Confounded association between proton pump inhibitor use and risk of biliary tract cancer: Result from three cohorts

Recent epidemiological studies suggested that proton pump inhibitor (PPI) use was associated with an increased risk of biliary tract cancer (BTC), however, confounders were not adequately controlled. Our study aimed to evaluate PPI use and subsequent risk of BTC and its subtypes in three well-established cohorts. We conducted a pooled analysis of the subjects free of cancers in UK Biobank (n = 463 643), Nurses' Health Study (NHS, n = 80 235) and NHS II (n = 95 869). Propensity score weighted Cox models were used to estimate marginal HRs of PPIs use on BTC risk, accounting for potential confounders. We documented 284 BTC cases in UK Biobank (median follow-up: 7.6 years), and 91 cases in NHS and NHS II cohorts (median follow-up: 15.8 years). In UK biobank, PPI users had a 96% higher risk of BTC compared to nonusers in crude model (HR 1.96, 95% CI 1.44-2.66), but the effect was attenuated to null after adjusting for potential confounders (HR 0.95, 95% CI 0.60-1.49). PPI use was not associated with risk of BTC in the pooled analysis of three cohorts (HR 0.93, 95% CI 0.60-1.43). We also observed no associations between PPI use with risk of intrahepatic (HR 1.00, 95% CI 0.49-2.04), extrahepatic bile duct (HR 1.09, 95% CI 0.52-2.27) and gallbladder cancers (HR 0.66, 95% CI 0.26-1.66) in UK Biobank. In summary, regular use of PPIs was not associated with the risk of BTC and its subtypes.     

Simultaneous Determination of Six Compounds in Rat Plasma by Ultra‑Performance Liquid Chromatography with Tandem Mass Spectrometry: Application in the Pharmacokinetic Study of Qing Gan‑Shu Yu‑Fang

A rapid and high selective ultra?performance liquid chromatography (UPLC) with tandem mass spectrometry method for simultaneous determination of six compounds including albiflorin, paeoniflorin, picroside I, picroside II, saikosaponin A, and saikosaponin D in rat plasma was developed and validated using butyl p-hydroxybenzoate as an internal standard. One-step direct protein precipitation with acetonitrile was used to extract the compounds from the rat plasma samples. Chromatographic separation was achieved using an ACQUITY UPLC BEH C18 column (100 mm × 2.1 mm, 1.7 µm) at a flow rate of 0.4 mL/min, using gradient mode containing 0.1% formic acid in water and acetonitrile were used as the Mobile phase A and B. Electrospray ionization in negative ion mode and multiple reaction monitoring were used to identify and quantify active components. Calibration curves showed good linearity (R2 > 0.9908) over a wide concentration range for all compounds. The intra- and interday precision (relative standard deviation) ranged 2.4%–7.0% and 2.6%–8.0%, respectively. The accuracy (relative error) was from ?13.0% to 13.2% at all quality control levels. The recovery ranged from 81.1% to 92.5%. The validated method was successfully applied to pharmacokinetic study in rats after oral administration of Qing Gan?Shu Yu?Fang. The results show that one can draw a conclusion that these six active ingredients can be quickly absorbed and play a pharmacodynamic role rapidly in vivo.     

Computer-generated vegan menus: The importance of food composition database choice

Precise data on food chemical composition is crucial for any quantitative nutrition research and indispensable for evaluation and planning of computer-based menus. Moreover, exact food chemical composition is of the utmost importance in composing specific diets that may be low in certain essential nutrients. Therefore, this research evaluated a weekly vegan menu using three different food composition databases (FCDB): the Croatian, the official Danish Food Composition Database and the United States Department of Agriculture (USDA) – National Nutrient Database for Standard Reference. Significant differences (p < 0.05) were determined in evaluated and optimised offers for the same menu when using different FCDBs. Furthermore, in these menus quantities of some nutrients have not reached official daily recommendations needed for prevention of non-communicable diseases. In this research, new vegan menus that can provide adequate quantity of essential nutrients, regardless of FCDB utilized, were created with linear optimisation (LO). This resulted in high quality daily menu offers. However, depending on the FCDB that was used, optimal menus differed in daily meal combinations, and the effectiveness of the optimisation in the minimisation of differences caused by differences of data in FCDBs was tested. Linear optimisation has been proved to be an effective tool in planning of specific diets, such as vegan diets. Nonetheless, the quality of computer-planned menus depends heavily on the quality of FCDB used.     

Clinical characteristics and surgical outcomes of resectable acinar cell carcinoma of the pancreas-propensity score matching analysis with pancreatic ductal adenocarcinoma

BackgroundSurgical resection is recommended for patients with resectable acinar cell carcinoma (ACC). The aim of this study was to investigate the clinical characteristics and surgical outcomes of resectable ACC in comparison to pancreatic ductal adenocarcinoma (PDAC).MethodA retrospective analysis was performed on all patients who consecutively underwent radical resection with pathologically confirmed ACC and PDAC from December 2011 to December 2018. Clinicopathologic characteristics and follow-up information were analyzed. A 1:3 propensity score matching (PSM) method was used to minimize the bias between ACC and PDAC.ResultsA total of 26 patients with ACC and 1351 with PDAC were included. Compared to PDAC, ACC tended to be larger (4.5 vs. 3.0 cm; p < 0.001) and more frequently located in the pancreatic body/tail (61.5% vs. 36.6%, p = 0.009), with lower total bilirubin levels, lower neutrophil lymphocyte ratio (NLR) levels and lower carbohydrate antigen 19-9 (CA19-9) levels and carcinoembryonic antigen (CEA) levels. There was no difference in postoperative morbidities in patients with ACC and PDAC. The median OS and RFS were longer in ACC when compared to PDAC (OS: 43.5 mo vs. 19.0 mo, p = 0.004; RFS: 24.5 mo vs. 11.6 mo, p = 0.023). After the 1:3 PSM, ACC remained to be a better histological type for OS (p = 0.024), but had comparable RFS with PDAC (p = 0.164).ConclusionPatients with ACC after radical resection had better OS than that with PDAC. However, ACC is also an aggressive tumor with a similar trend of RFS with PDAC after the matching, necessitating the multidisciplinary treatment for resectable ACC disease.     

Four new diarylheptanoids from Rhizoma Zingiberis

Four new diarylheptanoids, (1S, 3R, 5R, 6R)-1, 5-epoxy-3, 6 dihydroxy-1-(4-hydroxy-3, 5-dimethoxyphenyl)-7-(4-hydroxy-3-methoxyphenyl) heptane (1), (1R, 3R, 5S)-1, 5-epoxy-3-acetoxy-1-(4, 5-dihydroxy-3-methoxyphenyl)-7-(3, 4- hydroxyphenyl) heptane (2), (3R, 5S, 6R, 7S)-3, 6-epoxy-7-hydroxyl-1-(4-hydroxyphenyl)-7-(3-methoxy-4-hydroxyphenyl) heptane (3), (E)-3-keto-1-(3-methoxy-4-hydroxyphenyl)-7-(4, 5-dihydroxy-3-methoxyphenyl)-4- heptene (4), were isolated from Rhizoma Zingiberis, and their structures were determined based on HR-ESI-MS and extensive spectroscopic techniques (UV, IR, 1D-NMR and 2D-NMR). Compounds 1–4 exhibited no cytotoxicity against HepG2 cell lines.

     

The potential mechanism of Bupleurum against anxiety was predicted by network pharmacology study and molecular docking

Metabolic Brain Disease - Bupleurum chinense DC. (Chaihu) is a traditional Chinese medicine (TCM) used in the treatment of anxiety. But the anxiolytic mechanisms of bupleurum are still unclear....     

Deep Phenotyping and Prediction of Long-term Cardiovascular Disease: Optimized by Machine Learning

BackgroundPrediction of cardiovascular disease (CVD) is important in clinical practice. Machine learning (ML) may offer an improved alternative to current CVD risk stratification in individual patients. We aim to identify important predictors and compare ML models with traditional models according to their prediction performance in a large long-term follow-up cohort.MethodsThe Atherosclerosis Risk in Communities (ARIC) study was designed to study the progression of subclinical disease to cardiovascular events over a 25-year follow-up period. All phenotypic variables at visit 1 were obtained. All-cause death, CVD, and coronary heart disease were the outcomes for analysis. The ML framework involved variable selection using the random survival forest (RSF) method, model building, and 5-fold cross-validation. Model performance was evaluated by discrimination using the Harrell concordance index (C-index), accuracy using the Brier score (BS), and interpretability using the number of variables in the model.ResultsOf the 14,842 participants in ARIC, the average age was 54.2 years, with 45.2% male and 26.2% Black participants. Thirty-eight unique variables were selected in the RSF top 20 importance ranking of all 6 outcomes. Aging, hypertension, glucose metabolism, renal function, coagulation, adiposity, and sodium retention dominated the predictions of all outcomes. The ML models outperformed the regression models and established risk scores with a higher C-index, lower BS, and varied interpretability.ConclusionsThe ML framework is useful for identifying important predictors of CVD and for developing models with robust performance compared with existing risk models.     

师承教育在医教协同创新中医人才培养中的作用

中医师承教育是当今社会中医传承的主要方式,其培养了大量的中医优秀人才,促进中医药文化和中医学术思想传播,是现代中医药发展不可缺少的组成部分。但近代以来,人们脑海中逐渐形成了以西方医学为体系的思维模式时,中医学教育该如何的发展与传承。针对这一现象,我们分析了现代院校教育与师承教育的特点,阐述师承教育在现代中医人才培养中的作用,以期探讨高等中医药院校教育改革的模式。