Abstract. Lung cancer is the most prevalent type of cancer and the leading cause of cancer-related deaths worldwide. Coherent anti-Stokes Raman scattering (CARS) is capable of providing cellular-level images and resolving pathologically related features on human lung tissues. However, conventional means of analyzing CARS images requires extensive image processing, feature engineering, and human intervention. This study demonstrates the feasibility of applying a deep learning algorithm to automatically differentiate normal and cancerous lung tissue images acquired by CARS. We leverage the features learned by pretrained deep neural networks and retrain the model using CARS images as the input. We achieve 89.2% accuracy in classifying normal, small-cell carcinoma, adenocarcinoma, and squamous cell carcinoma lung images. This computational method is a step toward on-the-spot diagnosis of lung cancer and can be further strengthened by the efforts aimed at miniaturizing the CARS technique for fiber-based microendoscopic imaging. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

Ce(1-x)M(x)O(2) (M = Ti, Zr, and Hf) nanomaterials with controlled morphology and composition were synthesized by a two-step route for the first time. These nanomaterials exhibit high activities for hydrogen reduction and ethanol reforming reactions, and therefore, they may be useful for applications in catalysis and solid oxide fuel cells.

Since the KCNB1 encoding Kv2.1 channel accounts for the majority of Kv currents modulating insulin secretion by pancreatic islet beta-cells, we postulated that KCNB1 is a plausible candidate gene for genetic variation contributing to the variable compensatory secretory function of beta-cells in type-2 diabetes (T2D). We conducted two studies, a case-control study and a cross-section study, to investigate the association of common single-nucleotide polymorphisms (SNPs) in KCNB1 with T2D and its linking traits. In the case-control study, we first examined the association of 20 tag SNPs of KCNB1 with T2D in a population with 226 T2D patients and non-diabetic subjects (screening study). We then identified the association in an enlarged population of 412 T2D patients and non-diabetic subjects (replication study). In the cross-sectional study, we investigated the linkage between the candidate SNP rs1051295 and T2D by comparing beta-cell function and insulin sensitivity among rs1051295 genotypes in a general population of 1051 subjects at fasting and after glucose loading (oral glucose tolerance tests, OGTT) in 84 fasting glucose impaired subjects, and several T2D-related traits. We found that among the 19 available tag SNPs, only the KCNB1 rs1051295 was associated with T2D (P = 0.027), with the rs1051295 TT genotype associated with an increased risk of T2D compared with genotypes CC (P = 0.009). At fasting, rs1051295 genotype TT was associated with a 9.8% reduction in insulin sensitivity compared to CC (P = 0.008); along with increased plasma triglycerides (TG) levels (TT/CC: P = 0.046) and increased waist/hip (W/H) ratio (TT/CC: P = 0.013; TT/TC: P = 0.002). OGTT confirmed that genotype TT exhibited reduced insulin sensitivity by 16.3% (P = 0.030) compared with genotype TC+CC in a fasting glucose impaired population. The KCNB1 rs1051295 genotype TT in the Chinese Han population is associated with decreased insulin sensitivity and increased plasma TG and W/H ratio, which together contribute to an increased risk for T2D.