly from European and Asian countries and studies with outcomes other than chronic cough. In this study, we aimed to estimate the prevalence of chronic cough across a large number of study sites as well as to identify its main risk factors using a standardised protocol and definition. Methods: We analysed cross-sectional data from 33,983 adults (≥40 years), recruited between Jan 2, 2003 and Dec 26, 2016, in 41 sites (34 countries) from the Burden of Obstructive Lung Disease (BOLD) study. We estimated the prevalence of chronic cough for each site accounting for sampling design. To identify risk factors, we conducted multivariable logistic regression analysis within each site and then pooled estimates using random-effects meta-analysis. We also calculated the population attributable risk (PAR) associated with each of the identifed risk factors. Findings: The prevalence of chronic cough varied from 3% in India (rural Pune) to 24% in the United States of America (Lexington,KY). Chronic cough was more common among females, both current and passive smokers, those working in a dusty job, those with a history of tuberculosis, those who were obese, those with a low level of education and those with hypertension or airflow limitation. The most influential risk factors were current smoking and working in a dusty job. Interpretation: Our findings suggested that the prevalence of chronic cough varies widely across sites in different world regions. Cigarette smoking and exposure to dust in the workplace are its major risk factors.info:eu-repo/semantics/publishedVersio

Burden of disease scenarios for 204 countries and territories, 2022–2050: a forecasting analysis for the Global Burden of Disease Study 2021

Background: Future trends in disease burden and drivers of health are of great interest to policy makers and the public at large. This information can be used for policy and long-term health investment, planning, and prioritisation. We have expanded and improved upon previous forecasts produced as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) and provide a reference forecast (the most likely future), and alternative scenarios assessing disease burden trajectories if selected sets of risk factors were eliminated from current levels by 2050. Methods: Using forecasts of major drivers of health such as the Socio-demographic Index (SDI; a composite measure of lag-distributed income per capita, mean years of education, and total fertility under 25 years of age) and the full set of risk factor exposures captured by GBD, we provide cause-specific forecasts of mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) by age and sex from 2022 to 2050 for 204 countries and territories, 21 GBD regions, seven super-regions, and the world. All analyses were done at the cause-specific level so that only risk factors deemed causal by the GBD comparative risk assessment influenced future trajectories of mortality for each disease. Cause-specific mortality was modelled using mixed-effects models with SDI and time as the main covariates, and the combined impact of causal risk factors as an offset in the model. At the all-cause mortality level, we captured unexplained variation by modelling residuals with an autoregressive integrated moving average model with drift attenuation. These all-cause forecasts constrained the cause-specific forecasts at successively deeper levels of the GBD cause hierarchy using cascading mortality models, thus ensuring a robust estimate of cause-specific mortality. For non-fatal measures (eg, low back pain), incidence and prevalence were forecasted from mixed-effects models with SDI as the main covariate, and YLDs were computed from the resulting prevalence forecasts and average disability weights from GBD. Alternative future scenarios were constructed by replacing appropriate reference trajectories for risk factors with hypothetical trajectories of gradual elimination of risk factor exposure from current levels to 2050. The scenarios were constructed from various sets of risk factors: environmental risks (Safer Environment scenario), risks associated with communicable, maternal, neonatal, and nutritional diseases (CMNNs; Improved Childhood Nutrition and Vaccination scenario), risks associated with major non-communicable diseases (NCDs; Improved Behavioural and Metabolic Risks scenario), and the combined effects of these three scenarios. Using the Shared Socioeconomic Pathways climate scenarios SSP2-4.5 as reference and SSP1-1.9 as an optimistic alternative in the Safer Environment scenario, we accounted for climate change impact on health by using the most recent Intergovernmental Panel on Climate Change temperature forecasts and published trajectories of ambient air pollution for the same two scenarios. Life expectancy and healthy life expectancy were computed using standard methods. The forecasting framework includes computing the age-sex-specific future population for each location and separately for each scenario. 95% uncertainty intervals (UIs) for each individual future estimate were derived from the 2·5th and 97·5th percentiles of distributions generated from propagating 500 draws through the multistage computational pipeline. Findings: In the reference scenario forecast, global and super-regional life expectancy increased from 2022 to 2050, but improvement was at a slower pace than in the three decades preceding the COVID-19 pandemic (beginning in 2020). Gains in future life expectancy were forecasted to be greatest in super-regions with comparatively low life expectancies (such as sub-Saharan Africa) compared with super-regions with higher life expectancies (such as the high-income super-region), leading to a trend towards convergence in life expectancy across locations between now and 2050. At the super-region level, forecasted healthy life expectancy patterns were similar to those of life expectancies. Forecasts for the reference scenario found that health will improve in the coming decades, with all-cause age-standardised DALY rates decreasing in every GBD super-region. The total DALY burden measured in counts, however, will increase in every super-region, largely a function of population ageing and growth. We also forecasted that both DALY counts and age-standardised DALY rates will continue to shift from CMNNs to NCDs, with the most pronounced shifts occurring in sub-Saharan Africa (60·1% [95% UI 56·8–63·1] of DALYs were from CMNNs in 2022 compared with 35·8% [31·0–45·0] in 2050) and south Asia (31·7% [29·2–34·1] to 15·5% [13·7–17·5]). This shift is reflected in the leading global causes of DALYs, with the top four causes in 2050 being ischaemic heart disease, stroke, diabetes, and chronic obstructive pulmonary disease, compared with 2022, with ischaemic heart disease, neonatal disorders, stroke, and lower respiratory infections at the top. The global proportion of DALYs due to YLDs likewise increased from 33·8% (27·4–40·3) to 41·1% (33·9–48·1) from 2022 to 2050, demonstrating an important shift in overall disease burden towards morbidity and away from premature death. The largest shift of this kind was forecasted for sub-Saharan Africa, from 20·1% (15·6–25·3) of DALYs due to YLDs in 2022 to 35·6% (26·5–43·0) in 2050. In the assessment of alternative future scenarios, the combined effects of the scenarios (Safer Environment, Improved Childhood Nutrition and Vaccination, and Improved Behavioural and Metabolic Risks scenarios) demonstrated an important decrease in the global burden of DALYs in 2050 of 15·4% (13·5–17·5) compared with the reference scenario, with decreases across super-regions ranging from 10·4% (9·7–11·3) in the high-income super-region to 23·9% (20·7–27·3) in north Africa and the Middle East. The Safer Environment scenario had its largest decrease in sub-Saharan Africa (5·2% [3·5–6·8]), the Improved Behavioural and Metabolic Risks scenario in north Africa and the Middle East (23·2% [20·2–26·5]), and the Improved Nutrition and Vaccination scenario in sub-Saharan Africa (2·0% [–0·6 to 3·6]). Interpretation: Globally, life expectancy and age-standardised disease burden were forecasted to improve between 2022 and 2050, with the majority of the burden continuing to shift from CMNNs to NCDs. That said, continued progress on reducing the CMNN disease burden will be dependent on maintaining investment in and policy emphasis on CMNN disease prevention and treatment. Mostly due to growth and ageing of populations, the number of deaths and DALYs due to all causes combined will generally increase. By constructing alternative future scenarios wherein certain risk exposures are eliminated by 2050, we have shown that opportunities exist to substantially improve health outcomes in the future through concerted efforts to prevent exposure to well established risk factors and to expand access to key health interventions

Prevalence of chronic cough, its risk factors and population attributable risk in the Burden of Obstructive Lung Disease (BOLD) study: a multinational cross-sectional study

Background: Chronic cough is a common respiratory symptom with an impact on daily activities and quality of life. Global prevalence data are scarce and derive mainly from European and Asian countries and studies with outcomes other than chronic cough. In this study, we aimed to estimate the prevalence of chronic cough across a large number of study sites as well as to identify its main risk factors using a standardized protocol and definition. Methods: We analyzed cross-sectional data from 33,983 adults (≥40 years), recruited between Jan 2, 2003 and Dec 26, 2016, in 41 sites (34 countries) from the Burden of Obstructive Lung Disease (BOLD) study. We estimated the prevalence of chronic cough for each site accounting for sampling design. To identify risk factors, we conducted multivariable logistic regression analysis within each site and then pooled estimates using random-effects meta-analysis. We also calculated the population-attributable risk (PAR) associated with each of the identified risk factors. Findings: The prevalence of chronic cough varied from 3% in India (rural Pune) to 24% in the United States of America (Lexington, KY). Chronic cough was more common among females, both current and passive smokers, those working in a dusty job, those with a history of tuberculosis, those who were obese, those with a low level of education, and those with hypertension or airflow limitation. The most influential risk factors were current smoking and working in a dusty job. Interpretation: Our findings suggested that the prevalence of chronic cough varies widely across sites in different world regions. Cigarette smoking and exposure to dust in the workplace are its major risk factors.info:eu-repo/semantics/publishedVersio

SARS-CoV-2 susceptibility and COVID-19 disease severity are associated with genetic variants affecting gene expression in a variety of tissues

Variability in SARS-CoV-2 susceptibility and COVID-19 disease severity between individuals is partly due to genetic factors. Here, we identify 4 genomic loci with suggestive associations for SARS-CoV-2 susceptibility and 19 for COVID-19 disease severity. Four of these 23 loci likely have an ethnicity-specific component. Genome-wide association study (GWAS) signals in 11 loci colocalize with expression quantitative trait loci (eQTLs) associated with the expression of 20 genes in 62 tissues/cell types (range: 1:43 tissues/gene), including lung, brain, heart, muscle, and skin as well as the digestive system and immune system. We perform genetic fine mapping to compute 99% credible SNP sets, which identify 10 GWAS loci that have eight or fewer SNPs in the credible set, including three loci with one single likely causal SNP. Our study suggests that the diverse symptoms and disease severity of COVID-19 observed between individuals is associated with variants across the genome, affecting gene expression levels in a wide variety of tissue types

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery