Ginsenoside Rg1 prevents acetaminophen-induced oxidative stress and apoptosis via Nrf2/ARE signaling pathway

Inappropriate use of acetaminophen (APAP) can lead to morbidity and mortality secondary to hepatic necrosis. Ginsenoside Rg1 is a major active ingredient in processed Panax ginseng, which is proved to elicit biological effects. We hypothesized the beneficial effect of Rg1 on APAP-mediated hepatotoxicity was through Nrf2/ARE pathway. The study was conducted in cells and mice, comparing the actions of Rg1. Rg1 significantly improved cell survival rates and promoted the expression of antioxidant proteins. Meanwhile, Rg1 reduced the excessive ROS and the occurrence of cell apoptosis, which were related to Nrf2/ARE pathway. Expression of Nrf2 has a certain cell specificity.

     

Development and validation of the EUROFORGEN NAME (North African and Middle Eastern) ancestry panel

Inference of biogeographic origin is an important factor in clinical, population and forensic genetics. The information provided by AIMs (Ancestry Informative Markers) can allow the differentiation of major continental population groups, and several AIM panels have been developed for this purpose. However, from these major population groups, Eurasia covers a wide area between two continents that is difficult to differentiate genetically. These populations display a gradual genetic cline from West Europe to South Asia in terms of allele frequency distribution. Although differences have been reported between Europe and South Asia, Middle East populations continue to be a target of further investigations due to the lack of genetic variability, therefore hampering their genetic differentiation from neighboring populations. In the present study, a custom-built ancestry panel was developed to analyze North African and Middle Eastern populations, designated the ‘NAME’ panel. The NAME panel contains 111 SNPs that have patterns of allele frequency differentiation that can distinguish individuals originating in North Africa and the Middle East when combined with a previous set of 126 Global AIM-SNPs.     

学龄前儿童心理行为问题与家长健康素养的关系

目的探讨家长健康素养与学龄前儿童心理行为问题的关系,为学龄前儿童心理行为问题早期干预提供参考。方法 2018年5-7月采用分层整群随机抽样的方法,抽取深圳市光明区13所幼儿园4 120名学龄前儿童及其家长,使用长处与困难问卷(SDQ)家长版和深圳市居民健康素养问卷分别对学龄前儿童心理行为问题及家长健康素养水平进行调查;采用非条件Logistic回归分析家长健康素养与儿童心理行为问题的关系。结果学龄前儿童心理行为问题检出率为34.9%(1 436/4 120)。其中困难总分6.5%、情绪问题5.6%、品行问题8.9%、多动注意不能13.4%、同伴交往问题14.4%、亲社会行为9.2%。男童和女童困难总分、情绪问题、品行问题、多动注意不能、同伴交往问题、亲社会行为检出率分别为7.3%和5.5%,4.8%和6.5%,9.7%和7.8%,15.5%和10.7%,15.8%和12.7%,11.1%和6.7%,差异有统计学意义(χ^2值分别为5.626,5.506,4.212,19.846,7.650,23.863,P<0.05)。多因素Logistic回归分析显示,家长健康素养(OR=1.294)、健康知识(OR=1.293)、健康行为(OR=1.412)及健康技能(OR=1.193)水平低是学龄前儿童心理行为问题产生的危险因素(P<0.05)。结论学龄前儿童心理行为问题发生率较高,问题突出,家长健康素养水平低是学龄前儿童心理行为问题的重要影响因素。     

Assessing perceived quality of primary care under hospital-township health centre integration: A cross-sectional study in China

Since the new round of health care reform in 2009, the vertical integration of hospitals and primary health institutions has become widely implemented in China as an efficient method for improving quality of primary care. This study aimed to answer the following questions: (a) What is the perceived quality of township health centres (THCs) under integration? (B) What differences could be observed among the three typical integration models, namely, private hospital-THC integration, public hospital-THC integration, and loose collaboration? Two rounds of cross-sectional surveys were conducted from November 2016 to June 2018. The Chinese version of the Primary Care Assessment Tool was used to evaluate perceived quality of sample THCs, and 1118 adult patients were interviewed in total. Multiple linear regressions were employed to compare the quality scores between two survey rounds and among different integration models after controlling for potential confounders. The results revealed that the quality of care significantly improved under private hospital-THC integration as observed by comparing two survey rounds, while no change or slight changes were observed in the other two models. The difference observed among the three models was that the perceived quality of THCs integrated with private hospitals was worse than that of THCs integrated with public hospitals and THCs under loose collaboration, while no significant difference was observed between public hospital-THC integration and loose collaboration. Increased attention should be given to highlighting the tight integration between hospitals and THCs and the different roles played by private and public hospitals in the current reform.     

Deep learning with biopsy whole slide images for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer：A multicenter study

IntroductionPredicting pathological complete response (pCR) for patients receiving neoadjuvant chemotherapy (NAC) is crucial in establishing individualized treatment. Whole-slide images (WSIs) of tumor tissues reflect the histopathologic information of the tumor, which is important for therapeutic response effectiveness. In this study, we aimed to investigate whether predictive information for pCR could be detected from WSIs.Materials and methodsWe retrospectively collected data from four cohorts of 874 patients diagnosed with biopsy-proven breast cancer. A deep learning pathological model (DLPM) was constructed to predict pCR using biopsy WSIs in the primary cohort, and it was then validated in three external cohorts. The DLPM could generate a deep learning pathological score (DLPs) for each patient; stromal tumor-infiltrating lymphocytes (TILs) were selected for comparison with DLPs.ResultsThe WSI feature-based DLPM showed good predictive performance with the highest area under the curve (AUC) of 0.72 among the cohorts. Alternatively, the combination of the DLPM and clinical characteristics offered a better prediction performance (AUC >0.70) in all cohorts. We also evaluated the performance of DLPM in three different breast subtypes with the best prediction for the triple-negative breast cancer (TNBC) subtype (AUC: 0.73). Moreover, DLPM combined with clinical characteristics and stromal TILs achieved the highest AUC in the primary cohort (AUC: 0.82) and validation cohort 1 (AUC: 0.80).ConclusionOur study suggested that WSIs integrated with deep learning could potentially predict pCR to NAC in breast cancer. The predictive performance will be improved by combining clinical characteristics. DLPs from DLPM can provide more information compared to stromal TILs for pCR prediction.     

Uncertainty evaluation at three spatial scales for the NDVI-based VWC estimation method used in the SMAP algorithm

Vegetation water content (VWC) is the key input parameter for a soil moisture retrieval algorithm based on microwave remote sensing, and VWC uncertainty can limit the estimated accuracy of soil moisture. There has been little research on VWC algorithm development and validation in China, and the uncertainty of the VWC estimation method has not been well evaluated. Therefore, the aim of this study is to evaluate the uncertainty of the VWC estimation method used in the SMAP (Soil Moisture Active Passive) algorithm on three spatial scales (the point-scale, 30 m scale, and 1 km scale) for maize in northeast China. Results from three ground experimental datasets showed that the SMAP VWC estimation method was strongly biased with an average overestimation of 1.16 kg m^?2,1.04 kg m^?2, and 1.13 kg m^?2 for the point-scale, 30 m scale, and 1 km scale respectively, and maximum bias occurred in the mid-stage of maize. Also, a new power relationship between NDVI (Normalized Difference Vegetation Index) and VWC was proposed for the 30 m scale based on Sentinel 2 NDVI and field VWC values from 2017 experiment, with respective R² (coefficient of determination) and Root Mean Squared Error (RMSE) values of 0.80 and 0.67 kg m^?2. The results confirmed that this power relationship was still suitable for VWC estimation at the 1 km scale, and it has smaller bias than the original SMAP VWC method. Future work will be carried out to evaluate the applicability of this VWC estimation method over a lager region. It is expected that it can improve the accuracy of soil moisture by providing high precision VWC input parameters.