Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND

Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations.

METHODS

The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds.

FINDINGS

The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles.

INTERPRETATION

Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere.

FUNDING

Bill & Melinda Gates Foundation.

Global fertility in 204 countries and territories, 1950-2021, with forecasts to 2100: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

BACKGROUND

Accurate assessments of current and future fertility-including overall trends and changing population age structures across countries and regions-are essential to help plan for the profound social, economic, environmental, and geopolitical challenges that these changes will bring. Estimates and projections of fertility are necessary to inform policies involving resource and health-care needs, labour supply, education, gender equality, and family planning and support. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 produced up-to-date and comprehensive demographic assessments of key fertility indicators at global, regional, and national levels from 1950 to 2021 and forecast fertility metrics to 2100 based on a reference scenario and key policy-dependent alternative scenarios.

METHODS

To estimate fertility indicators from 1950 to 2021, mixed-effects regression models and spatiotemporal Gaussian process regression were used to synthesise data from 8709 country-years of vital and sample registrations, 1455 surveys and censuses, and 150 other sources, and to generate age-specific fertility rates (ASFRs) for 5-year age groups from age 10 years to 54 years. ASFRs were summed across age groups to produce estimates of total fertility rate (TFR). Livebirths were calculated by multiplying ASFR and age-specific female population, then summing across ages 10-54 years. To forecast future fertility up to 2100, our Institute for Health Metrics and Evaluation (IHME) forecasting model was based on projections of completed cohort fertility at age 50 years (CCF50; the average number of children born over time to females from a specified birth cohort), which yields more stable and accurate measures of fertility than directly modelling TFR. CCF50 was modelled using an ensemble approach in which three sub-models (with two, three, and four covariates variously consisting of female educational attainment, contraceptive met need, population density in habitable areas, and under-5 mortality) were given equal weights, and analyses were conducted utilising the MR-BRT (meta-regression-Bayesian, regularised, trimmed) tool. To capture time-series trends in CCF50 not explained by these covariates, we used a first-order autoregressive model on the residual term. CCF50 as a proportion of each 5-year ASFR was predicted using a linear mixed-effects model with fixed-effects covariates (female educational attainment and contraceptive met need) and random intercepts for geographical regions. Projected TFRs were then computed for each calendar year as the sum of single-year ASFRs across age groups. The reference forecast is our estimate of the most likely fertility future given the model, past fertility, forecasts of covariates, and historical relationships between covariates and fertility. We additionally produced forecasts for multiple alternative scenarios in each location: the UN Sustainable Development Goal (SDG) for education is achieved by 2030; the contraceptive met need SDG is achieved by 2030; pro-natal policies are enacted to create supportive environments for those who give birth; and the previous three scenarios combined. Uncertainty from past data inputs and model estimation was propagated throughout analyses by taking 1000 draws for past and present fertility estimates and 500 draws for future forecasts from the estimated distribution for each metric, with 95% uncertainty intervals (UIs) given as the 2·5 and 97·5 percentiles of the draws. To evaluate the forecasting performance of our model and others, we computed skill values-a metric assessing gain in forecasting accuracy-by comparing predicted versus observed ASFRs from the past 15 years (2007-21). A positive skill metric indicates that the model being evaluated performs better than the baseline model (here, a simplified model holding 2007 values constant in the future), and a negative metric indicates that the evaluated model performs worse than baseline.

FINDINGS

During the period from 1950 to 2021, global TFR more than halved, from 4·84 (95% UI 4·63-5·06) to 2·23 (2·09-2·38). Global annual livebirths peaked in 2016 at 142 million (95% UI 137-147), declining to 129 million (121-138) in 2021. Fertility rates declined in all countries and territories since 1950, with TFR remaining above 2·1-canonically considered replacement-level fertility-in 94 (46·1%) countries and territories in 2021. This included 44 of 46 countries in sub-Saharan Africa, which was the super-region with the largest share of livebirths in 2021 (29·2% [28·7-29·6]). 47 countries and territories in which lowest estimated fertility between 1950 and 2021 was below replacement experienced one or more subsequent years with higher fertility; only three of these locations rebounded above replacement levels. Future fertility rates were projected to continue to decline worldwide, reaching a global TFR of 1·83 (1·59-2·08) in 2050 and 1·59 (1·25-1·96) in 2100 under the reference scenario. The number of countries and territories with fertility rates remaining above replacement was forecast to be 49 (24·0%) in 2050 and only six (2·9%) in 2100, with three of these six countries included in the 2021 World Bank-defined low-income group, all located in the GBD super-region of sub-Saharan Africa. The proportion of livebirths occurring in sub-Saharan Africa was forecast to increase to more than half of the world's livebirths in 2100, to 41·3% (39·6-43·1) in 2050 and 54·3% (47·1-59·5) in 2100. The share of livebirths was projected to decline between 2021 and 2100 in most of the six other super-regions-decreasing, for example, in south Asia from 24·8% (23·7-25·8) in 2021 to 16·7% (14·3-19·1) in 2050 and 7·1% (4·4-10·1) in 2100-but was forecast to increase modestly in the north Africa and Middle East and high-income super-regions. Forecast estimates for the alternative combined scenario suggest that meeting SDG targets for education and contraceptive met need, as well as implementing pro-natal policies, would result in global TFRs of 1·65 (1·40-1·92) in 2050 and 1·62 (1·35-1·95) in 2100. The forecasting skill metric values for the IHME model were positive across all age groups, indicating that the model is better than the constant prediction.

INTERPRETATION

Fertility is declining globally, with rates in more than half of all countries and territories in 2021 below replacement level. Trends since 2000 show considerable heterogeneity in the steepness of declines, and only a small number of countries experienced even a slight fertility rebound after their lowest observed rate, with none reaching replacement level. Additionally, the distribution of livebirths across the globe is shifting, with a greater proportion occurring in the lowest-income countries. Future fertility rates will continue to decline worldwide and will remain low even under successful implementation of pro-natal policies. These changes will have far-reaching economic and societal consequences due to ageing populations and declining workforces in higher-income countries, combined with an increasing share of livebirths among the already poorest regions of the world.

FUNDING

Bill & Melinda Gates Foundation.

Global burden and strength of evidence for 88 risk factors in 204 countries and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND

Understanding the health consequences associated with exposure to risk factors is necessary to inform public health policy and practice. To systematically quantify the contributions of risk factor exposures to specific health outcomes, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 aims to provide comprehensive estimates of exposure levels, relative health risks, and attributable burden of disease for 88 risk factors in 204 countries and territories and 811 subnational locations, from 1990 to 2021.

METHODS

The GBD 2021 risk factor analysis used data from 54 561 total distinct sources to produce epidemiological estimates for 88 risk factors and their associated health outcomes for a total of 631 risk-outcome pairs. Pairs were included on the basis of data-driven determination of a risk-outcome association. Age-sex-location-year-specific estimates were generated at global, regional, and national levels. Our approach followed the comparative risk assessment framework predicated on a causal web of hierarchically organised, potentially combinative, modifiable risks. Relative risks (RRs) of a given outcome occurring as a function of risk factor exposure were estimated separately for each risk-outcome pair, and summary exposure values (SEVs), representing risk-weighted exposure prevalence, and theoretical minimum risk exposure levels (TMRELs) were estimated for each risk factor. These estimates were used to calculate the population attributable fraction (PAF; ie, the proportional change in health risk that would occur if exposure to a risk factor were reduced to the TMREL). The product of PAFs and disease burden associated with a given outcome, measured in disability-adjusted life-years (DALYs), yielded measures of attributable burden (ie, the proportion of total disease burden attributable to a particular risk factor or combination of risk factors). Adjustments for mediation were applied to account for relationships involving risk factors that act indirectly on outcomes via intermediate risks. Attributable burden estimates were stratified by Socio-demographic Index (SDI) quintile and presented as counts, age-standardised rates, and rankings. To complement estimates of RR and attributable burden, newly developed burden of proof risk function (BPRF) methods were applied to yield supplementary, conservative interpretations of risk-outcome associations based on the consistency of underlying evidence, accounting for unexplained heterogeneity between input data from different studies. Estimates reported represent the mean value across 500 draws from the estimate's distribution, with 95% uncertainty intervals (UIs) calculated as the 2·5th and 97·5th percentile values across the draws.

FINDINGS

Among the specific risk factors analysed for this study, particulate matter air pollution was the leading contributor to the global disease burden in 2021, contributing 8·0% (95% UI 6·7-9·4) of total DALYs, followed by high systolic blood pressure (SBP; 7·8% [6·4-9·2]), smoking (5·7% [4·7-6·8]), low birthweight and short gestation (5·6% [4·8-6·3]), and high fasting plasma glucose (FPG; 5·4% [4·8-6·0]). For younger demographics (ie, those aged 0-4 years and 5-14 years), risks such as low birthweight and short gestation and unsafe water, sanitation, and handwashing (WaSH) were among the leading risk factors, while for older age groups, metabolic risks such as high SBP, high body-mass index (BMI), high FPG, and high LDL cholesterol had a greater impact. From 2000 to 2021, there was an observable shift in global health challenges, marked by a decline in the number of all-age DALYs broadly attributable to behavioural risks (decrease of 20·7% [13·9-27·7]) and environmental and occupational risks (decrease of 22·0% [15·5-28·8]), coupled with a 49·4% (42·3-56·9) increase in DALYs attributable to metabolic risks, all reflecting ageing populations and changing lifestyles on a global scale. Age-standardised global DALY rates attributable to high BMI and high FPG rose considerably (15·7% [9·9-21·7] for high BMI and 7·9% [3·3-12·9] for high FPG) over this period, with exposure to these risks increasing annually at rates of 1·8% (1·6-1·9) for high BMI and 1·3% (1·1-1·5) for high FPG. By contrast, the global risk-attributable burden and exposure to many other risk factors declined, notably for risks such as child growth failure and unsafe water source, with age-standardised attributable DALYs decreasing by 71·5% (64·4-78·8) for child growth failure and 66·3% (60·2-72·0) for unsafe water source. We separated risk factors into three groups according to trajectory over time: those with a decreasing attributable burden, due largely to declining risk exposure (eg, diet high in trans-fat and household air pollution) but also to proportionally smaller child and youth populations (eg, child and maternal malnutrition); those for which the burden increased moderately in spite of declining risk exposure, due largely to population ageing (eg, smoking); and those for which the burden increased considerably due to both increasing risk exposure and population ageing (eg, ambient particulate matter air pollution, high BMI, high FPG, and high SBP).

INTERPRETATION

Substantial progress has been made in reducing the global disease burden attributable to a range of risk factors, particularly those related to maternal and child health, WaSH, and household air pollution. Maintaining efforts to minimise the impact of these risk factors, especially in low SDI locations, is necessary to sustain progress. Successes in moderating the smoking-related burden by reducing risk exposure highlight the need to advance policies that reduce exposure to other leading risk factors such as ambient particulate matter air pollution and high SBP. Troubling increases in high FPG, high BMI, and other risk factors related to obesity and metabolic syndrome indicate an urgent need to identify and implement interventions.

FUNDING

Bill & Melinda Gates Foundation.

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950-2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

BACKGROUND

Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020-21 COVID-19 pandemic period.

METHODS

22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution.

FINDINGS

Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5-65·1] decline), and increased during the COVID-19 pandemic period (2020-21; 5·1% [0·9-9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98-5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50-6·01) in 2019. An estimated 131 million (126-137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7-17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8-24·8), from 49·0 years (46·7-51·3) to 71·7 years (70·9-72·5). Global life expectancy at birth declined by 1·6 years (1·0-2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67-8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4-52·7]) and south Asia (26·3% [9·0-44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations.

INTERPRETATION

Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic.

FUNDING

Bill & Melinda Gates Foundation.

Clinico-genomic findings, molecular docking, and mutational spectrum in an understudied population with breast cancer patients from KP, Pakistan

In this study, we report the mutational profiles, pathogenicity, and their association with different clinicopathologic and sociogenetic factors in patients with Pashtun ethnicity for the first time. A total of 19 FFPE blocks of invasive ductal carcinoma (IDC) from the Breast Cancer (BC) tissue and 6 normal FFPE blocks were analyzed by whole-exome sequencing (WES). Various somatic and germline mutations were identified in cancer-related genes, i.e., ATM, CHEK2, PALB2, and XRCC2. Among a total of 18 mutations, 14 mutations were somatic and 4 were germline. The ATM gene exhibited the maximum number of mutations (11/18), followed by CHEK2 (3/18), PALB2 (3/18), and XRCC2 (1/18). Except one frameshift deletion, all other 17 mutations were nonsynonymous single-nucleotide variants (SNVs). SIFT prediction revealed 7/18 (38.8%) mutations as deleterious. PolyPhen-2 and MutationTaster identified 5/18 (27.7%) mutations as probably damaging and 10/18 (55.5%) mutations as disease-causing, respectively. Mutations like PALB2 p.Q559R (6/19; 31.5%), XRCC2 p.R188H (5/19; 26.31%), and ATM p.D1853N (4/19; 21.05%) were recurrent mutations and proposed to have a biomarker potential. The protein network prediction was performed using GeneMANIA and STRING. ISPRED-SEQ indicated three interaction site mutations which were further used for molecular dynamic simulation. An average increase in the radius of gyration was observed in all three mutated proteins revealing their perturbed folding behavior. Obtained SNVs were further correlated with various parameters related to the clinicopathological status of the tumors. Three mutation positions (ATM p. D1853N, CHEK2 p.M314I, and PALB2 p.T1029S) were found to be highly conserved. Finally, the wild- and mutant-type proteins were screened for two drugs: elagolix (DrugBank ID: DB11979) and LTS0102038 (a triterpenoid, isolated from the anticancer medicinal plant Fagonia indica). Comparatively, a higher number of interactions were noted for normal ATM with both compounds, as compared to mutants.

Global, regional, and national incidence and mortality burden of non-COVID-19 lower respiratory infections and aetiologies, 1990-2021: a systematic analysis from the Global Burden of Disease Study 2021

BACKGROUND

Lower respiratory infections (LRIs) are a major global contributor to morbidity and mortality. In 2020-21, non-pharmaceutical interventions associated with the COVID-19 pandemic reduced not only the transmission of SARS-CoV-2, but also the transmission of other LRI pathogens. Tracking LRI incidence and mortality, as well as the pathogens responsible, can guide health-system responses and funding priorities to reduce future burden. We present estimates from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 of the burden of non-COVID-19 LRIs and corresponding aetiologies from 1990 to 2021, inclusive of pandemic effects on the incidence and mortality of select respiratory viruses, globally, regionally, and for 204 countries and territories.

METHODS

We estimated mortality, incidence, and aetiology attribution for LRI, defined by the GBD as pneumonia or bronchiolitis, not inclusive of COVID-19. We analysed 26 259 site-years of mortality data using the Cause of Death Ensemble model to estimate LRI mortality rates. We analysed all available age-specific and sex-specific data sources, including published literature identified by a systematic review, as well as household surveys, hospital admissions, health insurance claims, and LRI mortality estimates, to generate internally consistent estimates of incidence and prevalence using DisMod-MR 2.1. For aetiology estimation, we analysed multiple causes of death, vital registration, hospital discharge, microbial laboratory, and literature data using a network analysis model to produce the proportion of LRI deaths and episodes attributable to the following pathogens: Acinetobacter baumannii, Chlamydia spp, Enterobacter spp, Escherichia coli, fungi, group B streptococcus, Haemophilus influenzae, influenza viruses, Klebsiella pneumoniae, Legionella spp, Mycoplasma spp, polymicrobial infections, Pseudomonas aeruginosa, respiratory syncytial virus (RSV), Staphylococcus aureus, Streptococcus pneumoniae, and other viruses (ie, the aggregate of all viruses studied except influenza and RSV), as well as a residual category of other bacterial pathogens.

FINDINGS

Globally, in 2021, we estimated 344 million (95% uncertainty interval [UI] 325-364) incident episodes of LRI, or 4350 episodes (4120-4610) per 100 000 population, and 2·18 million deaths (1·98-2·36), or 27·7 deaths (25·1-29·9) per 100 000. 502 000 deaths (406 000-611 000) were in children younger than 5 years, among which 254 000 deaths (197 000-320 000) occurred in countries with a low Socio-demographic Index. Of the 18 modelled pathogen categories in 2021, S pneumoniae was responsible for the highest proportions of LRI episodes and deaths, with an estimated 97·9 million (92·1-104·0) episodes and 505 000 deaths (454 000-555 000) globally. The pathogens responsible for the second and third highest episode counts globally were other viral aetiologies (46·4 million [43·6-49·3] episodes) and Mycoplasma spp (25·3 million [23·5-27·2]), while those responsible for the second and third highest death counts were S aureus (424 000 [380 000-459 000]) and K pneumoniae (176 000 [158 000-194 000]). From 1990 to 2019, the global all-age non-COVID-19 LRI mortality rate declined by 41·7% (35·9-46·9), from 56·5 deaths (51·3-61·9) to 32·9 deaths (29·9-35·4) per 100 000. From 2019 to 2021, during the COVID-19 pandemic and implementation of associated non-pharmaceutical interventions, we estimated a 16·0% (13·1-18·6) decline in the global all-age non-COVID-19 LRI mortality rate, largely accounted for by a 71·8% (63·8-78·9) decline in the number of influenza deaths and a 66·7% (56·6-75·3) decline in the number of RSV deaths.

INTERPRETATION

Substantial progress has been made in reducing LRI mortality, but the burden remains high, especially in low-income and middle-income countries. During the COVID-19 pandemic, with its associated non-pharmaceutical interventions, global incident LRI cases and mortality attributable to influenza and RSV declined substantially. Expanding access to health-care services and vaccines, including S pneumoniae, H influenzae type B, and novel RSV vaccines, along with new low-cost interventions against S aureus, could mitigate the LRI burden and prevent transmission of LRI-causing pathogens.

FUNDING

Bill & Melinda Gates Foundation, Wellcome Trust, and Department of Health and Social Care (UK).

Pan-genome mediated therapeutic target mining in Kingella kingae and inhibition assessment using traditional Chinese medicinal compounds: an informatics approach

Kingella kingae causes bacteremia, endocarditis, osteomyelitis, septic arthritis, meningitis, spondylodiscitis, and lower respiratory tract infections in pediatric patients. Usually it demonstrates disease after inflammation of mouth, lips or infections of the upper respiratory tract. To date, therapeutic targets in this bacterium remain unexplored. We have utilized a battery of bioinformatics tools to mine these targets in this study. Core genes were initially inferred from 55 genomes of K. kingae and 39 therapeutic targets were mined using an in-house pipeline. We selected aroG product (KDPG aldolase) involved in chorismate pathway, for inhibition analysis of this bacterium using lead-like metabolites from traditional Chinese medicinal plants. Pharmacophore generation was done using control ZINC36444158 (1,16-bis[(dihydroxyphosphinyl)oxy]hexadecane), followed by molecular docking of top hits from a library of 36,000 compounds. Top prioritized compounds were ZINC95914016, ZINC33833283 and ZINC95914219. ADME profiling and simulation of compound dosing (100 mg tablet) was done to infer compartmental pharmacokinetics in a population of 300 individuals in fasting state. PkCSM based toxicity analysis revealed the compounds ZINC95914016 and ZINC95914219 as safe and with almost similar bioavailability. However, ZINC95914016 takes less time to reach maximum concentration in the plasma and shows several optimal parameters compared to other leads. In light of obtained data, we recommend this compound for further testing and induction in experimental drug design pipeline.Communicated by Ramaswamy H. Sarma.

Therapeutic target mapping from the genome of Kingella negevensis and biophysical inhibition assessment through PNP synthase binding with traditional medicinal compounds

Kingella negevensis belongs to the Neisseriaceae family. It is implied that it has significant virulence potential due to RTX toxin production, which can cause hemolysis. It usually colonizes the orophayrynx of pediatric population, along with Kingella kingae but has also been isolated from vagina. Todate no report on its drug targets is present, therefore putative therapeutic targets were identified from its genomic sequence data. Traditional Chinese (n > 36,000) and Indian medicinal compounds (n > 2000) were then screened against its pyridoxine 5'-phosphate synthase, a vital therapeutic target. Prioritized TCM compounds included ZINC02525131, ZINC33833737 and ZINC85486932, and Cadiyenol, 9,11,13-Octadecatrienoic acid and 6-Gingerol from Indian medicinal library. Molecular dynamics simulation of top compounds revealed ZINC02525131 as having best stability for 100 ns, compared to Cadiyenol. ADMET profiling was then done, along with physiologically based pharmacokinetic simulation of these compounds in a population of 200 individuals, for 12 h to see fate of the ingested compound. Additionally, the impact of these compounds in a population with cirrhosis and renal impairment was also simulated. We imply in light of all the studied parameters of safety and bioavailability, etc., that 6-Gingerol from Zingiber officinalis rhizome must be proceeded further for in vitro and in vivo testing for inhibition of K. negevensis.

Subtractive sequence-mediated therapeutic targets from the conserved gene clusters of Campylobacter hyointestinalis and computational inhibition assessment

Campylobacter hyointestinalis is a causative agent of enteritis, proctitis, human gastroenteritis, and diarrhea. Reported transmission is from pigs to humans. Link with gastrointestinal carcinoma has also been established in non-Helicobacter pylori patients carrying this strain. The genome size of the strain LMG9260 is 1.8 MB with 1785 chromosomal and seven plasmid proteins. No therapeutic targets have been identified and reported in this bacterium. Therefore, subtractive computational screening of its genome was carried out for the purpose. In total, 31 such targets were mined and riboflavin synthase was utilized for screening natural product inhibitors against it. Among more than 30,000 screened natural compounds from the NPASS library, three (NPC472060, NPC33653, and NPC313886) were prioritized to have the potential to be developed into new antimicrobial drugs. Dynamics simulation assay along with other relevant parameters like absorption, toxicity, and distribution of the inhibiting compounds were also predicted and NPC33653 was identified as having the best drug-like properties among the prioritized compounds. Thus, it has potential to be pursued further for the inhibition of riboflavin synthesis in C. hyointestinalis for subsequent obstruction of its growth and survival.Communicated by Ramaswamy H. Sarma.

Global, regional, and national age-specific progress towards the 2020 milestones of the WHO End TB Strategy: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND

Global evaluations of the progress towards the WHO End TB Strategy 2020 interim milestones on mortality (35% reduction) and incidence (20% reduction) have not been age specific. We aimed to assess global, regional, and national-level burdens of and trends in tuberculosis and its risk factors across five separate age groups, from 1990 to 2021, and to report on age-specific progress between 2015 and 2020.

METHODS

We used the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 (GBD 2021) analytical framework to compute age-specific tuberculosis mortality and incidence estimates for 204 countries and territories (1990-2021 inclusive). We quantified tuberculosis mortality among individuals without HIV co-infection using 22 603 site-years of vital registration data, 1718 site-years of verbal autopsy data, 825 site-years of sample-based vital registration data, 680 site-years of mortality surveillance data, and 9 site-years of minimally invasive tissue sample (MITS) diagnoses data as inputs into the Cause of Death Ensemble modelling platform. Age-specific HIV and tuberculosis deaths were established with a population attributable fraction approach. We analysed all available population-based data sources, including prevalence surveys, annual case notifications, tuberculin surveys, and tuberculosis mortality, in DisMod-MR 2.1 to produce internally consistent age-specific estimates of tuberculosis incidence, prevalence, and mortality. We also estimated age-specific tuberculosis mortality without HIV co-infection that is attributable to the independent and combined effects of three risk factors (smoking, alcohol use, and diabetes). As a secondary analysis, we examined the potential impact of the COVID-19 pandemic on tuberculosis mortality without HIV co-infection by comparing expected tuberculosis deaths, modelled with trends in tuberculosis deaths from 2015 to 2019 in vital registration data, with observed tuberculosis deaths in 2020 and 2021 for countries with available cause-specific mortality data.

FINDINGS

We estimated 9·40 million (95% uncertainty interval [UI] 8·36 to 10·5) tuberculosis incident cases and 1·35 million (1·23 to 1·52) deaths due to tuberculosis in 2021. At the global level, the all-age tuberculosis incidence rate declined by 6·26% (5·27 to 7·25) between 2015 and 2020 (the WHO End TB strategy evaluation period). 15 of 204 countries achieved a 20% decrease in all-age tuberculosis incidence between 2015 and 2020, eight of which were in western sub-Saharan Africa. When stratified by age, global tuberculosis incidence rates decreased by 16·5% (14·8 to 18·4) in children younger than 5 years, 16·2% (14·2 to 17·9) in those aged 5-14 years, 6·29% (5·05 to 7·70) in those aged 15-49 years, 5·72% (4·02 to 7·39) in those aged 50-69 years, and 8·48% (6·74 to 10·4) in those aged 70 years and older, from 2015 to 2020. Global tuberculosis deaths decreased by 11·9% (5·77 to 17·0) from 2015 to 2020. 17 countries attained a 35% reduction in deaths due to tuberculosis between 2015 and 2020, most of which were in eastern Europe (six countries) and central Europe (four countries). There was variable progress by age: a 35·3% (26·7 to 41·7) decrease in tuberculosis deaths in children younger than 5 years, a 29·5% (25·5 to 34·1) decrease in those aged 5-14 years, a 15·2% (10·0 to 20·2) decrease in those aged 15-49 years, a 7·97% (0·472 to 14·1) decrease in those aged 50-69 years, and a 3·29% (-5·56 to 9·07) decrease in those aged 70 years and older. Removing the combined effects of the three attributable risk factors would have reduced the number of all-age tuberculosis deaths from 1·39 million (1·28 to 1·54) to 1·00 million (0·703 to 1·23) in 2020, representing a 36·5% (21·5 to 54·8) reduction in tuberculosis deaths compared to those observed in 2015. 41 countries were included in our analysis of the impact of the COVID-19 pandemic on tuberculosis deaths without HIV co-infection in 2020, and 20 countries were included in the analysis for 2021. In 2020, 50 900 (95% CI 49 700 to 52 400) deaths were expected across all ages, compared to an observed 45 500 deaths, corresponding to 5340 (4070 to 6920) fewer deaths; in 2021, 39 600 (38 300 to 41 100) deaths were expected across all ages compared to an observed 39 000 deaths, corresponding to 657 (-713 to 2180) fewer deaths.

INTERPRETATION

Despite accelerated progress in reducing the global burden of tuberculosis in the past decade, the world did not attain the first interim milestones of the WHO End TB Strategy in 2020. The pace of decline has been unequal with respect to age, with older adults (ie, those aged >50 years) having the slowest progress. As countries refine their national tuberculosis programmes and recalibrate for achieving the 2035 targets, they could consider learning from the strategies of countries that achieved the 2020 milestones, as well as consider targeted interventions to improve outcomes in older age groups.

FUNDING

Bill & Melinda Gates Foundation.

A comparison of interpretable XGBoost and artificial neural network model for the prediction of severe acute pancreatitis

INTRODUCTION

Acute pancreatitis (AP) that progresses to persistent organ failure is defined as severe acute pancreatitis (SAP) which has a relatively high mortality. Early establishment of a prediction model is crucial for the improvement of disease prognosis.

OBJECTIVES

The aim of this study was to evaluate the accuracy of Extreme Gradient Boosting (XGBoost) and artificial neural network model (ANN) for predicting SAP.

PATIENTS AND METHODS

A total of 648 patients with AP were enrolled. XGBoost and ANN models were developed and valuated in the training set (519 patients) and test set (129 patients), respectively. The accuracy and results of XGBoost and ANN models were evaluated both by area under the receiver operating characteristic curves (AUC) and the area under precision recall curve.

RESULTS

15 variables were selected for model construction through univariable analysis. The AUCs of XGBoost model and ANN model in five-fold cross-validation of the training set were 0.92 (95%CI, 0.87-0.97) and 0.86 (95%CI, 0.78-0.92), respectively. AUCs of XGBoost model and ANN model for the test set were 0.93 (95%CI, 0.85-1.00) and 0.87 (95%CI, 0.79-0.96). XGBoost outperformed ANN in terms of both diagnostic accuracy and the area under the precision recall curve. Individualized prediction by XGBoost model was explained by local interpretable model-agnostic explanations (LIME) plot.

CONCLUSIONS

An interpretable XGBoost model showed higher discriminatory efficiency in predicting SAP compared to ANN.

Screening inhibitors against the Ef-Tu of Fusobacterium nucleatum: a docking, ADMET and PBPK assessment study

The oral pathogen Fusobacterium nucleatum has recently been associated with an elevated risk of colorectal cancer (CRC), endometrial metastasis, chemoresistance, inflammation, metastasis, and DNA damage, along with several other diseases. This study aimed to explore the disruption of protein machinery of F. nucleatum via inhibition of elongation factor thermo unstable (Ef-Tu) protein, through natural products. No study on Ef-Tu inhibition by natural products or in Fusobacterium spp. exists till todate. Ef-Tu is an abundant specialized drug target in bacteria that varies from human Ef-Tu. Elfamycins target Ef-Tu and hence, Enacyloxin IIa was used to generate pharmacophore for virtual screening of three natural product libraries, Natural Product Activity and Species Source (NPASS) (n = 30000 molecules), Tibetan medicinal plant database (n = 54 molecules) and African medicinal plant database (n > 6000 molecules). Peptaibol Septocylindrin B (NPC141050), Hirtusneanoside, and ZINC95486259 were prioritized from these libraries as potential therapeutic candidates. ADMET profiling was done for safety assessment, physiological-based pharmacokinetic modeling in human and mouse for getting insight into drug interaction with body tissues and molecular dynamics was used to assess stability of the best hit NPC141050 (Septocylindrin B). Based on the promising results, we propose further in vitro, in vivo and pharmacokinetic testing on the lead Septocylindrin B, for possible translation into therapeutic interventions.

Screening Marine Microbial Metabolites as Promising Inhibitors of Borrelia garinii: A Structural Docking Approach towards Developing Novel Lyme Disease Treatment

Lyme disease caused by the Borrelia species is a growing health concern in many parts of the world. Current treatments for the disease may have side effects, and there is also a need for new therapies that can selectively target the bacteria. Pathogens responsible for Lyme disease include B. burgdorferi, B. afzelii, and B. garinii. In this study, we employed structural docking-based screening to identify potential lead-like inhibitors against the bacterium. We first identified the core essential genome fraction of the bacterium, using 37 strains. Later, we screened a library of lead-like marine microbial metabolites (n = 4730) against the arginine deiminase (ADI) protein of Borrelia garinii. This protein plays a crucial role in the survival of the bacteria, and inhibiting it can kill the bacterium. The prioritized lead compounds demonstrating favorable binding energies and interactions with the active site of ADI were then evaluated for their drug-like and pharmacokinetic parameters to assess their suitability for development as drugs. Results from molecular dynamics simulation (100 ns) and other scoring parameters suggest that the compound CMNPD18759 (common name: aureobasidin; IUPAC name: 2-[(4R,6R)-4,6-dihydroxydecanoyl]oxypropan-2-yl (3S,5R)-3,5-dihydroxydecanoate) holds promise as a potential drug candidate for the treatment of Lyme disease, caused by B. garinii. However, further experimental studies are needed to validate the efficacy and safety of this compound in vivo.

Fabp5 is a common gene between a high-cholesterol diet and acute pancreatitis

Background and aims

Hypercholesterolemia has been identified as risk factor for severe acute pancreatitis (AP). We aimed to identify the common differentially expressed genes (DEGs) between a high-cholesterol diet and AP.

Methods

We retrived gene expression profiles from the GEO database. DEGs were assessed using GEO2R. For AP hub genes, we conducted functional enrichment analysis and protein-protein interaction (PPI) analysis. GeneMANIA and correlation analysis were employed to predict potential DEG mechanisms. Validation was done across various healthy human tissues, pancreatic adenocarcinoma, peripheral blood in AP patients, and Sprague-Dawley rats with AP.

Results

The gene "Fabp5" emerged as the sole common DEG shared by a high-cholesterol diet and AP. Using the 12 topological analysis methods in PPI network analysis, Rela, Actb, Cdh1, and Vcl were identified as hub DEGs. GeneMANIA revealed 77.6% physical interactions among Fabp5, TLR4, and Rela, while genetic correlation analysis indicated moderate associations among them. Peripheral blood analysis yielded area under the ROC curve (AUC) values of 0.71, 0.63, 0.74, 0.64, and 0.91 for Fabp5, TLR4, Actb, Cdh1 genes, and artificial neural network (ANN) model respectively, in predicting severe AP. In vivo immunohistochemical analysis demonstrated higher Fabp5 expression in the hyperlipidemia-associated AP group compared to the AP and control groups.

Conclusion

Fabp5 emerged as the common DEG connecting a high-cholesterol diet and AP. Rela was highlighted as a crucial hub gene in AP. Genetic interactions were observed among Fabp5, TLR4, and Rela. An ANN model consisting of Fabp5, TLR4, Actb, and Cdh1 was helpful in predicting severe AP.

Subtractive sequence analysis aided druggable targets mining in Burkholderia cepacia complex and finding inhibitors through bioinformatics approach

Burkholderia cepacia complex (BCC) is a group of gram-negative bacteria composed of at least 20 different species that cause diseases in plants, animals as well as humans (cystic fibrosis and airway infection). Here, we analyzed the proteomic data of 47 BCC strains by classifying them in three groups. Phylogenetic analyses were performed followed by individual core region identification for each group. Comparative analysis of the three individual core protein fractions resulted in 1766 ortholog/proteins. Non-human homologous proteins from the core region gave 1680 proteins. Essential protein analyses reduced the target list to 37 proteins, which were further compared to a closely related out-group, Burkholderia gladioli ATCC 10,248 strain, resulting in 21 proteins. 3D structure modeling, validation, and druggability step gave six targets that were subjected to further target prioritization parameters which ultimately resulted in two BCC targets. A library of 12,000 ZINC drug-like compounds was screened, where only the top hits were selected for docking orientations. These included ZINC01405842 (against Chorismate synthase aroC) and ZINC06055530 (against Bifunctional N-acetylglucosamine-1-phosphate uridyltransferase/Glucosamine-1-phosphate acetyltransferase glmU). Finally, dynamics simulation (200 ns) was performed for each ligand-receptor complex, followed by ADMET profiling. Of these targets, details of their applicability as drug targets have not yet been elucidated experimentally, hence making our predictions novel and it is suggested that further wet-lab experimentations should be conducted to test the identified BCC targets and ZINC scaffolds to inhibit them.

Chimeric vaccine design against the conserved TonB-dependent receptor-like β-barrel domain from the outer membrane tbpA and hpuB proteins of Kingella kingae ATCC 23330

Kingella kingae is a Gram-negative bacterium that primarily causes pediatric infections such as septicemia, endocarditis, and osteoarticular infections. Its virulence is attributed to the outer membrane proteins having implications in bacterial adhesion, invasion, nutrition, and host tissue damage. TonB-dependent receptors (TBDRs) play an important role in nutrition and were previously implicated as vaccine targets in other bacteria. Therefore, we targeted the conserved β-barrel TBDR domain of these proteins for designing a vaccine construct that could elicit humoral and cellular immune responses. We used bioinformatic tools to mine TBDR-containing proteins from K. kingae ATCC 23330 and then predict B- and T-cell epitopes from their conserved β-barrel TDR domain. A chimeric vaccine construct was designed using three antigenic epitopes, covering >98% of the world population and capable of inciting humoral and adaptive immune responses. The final construct elicited a robust immune response. Docking and dynamics simulation showed good binding affinity of the vaccine construct to various receptors of the immune system. Additionally, the vaccine was predicted to be safe and non-allergenic, making it a promising candidate for further development. In conclusion, our study demonstrates the potential of immunoinformatics approaches in designing chimeric vaccines against K. kingae infections. The chimeric vaccine we designed can serve as a blueprint for future experimental studies to develop an effective vaccine against this pathogen, which can serve as a potential strategy to prevent K. kingae infections.

Comprehensive review on the pathogenesis of hypertriglyceridaemia-associated acute pancreatitis

It is well known, that the inflammatory process that characterizes acute pancreatitis (AP) can lead to both pancreatic damage and systemic inflammatory response syndrome (SIRS). During the last 20 years, there has been a growing incidence of episodes of acute pancreatitis associated with hypertriglyceridaemia (HTAP). This review provides an overview of triglyceride metabolism and the potential mechanisms that may contribute to developing or exacerbating HTAP. The article comprehensively discusses the various pathological roles of free fatty acid, inflammatory response mechanisms, the involvement of microcirculation, serum calcium overload, oxidative stress and the endoplasmic reticulum, genetic polymorphism, and gut microbiota, which are known to trigger or escalate this condition. Future perspectives on HTAP appear promising, with ongoing research focused on developing more specific and effective treatment strategies.

Mining Autoimmune-Disorder-Linked Molecular-Mimicry Candidates in Clostridioides difficile and Prospects of Mimic-Based Vaccine Design: An In Silico Approach

Molecular mimicry, a phenomenon in which microbial or environmental antigens resemble host antigens, has been proposed as a potential trigger for autoimmune responses. In this study, we employed a bioinformatics approach to investigate the role of molecular mimicry in Clostridioides difficile-caused infections and the induction of autoimmune disorders due to this phenomenon. Comparing proteomes of host and pathogen, we identified 23 proteins that exhibited significant sequence homology and were linked to autoimmune disorders. The disorders included rheumatoid arthritis, psoriasis, Alzheimer's disease, etc., while infections included viral and bacterial infections like HIV, HCV, and tuberculosis. The structure of the homologous proteins was superposed, and RMSD was calculated to find the maximum deviation, while accounting for rigid and flexible regions. Two sequence mimics (antigenic, non-allergenic, and immunogenic) of ≥10 amino acids from these proteins were used to design a vaccine construct to explore the possibility of eliciting an immune response. Docking analysis of the top vaccine construct C2 showed favorable interactions with HLA and TLR-4 receptor, indicating potential efficacy. The B-cell and T-helper cell activity was also simulated, showing promising results for effective immunization against C. difficile infections. This study highlights the potential of C. difficile to trigger autoimmunity through molecular mimicry and vaccine design based on sequence mimics that trigger a defensive response.

Identification of Disalicyloyl Curcumin as a Potential DNA Polymerase Inhibitor for Marek's Disease Herpesvirus: A Computational Study Using Virtual Screening and Molecular Dynamics Simulations

Marek's disease virus (MDV) is a highly contagious and persistent virus that causes T-lymphoma in chickens, posing a significant threat to the poultry industry despite the availability of vaccines. The emergence of new virulent strains has further intensified the challenge of designing effective antiviral drugs for MDV. In this study, our main objective was to identify novel antiviral phytochemicals through in silico analysis. We employed Alphafold to construct a three-dimensional (3D) structure of the MDV DNA polymerase, a crucial enzyme involved in viral replication. To ensure the accuracy of the structural model, we validated it using tools available at the SAVES server. Subsequently, a diverse dataset containing thousands of compounds, primarily derived from plant sources, was subjected to molecular docking with the MDV DNA polymerase model, utilizing AutoDock software V 4.2. Through comprehensive analysis of the docking results, we identified Disalicyloyl curcumin as a promising drug candidate that exhibited remarkable binding affinity, with a minimum energy of -12.66 Kcal/mol, specifically targeting the DNA polymerase enzyme. To further assess its potential, we performed molecular dynamics simulations, which confirmed the stability of Disalicyloyl curcumin within the MDV system. Experimental validation of its inhibitory activity in vitro can provide substantial support for its effectiveness. The outcomes of our study hold significant implications for the poultry industry, as the discovery of efficient antiviral phytochemicals against MDV could substantially mitigate the economic losses associated with this devastating disease.

Burden of non-communicable diseases among adolescents and young adults aged 10-24 years in the South-East Asia and Western Pacific regions, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019

BACKGROUND

Although non-communicable diseases (NCDs) remain the leading causes of mortality and disability worldwide, little comprehensive or recent evidence of the burden of NCDs among adolescents and young adults in the South-East Asia and Western Pacific regions is available. We aimed to report population shifts in people aged 10-24 years and their NCD burden from 1990 to 2019 using data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019.

METHODS

We retrieved data from GBD 2019 for people aged 10-24 years in the South-East Asia and Western Pacific regions from 1990 to 2019. We presented population shifts and analysed deaths, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) for NCDs. We also quantified the associations of deaths and DALYs with the Socio-demographic Index (SDI) and Universal Health Coverage (UHC) effective coverage index using Spearman correlation and linear regression analyses. Percentages are reported to 1 decimal place and rates are reported to 2 decimal places.

FINDINGS

In 2019, there were 559·2 million young people aged 10-24 years in the South-East Asia region and 335·0 million in the Western Pacific region; India and China remained the countries with greatest number of this age group. In 1990-2019, India had an absolute increase of 139·4 million adolescents, while China had a decrease of 134·3 million. In 2019, NCDs accounted for 27·3% (95% uncertainty interval 25·1 to 29·2) and 34·6% (33·5 to 36·1) of total deaths, and 49·8% (45·3 to 54·4) and 65·1% (60·6 to 69·3) of total DALYs in the South-East Asia and the Western Pacific regions, respectively. Neoplasms, cardiovascular diseases, and mental disorders were the leading causes of NCD burden in 42 countries. Kiribati had the highest rates of deaths (62·82 [50·77 to 76·11] per 100 000 population), YLLs (4364·73 [3545·04 to 5275·63] per 100 000 population), and DALYs (9368·73 [7713·65 to 11340·99] per 100 000 population) for NCDs, whereas Australia (6976·51 [5044·46 to 9190·01] per 100 000 population) and New Zealand (6716·81 [4827·25 to 8827·69] per 100 000 population) had the largest rates of YLDs due to NCDs. From 1990 to 2019 across both regions, the rate of death due to NCDs declined by over a third (-32·8% [-41·1 to -22·9] in the South-East Asia region and -40·0% [-48·6 to -30·4] in the Western Pacific region), and DALYs decreased by about 12% (-12·0% [-16·8 to -7·7] in the South-East Asia region and -12·8% [-17·7 to -8·7] in the Western Pacific region), whereas the proportion of NCD burden relative to all-cause burden increased (45·7% [32·9 to 61·7] for deaths and 41·2% [35·2 to 48·8] for DALYs in the South-East Asia region; 11·8% [7·1 to 21·5] for deaths and 18·2% [14·6 to 22·0] for DALYs in the Western Pacific region). The rate of deaths and DALYs due to NCDs decreased monotonically alongside increases in SDI (rs=-0·57 [95% CI -0·81 to -0·32] for deaths and rs=-0·30 [-0·61 to 0·03] for DALYs). The rate of deaths (rs=-0·89 [95% CI -0·97 to -0·80]) and DALYs (rs=-0·67 [-0·93 to -0·41]) due to NCDs also decreased alongside increases in the UHC effective coverage index.

INTERPRETATION

Specific preventive and health service measures are needed for adolescents and young adults in countries with different levels of socioeconomic development to reduce the burden from NCDs.

FUNDING

National Natural Science Foundation of China.

TRANSLATION

For the Chinese translation of the abstract see Supplementary Materials section.

Global, regional, and national burden of meningitis and its aetiologies, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019

BACKGROUND

Although meningitis is largely preventable, it still causes hundreds of thousands of deaths globally each year. WHO set ambitious goals to reduce meningitis cases by 2030, and assessing trends in the global meningitis burden can help track progress and identify gaps in achieving these goals. Using data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we aimed to assess incident cases and deaths due to acute infectious meningitis by aetiology and age from 1990 to 2019, for 204 countries and territories.

METHODS

We modelled meningitis mortality using vital registration, verbal autopsy, sample-based vital registration, and mortality surveillance data. Meningitis morbidity was modelled with a Bayesian compartmental model, using data from the published literature identified by a systematic review, as well as surveillance data, inpatient hospital admissions, health insurance claims, and cause-specific meningitis mortality estimates. For aetiology estimation, data from multiple causes of death, vital registration, hospital discharge, microbial laboratory, and literature studies were analysed by use of a network analysis model to estimate the proportion of meningitis deaths and cases attributable to the following aetiologies: Neisseria meningitidis, Streptococcus pneumoniae, Haemophilus influenzae, group B Streptococcus, Escherichia coli, Klebsiella pneumoniae, Listeria monocytogenes, Staphylococcus aureus, viruses, and a residual other pathogen category.

FINDINGS

In 2019, there were an estimated 236 000 deaths (95% uncertainty interval [UI] 204 000-277 000) and 2·51 million (2·11-2·99) incident cases due to meningitis globally. The burden was greatest in children younger than 5 years, with 112 000 deaths (87 400-145 000) and 1·28 million incident cases (0·947-1·71) in 2019. Age-standardised mortality rates decreased from 7·5 (6·6-8·4) per 100 000 population in 1990 to 3·3 (2·8-3·9) per 100 000 population in 2019. The highest proportion of total all-age meningitis deaths in 2019 was attributable to S pneumoniae (18·1% [17·1-19·2]), followed by N meningitidis (13·6% [12·7-14·4]) and K pneumoniae (12·2% [10·2-14·3]). Between 1990 and 2019, H influenzae showed the largest reduction in the number of deaths among children younger than 5 years (76·5% [69·5-81·8]), followed by N meningitidis (72·3% [64·4-78·5]) and viruses (58·2% [47·1-67·3]).

INTERPRETATION

Substantial progress has been made in reducing meningitis mortality over the past three decades. However, more meningitis-related deaths might be prevented by quickly scaling up immunisation and expanding access to health services. Further reduction in the global meningitis burden should be possible through low-cost multivalent vaccines, increased access to accurate and rapid diagnostic assays, enhanced surveillance, and early treatment.

FUNDING

Bill & Melinda Gates Foundation.

Exploring and targeting potential druggable antimicrobial resistance targets ArgS, SecY, and MurA in Staphylococcus sciuri with TCM inhibitors through a subtractive genomics strategy

Staphylococcus sciuri (also currently Mammaliicoccus sciuri) are anaerobic facultative and non-motile bacteria that cause significant human pathogenesis such as endocarditis, wound infections, peritonitis, UTI, and septic shock. Methicillin-resistant S. sciuri (MRSS) strains also infects animals that include healthy broilers, cattle, dogs, and pigs. The emergence of MRSS strains thereby poses a serious health threat and thrives the scientific community towards novel treatment options. Herein, we investigated the druggable genome of S. sciuri by employing subtractive genomics that resulted in seven genes/proteins where only three of them were predicted as final targets. Further mining the literature showed that the ArgS (WP_058610923), SecY (WP_058611897), and MurA (WP_058612677) are involved in the multi-drug resistance phenomenon. After constructing and verifying the 3D protein homology models, a screening process was carried out using a library of Traditional Chinese Medicine compounds (consisting of 36,043 compounds). The molecular docking and simulation studies revealed the physicochemical stability parameters of the docked TCM inhibitors in the druggable cavities of each protein target by identifying their druggability potential and maximum hydrogen bonding interactions. The simulated receptor-ligand complexes showed the conformational changes and stability index of the secondary structure elements. The root mean square deviation (RMSD) graph showed fluctuations due to structural changes in the helix-coil-helix and beta-turn-beta changes at specific points where the pattern of the RMSD and root mean square fluctuation (RMSF) (< 1.0 Å) support any major domain shifts within the structural framework of the protein-ligand complex and placement of ligand was well complemented within the binding site. The β-factor values demonstrated instability at few points while the radius of gyration for structural compactness as a time function for the 100-ns simulation of protein-ligand complexes showed favorable average values and denoted the stability of all complexes. It is assumed that such findings might facilitate researchers to robustly discover and develop effective therapeutics against S. sciuri alongside other enteric infections.

Molecular Mimicry Mapping in Streptococcus pneumoniae: Cues for Autoimmune Disorders and Implications for Immune Defense Activation

Streptococcus pneumoniae contributes to a range of infections, including meningitis, pneumonia, otitis media, and sepsis. Infections by this bacterium have been associated with the phenomenon of molecular mimicry, which, in turn, may contribute to the induction of autoimmunity. In this study, we utilized a bioinformatics approach to investigate the potential for S. pneumoniae to incite autoimmunity via molecular mimicry. We identified 13 S. pneumoniae proteins that have significant sequence similarity to human proteins, with 11 of them linked to autoimmune disorders such as psoriasis, rheumatoid arthritis, and diabetes. Using in silico tools, we predicted the sequence as well as the structural homology among these proteins. Database mining was conducted to establish links between these proteins and autoimmune disorders. The antigenic, non-allergenic, and immunogenic sequence mimics were employed to design and validate an immune response via vaccine construct design. Mimic-based vaccine construct can prove effective for immunization against the S. pneumoniae infections. Immune response simulation and binding affinity was assessed through the docking of construct C8 to human leukocyte antigen (HLA) molecules and TLR4 receptor, with promising results. Additionally, these mimics were mapped as conserved regions on their respective proteins, suggesting their functional importance in S. pneumoniae pathogenesis. This study highlights the potential for S. pneumoniae to trigger autoimmunity via molecular mimicry and the possibility of vaccine design using these mimics for triggering defense response.

Leveraging molecular docking to understand Congo red degradation by Staphylococcus caprae MB400

Congo red (CR) is a genotoxic, sulphonated azo dye and poses significant pollution problem. We hereby report its degradation by Staphylococcus caprae MB400. The bacterium initially propagated as a suspected contaminant upon CR dye supplemented nutrient agar plates, forming zones of clearance around its growth area. The bacterium was purified, gram stained and identified as Staphylococcus caprae via 16S rRNA gene sequencing. Dye decolourization was analysed in liquid culture, and Fourier-transform infrared spectroscopy (FTIR) was conducted for analysis of degraded product/metabolites. A decolourization of ~ 96.0% at 100 µg/ml concentration and pH 7 after 24 h of incubation was observed. Structure of the azoreductase enzyme, responsible for breakage of the bond in the dye and ultimately decolourization, was predicted, and molecular docking was harnessed for understanding the mechanism behind the reduction of azo bond (-N=N-) and conversion to metabolites. Our analysis revealed 12 residues critical for structural interaction of the azoreductase enzyme with this dye. Among these, protein backbone region surrounding four residues, i.e. Lys65, Phe122, Ile166 and Phe169, showed major displacement changes, upon binding with the dye. However, overall the conformational changes were not large.

An integrated computational approach to infer therapeutic targets from Campylobacter concisus and peptidomimetic based inhibition of its pyrimidine metabolism pathway

Campylobacter concisus is a commensal of the human oral flora that has been allied with persistent diarrhea and inflammatory bowel disease (IBD). In children under the age of two, Campylobacter infections are common in the developing countries and have frequently been associated with mortality. They are becoming a prevalent cause of bacterial diarrhea in early adulthood in developed countries as well. The need for identifying new therapeutic targets and drugs is crucial for curbing such infections. Therefore, we identified 18 cytoplasmic potential therapeutic candidates against the type strain of C. concisus and deoxycytidine triphosphate deaminase (dCTP deaminase), involved in pyrimidine synthesis was selected for screening of peptidomimetic inhibitors (n > 30,000 peptidomimetics) against it. To the best of our knowledge, this target has not been studied for Campylobacter spp. Three potent inhibitors of this enzyme were prioritized i.e. peptidomimetic 27, 64, and 150. Dynamics simulation of 100 ns was carried out to validate findings for top-scored inhibitors along with physiology-based pharmacokinetics to estimate behavior in human body and predict dosing parameters. This verification demonstrates a first-in-human pharmacokinetic simulation for these peptidomimetics and can help enhance confidence in these peptide-like structures. Moiety 27 (IUPAC name: 5-[(3,5-dimethyl-1H-pyrazol-1-yl)methyl]-N-{[2-(2-methoxyethyl)-1-oxo-1H,2H,3H,4H-pyrrolo[1,2-a]pyrazin-3-yl]methyl}furan-2-carboxamide), 64 (IUPAC name: 3-(2-methylpropyl)-1-{3-[5-(5-oxo-1-phenylpyrrolidin-3-yl)-1,2,4-oxadiazol-3-yl]phenyl}urea), and 150 (IUPAC name: N-(3-methoxypropyl)-1-[6-(4-methylphenyl)-4H,6H,7H-[1,2,3]triazolo[4,3-c][1,4]oxazine-3-carbonyl]piperidine-4-carboxamide) were identified as potent inhibitors of C. concisus.Communicated by Ramaswamy H. Sarma.

Relationship between Cholesterol-Related Lipids and Severe Acute Pancreatitis: From Bench to Bedside

It is well known that hypercholesterolemia in the body has pro-inflammatory effects through the formation of inflammasomes and augmentation of TLR (Toll-like receptor) signaling, which gives rise to cardiovascular disease and neurodegenerative diseases. However, the interaction between cholesterol-related lipids and acute pancreatitis (AP) has not yet been summarized before. This hinders the consensus on the existence and clinical importance of cholesterol-associated AP. This review focuses on the possible interaction between AP and cholesterol-related lipids, which include total cholesterol, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C) and apolipoprotein (Apo) A1, from the bench to the bedside. With a higher serum level of total cholesterol, LDL-C is associated with the severity of AP, while the persistent inflammation of AP is allied with a decrease in serum levels of cholesterol-related lipids. Therefore, an interaction between cholesterol-related lipids and AP is postulated. Cholesterol-related lipids should be recommended as risk factors and early predictors for measuring the severity of AP. Cholesterol-lowering drugs may play a role in the treatment and prevention of AP with hypercholesterolemia.

Inhibitor Assessment against the LpxC Enzyme of Antibiotic-resistant Acinetobacter baumannii Using Virtual Screening, Dynamics Simulation, and in vitro Assays

BACKGROUND

Bacterial resistance is currently a significant global public health problem. Acinetobacter baumannii has been ranked in the list of the World Health Organization as the most critical and priority pathogen for which new antibiotics are urgently needed. In this context, computational methods play a central role in the modern drug discovery process. The purpose of the current study was to identify new potential therapeutic molecules to neutralize MDR A. baumannii bacteria.

METHODS

A total of 3686 proteins retrieved from the A. baumannii proteome were subjected to subtractive proteomic analysis to narrow down the spectrum of drug targets. The SWISS-MODEL server was used to perform a 3D homology model of the selected target protein. The SAVES server was used to evaluate the overall quality of the model. A dataset of 74500 analogues retrieved from the PubChem database was docked with LpxC using the AutoDock software.

RESULTS

In this study, we predicted a putative new inhibitor for the Lpxc enzyme of A. baumannii. The LpxC enzyme was selected as the most appropriate drug target for A. baumannii. According to the virtual screening results, N-[(2S)-3-amino-1-(hydroxyamino)-1-oxopropan-2-yl]-4-(4-bromophenyl) benzamide (CS250) could be a promising drug candidate targeting the LpxC enzyme. This molecule shows polar interactions with six amino acids and non-polar interactions with eight other residues. In vitro experimental validation was performed through the inhibition assay.

CONCLUSION

To the best of our knowledge, this is the first study that suggests CS250 as a promising inhibitory molecule that can be exploited to target this gram-negative pathogen.

Genomic landscape of the emerging XDR Salmonella Typhi for mining druggable targets clpP, hisH, folP and gpmI and screening of novel TCM inhibitors, molecular docking and simulation analyses

Typhoid fever is transmitted by ingestion of polluted water, contaminated food, and stool of typhoid-infected individuals, mostly in developing countries with poor hygienic environments. To find novel therapeutic targets and inhibitors, We employed a subtractive genomics strategy towards Salmonella Typhi and the complete genomes of eight strains were primarily subjected to the EDGAR tool to predict the core genome (n = 3207). Human non-homology (n = 2450) was followed by essential genes identification (n = 37). The STRING database predicted maximum protein-protein interactions, followed by cellular localization. The virulent/immunogenic ability of predicted genes were checked to differentiate drug and vaccine targets. Furthermore, the 3D models of the identified putative proteins encoded by the respective genes were constructed and subjected to druggability analyses where only "highly druggable" proteins were selected for molecular docking and simulation analyses. The putative targets ATP-dependent CLP protease proteolytic subunit, Imidazole glycerol phosphate synthase hisH, 7,8-dihydropteroate synthase folP and 2,3-bisphosphoglycerate-independent phosphoglycerate mutase gpmI were screened against a drug-like library (n = 12,000) and top hits were selected based on H-bonds, RMSD and energy scores. Finally, the ADMET properties for novel inhibitors ZINC19340748, ZINC09319798, ZINC00494142, ZINC32918650 were optimized followed by binding free energy (MM/PBSA) calculation for ligand-receptor complexes. The findings of this work are expected to aid in expediting the identification of novel protein targets and inhibitors in combating typhoid Salmonellosis, in addition to the already existing therapies.

Genomic analysis of Chryseobacterium indologenes and conformational dynamics of the selected DD-peptidase

Chrysobacterium indologenes is an emerging MDR pathogen that belongs to the family Flavobacteriaceae. The genome of the C. indologenes, isolated from the nephrotic patient, was sequenced through Illumina MiSeq. The pangenomics of available 56 C. indologenes strains using BPGA revealed an open pangenome (n=5553 CDS), core genome (2141), and accessory genome (2013). The CEG/DEG database identified 662 essential genes that drastically reduced to 68 genes after non-homology analyses towards human and gut microbiome. Further filtering the data for other drug target prioritizing parameters resulted in 32 putative targets. Keeping in view the crucial role played in cell wall biosynthesis, dacB was selected as the final target that encodes D-alanyl-d-alanine carboxypeptidase/endopeptidase (DD-peptidase). The 3D structure of dacB was modelled and rendered to docking analyses against two compound libraries of African plants (n=6842) and Tibetan medicines (n=52). The ADMET profiling exhibited the physicochemical properties of final compounds. The MD simulations showed the stability of inhibitor-DD-peptidase complex and interactions in terms of RMSD, RMSF, binding free energy calculation and H-bonding. We propose that the novel compounds Leptopene and ZINC95486338 from our findings might be potent DD-peptidase inhibitors that could aid in the development of new antibiotic-resistant therapy for the emerging MDR C. indologenes.

Non-linear correlation between amylase day 2 to day 1 ratio and incidence of severe acute pancreatitis

Background

This study aimed to assess whether the amylase day 2/amylase day 1 ratio was associated with severe acute pancreatitis (SAP).

Methods

We retrospectively enrolled 464 patients with acute pancreatitis. Serum amylase was measured on admission (day 1) and 24 h later (day 2). Univariable logistic regression with restricted cubic spline analysis, multivariable logistic analysis, and receiver operating characteristic curve analysis was used to evaluate the relationship between the amylase day 2/amylase day 1 ratio and SAP.

Results

A non-linear association between the amylase day 2/amylase day 1 ratio and SAP was observed. The multivariable logistic analysis confirmed that a high amylase day 2/amylase day 1 ratio (≥0.3) was independently associated with the development of SAP (OR: 6.62). The area under the receiver operating characteristic curve (AUC) of the amylase day 2/amylase day 1 ratio, as a predictive factor for SAP, was 0.65. When amylase ratio ≥0.3 was counted as 1 point and added to the BISAP score to build a new model named the BISAPA (BISAP plus Amylase ratio) score (AUC = 0.86), it improved the diagnostic power of the original BISAP score (AUC = 0.83) for SAP. With a cut-off value of 3, the BISAPA score achieved a sensitivity of 66.0%, a specificity of 86.7%, and diagnostic accuracy of 84.48%.

Conclusions

There is a non-linear correlation between the amylase day 2/amylase day 1 ratio and the incidence of SAP. BISAPA score might also be a useful tool for the same purpose.

Nomogram of intra-abdominal infection after surgery in patients with gastric cancer: A retrospective study

Background

Surgical resection is still the primary way to treat gastric cancer. Therefore, postoperative complications such as IAI (intra-abdominal infection) are major problems that front-line clinical workers should pay special attention to. This article was to build and validate IAI’s RF (regression function) model. Furthermore, it analyzed the prognosis in patients with IAI after surgery for stomach cancer. The above two points are our advantages, which were not involved in previous studies.

Methods

The data of this study was divided into two parts, the training data set and the validation data set. The training data for this article were from the patients treated surgically with gastric cancer in our center from December 2015 to February 2017. We examined IAI’s morbidity, etiological characteristics, and prognosis in the training data set. Univariate and multivariate logistic regression analyses were used to screen risk factors, establish an RF model and create a nomogram. Data from January to March 2021 were used to validate the accuracy of the RF model.

Results

The incidence of IAI was 7.2%. The independent risk factors for IAI were hypertension (Odds Ratio [OR] = 3.408, P = 0.001), history of abdominal surgery (OR = 2.609, P = 0.041), combined organ excision (OR = 4.123, P = 0.010), and operation time ≥240 min (OR = 3.091, P = 0.005). In the training data set and validation data set, the area under the ROC curve of IAI predicted by the RF model was 0.745 ± 0.048 (P<0.001) and 0.736 ± 0.069 (P=0.003), respectively. In addition, IAI significantly extended the length of hospital stay but had little impact on survival.

Conclusions

Patients with hypertension, combined organ excision, a history of abdominal surgery, and a surgical duration of 240 min or more are prone to IAI, and the RF model may help to identify them.

Damage associated molecular patterns and neutrophil extracellular traps in acute pancreatitis

Previous researches have emphasized a trypsin-centered theory of acute pancreatitis (AP) for more than a century. With additional studies into the pathogenesis of AP, new mechanisms have been explored. Among them, the role of immune response bears great importance. Pro-inflammatory substances, especially damage-associated molecular patterns (DAMPs), play an essential role in activating, signaling, and steering inflammation. Meanwhile, activated neutrophils attach great importance to the immune defense by forming neutrophil extracellular traps (NETs), which cause ductal obstruction, premature trypsinogen activation, and modulate inflammation. In this review, we discuss the latest advances in understanding the pathological role of DAMPs and NETs in AP and shed light on the flexible crosstalk between these vital inflammatory mediators. We, then highlight the potentially promising treatment for AP targeting DAMPs and NETs, with a focus on novel insights into the mechanism, diagnosis, and management of AP.

Reverse vaccinology approach for multi-epitope centered vaccine design against delta variant of the SARS-CoV-2

The ongoing COVID-19 outbreak, initially identified in Wuhan, China, has impacted people all over the globe and new variants of concern continue to threaten hundreds of thousands of people. The delta variant (first reported in India) is currently classified as one of the most contagious variants of SARS-CoV-2. It is estimated that the transmission rate of delta variant is 225% times faster than the alpha variant, and it is causing havoc worldwide (especially in the USA, UK, and South Asia). The mutations found in the spike protein of delta variant make it more infective than other variants in addition to ruining the global efficacy of available vaccines. In the current study, an in silico reverse vaccinology approach was applied for multi-epitope vaccine construction against the spike protein of delta variant, which could induce an immune response against COVID-19 infection. Non-toxic, highly conserved, non-allergenic and highly antigenic B-cell, HTL, and CTL epitopes were identified to minimize adverse effects and maximize the efficacy of chimeric vaccines that could be developed from these epitopes. Finally, V1 vaccine construct model was shortlisted and 3D modeling was performed by refinement, docking against HLAs and TLR4 protein, simulation and in silico expression. In silico evaluation showed that the designed chimeric vaccine could elicit an immune response (i.e., cell-mediated and humoral) identified through immune simulation. This study could add to the efforts of overcoming global burden of COVID-19 particularly the variants of concern.

Impact of Azo Dyes and Ibuprofen on the Proteome of Serratia nematodiphila sp. MB307

Background:

Micropollutants comprise of organic/mineral substances that cause undesirable impact on the environment, by effecting life at all scales. In this study, we explored the changes they impart on the global proteome of a soil bacterium Serratia nematodiphila MB307, for two classes of pollutants, i.e., Azo dyes (Methyl orange, Congo red) and a pharmaceutical (Ibuprofen).

Methods:

The 100 µg pollutant supplemented alteration of pure S. nematodiphila MB307 culture after 24 h of incubation at 37 ℃ and its control was analyzed using differential proteomics approach. MaxQuant software with Perseus package was used for data analysis purpose.

Results:

Prominently ribosomal proteins and chaperones were up or downregulated in the whole cell and membranous fraction.

Conclusion:

This illustrates dynamic protein production adaptation of bacteria, to cope with stress and cell growth/division trade-off for survival. A collective pattern of survival under stress or pollution resistance could not be decrypted for all classes of pollutants, portraying dissimilar mechanisms of coping differently structured pollutant moieties.

Heavy metal stress tolerance by Serratia nematodiphila sp. MB307: insights from mass spectrometry based proteomics

Background:

Heavy metals impact living organism deleteriously when exceed the required limits. Their remediation by bacteria is a much pursued area of environmental research. In this study, we explored the quantitative changes for four heavy metals (Cadmium, Chromium, Zinc, Copper), on global and membrane proteome of gram negative S. nematodiphila MB307. This is a versatile bacterium, isolated from rhizosphere of heavy metal tolerating plant and equipped with characteristics ranging from useful biopeptide production to remediation of metals.

Methods:

We explored changes in its static end products of coding DNA sequences i.e. proteins after 24 incubation under metal stress, using LC-MS/MS. Data analysis was done using MaxQuant software coupled with Perseus package.

Results:

Up and downregulated protein fractions consisted prominently of chaperones, membrane integrity proteins, mobility or transporter proteins. Comparative analysis with previously studied bacteria and functional contribution of these proteins in metal stress offers evidence for survival of S. nematodiphila under high concentrations of selected metals.

Conclusion:

The outcomes validate that this soil derived bacterium is well attuned to remove these metals from soil, water and may be additionally useful for boosting phytoremediation of metals. This study delivers interesting insights and overlays ground for further investigations into mechanistic activity of this bacterium under pollutant stress.

Mining therapeutic targets from the antibiotic-resistant Campylobacter coli and virtual screening of natural product inhibitors against its riboflavin synthase

Campylobacter coli resides in the intestine of several commonly consumed animals, as well as water and soil. It leads to campylobacteriosis when humans eat raw/undercooked meat or come into contact with infected animals. A common manifestation of the infection is fever, nausea, headache, and diarrhea. Increasing antibiotic resistance is being observed in this pathogen. The increased incidence of C. coli infection, and post-infection complications like Guillain-Barré syndrome, make it an important pathogen. It is essential to find novel therapeutic targets and drugs against it, especially with the emergence of antibiotic-resistant strains. In the current study, genomes of 89 antibiotic-resistant strains of C. coli were downloaded from the PATRIC database. Potent drug targets (n = 36) were prioritized from the core genome (n = 1,337 genes) of this species. Riboflavin synthase was selected as a drug target and pharmacophore-based virtual screening was performed to predict its inhibitors from the NPASS (n =  ~ 30,000 compounds) natural product library. The top three docked compounds (NPC115144, NPC307895, and NPC470462) were selected for dynamics simulation (for 50 ns) and ADMET profiling. These identified compounds appear safe for targeting this pathogen and can be further validated by experimental analysis before clinical trials.

Identification of Therapeutic Targets in an Emerging Gastrointestinal Pathogen Campylobacter ureolyticus and Possible Intervention through Natural Products

Campylobacter ureolyticus is a Gram-negative, anaerobic, non-spore-forming bacteria that causes gastrointestinal infections. Being the most prevalent cause of bacterial enteritis globally, infection by this bacterium is linked with significant morbidity and mortality in children and immunocompromised patients. No information on pan-therapeutic drug targets for this species is available yet. In the current study, a pan-genome analysis was performed on 13 strains of C. ureolyticus to prioritize potent drug targets from the identified core genome. In total, 26 druggable proteins were identified using subtractive genomics. To the best of the authors’ knowledge, this is the first report on the mining of drug targets in C. ureolyticus. UDP-3-O-acyl-N-acetylglucosamine deacetylase (LpxC) was selected as a promiscuous pharmacological target for virtual screening of two bacterial-derived natural product libraries, i.e., postbiotics (n = 78) and streptomycin (n = 737) compounds. LpxC inhibitors from the ZINC database (n = 142 compounds) were also studied with reference to LpxC of C. ureolyticus. The top three docked compounds from each library (including ZINC26844580, ZINC13474902, ZINC13474878, Notoginsenoside St-4, Asiaticoside F, Paraherquamide E, Phytoene, Lycopene, and Sparsomycin) were selected based on their binding energies and validated using molecular dynamics simulations. To help identify potential risks associated with the selected compounds, ADMET profiling was also performed and most of the compounds were considered safe. Our findings may serve as baseline information for laboratory studies leading to the discovery of drugs for use against C. ureolyticus infections.

Crystal Structure of an SSB Protein from Salmonella enterica and Its Inhibition by Flavanonol Taxifolin

Single-stranded DNA (ssDNA)-binding proteins (SSBs) play a central role in cells by participating in DNA metabolism, including replication, repair, recombination, and replication fork restart. SSBs are essential for cell survival and thus an attractive target for potential anti-pathogen chemotherapy. In this study, we determined the crystal structure and examined the size of the ssDNA-binding site of an SSB from Salmonella enterica serovar Typhimurium LT2 (SeSSB), a ubiquitous opportunistic pathogen which is highly resistant to antibiotics. The crystal structure was solved at a resolution of 2.8 Å (PDB ID 7F25), indicating that the SeSSB monomer possesses an oligonucleotide/oligosaccharide-binding (OB) fold domain at its N-terminus and a flexible tail at its C-terminus. The core of the OB-fold in the SeSSB is made of a six-stranded β-barrel capped by an α-helix. The crystal structure of the SeSSB contained two monomers per asymmetric unit, which may indicate the formation of a dimer. However, the gel-filtration chromatography analysis showed that the SeSSB forms a tetramer in solution. Through an electrophoretic mobility shift analysis, we characterized the stoichiometry of the SeSSB complexed with a series of ssDNA dA homopolymers, and the size of the ssDNA-binding site was determined to be around 22 nt. We also found the flavanonol taxifolin, also known as dihydroquercetin, capable of inhibiting the ssDNA-binding activity of the SeSSB. Thus, this result extended the SSB interactome to include taxifolin, a natural product with a wide range of promising pharmacological activities.

Differential analysis of Orientia tsutsugamushi genomes for therapeutic target identification and possible intervention through natural product inhibitor screening

Orientia tsutsugamushi (Ott) is a causative agent of scrub typhus, and one of the emerging pathogens that could affect a large human population. It is one of the misdiagnosed and under-reported, febrile illnesses that infects various body organs (skin, heart, lung, kidney, and brain). The control of this infection is hampered due to the lack of drugs or vaccine against it. This study was undertaken to identify potential drug targets from the core genome of Ott and investigate novel natural product inhibitors against them. Hence, the available genomes for 22 strains of Ott were downloaded from the PATRIC database, and pan-genomic analysis was performed. Only 202 genes were present in the core region. Among these, 94 were identified as essential, 32 non-homologous to humans, nine non-homologous to useful gut flora and a single gene dapD as a drug target. Product of this gene (2,3,4,5-tetrahydropyridine-2-carboxylate N-succinyltransferase) was modeled and docked against traditional Indian (Ayurvedic) and Chinese phytochemical libraries, with best hits selected for docking, based on multiple target-drug/s interactions and minimum energy scores. ADMET profiling and molecular dynamics simulation was performed for top three compounds from each library to assess the toxicity and stability, respectively. We presume that these compounds (ZINC8214635, ZINC32793028, ZINC08101133, ZINC85625167, ZINC06018678, and ZINC13377938) could be successful inhibitors of Ott. However, in-depth experimental and clinical research is needed for further validation.

Usefulness of Random Forest Algorithm in Predicting Severe Acute Pancreatitis

BACKGROUND AND AIMS

This study aimed to develop an interpretable random forest model for predicting severe acute pancreatitis (SAP).

METHODS

Clinical and laboratory data of 648 patients with acute pancreatitis were retrospectively reviewed and randomly assigned to the training set and test set in a 3:1 ratio. Univariate analysis was used to select candidate predictors for the SAP. Random forest (RF) and logistic regression (LR) models were developed on the training sample. The prediction models were then applied to the test sample. The performance of the risk models was measured by calculating the area under the receiver operating characteristic (ROC) curves (AUC) and area under precision recall curve. We provide visualized interpretation by using local interpretable model-agnostic explanations (LIME).

RESULTS

The LR model was developed to predict SAP as the following function: -1.10-0.13×albumin (g/L) + 0.016 × serum creatinine (μmol/L) + 0.14 × glucose (mmol/L) + 1.63 × pleural effusion (0/1)(No/Yes). The coefficients of this formula were utilized to build a nomogram. The RF model consists of 16 variables identified by univariate analysis. It was developed and validated by a tenfold cross-validation on the training sample. Variables importance analysis suggested that blood urea nitrogen, serum creatinine, albumin, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, calcium, and glucose were the most important seven predictors of SAP. The AUCs of RF model in tenfold cross-validation of the training set and the test set was 0.89 and 0.96, respectively. Both the area under precision recall curve and the diagnostic accuracy of the RF model were higher than that of both the LR model and the BISAP score. LIME plots were used to explain individualized prediction of the RF model.

CONCLUSIONS

An interpretable RF model exhibited the highest discriminatory performance in predicting SAP. Interpretation with LIME plots could be useful for individualized prediction in a clinical setting. A nomogram consisting of albumin, serum creatinine, glucose, and pleural effusion was useful for prediction of SAP.

A Comparison of XGBoost, Random Forest, and Nomograph for the Prediction of Disease Severity in Patients With COVID-19 Pneumonia: Implications of Cytokine and Immune Cell Profile

BACKGROUND AND AIMS

The aim of this study was to apply machine learning models and a nomogram to differentiate critically ill from non-critically ill COVID-19 pneumonia patients.

METHODS

Clinical symptoms and signs, laboratory parameters, cytokine profile, and immune cellular data of 63 COVID-19 pneumonia patients were retrospectively reviewed. Outcomes were followed up until Mar 12, 2020. A logistic regression function (LR model), Random Forest, and XGBoost models were developed. The performance of these models was measured by area under receiver operating characteristic curve (AUC) analysis.

RESULTS

Univariate analysis revealed that there was a difference between critically and non-critically ill patients with respect to levels of interleukin-6, interleukin-10, T cells, CD4⁺ T, and CD8⁺ T cells. Interleukin-10 with an AUC of 0.86 was most useful predictor of critically ill patients with COVID-19 pneumonia. Ten variables (respiratory rate, neutrophil counts, aspartate transaminase, albumin, serum procalcitonin, D-dimer and B-type natriuretic peptide, CD4⁺ T cells, interleukin-6 and interleukin-10) were used as candidate predictors for LR model, Random Forest (RF) and XGBoost model application. The coefficients from LR model were utilized to build a nomogram. RF and XGBoost methods suggested that Interleukin-10 and interleukin-6 were the most important variables for severity of illness prediction. The mean AUC for LR, RF, and XGBoost model were 0.91, 0.89, and 0.93 respectively (in two-fold cross-validation). Individualized prediction by XGBoost model was explained by local interpretable model-agnostic explanations (LIME) plot.

CONCLUSIONS

XGBoost exhibited the highest discriminatory performance for prediction of critically ill patients with COVID-19 pneumonia. It is inferred that the nomogram and visualized interpretation with LIME plot could be useful in the clinical setting. Additionally, interleukin-10 could serve as a useful predictor of critically ill patients with COVID-19 pneumonia.

Evaluation of the risk factors for severe complications and surgery of intestinal foreign bodies in adults: a single-center experience with 180 cases

BACKGROUND

Foreign bodies (FBs) lodged in the intestine or causing intestinal complications are uncommon in clinical practice but may pose diagnostic difficulties and prove life-threatening. This study aimed to evaluate the risk factors for severe complications and surgery to aid clinicians in the diagnosis and management of intestinal FBs.

METHODS

We performed a retrospective analysis of patients in whom FBs were lodged in the intestine or caused complications from 2010 to 2020 in the First Affiliated Hospital of Wenzhou Medical University (Zhejiang, China). The characteristics of the patients and FBs, symptoms, imaging findings, diagnostics, treatment strategies, and clinical outcomes were analysed. Furthermore, the risk factors for complications and surgery were investigated.

RESULTS

In total, 180 patients were included in our study. Most patients (76.1%) were unable to provide a history of ingestion. Bezoars were the most common FBs (35.6%). The FBs were mainly located in the duodenum (32.8%) and the ileum (27.8%). Surgical removal of FBs was successful in 89 (49.4%) patients and endoscopic removal in 54 (30.0%) patients. Eleven with perforations were treated conservatively. FBs located in the jejunum or ileum were more likely to cause severe complications than those located in the duodenum. FBs located in the jejunum, ileum, or sigmoid colon were more likely to undergo surgery, and severe complications were an independent risk factor for surgery.

CONCLUSION

Intestinal FBs, often localized in angulation, are likely to be misdiagnosed because most patients do not provide a history of FB ingestion. Surgery and endoscopic therapy are the most commonly used treatment modalities. Surgery is not mandatory in clinically stable patients with small and contained perforations. FBs located in the jejunum or ileum are risk factors for both complications and surgery.

An Overview of Algorithms and Associated Applications for Single Cell RNA-Seq Data Imputation

Single cell RNA-Seq technology enables the assessment of RNA expression in individual cells. This makes it popular in experimental biology for gleaning specifications of novel cell types as well as inferring heterogeneity. Experimental data conventionally contains zero counts or dropout events for many single cell transcripts. Such missing data hampers the accurate analysis using standard workflows, designed for massive RNA-Seq datasets. Imputation for single cell datasets is done to infer the missing values. This was traditionally done with ad-hoc code but later customized pipelines, workflows and specialized software appeared for this purpose. This made it easy to benchmark and cluster things in an organized manner. In this review, we have assembled a catalog of available RNA-Seq single cell imputation algorithms/workflows and associated softwares for the scientific community performing single-cell RNA-Seq data analysis. Continued development of imputation methods, especially using deep learning approaches, would be necessary for eradicating associated pitfalls and addressing challenges associated with future large scale and heterogeneous datasets.

Pan-Genome Reverse Vaccinology Approach for the Design of Multi-Epitope Vaccine Construct against Escherichia albertii

Escherichia albertii is characterized as an emerging pathogen, causing enteric infections. It is responsible for high mortality rate, especially in children, elderly, and immunocompromised people. To the best of our knowledge, no vaccine exists to curb this pathogen. Therefore, in current study, we aimed to identify potential vaccine candidates and design chimeric vaccine models against Escherichia albertii from the analysis of publicly available data of 95 strains, using a reverse vaccinology approach. Outer-membrane proteins (n = 4) were identified from core genome as vaccine candidates. Eventually, outer membrane Fimbrial usher (FimD) protein was selected as a promiscuous vaccine candidate and utilized to construct a potential vaccine model. It resulted in three epitopes, leading to the design of twelve vaccine constructs. Amongst these, V6 construct was found to be highly immunogenic, non-toxic, non-allergenic, antigenic, and most stable. This was utilized for molecular docking and simulation studies against six HLA and two TLR complexes. This construct can therefore be used for pan-therapy against different strains of E. albertii and needs to be tested in vitro and in vivo.

Therapeutic target identification via differential genome analysis of antibiotic resistant Shigella sonnei and inhibitor evaluation against a selected drug target

Shigella sonnei has been implicated in bloody diarrhea (accompanied by abdominal pain and fever) and is an emerging pathogen of concern, especially in developing countries. The major means of transmission is the fecal-oral route while sexual transmission has also been reported. In children, the impact might be stunted growth due to life-threatening illness. Resistance has been reported in this species for several types of antibiotics. In this study, we retrieved the antibiotic-resistant labeled whole genome sequences of the species from the PATRIC database and performed a pan-genome analysis to filter out core genes. Antibiotic resistance was studied in the core, accessory and unique genome. Core genes were utilized as seed substance for essentiality analysis and drug candidate assignment. Product of the gene aroG, i.e. chorismate biosynthetic process 3-deoxy-7-phosphoheptulonate synthase enzyme, responsible for aromatic amino acid family biosynthetic process, was taken for further downstream processing. Natural product libraries of flavonoids (n = 178), ZINC database derived inhibitor compounds of the 3-deoxy-7-phosphoheptulonate synthase enzyme (n = 112), and streptomycin compounds (n = 737) were docked to find out potent inhibitors, followed by dynamics simulation of 50 ns each for top compounds.. Physicochemical and ADMET profiling of the top compounds was done to analyze their safety for consumption. We propose that the top compounds: Phytoene from Streptomycin library and ZINC000036444158 (synonym:1,16-bis[(dihydroxyphosphinyl)oxy]hexadecane) from 3-deoxy-7-phosphoheptulonate synthase inhibitor library of ZINC database (and used as a control in this study) should be tested in vitro against Shigella sonnei, to fully determine their efficacy. This could add to the drying pipeline of potent drug molecules against emerging pathogens.

Screening of potent phytochemical inhibitors against SARS-CoV-2 protease and its two Asian mutants

Background

COVID-19, declared a pandemic in March 2020 by the World Health Organization is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The virus has already killed more than 2.3 million people worldwide.

Object

The principal intent of this work was to investigate lead compounds by screening natural product library (NPASS) for possible treatment of COVID-19.

Methods

Pharmacophore features were used to screen a large database to get a small dataset for structure-based virtual screening of natural product compounds. In the structure-based screening, molecular docking was performed to find a potent inhibitor molecule against the main protease (M^pro) of SARS-CoV-2 (PDB ID: 6Y7M). The predicted lead compound was further subjected to Molecular Dynamics (MD) simulation to check the stability of the leads compound with the evolution of time.

Results

In pharmacophore-based virtual screening, 2,361 compounds were retained out of 30,927. In the structure-based screening, the lead compounds were filtered based on their docking scores. Among the 2,360 compounds, 12 lead compounds were selected based on their docking score. Kazinol T with NPASS ID: NPC474104 showed the highest docking score of −14.355 and passed criteria of Lipinski's drug-like parameters. Monitoring ADMET properties, Kazinol T showed its safety for consumption. Docking of Kazinol T with two Asian mutants (R60C and I152V) showed variations in binding and energy parameters. Normal mode analysis for ligand-bound and unbound form of protease along with its mutants, revealed displacement and correlation parameters for C-alpha atoms. MD simulation results showed that all ligand-protein complexes remained intact and stable in a dynamic environment with negative Gibbs free energy.

Conclusions

The natural product Kazinol T was a predicted lead compound against the main protease of SARS-CoV-2 and will be the possible treatment for COVID-19.

Comprehensive comparative genomic and microsatellite analysis of SARS, MERS, BAT-SARS, and COVID-19 coronaviruses

The coronavirus disease 2019 (COVID‐19) pandemic has spread around the globe very rapidly. Previously, the evolution pattern and similarity among the COVID‐19 causative organism severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) and causative organisms of other similar infections have been determined using a single type of genetic marker in different studies. Herein, the SARS‐CoV‐2 and related β coronaviruses Middle East respiratory syndrome coronavirus (MERS‐CoV), SARS‐CoV,  bat coronavirus (BAT‐CoV) were comprehensively analyzed using a custom‐built pipeline that employed phylogenetic approaches based on multiple types of genetic markers including the whole genome sequences, mutations in nucleotide sequences, mutations in protein sequences, and microsatellites. The whole‐genome sequence‐based phylogeny revealed that the strains of SARS‐CoV‐2 are more similar to the BAT‐CoV strains. The mutational analysis showed that on average MERS‐CoV and BAT‐CoV genomes differed at 134.21 and 136.72 sites, respectively, whereas the SARS‐CoV genome differed at 26.64 sites from the reference genome of SARS‐CoV‐2. Furthermore, the microsatellite analysis highlighted a relatively higher number of average microsatellites for MERS‐CoV and SARS‐CoV‐2 (106.8 and 107, respectively), and a lower number for SARS‐CoV and BAT‐CoV (95.8 and 98.5, respectively). Collectively, the analysis of multiple genetic markers of selected β viral genomes revealed that the newly born SARS‐COV‐2 is closely related to BAT‐CoV, whereas, MERS‐CoV is more distinct from the SARS‐CoV‐2 than BAT‐CoV and SARS‐CoV.

Sex differences in the relationship among alcohol, smoking, and Helicobacter pylori infection in asymptomatic individuals

Objective

We aimed to investigate the relationship of Helicobacter pylori infection with alcohol and smoking.

Methods

We conducted a cross-sectional study among participants who underwent health check-ups for H. pylori infection between January 2013 and March 2017. We subsequently investigated the relationship of H. pylori infection with alcohol and smoking.

Results

A total of 7169 participants were enrolled in this study. The overall prevalence of H. pylori infection was 55.2%. Participants with H. pylori infection were more likely to be older than those without H. pylori infection. For male participants with H. pylori infection, multivariable logistic regression analysis indicated that both smoking (odds ratio (OR): 1.61; 95% confidence interval (CI): 1.41–1.83) and alcohol consumption (OR: 1.30; 95% CI: 1.10–1.52) were independently positively associated with H. pylori infection. For female participants, multivariable logistic regression analysis indicated that both smoking (OR: 0.03; 95% CI: 0.02–0.07) and alcohol consumption (OR: 0.20; 95% CI: 0.12–0.33) were inversely significantly associated with H. pylori infection after adjustment for age.

Conclusions

Smoking and alcohol consumption were risk factors for male participants but these were protective factors for female individuals with H. pylori infection.

Resveratrol supplementation and acute pancreatitis: A comprehensive review

Resveratrol, a natural polyphenolic ingredient extracted from herbs, suppresses oxidative stress and inflammation. We performed a comprehensive review to find any evidence about the effects of Resveratrol on acute pancreatitis (AP). Resveratrol has been found to directly impact cytokine generation. As these factors play a crucial role in the pathophysiology of AP, resveratrol might attenuate AP and its complications. Mechanistically, resveratrol exerts its pharmacological effects through anti-inflammatory and antioxidant mechanisms via interaction with different signaling molecules and transcription factors. Indeed, resveratrol might prove to be an effective therapeutic component for AP treatment in the future. In this review, we shed light on potential most recent pathways through which resveratrol might impact the management and control of AP.