Temporal relationship between hepatic steatosis and blood pressure elevation and the mediation effect in the development of cardiovascular disease.

The temporal relationship between non-alcoholic fatty liver disease (NAFLD) and hypertension remains highly controversial, with ongoing debates on whether NAFLD induces hypertension or vice versa. We employed cross-lagged panel models to investigate the temporal relationship between hepatic steatosis (assessed by Fatty Liver Index [FLI] in the main analysis, and by Proton Density Fat Fraction [PDFF] in the validation study) and blood pressure (systolic and diastolic blood pressure [SBP/ DBP]). Subsequently, we employed causal mediation models to explore the mediation effect in CVD development, including ischemic heart disease and stroke. The main analysis incorporated repeated measurement data of 5,047 participants from the China Multi-Ethnic Cohort (CMEC) and 5,685 participants from the UK Biobank (UKB). In both cohorts, the path coefficients from FLI to blood pressure were significant and greater than the path from blood pressure to FLI, with ßFLI→SBP = 0.081, P < 0.001 versus ßSBP→FLI = 0.020, P = 0.031; ßFLI→DBP = 0.082, P < 0.001 versus ßDBP→FLI = -0.006, P = 0.480 for CEMC, and ßFLI→SBP = 0.057, P < 0.001 versus ßSBP→FLI = -0.001, P = 0.727; ßFLI→DBP = 0.061, P < 0.001, versus ßDBP→FLI = -0.006, P = 0.263 for UKB. The validation study with 962 UKB participants using PDFF consistently supported these findings. In the mediation analyses encompassing 11,108 UKB participants, SBP and DBP mediated 12.2% and 5.2% of the hepatic steatosis-CVD association, respectively. The proportions were lower for ischemic heart disease (SBP: 6.1%, DBP: non-statistically significant -6.8%), and relatively stronger for stroke (SBP: 19.4%, DBP: 26.1%). In conclusion, hepatic steatosis more strongly contributes to elevated blood pressure than vice versa. Blood pressure elevation positively mediates the hepatic steatosis-CVD association, particularly in stroke compared to ischemic heart disease.

Long-term exposure to fine particulate matter constituents in relation to chronic kidney disease: evidence from a large population-based study in China.

The effects of long-term exposure to fine particulate matter (PM2.5) constituents on chronic kidney disease (CKD) are not fully known. This study sought to examine the association between long-term exposure to major PM2.5 constituents and CKD and look for potential constituents contributing substantially to CKD. This study included 81,137 adults from the 2018 to 2019 baseline survey of China Multi-Ethnic Cohort. CKD was defined by the estimated glomerular filtration rate. Exposure concentration data of 7 major PM2.5 constituents were assessed by satellite remote sensing. Logistic regression models were used to estimate the effect of each PM2.5 constituent exposure on CKD. The weighted quantile sum regression was used to estimate the effect of mixed exposure to all constituents. PM2.5 constituents had positive correlations with CKD (per standard deviation increase), with ORs (95% CIs) of 1.20 (1.02-1.41) for black carbon, 1.27 (1.07-1.51) for ammonium, 1.29 (1.08-1.55) for nitrate, 1.20 (1.01-1.43) for organic matter, 1.25 (1.06-1.46) for sulfate, 1.30 (1.11-1.54) for soil particles, and 1.63 (1.39-1.91) for sea salt. Mixed exposure to all constituents was positively associated with CKD (1.68, 1.32-2.11). Sea salt was the constituent with the largest weight (0.36), which suggested its importance in the PM2.5-CKD association, followed by nitrate (0.32), organic matter (0.18), soil particles (0.10), ammonium (0.03), BC (0.01). Sulfate had the least weight (< 0.01). Long-term exposure to PM2.5 sea salt and nitrate may contribute more than other constituents in increasing CKD risk, providing new evidence and insights for PM2.5-CKD mechanism research and air pollution control strategy.

SGCL: Spatial guided contrastive learning on whole-slide pathological images.

Self-supervised representation learning (SSL) has achieved remarkable success in its application to natural images while falling behind in performance when applied to whole-slide pathological images (WSIs). This is because the inherent characteristics of WSIs in terms of gigapixel resolution and multiple objects in training patches are fundamentally different from natural images. Directly transferring the state-of-the-art (SOTA) SSL methods designed for natural images to WSIs will inevitably compromise their performance. We present a novel scheme SGCL: Spatial Guided Contrastive Learning, to fully explore the inherent properties of WSIs, leveraging the spatial proximity and multi-object priors for stable self-supervision. Beyond the self-invariance of instance discrimination, we expand and propagate the spatial proximity for the intra-invariance from the same WSI and inter-invariance from different WSIs, as well as propose the spatial-guided multi-cropping for inner-invariance within patches. To adaptively explore such spatial information without supervision, we propose a new loss function and conduct a theoretical analysis to validate it. This novel scheme of SGCL is able to achieve additional improvements over the SOTA pre-training methods on diverse downstream tasks across multiple datasets. Extensive ablation studies have been carried out and visualizations of these results have been presented to aid understanding of the proposed SGCL scheme. As open science, all codes and pre-trained models are available at https://github.com/HHHedo/SGCL.

Diverse associations between adiposity and blood pressure among 80,000 multi-ethnic Chinese adults.

BACKGROUND: Adiposity is widely recognized as one of the risk factors for high blood pressure (BP) and increasing adiposity is associated with elevated BP. However, which measures of adiposity could be most strongly associated with BP in multi-ethnic population remains uncertain, giving rise to implications that population-based adiposity measures could be necessary. METHODS: 80,000 multi-ethnic adults recruited from 5 provinces across Southwest China during 2018 ~ 2019 were studied. Multiple linear regression was applied to investigate the associations of systolic blood pressure (SBP) with: (1) two measures of general adiposity, body mass index (BMI) and height-adjusted weight; and (2) three measures of central adiposity, waist circumference (WC), hip circumference (HC) and waist hip ratio (WHR). RESULTS: Two distinct population-specific patterns were identified, as "BMI to SBP" and "WC to SBP". 90% of the participants fall into "BMI to SBP" pattern, in which the associations of SBP with BMI were independent of WC, and SBP-WC associations were considerably decreased by adjustment for BMI. And in this pattern, 10 kg/m2 greater BMI was associated with 11.9 mm Hg higher SBP on average. As for the rest population (Han males in Yunnan and Tibetans in Lhasa), they are suited for "WC to SBP" pattern, 10 cm wider WC was associated with 3.4 mm Hg higher SBP. CONCLUSION: Our results indicated that when selecting proper predictors for BP, population-specific adiposity measures are needed, considering ethnicity, sex and residing regions. A better understanding of adiposity and BP may better contribute to the potential clinical practices and developing precision application strategies.

KenDTI: An Ensemble Model for Predicting Drug-Target Interaction by Integrating Multi-Source Information.

The identification of drug-target interactions (DTIs) is an essential step in the process of drug discovery. As experimental validation suffers from high cost and low success rate, various computational models have been exploited to infer potential DTIs. The performance of DTI prediction depends heavily on the features extracted from drugs and target proteins. The existing predictors vary in input information and each has its own advantages. Therefore, combining the advantages of individual models and generating high-quality representations for drug-target pairs are effective ways to improve the performance of DTI prediction. In this study, we exploit both biochemical characteristics of drugs via network integration and molecular sequences via word embeddings, then we develop an ensemble model, KenDTI, based on two types of methods, i.e., network-based and classification-based. We assess the performance of KenDTI on two large-scale datasets, The experimental results show that KenDTI outperforms the state-of-the-art DTI predictors by a large margin. Moreover, KenDTI is robust against missing data in input networks and lack of prior knowledge. It is able to predict for drug-candidate chemical compounds with scarce information.

Selective admission policy of medical undergraduates in western China: applicants' real attitudes to the choice of a rural medical career.

INTRODUCTION: Since 2010, the Chinese government has been introducing selective admission policy to recruit rural students for 5-year western medicine and traditional Chinese medicine undergraduate education in order to improve rural townships' medical services system in western China. This study aimed to analyse the selective admission policy in western China from the perspective of medical students' attitudes towards rural career choice. METHODS: A cross-sectional survey was conducted and an anonymous questionnaire was used to investigate a sample of medical undergraduates chosen under the selective admission policy. RESULTS: The results indicate that medical undergraduates' enthusiasm to work in rural areas was very limited in Gansu province, western China. Extrinsic motivation played a more important role in rural career choice than intrinsic motivation. The students' attitudes were affected by socioeconomic and cultural conditions, which determined their personal and professional environment. Course major and family economic conditions were associated with their self-decisions. CONCLUSION: Further educational intervention should emphasise the students' humanistic inner qualities and recognition of professional value. Further policy adjustment should considered, for example improving social policy-based regional character and national development strategies.