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Abstract The night-time temperature-related injury risks and burdens were unclear. Using 11,512,467 non-fatal injury cases in 243 surveillance hospitals across China from 2006-2021, we estimated the associations between daytime or night-time temperature and injury by a time-stratified case-crossover study, and compared their injury burden during 1990s–2010s. We found the excess risk (ER) for per 1°C rise in night-time temperature (ER = 1.21%, 95%CI:1.03%,1.39%) was greater than that in daytime (ER = 0.86%, 95%CI:0.72%,1.00%). Compared with the 1980s, the attributable fractions (AFs) for daytime and night-time temperature change during the 1990s–2010s were 0.59% (95%eCI:0.54%,0.67%) and 0.73% (95%eCI:0.69%,0.77%), respectively. Spatially, the higher AFs of night-time temperature were more widely distributed than daytime temperature. The non-fatal injury risk attributed to night-time temperature was stronger than daytime temperature, and increased night-time temperatures posed a heavier injury burden compared with daytime temperature in China. Our findings indicate that high night-time temperature is an important injury risk in the context of climate change.

BackgroundStudies have found decreased heat effect and increased minimum mortality temperature (MMT) during the past decades. However, it is unclear whether heat exposure or temperature adaptation play an important role in this change.MethodsThis is a cross-sectional study. Data were collected from 3,094 respondents aged 31–64 years old based on online questionnaire. The Cochran-Armitage test for trend and Cochran–Mantel–Haenszel (CMH) test were used for the difference between three decades. The Chi square test was employed to compare the difference between different demographic subgroups during 2014–2023. Multivariate logistic regression model was used to analyze the risk factors of air conditioner ownership.ResultsMost respondents (94.6%) thought ambient temperature had been increasing, and 57.0% people thought climate change impacted their health. Long duration outdoors work (≥4 h) decreased from 36.01, 30.93 to 24.53% (Z = −9.80, p

Introduction: The health effects of ambient ozone have been investigated in many previous studies. However, the effects of long-term exposure to ambient ozone on the incidence of cardiovascular disease (CVD) remain inconclusive. Objectives: To estimate the associations of long-term exposure to maximum daily 8-hours average ozone (MDA8 O3) with the incidence of total CVD, heart disease, hypertension, and stroke. Methods: This was a prospective cohort study, and the data was obtained from the China Health and Retirement Longitudinal Survey (CHARLS) implemented during 2011–2018 and the China Family Panel Studies (CFPS) implemented during 2010–2018. We applied a Cox proportional hazards regression model to evaluate the associations of MDA8 O3 with total CVD, heart disease, hypertension, and stroke risks, and the corresponding population-attributable fractions (PAF) attributable to MDA8 O3 were also calculated. All analyses were conducted by R software. Results: The mean MDA8 O3 concertation of all included participants in the CHARLS and CFPS were 51.03 part per billion (ppb) and 51.15 ppb, respectively. In the CHARLS including 18,177 participants, each 10 ppb increment in MDA8 O3 concentration was associated with a 31% increase [hazard ratio (HR) = 1.31, 95% confidence interval (CI): 1.22–1.42] in the risk of incident heart disease, and the corresponding population-attributable fractions (PAF) was 13.79% [10.12%-17.32%]. In the CFPS including 30,226 participants, each 10 ppb increment in MDA8 O3 concentration was associated with an increase in the risk of incident total CVD (1.07 [1.02–1.13]), and hypertension (1.10 [1.03–1.18]). The PAFs of total CVD, and hypertension attributable to MDA8 O3 were 3.53% [0.82%-6.16%], and 5.11% [1.73%-8.38%], respectively. Stratified analyses showed greater associations in males, urban areas, and Southern China. Conclusions: Long-term exposure to MDA8 O3 may increase the incidence of CVD. Therefore, the policies that control O3 and related precursors are persistently needed.

Abstract Background The Smoking paradox has generated inconsistent findings concerning the clinical prognosis of acute ST-segment elevation myocardial infarction (STEMI) patients, while providing limited insights into coronary anatomy and function which are crucial prognostic factors. Therefore, this study aimed to further investigate the existence of smoking paradox in coronary anatomy and function. Methods This study divided STEMI patients into smokers and non-smokers. Quantitative coronary angiography, angiography‑derived microcirculatory resistance (AMR) and quantitative flow ratio (QFR) were utilized to analyze coronary anatomy and function. These parameters were compared using multivariable analysis and propensity score matching. The clinical outcomes were evaluated using Kaplan-Meier curve and Cox regression. Results The study included 1258 patients, with 730 in non-smoker group and 528 in smoker group. Smokers were significantly younger, predominantly male, and had fewer comorbidities. Without adjusting for confounders, smokers exhibited larger lumen diameter [2.03(1.45–2.57) vs. 1.90(1.37–2.49), P = 0.033] and lower AMR [244(212–288) vs. 260(218–301), P = 0.006]. After matching and multivariate adjustment, smokers exhibited inversely smaller lumen diameter [1.97(1.38–2.50) vs. 2.15(1.63–2.60), P = 0.002] and higher incidence of coronary microvascular dysfunction [233(53.9%) vs. 190(43.6%), P = 0.002], but showed similar AMR and clinical outcomes compared to non-smokers. There was no difference in QFR between two groups. Conclusion Smoking among STEMI patients undergoing pPCI was associated with smaller lumen diameter and higher occurrence of coronary microvascular dysfunction, although it had no further impact on clinical prognosis. The smoking paradox observed in coronary anatomy or function may be explained by younger age, gender, and lower prevalence of comorbidities.

Abstract Background Chronic pain was associated with a higher risk of mental disorders (e.g., depression and anxiety). However, the role of 24-h movement behaviors in the association remains unclear. Methods A total of 72,800 participants with accelerometer data and free of mental disorders from the UK Biobank were analyzed. The compositional mediation model and isotemporal substitution model were used to explore the associations between chronic pain, 24-h movement behaviors, and the incidence of overall mental disorders, depression, and anxiety. Results With a median follow-up of 13.36 years, participants with chronic pain had a higher rate of incident overall mental disorders (hazard ratio (HR): 1.281, 95% confidence interval (CI): 1.219 to 1.344), anxiety (HR: 1.391, 95% CI: 1.280 to 1.536), and depression (HR: 1.703, 95% CI: 1.551 to 1.871). Increased sedentary behavior (SB) and reduced moderate-to-vigorous physical activity (MVPA) caused by chronic pain both increased the risk of mental disorders. Twenty-four-hour movement behaviors explained the relationship between chronic pain and overall mental disorders, depression, and anxiety by 10.77%, 5.70%, and 6.86%, respectively. Interaction effects were found between MVPA and chronic pain when predicting the incidence of depression and between MVPA, sleep (SLP), and chronic pain when predicting the incidence of mental disorders. People with chronic pain would recommend at least 0.5 h per day of MVPA and 7 h per day of SLP and restricting SB below 11.5 h per day. Conclusions Twenty-four-hour movement behaviors played a significant mediating role in the association between chronic pain and mental disorders. Individuals with chronic pain should engage in more MVPA, less sedentary behavior, and have 7-h sleep per day.

Background: Limited attention has been paid to the health effects of long-term PM1 exposure on stroke admission. Current investigations exploring the long-term PM exposure effect are largely based on observational studies, and PM generally is not allocated randomly to participants. Using traditional regression models might confuse messaging and hinder policy recommendations for pollution control and disease prevention policies. Methods: We conducted a cohort study among 36,271 adults from one of the largest cities in China in 2015 and followed up through 2020. Hazard ratios of stroke admissions following long-term PM1 exposure were estimated via a causal inference approach, marginal structural time-varying Cox proportional hazard model, accounting for multiple confounders. Additionally, several sensitivity analyses and impact modification analyses were carried out. Results and discussion: Associations with 1 μg/m3 increase in long-term PM1 were identified for total (HR, 1.079; 95 %CI, 1.012–1.151) and ischemic stroke admissions (HR, 1.092; 95 %CI, 1.018–1.171). The harmful associations varied with exposure duration, initially increasing and then decreasing. The 2–3 years cumulative exposure was associated with a 3.3–5.4 % raised risk for total stroke. For every 1 μg/m³ increase in long-term PM1 exposure, females exhibited a higher risk of both total and ischemic stroke (13 % and 16 %) than men (4 % and 5 %). Low-exposure individuals (whose annual PM1 concentrations were under the third quartile among the annual concentrations for all the participants) exhibited greater sensitivity to PM1 effects (total stroke: 1.079 vs. 1.107; ischemic stroke: 1.092 vs. 1.116). The results underline the importance of safeguarding low-exposed people in highly polluted areas and suggest that long-term PM1 exposure may increase stroke admission risk, warranting attention to vulnerable groups.

Background: The potential for residential greenness to improve cardiovascular health through both physical and psychological mechanisms is well recognized. However, evidence from rapidly urbanizing developing countries and cohort-based causal inference approaches, remains limited. We aim to examine the effect of residential greenness and time to cardiovascular mortality in South China. Methods: We utilized data from a community-based population survey involving 748,209 participants at baseline from 2009 to 2015, followed up until 2020. Residential greenness exposure was assessed by the annual Normalized Difference Vegetation Index (NDVI) in the 500 m radius of each participant’s residence. We used time-varying proportional hazard Cox models coupled with inverse probability weighting to fit marginal structural models and obtain hazard ratios (HRs) for cardiovascular disease (CVD) mortality after adjusting for confounders. Multiple effect modifiers on both additive and multiplicative scales were further explored. Results: A total of 15,139 CVD-related deaths were identified during a median of 7.9 years of follow-up. A protective effect was found between higher greenness exposure and reduced CVD mortality, with a 9.3 % lower rate of total CVD mortality (HR 0.907, 95 % CI 0.859–0.957) based on a 0.1 increase in annual average NDVI. Demographic (age, marital status) and lifestyle factors (smoking, drinking status) were found to modify the association between residential greenness and CVD mortality (all P interaction values

Abstract Background Given the increased risk of chronic diseases and comorbidity among middle-aged and older adults in China, it is pivotal to identify the disease trajectory of developing chronic multimorbidity and address the temporal correlation among chronic diseases. Method The data of 15895 participants from the China Health and Retirement Longitudinal Study (CHARLS 2011 – 2018) were analyzed in the current study. Binomial tests and the conditional logistic regression model were conducted to estimate the associations among 14 chronic diseases, and the disease trajectory network analysis was adopted to visualize the relationships. Results The analysis showed that hypertension is the most prevalent disease among the 14 chronic conditions, with the highest cumulative incidence among all chronic diseases. In the disease trajectory network, arthritis was found to be the starting point, and digestive diseases, hypertension, heart diseases, and dyslipidemia were at the center, while memory-related disease (MRD), stroke, and diabetes were at the periphery of the network. Conclusions With the chronic disease trajectory network analysis, we found that arthritis was prone to the occurrence and development of various other diseases. In addition, patients of heart diseases/hypertension/digestive disease/dyslipidemia were under higher risk of developing other chronic conditions. For patients with multimorbidity, early prevention can preclude them from developing into poorer conditions, such as stroke, MRD, and diabetes. By identifying the trajectory network of chronic disease, the results provided critical insights for developing early prevention and individualized support services to reduce disease burden and improve patients’ quality of life.

Evidence of the potential causal links between long-term exposure to particulate matters (PM, i.e., PM1, PM2.5, and PM1–2.5) and T2DM mortality based on large cohorts is limited. In contrast, the existing evidence usually suffers from inherent bias with the traditional association assessment.A prospective cohort of 580,757 participants in the southern region of China were recruited during 2009 and 2015 and followed up through December 2020. PM exposure at each residential address was estimated by linking to the well-established high-resolution simulation dataset. Hazard ratios (HRs) were calculated using time-varying marginal structural Cox models, an established causal inference approach, after adjusting for potential confounders.During follow-up, a total of 717 subjects died from T2DM. For every 1 μg/m3 increase in PM2.5, the adjusted HRs and 95% confidence interval (CI) for T2DM mortality was 1.036 (1.019–1.053). Similarly, for every 1 μg/m3 increase in PM1 and PM1–2.5, the adjusted HRs and 95% CIs were 1.032 (1.003–1.062) and 1.085 (1.054–1.116), respectively. Additionally, we observed a generally more pronounced impact among individuals with lower levels of education or lower residential greenness which as measured by the Normalized Difference Vegetation Index (NDVI). We identified substantial interactions between NDVI and PM1 (P-interaction = 0.003), NDVI and PM2.5 (P-interaction = 0.019), as well as education levels and PM1 (P-interaction = 0.049).The study emphasizes the need to consider environmental and socio-economic factors in strategies to reduce T2DM mortality. We found that PM1, PM2.5, and PM1–2.5 heighten the peril of T2DM mortality, with education and green space exposure roles in modifying it.

Abstract Neuropathic pain (NP) is a widespread chronic pain with a prevalence of 6.9–10% in the general population, severely affecting patients’ physical and mental health. Accumulating evidence indicated that the immune environment is an essential factor causing NP. However, the mechanism is unclear. This study attempted to analyze NP-related immune infiltration patterns. We downloaded the expression profiles from the Gene Expression Omnibus (GEO) database. The novel method of single-sample gene set enrichment analysis (ssGSEA) algorithm and weighted gene co-expression network analysis (WGCNA) was applied to identify immune-related genes and verified in vitro and in vivo experiments. The spared nerve injury (SNI) group was closely related to type1 T helper cells (Th1 cells), and two key genes (Abca1 and Fyb) positively correlated with Th1 cell infiltration. At the single-cell level, Abca1 and Fyb were significantly expressed in macrophages. In addition, we verified that Abca1 could affect the function of macrophages. Finally, we hypothesized that Abca1 is involved in the infiltration of Th1 cells into dorsal root ganglion (DRG) tissues and induces NP via immunoinflammatory response. Hence, the present study aimed to elucidate the correlation between NP and neuroinflammation and identify a new therapeutic target for treating NP.

Background: Epidemiological evidence on the association of PM2.5 (particulate matter with aerodynamic diameter ≤ 2.5 μm) and its specific components with hypertension and blood pressure is limited. Methods: We applied information of participants from the World Health Organization's (WHO) Study on Global Ageing and Adult Health (SAGE) to estimate the associations of long-term PM2.5 mass and its chemical components exposure with blood pressure (BP) and hypertension incidence in Chinese adults ≥ 50 years during 2007–2018. Generalized linear mixed model and Cox proportional hazard model were applied to investigate the effects of PM2.5 mass and its chemical components on the incidence of hypertension and BP, respectively. Results: Each interquartile range (IQR = 16.80 μg/m3) increase in the one-year average of PM2.5 mass concentration was associated with a 17 % increase in the risk of hypertension (HR = 1.17, 95 % CI: 1.10, 1.24), and the population attributable fraction (PAF) was 23.44 % (95 % CI: 14.69 %, 31.55 %). Each IQR μg/m3 increase in PM2.5 exposure was also related to increases of systolic blood pressure (SBP) by 2.54 mmHg (95 % CI:1.99, 3.10), and of diastolic blood pressure (DBP) by 1.36 mmHg (95 % CI: 1.04, 1.68). Additionally, the chemical components of SO42−, NO3−, NH4+, OM, and BC were also positively associated with an increased risk of hypertension incidence and elevated blood pressure. Conclusions: These results indicate that long-term exposure to PM2.5 mass and its specific components may be major drivers of escalation in hypertension diseases.

Purpose – The decarbonization of power generation is key to achieving carbon neutrality in China by the end of 2060. This paper aims to examine how green finance influences China’s low-carbon transition of power generation. Using a provincial panel data set as an empirical study example, green finance is assessed first, then empirically analyses the influences of green finance on the low-carbon transition of power generation, as well as intermediary mechanisms at play. Finally, this paper makes relevant recommendations for peak carbon and carbon neutrality in China. Design/methodology/approach – To begin with, an evaluation index system with five indicators is constructed with entropy weighting method. Second, this paper uses the share of coal-fired power generation that takes in total power generation as an inverse indicator to measure the low-carbon transition in power generation. Finally, the authors perform generalized method of moments (GMM) econometric model to examine how green finance influences China’s low-carbon transition of power generation by taking advantage of 30 provincial panel data sets, spanning the period of 2007–2019. Meanwhile, the implementation of the 2016 Guidance on Green Finance is used as a turning point to address endogeneity using difference-in-difference method (DID). Findings – The prosperity of green finance can markedly reduce the share of thermal power generation in total electricity generation, which implies a trend toward China’s low-carbon transformation in the power generation industry. Urbanization and R&D investment are driving forces influencing low-carbon transition, while economic development hinders the low-carbon transition. The conclusions remain robust after a series of tests such as the DID method, instrumental variable method and replacement indicators. Notably, the results of the mechanism analysis suggest that green finance contributes to low-carbon transformation in power generation by reducing secondary sectoral share, reducing the production of export products, promoting the advancement of green technologies and expanding the proportion of new installed capacity of renewable energy. Research limitations/implications – This paper puts forward relevant suggestions for promoting the green finance development with countermeasures such as allowing low interest rate for renewable energy power generation, facilitating market function and using carbon trade market. Additional policy implication is to promote high quality urbanization and increase R&D investment while pursuing high quality economic development. The last implication is to develop mechanism to strengthen the transformation of industrial structure, to promote high quality trade from high carbon manufactured products to low-carbon products, to stimulate more investment in green technology innovation and to accelerate the greening of installed structure in power generation industry. Originality/value – This paper first attempts to examine the low-carbon transition in power generation from a new perspective of green finance. Second, this paper analyses the mechanism through several aspects: the share of secondary industry, the output of exported products, advances in green technology and the share of renewable energy in new installed capacity, which has not yet been done. Finally, this study constructs a system of indicators to evaluate green finance, including five indicators with entropy weighting method. In conclusion, this paper provides scientific references for sustainable development in China, and meanwhile for other developing countries with similar characteristics.

Abstract Post-traumatic stress disorder (PTSD) is a mental disorder diagnosed by clinical interviews, self-report measures and neuropsychological testing. Traumatic brain injury (TBI) can have neuropsychiatric symptoms similar to PTSD. Diagnosing PTSD and TBI is challenging and more so for providers lacking specialized training facing time pressures in primary care and other general medical settings. Diagnosis relies heavily on patient self-report and patients frequently under-report or over-report their symptoms due to stigma or seeking compensation. We aimed to create objective diagnostic screening tests utilizing Clinical Laboratory Improvement Amendments (CLIA) blood tests available in most clinical settings. CLIA blood test results were ascertained in 475 male veterans with and without PTSD and TBI following warzone exposure in Iraq or Afghanistan. Using random forest (RF) methods, four classification models were derived to predict PTSD and TBI status. CLIA features were selected utilizing a stepwise forward variable selection RF procedure. The AUC, accuracy, sensitivity, and specificity were 0.730, 0.706, 0.659, and 0.715, respectively for differentiating PTSD and healthy controls (HC), 0.704, 0.677, 0.671, and 0.681 for TBI vs. HC, 0.739, 0.742, 0.635, and 0.766 for PTSD comorbid with TBI vs HC, and 0.726, 0.723, 0.636, and 0.747 for PTSD vs. TBI. Comorbid alcohol abuse, major depressive disorder, and BMI are not confounders in these RF models. Markers of glucose metabolism and inflammation are among the most significant CLIA features in our models. Routine CLIA blood tests have the potential for discriminating PTSD and TBI cases from healthy controls and from each other. These findings hold promise for the development of accessible and low-cost biomarker tests as screening measures for PTSD and TBI in primary care and specialty settings.

Injury poses heavy burden on public health, but little evidence on the potential role of climate change on injury exists. Here, the authors collect data during 2013-2019 in six provinces of China to estimate the associations between temperature and injury mortality, and to project future mortality burden attributable to temperature change driven by climate change.

BackgroundDrowning is a serious public health problem worldwide. Previous epidemiological studies on the association between meteorological factors and drowning mainly focused on individual weather factors, and the combined effect of mixed exposure to multiple meteorological factors on drowning is unclear. ObjectiveWe aimed to investigate the combined effects of multiple meteorological factors on unintentional drowning mortality in China and to identify the important meteorological factors contributing to drowning mortality. MethodsUnintentional drowning death data (based on International Classification of Diseases, 10th Edition, codes W65-74) from January 1, 2013, to December 31, 2018, were collected from the Disease Surveillance Points System for Guangdong, Hunan, Zhejiang, Yunnan, and Jilin Provinces, China. Daily meteorological data, including daily mean temperature, relative humidity, sunlight duration, and rainfall in the same period were obtained from the Chinese Academy of Meteorological Science Data Center. We constructed a time-stratified case-crossover design and applied a generalized additive model to examine the effect of individual weather factors on drowning mortality, and then used quantile g-computation to estimate the joint effect of the mixed exposure to meteorological factors. ResultsA total of 46,179 drowning deaths were reported in the 5 provinces in China from 2013 to 2018. In an effect analysis of individual exposure, we observed a positive effect for sunlight duration, a negative effect for relative humidity, and U-shaped associations for temperature and rainfall with drowning mortality. In a joint effect analysis of the above 4 meteorological factors, a 2.99% (95% CI 0.26%-5.80%) increase in drowning mortality was observed per quartile rise in exposure mixture. For the total population, sunlight duration was the most important weather factor for drowning mortality, with a 93.1% positive contribution to the overall effects, while rainfall was mainly a negative factor for drowning deaths (90.5%) and temperature and relative humidity contributed 6.9% and –9.5% to the overall effects, respectively. ConclusionsThis study found that mixed exposure to temperature, relative humidity, sunlight duration, and rainfall was positively associated with drowning mortality and that sunlight duration, rather than temperature, may be the most important meteorological factor for drowning mortality. These findings imply that it is necessary to incorporate sunshine hours and temperature into early warning systems for drowning prevention in the future.

Background: Cardiovascular disease (CVD) mortality is associated with long-term particulate matter (PM) exposure. However, evidence from large, highly-exposed population cohort and observational-data-based causal inference approaches remains limited. Aims: We examined the potential causal links between PM exposure and the CVD mortality in South China. Methods: 580,757 participants were recruited during 2009–2015 and followed up through 2020. Satellite-based annual concentrations of PM2.5, PM10, and PMcoarse (i.e., PM10 - PM2.5) at 1 km2 spatial resolution were estimated and assigned to each participant. Marginal structural Cox models with time-varying covariates, adjusted using inverse probability weighting, were developed to evaluate the association between prolonged PM exposure and CVD mortality. Results: For overall CVD mortality, the hazard ratios and 95% confidence interval for each 1 μg/m3 increase in the annual average concentration of PM2.5, PM10, and PMcoarse were 1.033 (1.028–1.037), 1.028 (1.024–1.032), and 1.022 (1.012–1.033), respectively. All three PMs were linked to a higher mortality risk for myocardial infarction and ischemic heart disease (IHD). The mortality risk of chronic IHD and hypertension was linked to PM2.5 and PM10. Significant association between PMcoarse and other heart disease mortality was also observed. The older, women, less-educated participants, or inactive participants exhibited particularly higher susceptibility. Participants who were generally exposed to PM10 concentrations below 70 μg/m3 were more vulnerable to PM2.5-, PM10- and PMcoarse-CVD mortality risks. Conclusion: This large cohort study provides evidence for the potential causal links between increased CVD mortality and ambient PM exposure, as well as socio-demographics linked to the highest vulnerability.

Properly analyzing and reporting data remains a challenging task in epidemiologic research, as underreporting of data is often overlooked. The evaluation on the effect of underreporting remains understudied. In this study, we examined the effect of different scenarios of mortality underreporting on the relationship between PM10, temperature, and mortality. Mortality data, PM10, and temperature data in seven cities were obtained from Provincial Center for Disease Control and Prevention (CDC), China Meteorological Data Sharing Service System, and China National Environmental Monitoring Center, respectively. A time-series design with a distributed lag nonlinear model (DLNM) was used to examine the effects of five mortality underreporting scenarios: 1) Random underreporting of mortality; 2) Underreporting is monotonically increasing (MI) or monotonically decreasing (MD); 3) Underreporting due to holiday and weekends; 4) Underreporting occurs before the 20th day of each month, and these underreporting will be added after the 20th day of the month; and 5) Underreporting due to holiday, weekends, MI, and MD. We observed that underreporting at random (UAR) scenario had little effect on the association between PM10, temperature, and daily mortality. However, other four underreporting not at random (UNAR) scenarios mentioned above had varying degrees of influence on the association between PM10, temperature, and daily mortality. Additionally, in addition to imputation under UAR, the variation of minimum mortality temperature (MMT) and attributable fraction (AF) of mortality attributed to temperature in the same imputation scenarios is inconsistent in different cities. Finally, we observed that the pooled excess risk (ER) below MMT was negatively associated with mortality and the pooled ER above MMT was positively associated with mortality. This study showed that UNAR impacted the association between PM10, temperature, and mortality, and potential underreporting should be dealt with before analyzing data to avoid drawing invalid conclusions.

IntroductionOwing to advancements in non-invasive magnetic resonance imaging, many studies have repeatedly showed that diabetes affects the central nervous system in the presence of peripheral neuropathy, suggesting a common or interacting pathological mechanism for both complications.MethodsWe aimed to investigate the role of abnormal gut microbiota in rats with diabetic peripheral neuropathy (DPN) combined with cognitive dysfunction. Glucose-compliant rats with nerve conduction deficits were screened as a successful group of DPN rats. The DPN group was then divided into rats with combined cognitive impairment (CD) and rats with normal cognitive function (NCD) based on the results of the Novel object recognition test. Rat feces were then collected for 16S rRNA gene sequencing of the intestinal flora.Results and DiscussionThe results revealed that abnormalities in Firmicutes, Ruminococcaceae, Bacteroidia, and Actinobacteria-like microorganisms may induce DPN complicated by cognitive dysfunction.

Objective To systematically analyse global, regional and national burden change of unintentional drowning from 1990 to 2019, and to further quantify the contribution of social determinants of health (SDH) on the change.Design Data from the Global Burden of Disease Study 2019 were used in this study.Setting and participants Individuals of all ages and genders from 204 countries and territories.Main outcome measures The main outcomes were the age-standardised rates (ASRs) of mortality and disability-adjusted life-years (DALYs) of unintentional drowning. The percentage change in the ASRs were used to estimate the joint effect of SDH on trends in global burden of drowning.Results We observed that the global burden of unintentional drowning declined markedly from 1990 to 2019, with age-standardised mortality rate and DALYs rate decreasing by 61.5% and 68.2%, respectively. Women, children, middle Socio-Demographic Index (SDI) countries, South-East Asia and Western Pacific region had higher reduction. At national level, greater reductions were observed in Armenia and Republic of Korea, but significant increases in Cabo Verde and Vanuatu. We found that every one percentile increase in six SDHs (Gross Domestic Product (GDP) per person, SDI, educational attainment, health spending, health workers and urbanisation) was associated with a decrease of 0.15% and 0.16% in drowning age-standardised mortality rate and DALYs rate globally, respectively. Health spending and GDP per capita were the main contributors to the reduction of drowning globally.Conclusions The global burden of unintentional drowning significantly declined in the past three decades, and the improvement of SDHs such as GDP per capita and health spending mainly contributed to the decrease. Our findings indicate that improvement of SDHs is critical for drowning prevention and control.

Background: It remains unclear whether type 2 diabetes and the complication of arthritis are causally related to the PM2.5 pollutant. Therefore, we aimed to investigate the associations of long-term PM2.5 exposure with type 2 diabetes and with arthritis in type 2 diabetes patients. Materials and methods: This study used data from the China Health and Retirement Longitudinal Survey (CHARLS) implemented during 2011–2018. The associations were analyzed by Cox proportional hazards regression models, and the population-attributable fraction (PAF) was calculated to assess the burden of type 2 diabetes and arthritis-attributable to PM2.5. Results: A total of 21,075 participants were finally included, with 19,121 analyzed for PM2.5 and type 2 diabetes risk and 12,427 analyzed for PM2.5 and arthritis risk, of which 1,382 with newly-diagnosed type 2 diabetes and 1,328 with arthritis during the follow-up. Overall, each 10 μg/m3 increment in PM2.5 concentration was significantly associated with an increase in the risk of type 2 diabetes (HR = 1.26, 95 %CI1.22 to 1.31), and the PAF of type 2 diabetes attributable to PM2.5 was 13.54 %. In type 2 diabetes patients, each 10 μg/m3 increment in PM2.5 exposure was associated with an increase in arthritis (HR = 1.42, 95 %CI: 1.28 to 1.57), and the association was significantly greater than that (H = 1.23, 95 %CI: 1.19 to 1.28) in adults without type 2 diabetes. The PAFs of arthritis-attributable to PM2.5 in participants with and without type 2 diabetes were 18.54 % and 10.69 %, respectively. Conclusion: Long-term exposure to PM2.5 may increase the risk of type 2 diabetes and make type 2 diabetes patients susceptible to arthritis.

Increases in anti-Asian COVID-19 related discriminatory behaviors have been observed, many of which targeted older adults. Studies demonstrate that racial discrimination is associated with worse health outcomes, including anxiety, depression, and sleep difficulties. No previous studies have examined the impact of day-to-day experiences of discrimination before and during COVID-19 on both Asian and non-Asian older adults within the same sample. We examined whether everyday discrimination was associated with increased anxiety and depression symptoms, decreased levels of functioning, and increased sleep difficulties among Asian and non-Asian US older adults before and during the pandemic. Data came from the Positive Minds-Strong Bodies randomized clinical trial, an evidence-based mental health and disability prevention intervention for racially and ethnically diverse older adults with elevated depression or anxiety symptoms and minor to moderate disability. We conducted secondary data analyses in a cohort of 165 older adults (56 Asian and 109 non-Asian) assessed before COVID-19 (May 2015-May 2018) and during COVID-19 (March 2021-July 2022). Regression models examined whether everyday discrimination impacted health outcomes differently before and during COVID-19, and whether this effect was stronger among Asian compared to non-Asian older adults. Non-Asian older adults reported the same levels of everyday discrimination before and during COVID-19. Consistent with literature suggesting that social distancing has inadvertently kept US Asian populations from experiencing discrimination, Asian older adults reported marginally lower levels of everyday discrimination during the pandemic compared with pre-pandemic. We found that everyday discrimination was not associated with health outcomes before COVID-19. In contrast, during the pandemic, everyday discrimination was associated with worse anxiety and depression symptoms and worse levels of functioning, although only the impact on depression was significantly stronger compared with before the pandemic. This negative impact of everyday discrimination on health outcomes during the pandemic appeared to affect both Asian and non-Asian older adults similarly. Social support and social cohesion buffered against the negative effect of everyday discrimination on depression and level of functioning during the pandemic. Results suggest that public health interventions aimed at reducing everyday discrimination and emphasizing social support and cohesion can potentially improve health outcomes for all US older adult populations.Clinical trial registrationwww.ClinicalTrials.gov; identifier: NCT02317432.

Abstract We sought to find clinical subtypes of posttraumatic stress disorder (PTSD) in veterans 6–10 years post-trauma exposure based on current symptom assessments and to examine whether blood biomarkers could differentiate them. Samples were males deployed to Iraq and Afghanistan studied by the PTSD Systems Biology Consortium: a discovery sample of 74 PTSD cases and 71 healthy controls (HC), and a validation sample of 26 PTSD cases and 36 HC. A machine learning method, random forests (RF), in conjunction with a clustering method, partitioning around medoids, were used to identify subtypes derived from 16 self-report and clinician assessment scales, including the clinician-administered PTSD scale for DSM-IV (CAPS). Two subtypes were identified, designated S1 and S2, differing on mean current CAPS total scores: S2 = 75.6 (sd 14.6) and S1 = 54.3 (sd 6.6). S2 had greater symptom severity scores than both S1 and HC on all scale items. The mean first principal component score derived from clinical summary scales was three times higher in S2 than in S1. Distinct RFs were grown to classify S1 and S2 vs. HCs and vs. each other on multi-omic blood markers feature classes of current medical comorbidities, neurocognitive functioning, demographics, pre-military trauma, and psychiatric history. Among these classes, in each RF intergroup comparison of S1, S2, and HC, multi-omic biomarkers yielded the highest AUC-ROCs (0.819–0.922); other classes added little to further discrimination of the subtypes. Among the top five biomarkers in each of these RFs were methylation, micro RNA, and lactate markers, suggesting their biological role in symptom severity.

Evidence on the spatial-temporal distribution of tuberculosis (TB) mortality across China and its relationship with long-term particulate matter (PM _2.5 ) exposure is limited. We aimed to address significant gaps in our understanding of the spatial-temporal clustering patterns of TB mortality in China and provide evidence for its causal links with long-term PM _2.5 exposure. Annual pulmonary TB mortality, PM _2.5 concentrations, and socioeconomic factors for provinces in mainland China between 2004 and 2017 were obtained. Turning points in the temporal trend and spatial clustering patterns of pulmonary TB mortality were identified. A difference-in-differences causal inference approach was applied to estimate the long-term effect of PM _2.5 exposure on the mortality. The average annual percent change of pulmonary TB mortality in China was −2.5% (95% CI : −5.6%, 0.7%), with an 11.1% annual increase in the Northwest since 2012 ( P = 0.029). The hot and cold spots, determined by the local Moran’s I index, were all located in northern China, where Xinjiang in the Northwest had the highest mortality across the study period. We found a significant association between long-term PM _2.5 exposure and pulmonary TB mortality, with percent increase risk of mortality ( IR %) being 0.74% (95 CI %, 0.04%, 1.45%) for 1 µ g/m ^3 increase in PM _2.5 concentration. This association varied across multiple socioeconomic groups, with the highest IR % in provinces with lower level of latitude ( IR % = 0.83%, 95% CI : 0.01%, 1.65%), lower quartile of gross domestic product ( IR % = 1.01%, 95% CI: 0.23%, 1.80%) or higher proportion (⩾14%) of people >65 years of age ( IR % = 1.24%, 95% CI: 0.44%, 2.04%). Comprehensive sensitivity analyses showed a robust adverse effect of long-term PM _2.5 exposure on pulmonary TB mortality. Attention needs to be paid to the rising trend of pulmonary TB mortality in Northwest China. Our study provides the stable evidence to date of the causal association between long-term PM _2.5 exposure and the risk of death from pulmonary TB, especially in low-altitude, underdeveloped, and aged provinces.

The advancement of new energy vehicles has led to more demanding standards for detecting defects in cylindrical coated lithium batteries. The current research lacks robustness and has low performance. This paper seeks to provide real-time defect identification in cylindrical coated lithium batteries and improve the object detection method of the YOLOv5s model. This paper presents an MGSEC3 module with multi-scale feature extraction and integration of the SENet network in the YOLOv5 Backbone network. This module aims to reduce computational burden and enhance feature extraction efficiency as much as possible. At the same time, the CARAFE operator is utilized to enhance the up-sampling operator in order to reduce the loss of feature information. In addition, enhancements to the loss function enhance both the detection performance and convergence speed. The enhanced YOLOv5s model achieved an average detection rate of 82.4% on the custom cylindrical coated lithium battery dataset, 2.3% higher than the original YOLOv5s. This paper significantly enhances the precision and efficiency of flaw identification in cylindrical coated lithium batteries. [ABSTRACT FROM AUTHOR]

Abstract Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative condition that primarily affects the motor system and shares many features with frontotemporal dementia (FTD). Evidence suggests that ALS is a ‘dying-back’ disease, with peripheral denervation and axonal degeneration occurring before loss of motor neuron cell bodies. Distal to a nerve injury, a similar pattern of axonal degeneration can be seen, which is mediated by an active axon destruction mechanism called Wallerian degeneration. Sterile alpha and TIR motif-containing 1 (Sarm1) is a key gene in the Wallerian pathway and its deletion provides long-term protection against both Wallerian degeneration and Wallerian-like, non-injury induced axonopathy, a retrograde degenerative process that occurs in many neurodegenerative diseases where axonal transport is impaired. Here, we explored whether Sarm1 signalling could be a therapeutic target for ALS by deleting Sarm1 from a mouse model of ALS-FTD, a TDP-43Q331K, YFP-H double transgenic mouse. Sarm1 deletion attenuated motor axon degeneration and neuromuscular junction denervation. Motor neuron cell bodies were also significantly protected. Deletion of Sarm1 also attenuated loss of layer V pyramidal neuronal dendritic spines in the primary motor cortex. Structural MRI identified the entorhinal cortex as the most significantly atrophic region, and histological studies confirmed a greater loss of neurons in the entorhinal cortex than in the motor cortex, suggesting a prominent FTD-like pattern of neurodegeneration in this transgenic mouse model. Despite the reduction in neuronal degeneration, Sarm1 deletion did not attenuate age-related behavioural deficits caused by TDP-43Q331K. However, Sarm1 deletion was associated with a significant increase in the viability of male TDP-43Q331K mice, suggesting a detrimental role of Wallerian-like pathways in the earliest stages of TDP-43Q331K-mediated neurodegeneration. Collectively, these results indicate that anti-SARM1 strategies have therapeutic potential in ALS-FTD.

Abstract Background Long non‐coding RNA (lncRNA) plays an essential role in hepatitis B virus‐related hepatocellular carcinoma (HBV‐related HCC) occurrence and development. Single nucleotide polymorphism (SNP) may affect HBV‐related HCC susceptibility by altering the function of lncRNA. However, the relationship between lncRNA SNPs and HBV‐related HCC occurrence and development is still unclear. Methods In the present study, based on HBV‐related HCC genome‐wide association studies, eight potentially functional SNPs from two lncRNAs were predicted using a set of bioinformatics strategies. In 643 HBV‐related HCC patients, 549 CHB carriers, and 553 HBV natural clearance subjects from Southern Chinese, we evaluated associations between SNPs and HBV‐related HCC occurrence or development with odds ratio (OR) and 95% confidence interval (CI) under credible genetic models. Results In HBV‐related HCC patients, rs9908998 was found to significantly increase the risk of lymphatic metastasis under recessive model (Adjusted OR = 1.95, 95% CI = 1.20–3.17). Lnc‐RP11‐150O12.3 rs2275959, rs1008547, and rs11776545 with cancer family history may show significant multiplicative and additive interactions on HBV‐related HCC susceptibility (all pAdjusted < .05). The associations of rs2275959, rs1008547, and rs11776545 with distant metastasis of HBV‐related HCC patients were observed in additive model (Adjusted OR = 1.45, 95% CI = 1.06–1.97 for rs2275959; Adjusted OR = 1.45, 95% CI = 1.06–1.98 for rs1008547; Adjusted OR = 1.40, 95% CI = 1.03–1.91 for rs11776545). Conclusion Taken together, lnc‐ACACA‐1 rs9908998, lnc‐RP11‐150O12.3 rs2275959, rs1008547, and rs11776545 might be predictors for HBV‐related HCC risk or prognosis.

Clustering technology is one of the crucial technologies to prolong the lifetime in wireless sensor networks. However, most cluster schemes choose cluster head randomly to send data without considering load balancing and security. In addition, some cluster heads in the highly active area may be overloaded, while others in the low active area may be overloaded, which may easily lead to extreme imbalance in task allocation. Our research on relevant literature shows that the existing authentication schemes do not fully consider the load balancing of cluster heads, while the load balancing schemes ignore the security authentication of cluster heads. Therefore, this article effectively combines load balancing and security verification, and proposes a lightweight load balancing and verification scheme (secure load and energy balancing) based on clustered wireless sensor networks. Secure load and energy balancing implements cluster head’s authentication and confidentiality and integrity of all messages in load balancing. This scheme not only effectively maintains the energy balance of the whole network but also successfully improves the security overhead, thus prolonging the network lifetime. The simulation results show that compared with other similar schemes, this scheme has higher packet forwarding rate, longer network life, and lower overhead. This further proves that the scheme is energy-saving, safe, dynamic, stable, and sustainable.

Autosomal dominant tubulointerstitial kidney disease caused by mutations in uromodulin gene (ADTKD-UMOD) is a spectrum of hereditary renal disorders, characterized by early-onset hyperuricemia, gout and progressive nephropathy. This study presented a novel UMOD mutation in an ADTKD pedigree and reviewed studies in Chinese population. The index patient is a 16-year-old girl with hypertension, hyperuricemia and normal serum creatinine level. Four affected and six unaffected members were available for genetic screen. The mutation analysis was performed by next-generation sequencing and direct sequencing. A literature research was conducted to review Chinese ADTKD-UMOD cases. MEDLINE and Chinese Biomedicine Databases were searched with ‘uromodulin’, ‘juvenile gout’ and their related terms. Genetic sequencing revealed a de novo mutation within exon 3 (Cys223Gly), which was co-segregating with phenotype in this pedigree. In the review, four studies and our study involving a total of 67 ADTKD patients from 11 families were identified. Of these patients, 27 were confirmed to carry UMOD mutations. Mutations occurred in exon 3 were commonly observed, while mutations within exon 4, 5 and 9 occurred less frequently in Chinese ADTKD-UMOD cases. Among these cases, median age of symptom onset was 26.5 years, median age of end-stage renal diseases (ESRD) or death by ESRD was 41.9 years without renal replacement treatment. Phenotype caused by mutations in D8C domain seemed to be severe than those in GPI domain. Compared with patients of other race, Chinese ADTKD-UMOD patients advanced more aggressively to ESRD.

Meteorological exposures are well-documented factors underlying the dengue pandemics, and air pollution was reported to have the potential to change the behaviors and health conditions of mosquitos. However, it remains unclear whether air pollution could modify the association of meteorological exposures and the incidence of dengue fever. We matched the dengue surveillance data with the meteorological and air pollution data collected from monitoring sites from 2015 through 2019 in Guangzhou area. We developed generalized additive models with Poisson distribution to regress the daily counts of dengue against four meteorological exposures, while controlling for pollution and normalized difference vegetation index to evaluate the risk ratio (RR) of dengue for each unit increase in different exposures. The interaction terms of meteorological exposures and air pollution were then included to assess the modification effect of different pollution on the associations. Daily dengue cases were nonlinearly associated with one-week cumulative temperature and precipitation, while not associated with humidity and wind speed. RRs were 1.07 (1.04, 1.11) and 0.95 (0.88, 1.03) for temperature below and above 27.1 °C, 0.97 (0.96, 0.98) and 1.05 (1.01, 1.08) for precipitation below and above 20.3 mm, respectively. For the modification effect, the RRs of low-temperature, wind speed on higher SO _2 days and low-precipitation on both higher PM _2.5 and SO _2 days were greater compared to the low-pollution days with P _interaction being 0.037, 0.030, 0.022 and 0.018. But the RRs of both high-temperature on higher SO _2 days and high-precipitation on higher PM _2.5 d were smaller with P _interaction being 0.001 and 0.043. Air pollution could alter the meteorology-dengue associations. The impact of low-temperature, low-precipitation and wind speed on dengue occurrence tended to increase on days with high SO _2 levels while the impact of high-temperature decreased. The impact of low-precipitation increased on high-PM _2.5 d while the impact of high-precipitation decreased.

Background: While previous studies uncovered individual vulnerabilities to health risks during catastrophic storms, few evaluated the population vulnerability which is more important for identifying areas in greatest need of intervention. Objectives: We assessed the association between community factors and multiple health outcomes, and developed a community vulnerability index. Methods: We retained emergency department visits for several health conditions from the 2005–2014 New York Statewide Planning and Research Cooperative System. We developed distributed lag nonlinear models at each spatial cluster across eight counties in downstate New York to evaluate the health risk associated with Superstorm Sandy (10/28/2012–11/9/2012) compared to the same period in other years, then defined census tracts in clusters with an elevated risk as “risk-elevated communities”, and all others as “unelevated”. We used machine-learning techniques to regress the risk elevation status against community factors to determine the contribution of each factor on population vulnerability, and developed a community vulnerability index (CVI). Results: Overall, community factors had positive contributions to increased community vulnerabilities to Sandy-related substance abuse (91.35%), injuries (70.51%), cardiovascular diseases (8.01%), and mental disorders (2.71%) but reversely contributed to respiratory diseases (−34.73%). The contribution of low per capita income (max: 22.08%), the percentage of residents living in group quarters (max: 31.39%), the percentage of areas prone to flooding (max: 38.45%), and the percentage of green coverage (max: 29.73%) tended to be larger than other factors. The CVI based on these factors achieved an accuracy of 0.73–0.90 across outcomes. Conclusions: Our findings suggested that substance abuse was the most sensitive disease susceptible to less optimal community indicators, whereas respiratory diseases were higher in communities with better social environment. The percentage of residents in group quarters and areas prone to flooding were among dominant predictors for community vulnerabilities. The CVI based on these factors has an appropriate predictive performance. Keywords: Catastrophic storms, Multiple health outcomes, Community vulnerability index, ROC

Background More intense and longer‐lasting heat events are expected in the United States as a consequence of climate change. This study aimed to project the potential changes in maternal heat exposure during early pregnancy (3–8 weeks post conception) and the associated burden of congenital heart defects (CHDs) in the future. Methods and Results This study expanded on a prior nationwide case‐control study that evaluated the association between CHDs and maternal heat exposure during early pregnancy in summer and spring. We defined multiple indicators of heat exposure, and applied published odds ratios obtained for the matching season of the baseline (1995–2005) into the projection period (2025–2035) to estimate potential changes in CHD burden throughout the United States. Increases in maternal heat exposure were projected across the United States and to be larger in the summer. The Midwest will potentially have the highest increase in summer maternal exposure to excessively hot days (3.42; 95% CI, 2.99–3.88 per pregnancy), heat event frequency (0.52; 95% CI, 0.44–0.60) and heat event duration (1.73; 95% CI, 1.49–1.97). We also found large increases in specific CHD subtypes during spring, including a 34.0% (95% CI, 4.9%–70.8%) increase in conotruncal CHD in the South and a 38.6% (95% CI, 9.9%–75.1%) increase in atrial septal defect in the Northeast. Conclusions Projected increases in maternal heat exposure could result in an increased CHD burden in certain seasons and regions of the United States.