Outdoor Light at Night, Air Pollution, and Risk of Cerebrovascular Disease: A Cohort Study in China.

BACKGROUND: We sought to explore the associations of outdoor light at night (LAN) and air pollution with the risk of cerebrovascular disease (CeVD). METHODS: We included a total of 28 302 participants enrolled in Ningbo, China from 2015 to 2018. Outdoor LAN and air pollution were assessed by Satellite-derived images and land-use regression models. CeVD cases were confirmed by medical records and death certificates and further subdivided into ischemic and hemorrhagic stroke. Cox proportional hazard models were used to estimate hazard ratios and 95% CIs. RESULTS: A total of 1278 CeVD cases (including 777 ischemic and 133 hemorrhagic stroke cases) were identified during 127 877 person-years of follow-up. In the single-exposure models, the hazard ratios for CeVD were 1.17 (95% CI, 1.06-1.29) for outdoor LAN, 1.25 (1.12-1.39) for particulate matter with an aerodynamic diameter ≤2.5 µm, 1.14 (1.06-1.22) for particulate matter with aerodynamic diameter ≤10 µm, and 1.21 (1.06-1.38) for NO2 in every interquartile range increase. The results were similar for ischemic stroke, whereas no association was observed for hemorrhagic stroke. In the multiple-exposure models, the associations of outdoor LAN and PM with CeVD persisted but not for ischemic stroke. Furthermore, no interaction was observed between outdoor LAN and air pollution. CONCLUSIONS: Levels of exposure to outdoor LAN and air pollution were positively associated with the risk of CeVD. Furthermore, the detrimental effects of outdoor LAN and air pollution might be mutually independent.

Dynamic correction of soft measurement model for evaporation process parameters based on ARMA.

To address the significant soft measurement errors in traditional static models for evaporation process parameters, which are characterized by continuity and cumulativity, this paper proposes a dynamic correction method for soft measurement models of evaporation process parameters based on the autoregressive moving-average model (ARMA). Initially, the Powell's directional evolution (Powell-DE) algorithm is utilized to identify the autoregressive order and moving average order of the ARMA model. Subsequently, the prediction error of a mechanism-reduced robust least squares support vector machine ensemble model is utilized as input. An error time series prediction model, which compensates for the errors in the autoregressive moving average model, is then applied for dynamic estimation of the prediction error. Finally, an integration strategy using the entropy method is employed to combine the static soft measurement model, based on the mechanism-reduced robust least squares support vector machine, with the dynamic correction soft measurement model, which is based on the error time series compensation of the ARMA model. The new model is analyzed and validated using production data from an alumina plant's evaporation process. Compared to traditional models, the new model demonstrates significantly improved prediction accuracy and is capable of dynamic prediction of evaporation process parameters.

Soft-sensing modeling of mother liquor concentration in the evaporation process based on reduced robust least-squares support-vector machine.

The evaporation process is vital in alumina production, with mother liquor concentration serving as a critical control parameter. To address the challenge of online detection, we propose the introduction of a soft measurement strategy. First, due to the significant fluctuations in the production process variables and inter-variable coupling, comprehensive grey correlation analysis and kernel principal component analysis are employed to reduce the input dimension and computational complexity of the data, enhancing the efficiency of the soft sensing model. The reduced robust least-squares support-vector machine (LSSVM), with its commendable predictive performance, is used for modeling and predicting the principal components. Concurrently, an improved Pattern Search-Differential Evolution (PS-DE) algorithm is proposed for optimizing the pivotal parameters of the LSSVM network. Lastly, on-site industrial data validation indicates that the new model offers superior tracking capabilities and heightened accuracy. It is deemed aptly suitable for the online detection of mother liquor concentration.

Association between visit-to-visit lipid variability and risk of ischemic heart disease: a cohort study in China.

AIMS: To explore the associations between visit-to-visit lipid variability and risk of ischemic heart disease (IHD) in a population-based cohort in China. METHODS: We evaluated lipid variability in 30,217 individuals from the Yinzhou Health Information System who had ≥3 recorded lipid measurements during 2010-2014. We used various indicators including standard deviation (SD), coefficient of variation (CV), variability independent of the mean (VIM), and average real variability (ARV) to quantify the variability in triglycerides, total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C). We used Cox proportional hazards models to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). RESULTS: Overall, a total of 1305 participants with IHD were identified during the follow-up of 194,421 person-years. Subjects in Q4 had a 21% elevated risk of IHD (HR = 1.21, 95% CI: 1.03-1.41) for LDL-C variability (CV) compared with the reference (Q1). The HRs for Q4 vs Q1 were 1.21 (95% CI: 1.04-1.42) for HDL-C variability, and 1.28 (95% CI: 1.10-1.50) for TC variability. However, no association was observed between triglycerides variability and risk of IHD. CONCLUSIONS: Higher variability in LDL-C, HDL-C, and TC levels was associated with an elevated risk of IHD, suggesting that lipid variability could be considered as an independent risk factor of IHD.

Fuzzy super twisting mode control of a rigid-flexible robotic arm based on approximate inertial manifold dimensionality reduction.

Introduction: The control of infinite-dimensional rigid-flexible robotic arms presents significant challenges, with direct truncation of first-order modal models resulting in poor control quality and second-order models leading to complex hardware implementations. Methods: To address these issues, we propose a fuzzy super twisting mode control method based on approximate inertial manifold dimensionality reduction for the robotic arm. This innovative approach features an adjustable exponential non-singular sliding surface and a stable continuous super twisting algorithm. A novel fuzzy strategy dynamically optimizes the sliding surface coefficient in real-time, simplifying the control mechanism. Results: Our findings, supported by various simulations and experiments, indicate that the proposed method outperforms directly truncated first-order and second-order modal models. It demonstrates effective tracking performance under bounded external disturbances and robustness to system variability. Discussion: The method's finite-time convergence, facilitated by the modification of the nonlinear homogeneous sliding surface, along with the system's stability, confirmed via Lyapunov theory, marks a significant improvement in control quality and simplification of hardware implementation for rigid-flexible robotic arms.

Exposure to Neighborhood Walkability and Residential Greenness and Incident Fracture.

Importance: Emerging studies have suggested that environmental factors are associated with fracture. However, little is known about the association of neighborhood walkability and residential greenness with fracture. Objective: To investigate the association of long-term exposure to walkability and greenness with incident fracture and explore the potential interaction effect. Design, Setting, and Participants: This cohort study recruited participants aged 40 years or older in Ningbo, China from June 2015 to January 2018. Participants were observed for outcomes through February 2023, with data analysis conducted in March 2023. Exposures: Neighborhood walkability was measured by a modified walkability calculation method according to a walk score tool. Residential greenness was assessed by satellite-derived normalized difference vegetation index (NDVI) within a 1000-m buffer. Main Outcomes and Measures: Incident fracture was ascertained according to International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes via the Yinzhou Health Information System. Cox proportional hazards models were fit, with age as time scale to estimate the associations of walkability and greenness with fracture. Potential effect modification was explored by covariates, as well as the interactive effect of walkability and greenness. Results: A total of 23 940 participants were included in this study with 13 735 being female (57.4%). The mean (SD) age at baseline was 63.4 (9.4) years. During a follow-up period of 134 638 person-years, 3322 incident fractures were documented. In the full adjusted model, every IQR increment in neighborhood walkability and residential greenness was associated with a hazard ratio (HR) of 0.88 (95% CI, 0.83-0.92) and 0.84 (95% CI, 0.80-0.89), respectively, for fracture. Furthermore, the association of greenness and fracture was greater with an increase in walkability. The HR (Q4 vs Q1) for greenness was 0.62 (95% CI, 0.46-0.82) in neighborhoods with the highest quartile of walkability. Conclusions and Relevance: This population cohort study suggested that long-term exposure to neighborhood walkability and residential greenness were both associated with lower risk of incident fracture. The benefits of greenness increased in more walkable areas.

Rivaroxaban versus nadroparin for thromboprophylaxis following thoracic surgery for lung cancer: A randomized, noninferiority trial.

The benefit of rivaroxaban in thromboprophylaxis after oncologic lung surgery remains unknown. To evaluate the efficacy and safety of rivaroxaban, patients who underwent thoracic surgery for lung cancer were enrolled, and randomly assigned to rivaroxaban or nadroparin groups in a 1:1 ratio; anticoagulants were initiated 12-24 h after surgery and continued until discharge. Four hundred participants were required according to a noninferiority margin of 2%, assuming venous thromboembolism (VTE) occurrence rates of 6.0% and 12.6% for patients in the rivaroxaban and nadroparin groups, respectively. The primary efficacy outcome was any VTE during the treatment and 30-day follow-up periods. The safety outcome was any on-treatment bleeding event. Finally, 403 patients were randomized (intention-to-treat [ITT] population), with 381 included in per-protocol (PP) population. The primary efficacy outcomes occurred in 12.5% (25/200) of the rivaroxaban group and 17.7% (36/203) of the nadroparin group (absolute risk reduction, -5.2%; 95% confidence interval [CI], [-12.2-1.7]), indicating the noninferiority of rivaroxaban in ITT population. Sensitivity analysis was performed in the PP population and yielded similar results, confirming the noninferiority of rivaroxaban. In the safety analysis population, the incidence of any on-treatment bleeding events did not differ significantly between the groups (12.2% for rivaroxaban vs. 7.0% for nadroparin; relative risk [RR], 1.9; 95% CI, [0.9-3.7]; p = .08), including major bleeding (9.7% vs. 6.5%; RR, 1.6 [95% CI, 0.9-3.7]; p = .24), and nonmajor bleeding (2.6% vs. 0.5%; RR, 5.2 [95% CI, 0.6-45.2]; p = .13). Rivaroxaban for thromboprophylaxis after oncologic lung surgery was shown to be noninferior to nadroparin.

Age at diagnosis modifies associations of type 2 diabetes with cancer incidence and mortality: a retrospective matched-cohort study.

AIMS/HYPOTHESIS: The age-specific associations between type 2 diabetes and cancer risk are not fully understood. The aim of this study was to assess how age at diagnosis modifies the associations between type 2 diabetes and cancer risk. METHODS: We used data from the Yinzhou Health Information System, and included 42,279 individuals who were newly diagnosed with type 2 diabetes between 2010 and 2014, as well as 166,010 age- and sex-matched control individuals without diabetes who were selected randomly from the electronic health records of the entire population. Patients were divided into four age groups according to age at diagnosis: <50, 50-59, 60-69 and ≥70 years. Stratified Cox proportional hazards regression models, with age as the time scale, were used to estimate the HRs and 95% CIs for the associations of type 2 diabetes with the risks of overall and site-specific cancers. Population-attributable fractions were also calculated for outcomes associated with type 2 diabetes. RESULTS: During median follow-up periods of 9.20 and 9.32 years, we identified 15,729 incident cancer cases and 5383 cancer deaths, respectively. Patients diagnosed with type 2 diabetes before 50 years of age had the highest relative risks of cancer incidence and mortality, with HRs (95% CI) of 1.35 (1.20, 1.52) for overall cancer incidence, 1.39 (1.11, 1.73) for gastrointestinal cancer incidence, 2.02 (1.50, 2.71) for overall cancer mortality, and 2.82 (1.91, 4.18) for gastrointestinal cancer mortality. Risk estimates decreased gradually with each decade increase in diagnostic age. The population-attributable fractions for overall cancer and gastrointestinal cancer mortality also decreased with increasing age. CONCLUSIONS/INTERPRETATION: The associations of type 2 diabetes with cancer incidence and mortality varied by age at diagnosis, with a higher relative risk among patients who were diagnosed at a younger age.

Residential greenness, air pollution and incident neurodegenerative disease: A cohort study in China.

BACKGROUND: Neurodegenerative disease has a great adverse impact on population's death and disability worldwide. However, the association of air pollution and residential greenness with neurodegenerative disease and their potential mechanisms still remain uncertain. METHODS: We used data from a population-based prospective cohort in Ningbo, China. Exposure to PM2.5, PM10 and NO2 were assessed by land-use regression (LUR) models and residential greenness was estimated by Normalized Difference Vegetation Index (NDVI). Our primary outcomes were all neurodegenerative diseases, Parkinson's disease (PD) and Alzheimer's disease (AD). Cox proportional hazards regression models were used to examine the association of air pollution and residential greenness with risk of incident neurodegenerative disease. Furthermore, we also explored the potential mediation relationship and effect modification between greenness and air pollutants. RESULTS: During the follow-up period, we identified a total of 617 incident neurodegenerative diseases, 301 PD and 182 AD. In single-exposure models, PM2.5 was positively associated with all outcomes (e.g. AD hazard ratio (HR): 1.41, 95 % confidence interval (CI): 1.09-1.84, per interquartile range (IQR) increment), whereas residential greenness showed protective effects (e.g. neurodegenerative disease, HR: 0.82, 95%CI: 0.75-0.90, per IQR increment for NDVI in 1000 m buffer). NO2 was positively associated with risk of neurodegenerative disease and PM10 was associated with neurodegenerative disease and AD. In two-exposure models, after adjustment for PM2.5, the association for greenness generally attenuated towards null. Moreover, we identified the significant modification effect of greenness on PM2.5 on additive and multiplicative scales. CONCLUSION: In this prospective study, we found that exposure to higher residential greenness and lower concentrations of particulate matter were associated with lower risk of neurodegenerative disease, PD and AD. Residential greenness could modify the association of PM2.5 with neurodegenerative disease.

Estimation of cancer mortality attributable to excess body weight during 2006-2015 in China.

BACKGROUND: Cancer is one of the most common causes of death. Excess body weight (EBW), a risk factor for cancer, is highly prevalent in China. We aimed to estimate the number and proportion of cancer deaths attributed to EBW and their changes during 2006-2015 in China. METHODS: Population attributable fractions in 2006, 2010, and 2015 were calculated with 1) prevalence of overweight/obesity, exacted from the China Health and Nutrition Survey conducted in 8-9 provinces of China in 1997, 2000, and 2004; 2) relative risks for EBW and site-specific cancers, obtained from previous studies; 3) data on cancer deaths in 2006, 2010, and 2015, originated from the Chinese Cancer Registry Annual Report. RESULTS: In 2015, EBW contributed to 45,918 (3.1% of all) cancer deaths in China, with 24,978 (2.6%) in men and 20,940 (3.8%) in women. By region, the fraction of cancer deaths attributable to EBW ranged from 1.6% (West) to 4.1% (Northeast). Cancers of liver, stomach, and colorectum were the main EBW-attributable cancers. The fractions of cancer deaths attributable to EBW were 2.4% (95%CI: 0.8-4.2%) in 2006, 2.9% (95%CI: 1.0-5.2%) in 2010, and 3.1% (95%CI: 1.0-5.4%) in 2015, respectively, and increased for all gender, region, and cancer site during 2006-2015. CONCLUSIONS: The proportion of cancer deaths attributed to EBW was higher in women and Northeastern China, with an upward trend in the recent decade. A combination of comprehensive and individualized measures is necessary to reduce the prevalence of EBW and related cancer burden in China.

Involvement of Kir4.1 in pain insensitivity of the BTBR mouse model of autism spectrum disorder.

Autism spectrum disorder (ASD) is a severe neurodevelopmental disorder. Abnormal pain sensation is a common clinical symptom of ASD that seriously affects the quality of life of patients with ASD and their families. However, the underlying mechanism is unclear. It is believed to be related to the excitability of neurons and the expression of ion channels. Herein, we confirmed that baseline pain and Complete Freund's adjuvant (CFA)-induced chronic inflammatory pain were impaired in the BTBR T+ Itpr3tf/J (BTBR) mouse model of ASD. RNA sequencing (RNA-seq) analyses of the dorsal root ganglia (DRG), which are closely related to pain in ASD model mice, revealed that high expression of KCNJ10 (encoding Kir4.1) might be an important factor in ASD pain sensation abnormalities. The levels of Kir4.1 were further verified by western blotting, RT-qPCR, and immunofluorescence. By inhibiting Kir4.1, the pain insensitivity of BTBR mice improved, confirming that a high expression level of Kir4.1 was highly correlated with decreased pain sensitivity in ASD. Meanwhile, we found that the anxiety behaviours and the social novelty recognition were changed after CFA induced inflammatory pain. And after inhibiting Kir4.1, the stereotyped behaviours and social novelty recognition of BTBR mice were also improved. Further, we found that the expression levels of glutamate transporters, excitatory amino acid transporter 1 (EAAT1), and excitatory amino acid transporter 2 (EAAT2) were increased in the DRG of BTBR mice but decreased after inhibiting Kir4.1. This suggests that Kir4.1 may play a key role in the improvement of pain insensitivity in ASD by regulating glutamate transporters. In conclusion, our findings revealed the possible mechanism and role of Kir4.1 in the pain insensitivity in ASD, using bioinformatics analyses and animal experiments, and provided a theoretical basis for clinically targeted intervention in ASD.

Prediction Model for Sensory Perception Abnormality in Autism Spectrum Disorder.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by heterogeneous clinical phenotypes. Patients often experience abnormal sensory perception, which may further affect the ASD core phenotype, significantly and adversely affecting their quality of life. However, biomarkers for the diagnosis of ASD sensory perception abnormality are currently elusive. We sought to identify potential biomarkers related to ASD sensory perception abnormality to construct a prediction model that could facilitate the early identification of and screening for ASD. Differentially expressed genes in ASD were obtained from the Gene Expression Omnibus database and were screened for genes related to sensory perception abnormality. After enrichment analysis, the random forest method was used to identify disease-characteristic genes. A prediction model was constructed with an artificial neural network. Finally, the results were validated using data from the dorsal root ganglion, cerebral cortex, and striatum of the BTBR T+ Itpr3tf/J (BTBR) ASD mouse model. A total of 1869 differentially expressed genes in ASD were screened, among which 16 genes related to sensory perception abnormality were identified. According to enrichment analysis, these 16 genes were mainly related to actin, cholesterol metabolism, and tight junctions. Using random forest, 15 disease-characteristic genes were screened for model construction. The area under the curve of the training set validation result was 0.999, and for the model function validation, the result was 0.711, indicating high accuracy. The validation of BTBR mice confirmed the reliability of using these disease-characteristic genes for prediction of ASD. In conclusion, we developed a highly accurate model for predicting ASD sensory perception abnormality from 15 disease-characteristic genes. This model provides a new method for the early identification and diagnosis of ASD sensory perception abnormality.

Baseline and longitudinal trajectories of body-mass index and all-cause mortality among patients with type 2 diabetes.

AIM: To investigate the associations of baseline body mass index (BMI) and longitudinal BMI trajectories with all-cause mortality among patients with type 2 diabetes mellitus (T2DM). METHODS,: We used data from the diabetes surveillance system of Yinzhou Health Information System with T2DM patients registered from 2010 to 2015. Participants aged ≥ 40 years were included and were followed up until September 30, 2021. The latent class growth mixture model was used to identify different changing patterns in BMI for 5 years from registration. Cox proportional hazards models were used to examine the associations of baseline BMI and 5-year BMI trajectories with all-cause mortality. RESULTS: We observed a nonlinear association between baseline BMI and all-cause mortality (P for nonlinearity < 0.001), with an increased risk of death for low but not high BMI. However, compared with participants with medium-stable BMI for 5 years from baseline, individuals with increasing BMI had higher mortality, with adjusted hazard ratios (95% confidence intervals) 1.21 (1.02;1.43) for early-increasing and 1.47 (1.19;1.80) for late-sharp increasing groups. CONCLUSION: These findings suggest that while obesity itself may not be associated with an increased risk for mortality, weight gain, and in particular rapid weight gain, is a risk factor for mortality among patients with T2DM.

Association of walkability and NO2 with metabolic syndrome: A cohort study in China.

BACKGROUND: Epidemiological studies have reported an association between traffic-related pollution with risk of metabolic syndrome (MetS). However, evidence from prospective studies on the association of walkability and nitrogen dioxide (NO2) with MetS is still scarce. We, therefore, aimed to evaluate the association of long-term exposure to NO2 and walkability with hazards of incident MetS. METHODS: A total of 17,965 participants without MetS diagnosed within one year at baseline were included in our study from a population-based prospective cohort in Yinzhou District, Ningbo, Zhejiang Province, China. Participants were followed up by the regional Health Information System (HIS) until December 15, 2021. MetS was defined based on the criteria of Chinese Diabetes Society (CDS2004). We used walkscore tools, calculating with amenity categories and decay functions, and spatial-temporal land-use regression (LUR) models to estimate walkability and NO2 concentrations. We used Cox proportional hazards regression models to examine the association of walkability and NO2 with hazards of MetS incidence reporting with hazard ratios (HRs) and 95% confidence intervals (CIs). RESULTS: Overall, we followed up 77,303 person-years and identified 4040 incident cases of MetS in the entire cohort. Higher walkability was inversely associated with incident MetS (HR = 0.94, 95 % CI: 0.91-0.99), whereas NO2 was positively associated with MetS incidence (HR = 1.07, 95 %CI: 1.00-1.15) per interquartile range increment in two-exposure models. Furthermore, we found a significant multiplicative interaction between walkability and NO2. Stronger associations were observed for NO2 and incident MetS among men, smokers, drinkers and participants who aged < 60 years and had higher levels of income. CONCLUSION: In summary, we found living in areas with lower walkability and higher concentrations of NO2 were associated with increased incidence of MetS. The beneficial effect of higher walkability may be attenuated by exposure to NO2.

Synthesis of V2O5·nH2O nanobelts@polyaniline core-shell structures with highly efficient Zn2+ storage.

Aqueous zinc-ion batteries (AZIBs) are regarded as attractive candidates for next-generation energy storage devices. Among various cathode materials, V2O5·nH2O (VOH) possesses a high theoretical capacity but poor cycle stability due to the susceptibility of its open structure to damage by the quick shuttling of Zn2+. Herein, the structural stability of VOH is directly improved by wrapping polyaniline (PANI) on the VOH nanobelts (VOH@PANI). As a cathode material for AZIBs, the VOH nanobelts@PANI core-shell structures exhibit an outstanding cycle stability of 98% after 2000 cycles at 2 A g-1. The improved conductivity and additional energy storage contribution of the PANI endow VOH@PANI with a specific capacity as high as 440 mAh g-1 at 0.1 A g-1, substantially higher than pure VOH (291 mAh g-1). At the same time, high energy and power densities of 349 Wh kg-1 and 3347 W kg-1 are achieved. This work not only demonstrates that p-type doped PANI coatings on VOH can boost the Zn2+ storage of VOH, but also provides a novel method to enhance cathode materials for high electrochemical performance.

Association of walkability and fine particulate matter with chronic obstructive pulmonary disease: A cohort study in China.

BACKGROUND: Walkability has been considered to be associated with metabolic and cardiovascular diseases, but the relationship between walkability and chronic obstructive pulmonary disease (COPD) remains unclear. Moreover, fine particulate matter (PM2.5), possibly positively correlated to walkability, may lead to an increased risk of COPD. The separate and joint effects of PM2.5 and walkability on risk of COPD were explored in our study. METHODS: We used prospective data of 29,572 participants from the Yinzhou cohort in Ningbo, China. COPD cases were diagnosed based on the Yinzhou Health Information System (YHIS). Walkability was measured using walk score in relation to the built environment based on geographic information systems (GIS). Air pollution levels were assessed by fitting land use regression (LUR) models. Cox proportional hazards models were used to evaluate the relationships of PM2.5 and walkability with COPD. Furthermore, we also examined additive and multiplicative interactions between walkability and PM2.5. RESULTS: Overall, a total of 29,572 participants were included in the final analysis and 722 COPD incident cases were identified during 134,846 person-years of follow-up. Compared with subjects with lower walkability, individuals with higher walkability had a decreased risk of COPD (HR = 0.88, 95 % CI: 0.82-0.95) for every IQR increase. By contrast, exposure to PM2.5 (every IQR increase) was associated with an elevated risk of COPD (HR = 1.21, 95 % CI: 1.06-1.37). No interaction between PM2.5 and walkability was observed. CONCLUSIONS: Living in a highly walkable neighborhood could decrease risk of COPD, whereas high levels of PM2.5 were positively associated with COPD. In addition, the beneficial effects of walkability were not attenuated by exposure to PM2.5.

Analysis of a cohort study on myopia among primary and secondary school students in Henan Province, 2019-2020 / 中国学校卫生

Objective@#To track and investigate the changes in visual acuity of primary and secondary school students in Henan Province during the COVID-19 pandemic home confinement, so as to provide theoretical basis for the prevention and control of myopia.@*Methods@#A cohort study design was employed for this research. In September 2019, visual acuity tests were conducted among 2 222 primary and secondary school students by Multi stage random cluster sampling method from four cities in Henan Province, including Zhengzhou, Xinxiang, Zhoukou, and Pingdingshan. A follow up study was conducted in June 2020, with on site visual acuity tests and questionnaire surveys. Wilcoxon rank sum test, Kruskal Wallis rank sum test, Chi square test, one way analysis of variance, and multiple linear regression model were used to analyze the changes in visual acuity of primary and secondary school students and the influencing factors from 2019 to 2020.@*Results@#Compared with 2019, the overall myopia rate of students increased in 2020, and the difference was statistically significant (55.7%, 64.9%, χ 2=1 035.91, P <0.01), and the difference between mild, moderate and severe myopia rates occurred at 2 years (2019:32.4%, 18.8%, 4.4%, 2020:36.7%, 22.5%, 5.7%, χ 2= 8.43, 9.23, 3.94, P <0.05). The myopia incidence rate of primary and secondary school students in 2020 was 28.3%. As presented in multiple linear regression analysis, middle school, grade 4th-6th and grade 1st-3rd of primary school, low economic level, using television for online classes, the study desk being not bright on sunny days, without looking far away during breaks, the brightness of the study desk and desktop which was average on sunny days, and using roof lamp only when studying at night were associated with myopia progression among students ( B=-0.16, -0.18, -0.20, -0.06, -0.21, -0.13, -0.11, -0.40, P <0.05).@*Conclusions@#During 2019-2020, primary and secondary school students in Henan Province experience a progression towards myopia, which is comprehensively influenced by education stage, economic level, the habit of using eyes, and visual environment. Myopia prevention and control should be actively intervened and strengthened to improve the eye environment for primary and secondary school students, in order to slow down the development of myopia.

Proteomic-Based Approach Reveals the Involvement of Apolipoprotein A-I in Related Phenotypes of Autism Spectrum Disorder in the BTBR Mouse Model.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder. Abnormal lipid metabolism has been suggested to contribute to its pathogenesis. Further exploration of its underlying biochemical mechanisms is needed. In a search for reliable biomarkers for the pathophysiology of ASD, hippocampal tissues from the ASD model BTBR T+ Itpr3tf/J (BTBR) mice and C57BL/6J mice were analyzed, using four-dimensional (4D) label-free proteomic analysis and bioinformatics analysis. Differentially expressed proteins were significantly enriched in lipid metabolic pathways. Among them, apolipoprotein A-I (ApoA-I) is a hub protein and its expression was significantly higher in the BTBR mice. The investigation of protein levels (using Western blotting) also confirmed this observation. Furthermore, expressions of SphK2 and S1P in the ApoA-I pathway both increased. Using the SphK inhibitor (SKI-II), ASD core phenotype and phenotype-related protein levels of P-CREB, P-CaMKII, and GAD1 were improved, as shown via behavioral and molecular biology experiments. Moreover, by using SKI-II, we found proteins related to the development and function of neuron synapses, including ERK, caspase-3, Bax, Bcl-2, CDK5 and KCNQ2 in BTBR mice, whose levels were restored to protein levels comparable to those in the controls. Elucidating the possible mechanism of ApoA-I in ASD-associated phenotypes will provide new ideas for studies on the etiology of ASD.

Research progress and hotspot of the artificial intelligence application in the ultrasound during 2011-2021: A bibliometric analysis.

Ultrasound, as a common clinical examination tool, inevitably has human errors due to the limitations of manual operation. Artificial intelligence is an advanced computer program that can solve this problem. Therefore, the relevant literature on the application of artificial intelligence in the ultrasonic field from 2011 to 2021 was screened by authors from the Web of Science Core Collection, which aims to summarize the trend of artificial intelligence application in the field of ultrasound, meanwhile, visualize and predict research hotspots. A total of 908 publications were included in the study. Overall, the number of global publications is on the rise, and studies on the application of artificial intelligence in the field of ultrasound continue to increase. China has made the largest contribution in this field. In terms of institutions, Fudan University has the most number of publications. Recently, IEEE Access is the most published journal. Suri J. S. published most of the articles and had the highest number of citations in this field (29 articles). It's worth noting that, convolutional neural networks (CNN), as a kind of deep learning algorithm, was considered to bring better image analysis and processing ability in recent most-cited articles. According to the analysis of keywords, the latest keyword is "COVID-19" (2020.8). The co-occurrence analysis of keywords by VOSviewer visually presented four clusters which consisted of "deep learning," "machine learning," "application in the field of visceral organs," and "application in the field of cardiovascular". The latest hot words of these clusters were "COVID-19; neural-network; hepatocellular carcinoma; atherosclerotic plaques". This study reveals the importance of multi-institutional and multi-field collaboration in promoting research progress.

Long-term exposure to residential surrounding greenness and incidence of diabetes: A prospective cohort study.

Exposure to residential greenness might affect population health through increasing physical activity and social engagement, improving mental health, and reducing harmful environmental exposure. However, evidence on the association of greenness with risk of diabetes is still controversial. In this study, we recruited a total of 22,535 participants aged ≥18 years from Yinzhou District, Ningbo, Zhejiang Province, China to investigate the associations between residential greenness and risk of diabetes incidence. Residential greenness was estimated using Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Vegetation Continuous Field (VCF). We also calculated cumulative average NDVI, EVI and VCF values, and changes in NDVI, EVI and VCF during the follow-up period. We used Cox proportional hazards models controlling for demographic characteristics, lifestyles, individual socioeconomic status, history of diseases and particulate matter with an aerodynamic diameter ≤2.5 µm (PM2.5) to examine hazard ratios (HRs) and 95% confidence intervals (95% CIs) and assessed physical activity, body mass index (BMI) or PM2.5 as potential mediators. During 84,992.64 person-years of follow-up, a total of 1,154 incident cases of diabetes occurred. In multivariable models, living in the highest quartile of cumulative average NDVI, EVI and VCF within 250-m buffer was associated with 57% (HR = 0.43, 95% CI: 0.36, 0.52), 62% (HR = 0.38, 95% CI: 0.32, 0.45), and 55% (HR = 0.45, 95% CI: 0.38, 0.54) reduction in diabetes risk compared with the lowest quartile, respectively. Results remained similar for NDVI, EVI, and VCF within 500-m and 1000-m buffers. Stratified analyses showed stronger association for residential greenness and diabetes among older people. The association between greenness and diabetes did not appear to be mediated by physical activity, PM2.5 or BMI. Our findings suggested that higher residential greenness was significantly associated with lower risk of diabetes.