To construct a mitochondrial-related risk assessment model to explore the impact of mitochondria on the survival of patients with non-small cell lung cancer (NSCLC), predict immune status, and evaluate its potential value.

Mitochondrial and NSCLC-related data were downloaded from the MitoCarta3.0 database and The Cancer Genome Atlas (TCGA) database, respectively. Differentially expressed mitochondrial-related genes were screened, and a risk scoring model was constructed using Cox regression analysis. Based on the median risk score, NSCLC patients in the TCGA database were divided into high-risk and low-risk groups. The validity of the prognostic model was verified using Kaplan-Meier analysis, receiver operating characteristic (ROC) curves, clinical case feature analysis, and immune status assessment.

A total of 320 mitochondrial-related genes were obtained from NSCLC samples. Four key model genes (TIMM10, CYP24A1, BCL2L10, ACSM5) were selected through COX analysis, leading to the construction of a nomogram prediction model for NSCLC. Immune cell infiltration assessment revealed a negative correlation between risk scores and the enrichment of T cells, B cells, and macrophages; conversely, the enrichment of resting mast cells, cancer-associated fibroblasts, and myeloid progenitor cells was positively correlated with risk scores. Patients in the high-risk group had shorter overall survival and exhibited higher levels of immune suppressive cell infiltration. Validation of the IMvigor210 immunotherapy model showed significant differences in survival probabilities between high-risk and low-risk groups in bladder cancer.

This study established a mitochondrial gene risk scoring model for predicting the prognosis of NSCLC. TIMM10,CYP24A1, BCL2L10, and ACSM5 are promising potential targets for further research on NSCLC.

To investigate the longitudinal association between glycemic profile and the incidence of chronic kidney disease (CKD), as well as the impact of genetic susceptibility on this association.

Utilizing baseline survey and genetic data from the UK Biobank, Cox proportional hazards models were employed to assess the correlation between baseline hemoglobin A1c (HbA1c) levels and glycemic status with the onset of CKD, along with the role of genetic susceptibility in this relationship.

A total of 81 059 participants were included in this study, with 15.2% classified as prediabetic and 6.9%as diabetic. During a median follow-up period of 13.5 years, 3 637 new cases of CKD were observed. Multivariable-adjusted models indicated that both type 2 diabetes (T2D) and prediabetes significantly increased the risk of developing CKD compared to participants with normal blood glucose levels, with hazard ratios (HRs) and 95% confidence intervals (CIs) of 2.04 (95%CI:1.85-2.25) and 1.09 (95%CI: 1.00-1.18), respectively. A clear risk gradient was observed when HbA1c was below the diabetes threshold; participants with HbA1c ≥6.6% had approximately double the risk of CKD compared to those with HbA1c <5.0%. A significant multiplicative interaction between glycemic status and genetic risk was found (P _interaction <0.001). In all genetic risk groups, hyperglycemia significantly increased the risk of CKD. Among participants with high genetic risk, those with both T2D and high genetic risk exhibited the highest CKD risk (HR=6.67, 95%CI: 5.75-7.74) compared to those with low genetic risk and normal blood glucose.

Glycemic status is associated with the risk of CKD across all genetic risk groups.

To analyze the impact of smart healthcare on the health of middle-aged and elderly individuals and to provide references for exploring pathways to healthy aging.

The smart city pilot programs were treated as a quasi-natural experiment for smart healthcare. Utilizing data from the China Health and Retirement Longitudinal Study, this research employed the difference-in-differences approach and an improved mediation effect model to examine the impact of smart healthcare on the health of middle-aged and elderly individuals and the mechanisms involved.

Smart healthcare significantly improved the health levels of middle-aged and elderly individuals (P<0.05). Enhancing self-health management and promoting the utilization of medical services were important mechanisms through which smart healthcare improved health outcomes.

Smart healthcare has a positive impact on the health of middle-aged and elderly individuals, with variations observed among different subgroups. It is essential to eliminate barriers to the application of smart technologies, bridge the digital divide among the elderly population, and advance equitable access to healthcare services.

To explore the association between the systemic immune-inflammation index (SII) and adult obesity, and to provide insights for the prevention and early diagnosis of obesity.

This study was based on the survey data of the National Health and Nutrition Examination Survey (NHANES). A binary logistic regression model was used to evaluate the relationship between the systemic immune-inflammation index and obesity (general obesity, central obesity) by calculating the odds ratio (OR) and its 95% confidence interval (CI). In addition, stratified analysis was performed by age (< 60 years old and ≥ 60 years old) and gender.

After adjusting all covariates, when general obesity was taken as the outcome variable, compared with the subjects in the lowest quartile (Q1) of SII, the risk of general obesity in the subjects in the third quartile (Q3) increased by 0.374 times (OR=1.374, 95%CI: 1.111-1.699); the risk of general obesity in the subjects in the fourth quartile (Q4) increased by 0.843 times (OR=1.843, 95%CI: 1.490-2.281). When central obesity was taken as the outcome variable, compared with the subjects in Q1, the risk of central obesity in the subjects in Q3 increased by 0.542 times (OR=1.542, 95%CI: 1.189-2.000); the risk of central obesity in the subjects in Q4 increased by 1.036 times (OR=2.036, 95%CI: 1.553-2.670). In addition, the results of stratified analysis showed that the relationship between SII and obesity was different among different ages and was only significant in the population aged <60 years old.

SII is positively correlated with the risk of adult obesity, and the higher the SII, the higher the risk of obesity. In addition, the relationship between SII and obesity may be age-specific.

To investigate the association between blood ethylene oxide exposure levels and sleep disorders.

Utilizing data from the National Health and Nutrition Examination Survey (NHANES) 2015-2018, this study selected adults aged 18 and older. The relationship between blood ethylene oxide exposure levels and sleep disorders was analyzed using multivariable logistic regression, subgroup analysis, interaction analysis, and restricted cubic spline analysis.

A total of 2 579 participants were included, with a median blood ethylene oxide exposure concentration of 21.76 pmol/g Hb, and 753 (29.2%) individuals reported sleep disorders. Multivariable logistic regression indicated that compared to the lowest quartile, the risk of sleep disorders increased by 94% in the highest quartile of blood ethylene oxide exposure (OR=1.94, 95%CI: 1.27-2.95, P=0.012). Subgroup analysis revealed a significant association between ethylene oxide exposure and sleep disorders among women, individuals aged 40 to 59, those of other races, individuals with a poverty ratio of 1.3 to 3.5, those with an education level of high school or below or possessing a college degree, individuals engaging in moderate physical activity, and the unmarried population.Interaction analysis showed that these factors did not exhibit interaction effects on sleep. Restricted cubic spline analysis indicated no nonlinear association between ethylene oxide exposure levels and the occurrence of sleep disorders (P_non-linear=0.09).

There is a significant positive correlation between high blood ethylene oxide exposure levels and sleep disorders.

To investigate the impact of serum free fatty acids (FFA) on serum uric acid (SUA) levels in young and middleaged hyperuricemia (HUA) patients with different body mass indexes (BMI).

A total of 144 young and middle-aged male patients first diagnosed with HUA at Zhu Xianyi Memorial Hospital of Tianjin Medical University from March 2018 to May 2020 were selected. They were divided into three groups based on BMI: normal group (18.5 kg/m² ≤ BMI < 24.0 kg/m², n=42), overweight group (24.0 kg/m² ≤ BMI < 28.0 kg/m², n=58), and obese group (BMI ≥ 28 kg/m², n=44). Additionally, they were categorized into tertiles based on FFA levels: low tertile group (FFA ≤ 0.37 mmol/L, n=48), middle tertile group (0.37 mmol/L < FFA < 0.7 mmol/L, n=48),and high tertile group (FFA ≥ 0.7 mmol/L, n=48). General information and laboratory data, including FFA and SUA levels, were collected and statistically analyzed.

As BMI increased, FFA levels in the normal, overweight, and obese groups showed a significant upward trend (P < 0.05). SUA levels also increased, with statistically significant differences between the normal and obese groups and between the overweight and obese groups (P < 0.05), but no significant difference was observed between the normal and overweight groups (P > 0.05). With increasing FFA levels, BMI and SUA levels in the low, middle, and high FFA tertile groups also increased. Significant differences were observed between the low and high tertile groups and between the middle and high tertile groups (P < 0.05), but no significant difference was found between the low and middle tertile groups (P > 0.05). Two-way ANOVA revealed an interaction between FFA and obesity on the SUA levels (F=2.701, P=0.033), indicating that their combined effect further elevated SUA levels. Spearman correlation analysis showed a positive correlation between FFA and SUA levels in the obese group (r=0.428, P=0.004). However, no such correlation was observed in the normal and overweight groups (P > 0.05).

In young and middle-aged HUA patients, those with obesity and high FFA levels exhibit higher SUA levels. The effects of FFA and obesity on SUA levels are both additive and interactive.

To analyze the external exposure levels of nitrate in rural drinking water in Guangdong Province and provide technical support for the safety management of rural water supply.

Monitoring was conducted on the finished water and terminal water from rural drinking water supply units in Guangdong Province from 2018 to 2022. The compliance of nitrate levels in water quality was evaluated according to the Standards for Drinking Water Quality (GB 5749-2022). The Kolmogorov-Smirnov test was used to determine the normality of the data. For non-normally distributed data, the median was used for description. The chisquare test or Fisher’s exact test was employed to analyze differences in rates between groups. The Mann-Whitney U test was used to compare nitrate exposure levels between two groups, and the Kruskal-Wallis H test was applied for comparisons among multiple groups.

A total of 62 998 water samples were monitored, with an overall compliance rate of 99.52%. The nitrate exposure levels ranged from 0.001 to 63.80 mg/L. Significant differences in nitrate exposure levels were observed across different years (H=445.586, P<0.01), regions (H=2 050.151, P<0.01), water source types (Z=-5.268, P<0.01), sample types (Z=-11.888, P<0.05), water supply capacities (Z=-33.794, P<0.01), water treatment methods (H=27.750, P<0.01), and the presence or absence of advanced treatment (Z=-2.121, P<0.05).

The overall compliance rate of nitrate levels in rural drinking water in Guangdong Province is relatively high. However, special attention should be paid to nitrate pollution in certain areas of western and eastern Guangdong, groundwater sources, decentralized and small-scale centralized water supplies, as well as the high exposure risks for infants and young children.

To describe the changes in the burden of depression among Chinese residents from 1990 to 2021 and to forecast future trends, providing reference for the prevention and control of depression.

Based on the 2021 Global Burden of Disease data, indicators such as the number of cases, incidence rate, prevalence rate, and disability-adjusted life years (DALYs) along with DALYs rates were selected to calculate the rate of change. The Join point regression model was employed to calculate the annual percentage change (APC) and average annual percentage change (AAPC) to analyze the trend of disease burden. Future trends were predicted using R software combined with the GM (1,1) model.

In 2021, the total number of depression cases in China was approximately 42.36 million, with an incidence rate of 2 977.354 per 100 000 and a standardized incidence rate of 2 345.079 per 100 000. The total DALYs attributed to depression were 7.8659 million years, with a total DALYs rate of 552.87 per 100 000 and a standardized DALYs rate of 430.61 per 100 000. Compared to 1990, the standardized prevalence rates for the total population, males, and females decreased by 6.39%, 2.4%, and 9.17%, respectively; the standardized incidence rates decreased by 10.79%, 3.96%, and 14.93%; and the standardized DALYs rates decreased by 9.02%, 3.85%, and 12.43%. Join point regression analysis indicated that from 1990 to 1995 and from 2010 to 2015, the standardized incidence rate of depression among the total population in China showed an increasing trend (APC of 1.56% and 1.37%, respectively), while from 1995 to 2000 and 2005 to 2010, it exhibited a decreasing trend (APC of -2.74% and -1.45%,respectively). The standardized prevalence rates from 1990 to 1992, 1992 to 1995, and 2019 to 2021 all showed an upward trend (APC of 1.67%, 0.48%, and 0.96%, respectively), whereas from 1995 to 2000, 2000 to 2005, 2005 to 2010, and 2010 to 2019, they demonstrated a downward trend (APC of -1.06%, -0.22%, -1.02%, and -0.15%, respectively). The standardized DALYs rate increased from 1990 to 1994 (APC of 1.75%) and showed a decreasing trend from 1994 to 2000 and from 2000 to 2006 (APC of-1.55% and -0.46%, respectively).

The burden of depression among Chinese residents is increasing, with a higher burden observed in females. The risk of depression among the elderly should not be overlooked. Continued efforts are needed to enhance public awareness of depression-related health knowledge and implement preventive interventions.

To analyze the status of antiretroviral therapy (ART), survival outcomes, and influencing factors among HIV-infected children in Guangxi.

A retrospective cohort study was conducted, incorporating data from HIV-infected children aged ≤14 years in Guangxi, including treatment status, survival time, and influencing factors. Logistic regression and Cox proportional hazards regression models were employed for statistical analysis.

A total of 472 HIV-infected children were included, with 27 deaths reported. All children received ART. Factors such as age and clinical stage at diagnosis influenced the risk of delayed initiation of treatment. Survival analysis revealed that baseline CD4+ T-cell levels, clinical stage, and treatment regimen were key determinants of survival time. Children with baseline CD4+ T-cell counts >350 cells/μL had longer survival time compared to those with counts <200 cells/μL (aHR=0.31, 95%CI: 0.13-0.74). Children in WHO clinical stage IV had shorter survival times than those in stage I (aHR=3.22, 95%CI: 1.24-12.2). Additionally, children treated with the 3TC+ABC+LPV/r regimen had shorter survival time than those treated with the 3TC+AZT+EFV regimen (aHR=4.26, 95%CI: 1.16-15.61).

The coverage of ART among HIV-infected children in Guangxi is high, with relatively favorable survival rates. However, efforts should be intensified to educate caregivers and initiate treatment early, optimize treatment regimens, and improve quality of life.

To explore whether N⁶-methyl adenosine (m⁶A) is involved in arsenic-induced tau protein phosphorylation.

Neuroblastoma (SH-SY5Y) cells were treated with 0, 1, 5, 10 μmol/L sodium arsenate for 24 hours. Then, the intracellular m⁶A level was detected, the mRNA expression levels of m⁶A-related enzymes in the cells were detected by qPCR,and the total tau protein expression level, phosphorylated tau protein level and m⁶A-related enzyme expression levels in the cells were detected by Western Blot. After inhibiting the intracellular m⁶A level with 3-deoxyadenosines, the changes in the intracellular m⁶A level and tau protein phosphorylation level were verified. SPSS was used for analysis of variance of the experimental results, with α=0.05.

After SH-SY5Y cells were treated with various concentrations of arsenic for 24 hours, there was no significant difference in the total tau protein level in the cells (F=3.047, P > 0.05). After the cells were treated with 5 μmol/L arsenic for 24 hours, the intracellular m⁶A level increased by 31.4% (F=4.511, P < 0.05), and the phosphorylated tau protein (at site T231) level increased by 42.6% (95%CI: 0.165-0.689, P < 0.01). The level of phosphorylated tau protein (at sites S202 + T205) increased with the increase in arsenic concentration, and the highest increase was 55.2%(95%CI: 0.050-0.409, P < 0.05) after treatment with 10 μmol/L arsenic for 24 hours. As the arsenic treatment concentration increased, the METTL3 mRNA expression in the cells increased, with the highest increase of 73.2% (95%CI: 0.201-1.423, P < 0.05) at a concentration of 10 μmol/L. The mRNA expression levels of METTL14, WTAP and FTO decreased, and they decreased to 65.4% (95%CI:-1.055 to-0.337, P < 0.01), 64.8% (95%CI:-0.389 to -0.111, P < 0.05) and 85.4% (95%CI: -0.030 to -0.010, P < 0.01) of the control group respectively after treatment with 10 μmol/L arsenic. The ALKBH5 mRNA expression first increased and then decreased, with an increase of 27.5% (95%CI: 0.033-0.147, P < 0.05) after treatment with 1 μmol/L arsenic for 24 hours; while it decreased by 30.7% (95%CI:-1.62 to -0.038, P < 0.01) after treatment with 10 μmol/L arsenic. Arsenic treatment led to an increase in METTL3 protein expression, with the highest increase of 107.1% (95%CI: 0.331-1.009, P < 0.01) after treatment with 5 μmol/L arsenic for 24 hours, while the protein expression levels of METTL14, WTAP and ALKBH5 decreased to 20.4% (95%CI: -0.788 to -0.509, P < 0.001), 23.5% (95%CI:-1.371 to -0.685, P <0.001) and 49.2% (95%CI:-0.423 to -0.183, P < 0.001) of the control group respectively after treatment of SH-SY5Y cells with 10 μmol/L arsenic for 24 hours. The FTO protein expression level showed a decreasing trend with the increase in arsenic concentration, with the lowest decrease of 45.3% (95%CI:-0.709 to -0.413, P < 0.001) after treatment with 10 μmol/L arsenic for 24 hours. After DAA inhibited the intracellular m⁶A level, the phosphorylated tau protein levels were significantly decreased (P < 0.05).

Arsenic can increase the m⁶A level in SH-SY5Y cells by increasing the expression level of the m⁶A methylase METTL3 and decreasing the expression levels of the m⁶A demethylases FTO and ALKBH5, thereby inducing the phosphorylation of tau protein in the cells.