Explore the vast range of titles available.

Background This study aimed to examine the bidirectional relationship between social isolation and cognitive performance among Chinese middle-aged and older adults. Methods We used four waves of data from the China Health and Retirement Longitudinal Study. A latent growth model (LGM) was applied to examine the association between social isolation and cognitive performance across different characteristics. Results In the analysis, we ultimately included 9,367 participants after excluding respondents with missing key variables. Social isolation and cognitive performance showed significant differences across time. After adjusting for the confounders, there was a significant association between higher social isolation and poor cognitive performance ( β = −1.38, p  < 0.001), and higher levels of social isolation resulted in a more pronounced decline in cognition over time ( β  = 0.17, p  < 0.001). Additionally, the path coefficient between the initial level of cognition at baseline and the slope of social isolation was − 0.07 ( p  < 0.001) and 0.01 ( p  = 0.021), respectively. For the correlation between slopes, our study found that females’ cognition scores were more susceptible to social isolation ( β = − 2.78, p  < 0.001). Similarly, regarding cognition scores, the influence of social isolation was greater among people with education below the primary level ( β = − 2.89, p  = 0.002) or a greater number of chronic diseases ( β = − 2.56, p  = 0.001). Conclusion Our findings support the bidirectional association between social isolation and cognition. Specifically, higher baseline social isolation and its rate of increase over time contribute to an intensification of cognitive decline at follow-up. Besides, poorer cognitive performance predicted higher social isolation.

Background Stroke, especially ischemic stroke (IS), has been a severe public health problem around the world. However, the association between air pollution and ischemic stroke remains ambiguous. Methods A total of 63, 997 IS cases aged 18 years or above in Shenzhen were collected from 2008 to 2014. We used the time-stratified case-crossover design combining with distributed lag nonlinear model (DLNM) to estimate the association between air pollution and IS onset. Furthermore, this study explored the variability across gender and age groups. Results The cumulative exposure-response curves were J-shaped for SO 2 , NO 2 and PM 10 , and V-shaped for O 3 , and crossed over the relative risk ( RR ) of one. The 99th, 50th (median) and 1st percentiles of concentration (μg/m 3 ) respectively were 37.86, 10.06, 3.71 for SO 2 , 116.26, 41.29, 18.51 for NO 2 , 145.94, 48.29, 16.14 for PM 10 , and 111.57, 49.82, 16.00 for O 3 . Extreme high-SO 2 , high-NO 2 , high-PM 10 , high-O 3 , and low-O 3 concentration increased the risk of IS, with the maximum RR values and 95% CI s: 1.50(1.22, 1.84) (99th vs median) at 0–12 lag days, 1.37(1.13, 1.67) (99th vs median) at 0–10 lag days, 1.26(1.04, 1.53) (99th vs median) at 0–12 lag days, 1.25(1.04, 1.49) (99th vs median) at 0–14 lag days, and 1.29(1.03, 1.61) (1st vs median) at 0–14 lag days, respectively. The statistically significant minimal RR value and 95% CI was 0.79(0.66,0.94) at 0–10 lag days for extreme low-PM 10 . The elderly aged over 65 years were susceptible to extreme pollution conditions. Difference from the vulnerability of males to extreme high-SO 2 , high-NO 2 and low-O 3 , females were vulnerable to extreme high-PM 10 and high-O 3 . Comparing with the elderly, adults aged 18–64 year were immune to extreme low-NO 2 and low-PM 10 . However, no association between CO and IS onset was found. Conclusions SO 2 , NO 2 , PM 10 and O 3 exerted non-linear and delayed influence on IS, and such influence varied with gender and age. These findings may have significant public health implications for the prevention of IS.

Background This study aimed to analyze the trends of Parkinson’s disease (PD) mortality rates among Chinese residents from 2004 to 2021, provide evidence for the formulation of PD prevention and control strategies to improve the quality of life among PD residents. Methods Demographic and sociological data such as gender, urban or rural residency and age were obtained from the National Cause of Death Surveillance Dataset from 2004 to 2021. We then analyzed the trends of PD mortality rates by Joinpoint regression. Results The PD mortality and standardized mortality rates in China showed an overall increasing trend during 2004–2021 (average annual percentage change [AAPC] = 7.14%, AAPC ASMR =3.21%, P  < 0.001). The mortality and standardized mortality rate in male (AAPC = 7.65%, AAPC ASMR =3.18%, P  < 0.001) were higher than that of female (AAPC = 7.03%, AAPC ASMR =3.09%, P  < 0.001). The PD standardized mortality rates of urban (AAPC = 5.13%, AAPC ASMR =1.76%, P  < 0.001) and rural (AAPC = 8.40%, AAPC ASMR =4.29%, P  < 0.001) residents both increased gradually. In the age analysis, the mortality rate increased with age. And the mortality rates of those aged > 85 years was the highest. Considering gender, female aged > 85 years had the fastest mortality trend (annual percentage change [APC] = 5.69%, P  < 0.001). Considering urban/rural, rural aged 80–84 years had the fastest mortality trend (APC = 6.68%, P  < 0.001). Conclusions The mortality rate of PD among Chinese residents increased from 2004 to 2021. Male sex, urban residence and age > 85 years were risk factors for PD-related death and should be the primary focus for PD prevention.

To assess YLL and mortality burden attributable to non-optimum ambient temperature, we collected mortality and environmental data from June 1, 2012 to December 30, 2017 in Shenzhen. We applied distributed lag nonlinear models with 21 days of lag to examine temperature–YLL and temperature–mortality associations, and calculated the attributable fractions of YLL and deaths for non-optimum temperature, including four subranges, mild cold, mild heat, extreme cold, and extreme heat. Cold and heat were distinguished by the optimum temperature, and each was separated into extreme and mild by cutoffs at 2.5th (12.2 °C) and 97.5th (30.4 °C) temperature percentile further. The optimum temperature was defined as the temperature that had minimum effect on YLL or mortality risk. The optimum temperature for non-accidental YLL was 24.5 °C, and for mortality it was 25.4 °C. Except for the population older than 65 years, the optimum temperature was generally lower in the YLL model than the mortality model. Of the total 61,576 non-accidental deaths and 1,350,835.7 YLL within the study period, 17.28% (95% empirical CI 9.42–25.14%) of YLL and 17.27% (12.70–21.34%) of mortality were attributable to non-optimum temperature. More YLL was caused by cold (10.14%, 3.94–16.36%) than by heat (7.14%, 0.47–13.88%). Mild cold (12.2–24.5 °C) was responsible for far more YLL (8.78%, 3.00–14.61%) than extreme cold (3.5–12.2 °C). As for cardiovascular deaths, only the fractions attributable to overall and cold temperature were significant, with mild cold contributing the largest fraction to YLL (16.31%, 6.85–25.82%) and mortality (16.08%, 9.77–21.22%). Most of the temperature-related YLL and mortality was attributable to mild but non-optimum weather, especially mild cold, while the YLL model implied a more prominent heat effect on premature death. Our findings can supply additional evidence from multiperspectives for health planners to define priorities and make targeted policies for mitigating the burden of adverse temperatures.

Background Timely identification of epidermal growth factor receptor (EGFR) mutation and anaplastic lymphoma kinase (ALK) rearrangement status in patients with non-small cell lung cancer (NSCLC) is essential for tyrosine kinase inhibitors (TKIs) administration. We aimed to use artificial intelligence (AI) models to predict EGFR mutations and ALK rearrangement status using common demographic features, pathology and serum tumor markers (STMs). Methods In this single-center study, demographic features, pathology, EGFR mutation status, ALK rearrangement, and levels of STMs were collected from Wuhan Union Hospital. One retrospective set (N = 1089) was used to train diagnostic performance using one deep learning model and five machine learning models, as well as the stacked ensemble model for predicting EGFR mutations, uncommon EGFR mutations, and ALK rearrangement status. A consecutive testing cohort (n = 1464) was used to validate the predictive models. Results The final AI model using the stacked ensemble yielded optimal diagnostic performance with areas under the curve (AUC) of 0.897 and 0.883 for predicting EGFR mutation status and 0.995 and 0.921 for predicting ALK rearrangement in the training and testing cohorts, respectively. Furthermore, an overall accuracy of 0.93 and 0.83 in the training and testing cohorts, respectively, were achieved in distinguishing common and uncommon EGFR mutations, which were key evidence in guiding TKI selection. Conclusions In this study, driverless AI based on robust variables could help clinicians identify EGFR mutations and ALK rearrangement status and provide vital guidance in TKI selection for targeted therapy in NSCLC patients.

Background: A few studies have explored the association between meteorological factors and hand, foot, and mouth disease (HFMD) with inconsistent results. Besides, studies about the effects of air pollutants on HFMD are very limited. Methods: Daily HFMD cases among children aged 0–14 years in Shenzhen were collected from 2009 to 2017. A distributed lag nonlinear model (DLNM) model was fitted to simultaneously assess the nonlinear and lagged effects of meteorological factors and air pollutants on HFMD incidence, and to further examine the differences of the effect across different subgroups stratified by gender, age and childcare patterns. Results: The cumulative relative risk (cRR) (median as reference) of HFMD rose with the increase of daily temperature and leveled off at about 30 °C (cRR: 1.40, 95%CI: 1.29, 1.51). There was a facilitating effect on HFMD when relative humidity was 46.0% to 88.8% (cRR at 95th percentile: 1.18, 95%CI: 1.11, 1.27). Short daily sunshine duration (5th vs. 50th) promoted HFMD (cRR: 1.07, 95%CI: 1.02, 1.11). The positive correlation between rainfall and HFMD reversed when it exceeded 78.3 mm (cRR: 1.41, 95% CI: 1.22, 1.63). Ozone suppressed HFMD when it exceeded 104 µg /m3 (cRR at 99th percentile: 0.85, 95%CI: 0.76, 0.94). NO2 promoted HFMD among infants and the cRR peaked at lag 9 day (cRR: 1.47, 95%CI: 1.02, 2.13) (99th vs. 50th). Besides, children aged below one year, males and scattered children were more vulnerable to high temperature, high relative humidity, and short sunshine duration. Conclusions: Temperature, relative humidity, sunshine duration, rainfall, ozone and NO2 were significantly associated with HFMD, and such effects varied with gender age and childcare patterns. These findings highlight the need for more prevention effort to the vulnerable populations and may be helpful for developing an early environment-based warning system for HFMD.

Global climate change has increased concerns about the interactive effects of temperature and air pollution on human health. The aim of this study was to examine the effect of changes in season and temperature on cardiovascular mortality associated with nitrogen dioxide (NO2) air pollution. We obtained daily data about deaths between 2013 and 2017 from the Shenzhen Center for Disease Control and Prevention. The Meteorological Service Center and state-controlled monitoring stations in Shenzhen provided daily air temperature and NO2 air pollution data, respectively. We used two approaches to examine these modifications. First, we explored seasonal effects using distributed lag linear models (DLMs) to allow cumulative lag effects. Second, we examined variations in the effects by strata defined by the air temperature and different lag for air pollutants in assessing their interactions. We further investigated whether people aged 65 years or older were more affected by synergistic effects, because China has a high proportion of older people in the population ageing. In the cold season (November–April), the percentage increase in death for each 10 μg/m3 increment in NO2 concentration on cardiovascular mortality was associated with a 4·45% (95% CI 2·71–6·21) increase in mortality for lag0–2 (a cumulative effect over a 2-day lag) and 4·87% (2·73–7·05) increase in mortality for lag0–6 (a cumulative effect over a 6-day lag). However, there were no significant effects observed in the warm season (May–October). The NO2 effect was significantly stronger on low-temperature days (0–50th percentile) than on high-temperature days. Additionally, low air temperature made older people (aged ≥65 years) and men more susceptible to NO2 pollution. Season and temperature strongly modified the adverse effects of NO2: in the cold season and on days with low temperatures, the adverse effect of NO2 on cardiovascular mortality was significantly enhanced. These findings suggest that an increase in the number of low temperature days as a result of global climate change might alter the health effects of air pollution. National Natural Science Foundation of China (81573262).

It remains unclear whether discharged COVID-19 patients have fully recovered from severe complications, including the differences in the post-infection metabolomic profiles of patients with different disease severities. COVID-19-recovered patients, who had no previous underlying diseases and were discharged from Wuhan Union Hospital for 3 months, and matched healthy controls (HCs) were recruited in this prospective cohort study. We examined the blood biochemical indicators, cytokines, lung computed tomography scans, including 39 HCs, 18 recovered asymptomatic (RAs), 34 recovered moderate (RMs), and 44 recovered severe/ critical patients (RCs). A liquid chromatography-mass spectrometry-based metabolomics approach was employed to profile the global metabolites of fasting plasma of these participants. Clinical data and metabolomic profiles suggested that RAs recovered well, but some clinical indicators and plasma metabolites in RMs and RCs were still abnormal as compared with HCs, such as decreased taurine, succinic acid, hippuric acid, some indoles, and lipid species. The disturbed metabolic pathway mainly involved the tricarboxylic cycle, purine, and glycerophospholipid metabolism. Moreover, metabolite alterations differ between RMs and RCs when compared with HCs. Correlation analysis revealed that many differential metabolites were closely associated with inflammation and the renal, pulmonary, heart, hepatic, and coagulation system functions. We uncovered metabolite clusters pathologically relevant to the recovery state in discharged COVID-19 patients which may provide new insights into the pathogenesis of potential organ damage in recovered patients.

ObjectiveEvidence on the relationship between ambient temperature and morbidity of different stroke subtypes in China is limited. This study aimed to assess the influence of ambient temperature on stroke risk in Shenzhen, China.MethodsFrom 1 January 2003 to 31 December 2014, 114 552 stroke cases in Shenzhen were collected. A generalised additive model with quasi-Poisson regression combined with a distributed lag non-linear model was applied to evaluate the temperature effects on stroke subtypes. Furthermore, this study explored the variability of the effects across sex, age and education.ResultsThe immediate heat effects on ischaemic stroke (IS) and the persistent effects of ambient temperature on intracerebral haemorrhage (ICH) were significant. Overall, the cold-related relative risks (RRs) of IS, ICH and subarachnoid haemorrhage (SAH) were 1.02 (0.97–1.07), 1.16 (1.04–1.30) and 1.12 (0.61–2.04), whereas the heat-related RRs were 1.00 (0.97–1.04), 0.80 (0.73–0.88) and 1.05 (0.63–1.78), respectively. For IS, a weakly beneficial cold effect was found among men while a detrimental heat effect among both men and women, the elderly and higher-educated population at lag0. However, regarding ICH, the temperature effects in men, the young and higher-educated population are stronger at lag0–4, lag0–7 as cold reveals threat and heat reveals protection.ConclusionResponses of diverse stroke subtypes to ambient temperature varied. Effective measures should be taken to increase public awareness about the effects of ambient temperature on stroke attack and to educate the public about self-protection.

IntroductionThis study aimed to construct artificial intelligence models based on thoracic CT images to perform segmentation and classification of benign pleural effusion (BPE) and malignant pleural effusion (MPE).MethodsA total of 918 patients with pleural effusion were initially included, with 607 randomly selected cases used as the training cohort and the other 311 as the internal testing cohort; another independent external testing cohort with 362 cases was used. We developed a pleural effusion segmentation model (M1) by combining 3D spatially weighted U-Net with 2D classical U-Net. Then, a classification model (M2) was built to identify BPE and MPE using a CT volume and its 3D pleural effusion mask as inputs.ResultsThe average Dice similarity coefficient, Jaccard coefficient, precision, sensitivity, Hausdorff distance 95% (HD95) and average surface distance indicators in M1 were 87.6±5.0%, 82.2±6.2%, 99.0±1.0%, 83.0±6.6%, 6.9±3.8 and 1.6±1.1, respectively, which were better than those of the 3D U-Net and 3D spatially weighted U-Net. Regarding M2, the area under the receiver operating characteristic curve, sensitivity and specificity obtained with volume concat masks as input were 0.842 (95% CI 0.801 to 0.878), 89.4% (95% CI 84.4% to 93.2%) and 65.1% (95% CI 57.3% to 72.3%) in the external testing cohort. These performance metrics were significantly improved compared with those for the other input patterns.ConclusionsWe applied a deep learning model to the segmentation of pleural effusions, and the model showed encouraging performance in the differential diagnosis of BPE and MPE.