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PurposesTo evaluate the capability of PET/CT images for differentiating the histologic subtypes of non-small cell lung cancer (NSCLC) and to identify the optimal model from radiomics-based machine learning/deep learning algorithms.MethodsIn this study, 867 patients with adenocarcinoma (ADC) and 552 patients with squamous cell carcinoma (SCC) were retrospectively analysed. A stratified random sample of 283 patients (20%) was used as the testing set (173 ADC and 110 SCC); the remaining data were used as the training set. A total of 688 features were extracted from each outlined tumour region. Ten feature selection techniques, ten machine learning (ML) models and the VGG16 deep learning (DL) algorithm were evaluated to construct an optimal classification model for the differential diagnosis of ADC and SCC. Tenfold cross-validation and grid search technique were employed to evaluate and optimize the model hyperparameters on the training dataset. The area under the receiver operating characteristic curve (AUROC), accuracy, precision, sensitivity and specificity was used to evaluate the performance of the models on the test dataset.ResultsFifty top-ranked subset features were selected by each feature selection technique for classification. The linear discriminant analysis (LDA) (AUROC, 0.863; accuracy, 0.794) and support vector machine (SVM) (AUROC, 0.863; accuracy, 0.792) classifiers, both of which coupled with the ℓ2,1NR feature selection method, achieved optimal performance. The random forest (RF) classifier (AUROC, 0.824; accuracy, 0.775) and ℓ2,1NR feature selection method (AUROC, 0.815; accuracy, 0.764) showed excellent average performance among the classifiers and feature selection methods employed in our study, respectively. Furthermore, the VGG16 DL algorithm (AUROC, 0.903; accuracy, 0.841) outperformed all conventional machine learning methods in combination with radiomics.ConclusionEmploying radiomic machine learning/deep learning algorithms could help radiologists to differentiate the histologic subtypes of NSCLC via PET/CT images.

Background Metabolic syndrome (MetS) is a major risk factor for cardiovascular diseases. The objective of the study was to evaluate the updated prevalence of MetS and provide a comprehensive illustration of the possible temporal changes in MetS prevalence in China from 2011 to 2015. Methods The data for this study are from the 2011 and 2015 waves of the China Health and Retirement Longitudinal Study (CHARLS). CHARLS is a nationally representative survey targeting populations aged 45 and above from 28 provinces in mainland China. A total of 11,847 and 13,013 participants were eligible for data analysis at the two time points. Results The estimated prevalence of MetS in 2015 was 20.41% (95% CI: 19.02–21.8%) by the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (ATP III) criteria, 34.77% (95% CI: 33.12–36.42%) by the International Diabetes Federation (IDF) criteria, 39.68% (95% CI: 37.88–41.47%) by the revised ATP III criteria, and 25.55% (95% CI: 24.19–26.91%) by the Chinese Diabetes Society (CDS) criteria. The prevalence was higher among women and elderly adults and in urban and northern populations. Furthermore, the trends in the prevalence decreased significantly between 2011 and 2015 by the ATP III, revised ATP III and CDS criteria. However, trends increased significantly from 2011 to 2015 by the IDF criteria. Conclusions A higher prevalence of MetS is found in those who reported being middle aged and elderly, women, residing in northern China or living in urban areas. Additionally, temporal changes in the prevalence of MetS varied according to different criteria. Increased attention to the causes associated with populations who have higher levels of MetS is warranted.

Hand, foot, and mouth disease (HFMD) is a prevalent acute infectious disease caused by enteroviruses, presenting substantial public health challenges in Shanghai, especially among children. The dynamic nature of HFMD’s etiology necessitates an ongoing evaluation of its epidemiological and virological trends to inform effective control strategies. This study aims to investigate the epidemiological patterns and viral evolution of HFMD in Fengxian District, Shanghai, China, with a focus on shifts in predominant viral strains over a 14-year period. We conducted a retrospective analysis of HFMD cases reported to the National Notifiable Disease Reporting System in Fengxian District from January 1, 2009 to December 31, 2022. Epidemiological trends, strain prevalence, and demographic impacts were assessed. A total of 27,272 HFMD cases were documented during the study period, with incidence showing pronounced seasonal fluctuations—peaking in spring and summer and a lesser peak in autumn. The disease incidence demonstrated significant positive correlations with several meteorological variables: daily average temperature ( r  = 0.30, P < 0.05), relative humidity ( r  = 0.20, P < 0.05), wind speed ( r  = 0.17, P < 0.05), and precipitation ( r  = 0.17, P < 0.05). Geographically, Nanqiao Town, Fengcheng Town, and Xidu Subdistrict reported the highest incidence rates. The demographic analysis revealed a male-to-female ratio of 1.60:1, predominantly affecting children aged 1–3 years. Prior to 2017, Enterovirus 71 (EV71) and Coxsackievirus A16 (CoxA16) were the primary detected strains; post-2017, Coxsackievirus A6 (CoxA6) emerged as the dominant strain. Statistical analysis confirmed significant year-to-year variations in virus detection rates, with decreasing trends for EV71 and other enteroviruses and an increasing trend for CoxA6. The findings indicate a distinct seasonal incidence of HFMD in Fengxian District. This study underscores the need for targeted public health education, enhanced surveillance, and proactive measures in childcare facilities to mitigate disease spread during peak seasons. Moreover, the evolving viral landscape warrants accelerated efforts in vaccine development against new strains to reduce HFMD incidence.

Background Impaired sensitivity to thyroid hormones is a newly proposed clinical entity associated with hyperuricemia in the subclinical hypothyroid population. However, it is unknown whether the association exists in the euthyroid population. This study aimed to explore the association of impaired sensitivity to thyroid hormones (assessed by the thyroid feedback quantile-based index [TFQI], parametric thyroid feedback quantile-based index [PTFQI], thyrotrophic thyroxine resistance index [TT4RI] and thyroid-stimulating hormone index [TSHI]) with hyperuricemia and quantify the mediating effect of body mass index BMI in the euthyroid population. Methods This cross-sectional study enrolled Chinese adults aged ≥ 20 years who participated in the Beijing Health Management Cohort (2008–2019). Adjusted logistic regression models were used to explore the association between indices of sensitivity to thyroid hormones and hyperuricemia. Odds ratios [OR] and absolute risk differences [ARD] were calculated. Mediation analyses were performed to estimate direct and indirect effects through BMI. Results Of 30,857 participants, 19,031 (61.7%) were male; the mean (SD) age was 47.3 (13.3) years; and 6,515 (21.1%) had hyperuricemia. After adjusting for confounders, individuals in the highest group of thyroid hormone sensitivity indices were associated with an increased prevalence of hyperuricemia compared with the lowest group (TFQI: OR = 1.18, 95% CI 1.04–1.35; PTFQI: OR = 1.20, 95% CI 1.05–1.36; TT4RI: OR = 1.17, 95% CI 1.08–1.27; TSHI: OR = 1.12, 95% CI 1.04–1.21). BMI significantly mediated 32.35%, 32.29%, 39.63%, and 37.68% of the associations of TFQI, PTFQI, TT4RI and TSHI with hyperuricemia, respectively. Conclusions Our research revealed that BMI mediated the association between impaired sensitivity to thyroid hormones and hyperuricemia in the euthyroid population. These findings could provide useful evidence for understanding the interaction between impaired sensitivity to thyroid hormone and hyperuricemia in euthyroid individuals and suggest the clinical implications of weight control in terms of impaired thyroid hormones sensitivity. Keypoints Quetions To what extent is impaired sensitivity to thyroid hormones associated with hyperuricemia through body mass index (BMI) in the euthyroid population? Findings In this cross-sectional study of 30,857 euthyroid adults, impaired sensitivity to thyroid hormones was independently associated with an increased prevalence of hyperuricemia. BMI mediated 32.3–39.6% of the association between impaired sensitivity to thyroid hormones and hyperuricemia. Meaning This study revealed that BMI mediated the association between impaired sensitivity to thyroid hormones and hyperuricemia in the euthyroid population, suggesting the potential clinical benefit of weight control in maintaining metabolic health among individuals with impaired thyroid hormones sensitivity.

Background Cross-sectional studies have reported that insulin resistance (IR) is associated with arterial stiffness. However, the relationship between IR and arterial stiffness progression remains unclear. This study aims to evaluate the association of triglyceride glucose (TyG) index and triglyceride to high-density lipoprotein cholesterol (TG/HDL-C) ratio with arterial stiffness progression in a non-normotensive population. Methods A total of 1895 prehypertensive (systolic pressure 120–139 mmHg or diastolic pressure 80–90 mmHg) or hypertensive (systolic pressure ≥ 140 mmHg or diastolic pressure ≥ 90 mmHg or using antihypertensive medication) participants were enrolled in 2013 and 2014, and followed until December 31, 2019. Arterial stiffness progression was measured by brachial-ankle pulse wave velocity (baPWV) change (absolute difference between baseline and last follow-up), baPWV change rate (change divided by following years), and baPWV slope (regression slope between examination year and baPWV). Results During a median follow-up of 4.71 years, we observed an increasing trend of baPWV in the population. There were linear and positive associations of the TyG index and TG/HDL-C ratio with the three baPWV parameters. The difference (95% CI) in baPWV change (cm/s) comparing participants in the highest quartile versus the lowest of TyG index and TG/HDL-C ratio were 129.5 (58.7–200.0) and 133.4 (52.0–214.9), respectively. Similarly, the evaluated baPWV change rates (cm/s/year) were 37.6 (15.3–60.0) and 43.5 (17.8–69.2), while the slopes of baPWV were 30.6 (9.3–51.8) and 33.5 (9.0–58.0). The observed association was stronger in the hypertensive population. Conclusion Our study indicates that the TyG index and TG/HDL-C ratio are significantly associated with arterial stiffness progression in hypertensive population, not in prehypertensive population.

Heavy fine particulate matter (PM2.5) air pollution occurs frequently in China. However, epidemiological research on the association between short-term exposure to PM2.5 pollution and respiratory disease morbidity is still limited. This study aimed to explore the association between PM2.5 pollution and hospital emergency room visits (ERV) for total and cause-specific respiratory diseases in urban areas in Beijing. Daily counts of respiratory ERV from Jan 1 to Dec 31, 2013, were obtained from ten general hospitals located in urban areas in Beijing. Concurrently, data on PM2.5 were collected from the Beijing Environmental Protection Bureau, including 17 ambient air quality monitoring stations. A generalized-additive model was used to explore the respiratory effects of PM2.5, after controlling for confounding variables. Subgroup analyses were also conducted by age and gender. A total of 92,464 respiratory emergency visits were recorded during the study period. The mean daily PM2.5 concentration was 102.1±73.6 μg/m3. Every 10 μg/m3 increase in PM2.5 concentration at lag0 was associated with an increase in ERV, as follows: 0.23% for total respiratory disease (95% confidence interval [CI]: 0.11%-0.34%), 0.19% for upper respiratory tract infection (URTI) (95%CI: 0.04%-0.35%), 0.34% for lower respiratory tract infection (LRTI) (95%CI: 0.14%-0.53%) and 1.46% for acute exacerbation of chronic obstructive pulmonary disease (AECOPD) (95%CI: 0.13%-2.79%). The strongest association was identified between AECOPD and PM2.5 concentration at lag0-3 (3.15%, 95%CI: 1.39%-4.91%). The estimated effects were robust after adjusting for SO2, O3, CO and NO2. Females and people 60 years of age and older demonstrated a higher risk of respiratory disease after PM2.5 exposure. PM2.5 was significantly associated with respiratory ERV, particularly for URTI, LRTI and AECOPD in Beijing. The susceptibility to PM2.5 pollution varied by gender and age.

Background Associations between the frequency of social and intellectual activities and cognitive trajectories are understudied in Chinese middle-aged and older adults. We aimed to examine this association in a nationally representative longitudinal study. Methods The China Health and Retirement Longitudinal Study (CHARLS) is a nationally representative sample of Chinese middle-aged and older participants. The frequency of social and intellectual activities was measured at baseline. Interview-based cognitive assessments of orientation and attention, episodic memory, and visuospatial skills and the calculation of combined global scores were assessed every 2 years from 2011 to 2016. Cognitive aging trajectories over time were analyzed using group-based trajectory modeling, and the associations of the trajectory memberships with social and intellectual activities were analyzed using multinomial logistic regression. Odds ratios (OR) and 95% confidence intervals (CI) were reported. Results Among 8204 participants aged 50–75 years at baseline, trajectory analysis identified three longitudinal patterns of cognitive function based on the global cognitive scores: “persistently low trajectory” ( n  = 1550, 18.9%), “persistently moderate trajectory” ( n  = 3194, 38.9%), and “persistently high trajectory” ( n  = 3460, 42.2%). After adjustment for sociodemographic variables, lifestyles, geriatric symptoms, and health conditions, more frequent intellectual activities (OR 0.54, 95% CI 0.38–0.77) and social activities (OR 0.79, 95% CI 0.65–0.95) were both associated with a lower likelihood of being in the “persistently low trajectory” for global cognitive function. Conclusions These findings suggested that more frequent social and intellectual activities were associated with more favorable cognitive aging trajectories.

Limited information is available regarding spatiotemporal variations of particles with median aerodynamic diameter < 2.5 μm (PM2.5) at high resolutions, and their relationships with meteorological factors in Beijing, China. This study aimed to detect spatiotemporal change patterns of PM2.5 from August 2013 to July 2014 in Beijing, and to assess the relationship between PM2.5 and meteorological factors. Daily and hourly PM2.5 data from the Beijing Environmental Protection Bureau (BJEPB) were analyzed separately. Ordinary kriging (OK) interpolation, time-series graphs, Spearman correlation coefficient and coefficient of divergence (COD) were used to describe the spatiotemporal variations of PM2.5. The Kruskal-Wallis H test, Bonferroni correction, and Mann-Whitney U test were used to assess differences in PM2.5 levels associated with spatial and temporal factors including season, region, daytime and day of week. Relationships between daily PM2.5 and meteorological variables were analyzed using the generalized additive mixed model (GAMM). Annual mean and median of PM2.5 concentrations were 88.07 μg/m3 and 71.00 μg/m3, respectively, from August 2013 to July 2014. PM2.5 concentration was significantly higher in winter (P < 0.0083) and in the southern part of the city (P < 0.0167). Day to day variation of PM2.5 showed a long-term trend of fluctuations, with 2-6 peaks each month. PM2.5 concentration was significantly higher in the night than day (P < 0.0167). Meteorological factors were associated with daily PM2.5 concentration using the GAMM model (R2 = 0.59, AIC = 7373.84). PM2.5 pollution in Beijing shows strong spatiotemporal variations. Meteorological factors influence the PM2.5 concentration with certain patterns. Generally, prior day wind speed, sunlight hours and precipitation are negatively correlated with PM2.5, whereas relative humidity and air pressure three days earlier are positively correlated with PM2.5.

Coronavirus disease 2019 (COVID-19) is a global pandemic posing significant health risks. The diagnostic test sensitivity of COVID-19 is limited due to irregularities in specimen handling. We propose a deep learning framework that identifies COVID-19 from medical images as an auxiliary testing method to improve diagnostic sensitivity. We use pseudo-coloring methods and a platform for annotating X-ray and computed tomography images to train the convolutional neural network, which achieves a performance similar to that of experts and provides high scores for multiple statistical indices (F1 scores > 96.72% (0.9307, 0.9890) and specificity >99.33% (0.9792, 1.0000)). Heatmaps are used to visualize the salient features extracted by the neural network. The neural network-based regression provides strong correlations between the lesion areas in the images and five clinical indicators, resulting in high accuracy of the classification framework. The proposed method represents a potential computer-aided diagnosis method for COVID-19 in clinical practice.Liang, Gu and other colleagues develop a convoluted neural network (CNN)-based framework to diagnose COVID-19 infection from chest X-ray and computed tomography images, and comparison with other upper respiratory infections. Compared to expert evaluation of the images, the neural network achieved upwards of 99% specificity, showing promise for the automated detection of COVID-19 infection in clinical settings.

Background Arteriosclerosis and atherosclerosis are closely related with cardiovascular disease (CVD) risk. Remnant cholesterol (RC) could predict CVD. However, its effect on joint arteriosclerosis and atherosclerosis progression remains unclear. This study aims to evaluate the association of RC with joint arteriosclerosis and atherosclerosis progression trajectories in the general population. Methods This study collected data across five biennial surveys of the Beijing Health Management Cohort from 2010 to 2019. Multi-trajectory model was used to determine the joint arteriosclerosis and atherosclerosis progression patterns by brachial-ankle pulse wave velocity (baPWV) and ankle brachial index (ABI). We also performed discordance analyses for RC vs. low density lipoprotein cholesterol (LDL-C) using ordinal logistics model. Results A total of 3186 participants were included, with three clusters following distinct arteriosclerosis and atherosclerosis progression patterns identified using a multi-trajectory model. In the multivariable-adjusted ordinal logistics analyses, RC was significantly associated with baPWV and ABI progression (OR: 1.20; 95% CI: 1.13–1.28, per 10 mg/dL). For the discordance analyses, the discordant low RC group was associated with decreased risk compared to the concordant group (OR: 0.73; 95% CI: 0.60–0.89). People with a high RC level were at an increased risk of joint arteriosclerosis and atherosclerosis progression, even with optimal LDL-C. Conclusions RC is independently associated with joint arteriosclerosis and atherosclerosis progression beyond LDL-C. RC could be an earlier risk factor than LDL-C of arteriosclerosis and atherosclerosis in the general population.