The Emergent Landscape of Detecting EGFR Mutations Using Circulating Tumor DNA in Lung Cancer.

The advances in targeted therapies for lung cancer are based on the evaluation of specific gene mutations especially the epidermal growth factor receptor (EGFR). The assays largely depend on the acquisition of tumor tissue via biopsy before the initiation of therapy or after the onset of acquired resistance. However, the limitations of tissue biopsy including tumor heterogeneity and insufficient tissues for molecular testing are impotent clinical obstacles for mutation analysis and lung cancer treatment. Due to the invasive procedure of tissue biopsy and the progressive development of drug-resistant EGFR mutations, the effective initial detection and continuous monitoring of EGFR mutations are still unmet requirements. Circulating tumor DNA (ctDNA) detection is a promising biomarker for noninvasive assessment of cancer burden. Recent advancement of sensitive techniques in detecting EGFR mutations using ctDNA enables a broad range of clinical applications, including early detection of disease, prediction of treatment responses, and disease progression. This review not only introduces the biology and clinical implementations of ctDNA but also includes the updating information of recent advancement of techniques for detecting EGFR mutation using ctDNA in lung cancer

Increased risk of endometriosis in patients with endometritis — a nationwide cohort study involving 84,150 individuals

Objectives: To evaluate the incidence of endometriosis among endometritis patients and its association with confoundingcomorbidities.Material and methods: A population-based, retrospective cohort study of women aged between 20 to 55 years, who werenewly diagnosed with endometritis between 2000 to 2013. A total of 16,830 endometritis patients and 67,230 non-endometritisindividuals were enrolled by accessing data from the National Health Insurance Research Database of Taiwan.The comorbidities accessed were uterine leiomyoma, rheumatoid arthritis, ovarian cancer, infertility and allergic diseases.Results: The mean follow-up period was 9.15 years for the non-endometritis cohort and 9.13 years for the endometritiscohort. There were significantly higher percentages of uterine leiomyoma, rheumatoid arthritis, infertility, ovarian cancerand allergic diseases in the endometritis cohort than in the non-endometritis cohort. Patients with endometritis hada 1.5-fold increased risk of their condition advancing to endometriosis (HR 1.58, 95% CI 1.48–1.68).Conclusions: Our results suggest that patients with endometritis exhibited a positive correlation in developing endometriosis

Liquid biopsy genotyping in lung cancer: ready for clinical utility?

Liquid biopsy is a blood test that detects evidence of cancer cells or tumor DNA in the circulation. Despite complicated collection methods and the requirement for technique-dependent platforms, it has generated substantial interest due, in part, to its potential to detect driver oncogenes such as epidermal growth factor receptor (EGFR) mutants in lung cancer. This technology is advancing rapidly and is being incorporated into numerous EGFR tyrosine kinase inhibitor (EGFR-TKI) development programs. It appears ready for integration into clinical care. Recent studies have demonstrated that biological fluids such as saliva and urine can also be used for detecting EGFR mutant DNA through application other user-friendly techniques. This review focuses on the clinical application of liquid biopsies to lung cancer genotyping, including EGFR and other targets of genotype-directed therapy and compares multiple platforms used for liquid biopsy

Distributed Training Large-Scale Deep Architectures

Scale of data and scale of computation infrastructures together enable the current deep learning renaissance. However, training large-scale deep architectures demands both algorithmic improvement and careful system configuration. In this paper, we focus on employing the system approach to speed up large-scale training. Via lessons learned from our routine benchmarking effort, we first identify bottlenecks and overheads that hinter data parallelism. We then devise guidelines that help practitioners to configure an effective system and fine-tune parameters to achieve desired speedup. Specifically, we develop a procedure for setting minibatch size and choosing computation algorithms. We also derive lemmas for determining the quantity of key components such as the number of GPUs and parameter servers. Experiments and examples show that these guidelines help effectively speed up large-scale deep learning training

Orbital Symmetry and Electron Correlation in Na_{x}CoO_2

Measurements of polarization-dependent soft x-ray absorption reveal that the electronic states determining the low-energy excitations of Na$_{x}$CoO$_2$ have predominantly $a_{1g}$ symmetry with significant O $2p$ character. A large transfer of spectral weight observed in O $1s$ x-ray absorption provides spectral evidence for strong electron correlations in the layered cobaltates. Comparing Co $2p$ x-ray absorption with calculations based on a cluster model, we conclude that Na$_{x}$CoO$_2$ exhibits a charge-transfer electronic character rather than a Mott-Hubbard character