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Investigating the causal relationship between exposure and time-to-event outcome is an important topic in biomedical research. Previous literature has discussed the potential issues of using hazard ratio (HR) as the marginal causal effect measure due to noncollapsibility. In this article, we advocate using restricted mean survival time (RMST) difference as a marginal causal effect measure, which is collapsible and has a simple interpretation as the difference of area under survival curves over a certain time horizon. To address both measured and unmeasured confounding, a matched design with sensitivity analysis is proposed. Matching is used to pair similar treated and untreated subjects together, which is generally more robust than outcome modeling due to potential misspecifications. Our propensity score matched RMST difference estimator is shown to be asymptotically unbiased, and the corresponding variance estimator is calculated by accounting for the correlation due to matching. Simulation studies also demonstrate that our method has adequate empirical performance and outperforms several competing methods used in practice. To assess the impact of unmeasured confounding, we develop a sensitivity analysis strategy by adapting the E-value approach to matched data. We apply the proposed method to the Atherosclerosis Risk in Communities Study (ARIC) to examine the causal effect of smoking on stroke-free survival.

Generation of TNF-α in response to chemotherapy has been associated with chemo-resistance and metastasis propagation. Here the authors show that the combination of an anti-TNF antibody and cisplatin doublet chemotherapy increases anti-tumor efficacy in lung cancer preclinical models and report the results of a phase I clinical trial of the anti-TNF-α antibody certolizumab in combination with chemotherapy in patients with stage IV lung adenocarcinomas.

Abstract Background Cancer is a heterogeneous disease with many genetic variations. Lines of evidence have shown copy number variations (CNVs) of certain genes are involved in development and progression of many cancers through the alterations of their gene expression levels on individual or several cancer types. However, it is not quite clear whether the correlation will be a general phenomenon across multiple cancer types. Methods In this study we applied a bioinformatics approach integrating CNV and differential gene expression mathematically across 1025 cell lines and 9159 patient samples to detect their potential relationship. Results Our results showed there is a close correlation between CNV and differential gene expression and the copy number displayed a positive linear influence on gene expression for the majority of genes, indicating that genetic variation generated a direct effect on gene transcriptional level. Another independent dataset is utilized to revalidate the relationship between copy number and expression level. Further analysis show genes with general positive linear influence on gene expression are clustered in certain disease-related pathways, which suggests the involvement of CNV in pathophysiology of diseases. Conclusions This study shows the close correlation between CNV and differential gene expression revealing the qualitative relationship between genetic variation and its downstream effect, especially for oncogenes and tumor suppressor genes. It is of a critical importance to elucidate the relationship between copy number variation and gene expression for prevention, diagnosis and treatment of cancer.

Summary: Recent advancements in single-cell RNA sequencing (scRNA-seq) have facilitated the classification of thousands of cells through transcriptome profiling, wherein accurate cell type identification is critical for mechanistic studies. In most current analysis protocols, cell type-based cluster annotation is manually performed and heavily relies on prior knowledge, resulting in poor replicability of cell type annotation. This study aimed to introduce a single-cell Cluster-based Automatic Annotation Toolkit for Cellular Heterogeneity (scCATCH, https://github.com/ZJUFanLab/scCATCH). Using three benchmark datasets, the feasibility of evidence-based scoring and tissue-specific cellular annotation strategies were demonstrated by high concordance among cell types, and scCATCH outperformed Seurat, a popular method for marker genes identification, and cell-based annotation methods. Furthermore, scCATCH accurately annotated 67%–100% (average, 83%) clusters in six published scRNA-seq datasets originating from various tissues. The present results show that scCATCH accurately revealed cell identities with high reproducibility, thus potentially providing insights into mechanisms underlying disease pathogenesis and progression. : Quantitative Genetics; Cell Biology; Bioinformatics; Automation in Bioinformatics Subject Areas: Quantitative Genetics, Cell Biology, Bioinformatics, Automation in Bioinformatics

Abstract This study proposes a novel algorithm that enhances the distinctiveness of the traditional vector of locally aggregated descriptors (VLAD) using spatial distribution clue of local features. The algorithm introduces a new method to compute the spatial distribution entropy (SDE) of clusters. Unlike conventional methods, this algorithm considers the distribution of full spatial information provided by local feature detectors rather than only utilizing the spatial coordinate statistics. For each cluster, the corresponding spatial distribution is computed using a histogram of spatial locations, scales, and orientations of all local features inside the cluster. Entropy is calculated from the spatial distributions of all clusters of an image to create a distribution function, which is further normalized and concatenated with the VLAD vector to generate the final representation. Image retrieval and classification experiments on public datasets are performed. Experimental results show that the proposed algorithms produce better or comparable retrieval performance than several state-of-the-art algorithms. In addition, we extend our SDE to the convolutional neural network (CNN) feature, which further improves the CNN feature result in image retrieval.

Room temperature phosphorescence (RTP) materials have wide applications, and guest/host doping is an important method to achieve RTP. Although weak host–guest interactions (such as hydrogen bonding and π–π stacking) are considered to play a key role in inducing RTP in most doped systems (DSs), stronger and facile coordination bonds can achieve RTP more effectively and are believed to do so in DSs in related research. However, there is a lack of solid experimental evidence. Herein a new stable ligand‐modified lead halide (PCB) is synthesized and used as matrix to prepare RTP NA/PCB DSs with naphthalene derivatives (NA) as guests. Remarkably, a coordination bond between host and guest is experimentally demonstrated and revealed to play a decisive role in the generation of efficient RTP. On this basis, a coordination‐driven doping strategy is proposed to achieve efficient, multicolored, and long‐lived RTP of the DSs. In addition, NA/PCB shows excellent RTP stability and can be used in advanced security encryption, white light emitting diodes, and phosphorescent temperature sensors. This work not only proves the important role of coordination bonds in the RTP DSs, but also shows the potential of the ligand‐modified lead halide matrix as the host material of RTP. [ABSTRACT FROM AUTHOR]

Abstract Background Valuable scientific results on biomedicine are very rich, but they are widely scattered in the literature. Topic modeling enables researchers to discover themes from an unstructured collection of documents without any prior annotations or labels. In this paper, taking ginseng as an example, biological dynamic topic model (Bio-DTM) was proposed to conduct a retrospective study and interpret the temporal evolution of the research of ginseng. Methods The system of Bio-DTM mainly includes four components, documents pre-processing, bio-dictionary construction, dynamic topic models, topics analysis and visualization. Scientific articles pertaining to ginseng were retrieved through text mining from PubMed. The bio-dictionary integrates MedTerms medical dictionary, the second edition of side effect resource, a dictionary of biology and HGNC database of human gene names (HGNC). A dynamic topic model, a text mining technique, was used to emphasize on capturing the development trends of topics in a sequentially collected documents. Besides the contents of topics taken on, the evolution of topics was visualized over time using ThemeRiver. Results From the topic 9, ginseng was used in dietary supplements and complementary and integrative health practices, and became very popular since the early twentieth century. Topic 6 reminded that the planting of ginseng is a major area of research and symbiosis and allelopathy of ginseng became a research hotspot in 2007. In addition, the Bio-DTM model gave an insight into the main pharmacologic effects of ginseng, such as anti-metabolic disorder effect, cardioprotective effect, anti-cancer effect, hepatoprotective effect, anti-thrombotic effect and neuroprotective effect. Conclusion The Bio-DTM model not only discovers what ginseng’s research involving in but also displays how these topics evolving over time. This approach can be applied to the biomedical field to conduct a retrospective study and guide future studies.

Abstract Current chemical markers based quality assessment methods largely fail to reflect intrinsic chemical complexity and multiple mechanisms of action of botanical drugs (BD). The development of novel quality markers is greatly needed. Here we propose bioactive chemical markers (BCM), defined as a group of chemo-markers that exhibit similar pharmacological activities comparable to the whole BD, which can therefore be used to effectively assess the quality of BD. As a proof-of-concept, a BCM-based strategy was developed and applied to Xuesaitong Injection (XST) for assessing the efficacy and consistency of different batches. Firstly, systemic characterization of chemical profile of XST revealed a total number of 97 compounds. Secondly, notoginsenoside R1, ginsenoside Rg1, Re, Rb1 and Rd were identified as BCM of XST on treating cardiovascular and cerebrovascular diseases according to Adjusted Efficacy Score following an in vivo validation. Analytical method for quantification of BCM was then developed to ensure the efficacy of XST. Finally, chemical fingerprinting was developed and used to evaluate the batch-to-batch consistency. Our present case study on XST demonstrates that BCM-based strategy offers a rational approach for quality assessment of BD and provides a workflow for chemistry, manufacturing, and controls (CMC) study of BD required by regulatory authority.