钙离子依赖型凝集素样受体2 (CLEC-2)研究进展

 钙离子依赖型凝集素样受体2(calcium dependent lectin like receptor 2,CLEC-2)是近年发现的血小板表面受体,相对分子质量(Mr)约32 000,是一种Ⅱ型跨膜受体。蛇毒蛋白Rhodocytin和唾液酸样糖蛋白Podoplanin是目前被分别鉴定出的CLEC-2的外源性和内源性配体。配体与受体相互作用后,通过Src及Syk依赖的酪氨酸激酶途径活化下游分子,最终激活PLCγ2,产生第二信使的生理效应,引起血小板活化聚集。研究表明CLEC-2与配体的相互作用和血栓性疾病、肿瘤转移和进展以及血小板捕获HIV 1并呈递给靶细胞等过程密切相关。因此,阻断两者相互作用可能成为治疗上述疾病的新靶点。     

Physiological variables involved in heart valve substitute calcification

Biochemical, histological and genetic studies using in vitro/in vivo models have demonstrated that pathological calcification of bioprosthetic heart valves (BHV) is regulated by various mechanisms associated with physiological variables. The major objective of this review is to characterize physiological variables involved in BHV calcification. This review examines our understanding of the systemic cellular behavior and physiological regulation processes behind BHV calcification and its clinical applications.     

Large-scale analysis of interindividual variability in single and paired-pulse TMS data

ObjectiveThis study brought together over 60 transcranial magnetic stimulation (TMS) researchers to create the largest known sample of individual participant single and paired-pulse TMS data to date, enabling a more comprehensive evaluation of factors driving response variability.MethodsAuthors of previously published studies were contacted and asked to share deidentified individual TMS data. Mixed-effects regression investigated a range of individual and study level variables for their contribution to variability in response to single and paired-pulse TMS data.Results687 healthy participant’s data were pooled across 35 studies. Target muscle, pulse waveform, neuronavigation use, and TMS machine significantly predicted an individual’s single-pulse TMS amplitude. Baseline motor evoked potential amplitude, motor cortex hemisphere, and motor threshold (MT) significantly predicted short-interval intracortical inhibition response. Baseline motor evoked potential amplitude, test stimulus intensity, interstimulus interval, and MT significantly predicted intracortical facilitation response. Age, hemisphere, and TMS machine significantly predicted MT.ConclusionsThis large-scale analysis has identified a number of factors influencing participants’ responses to single and paired-pulse TMS. We provide specific recommendations to minimise interindividual variability in single and paired-pulse TMS data.SignificanceThis study has used large-scale analyses to give clarity to factors driving variance in TMS data. We hope that this ongoing collaborative approach will increase standardisation of methods and thus the utility of single and paired-pulse TMS.     

Factor analysis for survival time prediction with informative censoring and diverse covariates

Fulfilling the promise of precision medicine requires accurately and precisely classifying disease states. For cancer, this includes prediction of survival time from a surfeit of covariates. Such data presents an opportunity for improved prediction, but also a challenge due to high dimensionality. Furthermore, disease populations can be heterogeneous. Integrative modeling is sensible, as the underlying hypothesis is that joint analysis of multiple covariates provides greater explanatory power than separate analyses. We propose an integrative latent variable model that combines factor analysis for various data types and an exponential proportional hazards (EPH) model for continuous survival time with informative censoring. The factor and EPH models are connected through low-dimensional latent variables that can be interpreted and visualized to identify subpopulations. We use this model to predict survival time. We demonstrate this model's utility in simulation and on four Cancer Genome Atlas datasets: diffuse lower-grade glioma, glioblastoma multiforme, lung adenocarcinoma, and lung squamous cell carcinoma. These datasets have small sample sizes, high-dimensional diverse covariates, and high censorship rates. We compare the predictions from our model to three alternative models. Our model outperforms in simulation and is competitive on real datasets. Furthermore, the low-dimensional visualization for diffuse lower-grade glioma displays known subpopulations.     

Aerosol particle generation from solution-based pressurized metered dose inhalers: a technical overview of parameters that influence respiratory deposition

Abstract

There are a multitude of formulation factors to consider when developing a solution-based pressurized metered dose inhaler (pMDI). Evaluation of these variables and their underpinning driving force has been performed over the years. Key components, including formulation composition and device design, play significant roles in determining the aerosol performance of these solution-based formulations. This review outlines research efforts that have focused on these essential governing factors, how the aerosol performance changes when these variables are modified and fundamental mechanisms affecting the delivery efficiency of such formulations.     

Alcances de la aleatorización mendeliana para el control de confusores no observables en epidemiología

The Mendelian randomization is an epidemiologic method proposed to control for spurious associations in observational studies. These associations are commonly caused by confusion derived from social, environmental, and behavioral factors, which can be difficult to measure. Mendelian randomization is based on the selection of genetic variants that are used as instrumental variables that influence exposure patterns or are associated with an intermediate phenotype of the disease. The present work aims to discuss how to select the appropriate genetic variants as instrumental variables and to present methodological tools to deal with the limitations of this epidemiological method. The use of instrumental variables for modifiable exposures has the potential to mitigate the effects of common limitations, such as confusion, when robust genetic variants are chosen as instrumental variables.     

Not just data: A method for improving prediction with knowledge

Many medical conditions are only indirectly observed through symptoms and tests. Developing predictive models for such conditions is challenging since they can be thought of as ‘latent’ variables. They are not present in the data and often get confused with measurements. As a result, building a model that fits data well is not the same as making a prediction that is useful for decision makers. In this paper, we present a methodology for developing Bayesian network (BN) models that predict and reason with latent variables, using a combination of expert knowledge and available data. The method is illustrated by a case study into the prediction of acute traumatic coagulopathy (ATC), a disorder of blood clotting that significantly increases the risk of death following traumatic injuries. There are several measurements for ATC and previous models have predicted one of these measurements instead of the state of ATC itself. Our case study illustrates the advantages of models that distinguish between an underlying latent condition and its measurements, and of a continuing dialogue between the modeller and the domain experts as the model is developed using knowledge as well as data.     

Targeting the PI3K/AKT/mTOR pathway in biliary tract cancers: A review of current evidences and future perspectives

Biliary tract cancers (BTCs) are a group of invasive neoplasms, with increasing incidence and dismal prognosis. In advanced disease, the standard of care is represented by first-line chemotherapy with cisplatin and gemcitabine. In subsequent lines, no clear recommendations are currently available, highlighting the need for novel therapeutic approaches.The PI3K/AKT/mTOR pathway is a core regulator of cell metabolism, growth and survival, and is involved in BTCs carcinogenesis and progression. Mutations, gene copy number alterations and aberrant protein phosphorylation of PI3K, AKT, mTOR and PTEN have been thoroughly described in BTCs and correlate with poor survival outcomes.Several pre-clinical evidences state the efficacy of PI3K/AKT/mTOR pathway inhibitors in BTCs, both in vitro and in vivo. In the clinical setting, initial studies with rapamycin analogs have shown interesting activity with an acceptable toxicity profile. Novel strategies evaluating AKT and PI3K inhibitors have risen serious safety concerns, pointing out the need for improved patient selection and increased target specificity for the clinical development of these agents, both alone and in combination with chemotherapy.This review extensively describes the role of the PI3K/AKT/mTOR pathway in BTCs and examines the rationale of its targeting in these tumors, with particular focus on clinical activity, toxicities and perspectives on further development of PI3K/AKT/mTOR pathway inhibitors.