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We conducted a joint (pooled) analysis of three genome-wide association studies (GWAS) 1-3 of esophageal squamous cell carcinoma (ESCC) in ethnic Chinese (5,337 ESCC cases and 5,787 controls) with 9,654 ESCC cases and 10,058 controls for follow-up. In a logistic regression model adjusted for age, sex, study, and two eigenvectors, two new loci achieved genome-wide significance, marked by rs7447927 at 5q31.2 (per-allele odds ratio (OR) = 0.85, 95% CI 0.82-0.88; P=7.72x10−20) and rs1642764 at 17p13.1 (per-allele OR= 0.88, 95% CI 0.85-0.91; P=3.10x10−13). rs7447927 is a synonymous single nucleotide polymorphism (SNP) in TMEM173 and rs1642764 is an intronic SNP in ATP1B2, near TP53. Furthermore, a locus in the HLA class II region at 6p21.32 (rs35597309) achieved genome-wide significance in the two populations at highest risk for ESSC (OR=1.33, 95% CI 1.22-1.46; P=1.99x10−10). Our joint analysis identified new ESCC susceptibility loci overall as well as a new locus unique to the ESCC high risk Taihang Mountain region.

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Breast cancer (BC) is one of the most common cancers among women worldwide, representing the majority of new cancer cases and cancer-related deaths according to global statistics, making it a significant public health problem in today's society. The early diagnosis of BC can improve the prognosis and chance of survival significantly, as it can promote timely clinical treatment to patients. Further accurate classification of benign tumours can prevent patients undergoing unnecessary treatments. Thus, the correct diagnosis of BC and classification of patients into malignant or benign groups is the subject of much research. Because of its unique advantages in critical features detection from complex BC datasets, machine learning (ML) is widely recognised as the methodology of choice in BC pattern classification and forecast modelling. In this paper, we aim to review ML techniques and their applications in BC diagnosis and prognosis. Firstly, we provide an overview of ML techniques including artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs), and k-nearest neighbors (k-NNs). Then, we investigate their applications in BC. Our primary data is drawn from the Wisconsin breast cancer database (WBCD) which is the benchmark database for comparing the results through different algorithms. Finally, a healthcare system model of our recent work is also shown.

The aim of this study was to evaluate the effect of nano-selenium (NS) and yeast-selenium (YS) supplementation on feed digestibility, rumen fermentation, and urinary purine derivatives in sheep. Six male ruminally cannulated sheep, average 43.32 ± 4.8 kg of BW, were used in a replicated 3 × 3 Latin square experiment. The treatments were control (without NS and YS), NS with 4 g nano-Se (provide 4 mg Se), and YS with 4 g Se-yeast (provide 4 mg Se) per kilogram of diet dry matter (DM), respectively. Experimental periods were 25 days with 15 days of adaptation and 10 days of sampling. Ruminal pH, ammonia N concentration, molar proportion of propionate, and ratio of acetate to propionate were decreased (P < 0.01), and total ruminal VFA concentration was increased with NS and YS supplementation (P < 0.01). In situ ruminal neutral detergent fiber (aNDF) degradation of Leymus chinensis (P < 0.01) and crude protein (CP) of soybean meal (P < 0.01) were significantly improved by Se supplementation. Digestibilities of DM, organic matter, crude protein, ether extract, aNDF, and ADF in the total tract and urinary excretion of purine derivatives were also affected by feeding Se supplementation diets (P < 0.01). Ruminal fermentation was improved by feeding NS, and feed conversion efficiency was also increased compared with YS (P < 0.01). We concluded that nano-Se can be used as a preferentially available selenium source in ruminant nutrition.

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