SERPINs shelter the endowed migrants in a hostile land

Since metastatic lesions of solid tumors are the major cause of mortality in cancer patients, understanding the molecular mechanisms of metastasis is of paramount importance. Although extensive knowledge has been accumulated regarding the early steps in metastasis—starting with the departure of cancer cells from their primary sites, to their transit through the hematogenous and/or lymphatic systems, and ending with their entrance into the parenchyma of distant organs—it is difficult if not impossible to translate such knowledge into medicine due to the challenge of identifying patients with only primary tumors but otherwise pristine organs. In other words, autopsy studies indicate that a large proportion of patients already harbor dormant, undetectable micrometastases at the time of cancer diagnosis (Hensel et al, 2013 ). Accordingly, stopping tumor cell dissemination is too late for these patients. Therefore, understanding the survival and outgrowth of micrometastases may hold greater promise to combat metastatic disease.     

Bayesian nonparametric inference for panel count data with an informative observation process

In this paper, the panel count data analysis for recurrent events is considered. Such analysis is useful for studying tumor or infection recurrences in both clinical trial and observational studies. A bivariate Gaussian Cox process model is proposed to jointly model the observation process and the recurrent event process. Bayesian nonparametric inference is proposed for simultaneously estimating regression parameters, bivariate frailty effects, and baseline intensity functions. Inference is done through Markov chain Monte Carlo, with fully developed computational techniques. Predictive inference is also discussed under the Bayesian setting. The proposed method is shown to be efficient via simulation studies. A clinical trial dataset on skin cancer patients is analyzed to illustrate the proposed approach.     

墨江蜈蚣与少棘蜈蚣的比较研究──Ⅱ．药效和毒理

本文比较了墨江蜈蚣与少棘蜈蚣的药效学和毒理学作用，实验包括抗惊厥试验、对致病性真菌和细菌体外生长的影响、急性毒性试验和染色体畸变实验等。结果两种蜈蚣的３％醋酸提取液对所试的真菌有抑制作用但无杀真菌作用，而对细菌无抑制作用。两种蜈蚣的毒性很低，在给小鼠５０ｇ／ｋｇ的剂量下都无法测出ＬＤ５０。在２０５ｍｇ／ｋｇ条件下的致突变率与未给药的突变率接近，表明两者都无遗传毒性。     

Complementary Aluminum Nanopatch/Nanohole Arrays for Broad Palettes of Colors

Plasmonics - We investigate aluminum nanopatch/nanohole arrays surrounded by a dielectric material on plastic substrates for large area color printing. In this specific arrangement, metallic...     

Cranial Suture Regeneration Mitigates Skull and Neurocognitive Defects in Craniosynostosis

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A Prostate Cancer Model Build by a Novel SVM-ID3 Hybrid Feature Selection Method Using Both Genotyping and Phenotype Data from dbGaP

Through Genome Wide Association Studies (GWAS) many Single Nucleotide Polymorphism (SNP)-complex disease relations can be investigated. The output of GWAS can be high in amount and high dimensional, also relations between SNPs, phenotypes and diseases are most likely to be nonlinear. In order to handle high volume-high dimensional data and to be able to find the nonlinear relations we have utilized data mining approaches and a hybrid feature selection model of support vector machine and decision tree has been designed. The designed model is tested on prostate cancer data and for the first time combined genotype and phenotype information is used to increase the diagnostic performance. We were able to select phenotypic features such as ethnicity and body mass index, and SNPs those map to specific genes such as CRR9, TERT. The performance results of the proposed hybrid model, on prostate cancer dataset, with 90.92% of sensitivity and 0.91 of area under ROC curve, shows the potential of the approach for prediction and early detection of the prostate cancer.