• Pratt Demant posted an update 6 months, 4 weeks ago

    Non-persistence with OACs was associated with an increased risk of the composite outcome of ischaemic stroke and ischaemic stroke-related death and ischaemic stroke (aHR 1.58, 95% CI 1.29-1.93) compared with being persistent with OACs.

    At least a quarter of NVAF patients were non-persistent with OACs within 4 years, which was associated with poor efficacy of ischaemic stroke prevention. The identified baseline characteristics may help identify patients at risk of non-persistence.

    At least a quarter of NVAF patients were non-persistent with OACs within 4 years, which was associated with poor efficacy of ischaemic stroke prevention. The identified baseline characteristics may help identify patients at risk of non-persistence.A microfluidic array was constructed for trapping single cell and loading identical dynamic biochemical stimulation for gain a better understanding of Ca2+ signalling in single cells by applying extracellular dynamic biochemical stimulus. This microfluidic array consists of multiple radially aligned flow channels with equal intersection angles, which was designed by a combination of stagnation point flow and physical barrier. Numerical simulation results and trajectory analysis shown the effectiveness of this single cell trapping device. Fluorescent experiment results demonstrated the effects of flow rate and frequency of dynamic stimulus on the profiles of biochemical concentration which exposed on captured cells. In this array chip, the captured single cells in each trapping channels were able to receive identical extracellular dynamic biochemical stimuli which being transmitted from the entrance at the middle of the microfluidic array. Besides, after loading dynamic Adenosine Triphosphate (ATP) stimulation on captured cells by this device, consistent average intracellular Ca2+ dynamics phase and cellular heterogeneity were observed in captured single K562 cells. Furthermore, this device is able to be used for investigating cellular respond in single cells to temporally varying environments by modulating the stimulation signal in terms of concentration, pattern, and duration of exposure.Medical research institutions have generated massive amounts of biological data by genetically profiling hundreds of cancer cell lines. In parallel, academic biology labs have conducted genetic screens on small numbers of cancer cell lines under custom experimental conditions. In order to share information between these two approaches to scientific discovery, this article proposes a “frequentist assisted by Bayes” (FAB) procedure for hypothesis testing that allows auxiliary information from massive genomics datasets to increase the power of hypothesis tests in specialized studies. The exchange of information takes place through a novel probability model for multimodal genomics data, which distills auxiliary information pertaining to cancer cell lines and genes across a wide variety of experimental contexts. If the relevance of the auxiliary information to a given study is high, then the resulting FAB tests can be more powerful than the corresponding classical tests. If the relevance is low, then the FAB tests yield as many discoveries as the classical tests. Lonidamine supplier Simulations and practical investigations demonstrate that the FAB testing procedure can increase the number of effects discovered in genomics studies while still maintaining strict control of type I error and false discovery rate.Forkhead box D1 (FOXD1) is a new member of FOX transcription factor family. FOXD1 has demonstrated multi-level roles during normal development and several diseases’ pathogenesis. However, litter is known about the role of FOXD1 in the progression of head and neck squamous cancer (HNSC). In the present study, we analyzed FOXD1 expression pattern using TCGA dataset, GEO datasets, HNSC cell lines and HNSC tissues. Then, we analyzed the correlation between FOXD1 expression and clinical characteristics, and evaluated the prognostic value of FOXD1 in HNSC. Moreover, we assessed the relationship between FOXD1 expression and tumor environment (TME) and immune cell infiltration using ESTIMATE and CIBERSORT algorithms. Finally, we predicted the FOXD1-related biological processes and signal pathways. FOXD1 was up-regulated in HNSC tissues in TCGA datasets, validated by GEO datasets, HNSC cell lines and HNSC tissues. FOXD1 expression was significantly associated with tumor site and HPV infection. Univariate and multivariate Cox regression analyses showed that FOXD1 expression was an independent prognostic factor. Moreover, we found that the proportions of naïve B cells, plasma cells, and resting dendritic cells were negatively correlated with FOXD1 expression, otherwise, the proportion of activated mast cells was positively correlated with FOXD1 expression using CIBERSORT algorithm. GSEA analyses revealed that FOXD1 was mainly involved in cancer-related signaling pathway and metabolism-related pathways. FOXD1 was a potential oncogene, and might represent an indicator for predicting overall survival of HNSC patients. Moreover, many cancer-related pathways and metabolism-related processes may be regulated by FOXD1.It is becoming increasingly common for researchers to consider incorporating external information from large studies to improve the accuracy of statistical inference instead of relying on a modestly sized data set collected internally. With some new predictors only available internally, we aim to build improved regression models based on individual-level data from an “internal” study while incorporating summary-level information from “external” models. We propose a meta-analysis framework along with two weighted estimators as the composite of empirical Bayes estimators, which combines the estimates from different external models. The proposed framework is flexible and robust in the ways that (i) it is capable of incorporating external models that use a slightly different set of covariates; (ii) it is able to identify the most relevant external information and diminish the influence of information that is less compatible with the internal data; and (iii) it nicely balances the bias-variance trade-off while preserving the most efficiency gain.

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