Cardiac abnormalities after induction of endoplasmic reticulum stress are associated with mitochondrial dysfunction and connexin43 expression

The endoplasmic reticulum (ER) is responsible for protein synthesis and calcium storage. ER stress, reflected by protein unfolding and calcium handling abnormalities, has been studied as a pathogenic factor in cardiovascular diseases. The aim of this study is to examine the effects of ER stress on mechanical and electrophysiological functions in the heart and explore the underlying molecular mechanisms. A total of 30 rats were randomly divided into control, ER stress inducer (tunicamycin[TN]) and ER stress inhibitor (tunicamycin+4-phenylbutyric acid [4-PBA]) groups. ER stress induction led to significantly systolic and diastolic dysfunction as reflected by maximal increasing/decreasing rate of left intraventricular pressure (±dp/dt), left ventricular peaksystolic pressure(LVSP) and LV end-diastolic pressure(LVEDP). Epicardial mapping performed in vivo revealed reduced conduction velocity and increased conduction heterogeneity associated with the development of spontaneous ventricular tachycardia. Masson’s trichrome staining revealed marked fibrosis in the myocardial interstitial and sub-pericardial regions, and thickened epicardium. Western blot analysis revealed increased pro-fibrotic factor transforming growth factor-β1 (TGF-β1), decreased mitochondrial biogenesis protein peroxlsome proliferator-activated receptor-γ coactlvator-1α （PGC-1a）, and decreased mitochondrial fusion protein mitofusin-2 (MFN2). These changes were associated with mitochondria dysfunction and connexin 43(CX43)translocation to mitochondria. These abnormalities can be partially prevented by the ER stress inhibitor 4-PBA. Our study shows that ER stress induction can produce cardiac electrical and mechanism dysfunction as well as structural remodelling. Mitochondrial function alterations are contributed by CX43 transposition to mitochondria. These abnormalities can be partially prevented by the ER stress inhibitor 4-PBA

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

Identification of new diagnostic biomarkers for Mycobacterium tuberculosis and the potential application in the serodiagnosis of human tuberculosis

Mycobacterium tuberculosis (M. tuberculosis) regions of difference (RD) encode proteins which are potentially useful as diagnostic reagents for tuberculosis (TB). In this study, 75 genes from M. tuberculosis RD1-RD16 were successfully cloned from which 68 proteins were expressed and purified. Three serum pools from patients with pulmonary TB (PTB), extra-pulmonary tuberculosis (EPTB) and healthy controls (HC) were used to preliminarily screen individual RD proteins. The OD630 ratio of the PTB or EPTB to the HC group ≥ 2-fold was positive. As a result, 29 proteins were obtained. The serological response to the identified antigens was further verified using 58 PTB samples with 38 sera from smear-positive PTB (PTB-SP) patients and 20 sera from smear-negative PTB (PTB-SN) patients, 16 EPTB samples, 42 latent M. tuberculosis infection samples and 40 HCs by indirect ELISA. With respect to the PTB diagnosis, receiver operating characteristic analysis showed that Rv0222 [area under the curve (AUC), 0.8129; 95% confidence interval (CI), 0.7280–0.8979] and Rv3403c (AUC, 0.8537; 95% CI, 0.7779–0.9294) performed better than ESAT6/CFP10 (AUC, 0.7435; 95% CI, 0.6465–0.8406). Rv0222 and Rv3403c demonstrated the highest diagnostic ability in the PTB-SP group (sensitivity, 86.8%; specificity, 80%), while Rv3403c demonstrated the highest diagnostic ability in the PTB-SN group (sensitivity, 70%; specificity, 80%). With respect to the EPTB diagnosis, Rv0222 exhibited the highest diagnostic value (AUC, 0.7523; sensitivity, 68.8%; specificity, 87.5%). In addition, the combination of Rv0222 and Rv3403c improved the test for PTB-SN. These results indicate that Rv0222 and Rv3403c would be potential diagnostic biomarkers for active TB serodiagnosis. Mouse experiments demonstrated that Rv0222 and Rv3403c elicited specific cellular and humoral responses which were characterized by production of IFN-γ, IgG1, and IgG2a, but a higher level of IgG1 than IgG2a