Assessment of selective breeding effects and selection signatures in Qingyuan partridge chicken and its strains.

Qingyuan partridge chicken (QYM) is a highly regarded native breed in China, highly esteemed for its exceptional breeding characteristics. However, the investigation into the selection signatures and its strains remains largely unexplored. In this study, blood sampling, DNA extracting, and high-depth resequencing were performed in 27 QYMs. Integrating the genomic data of 14 chicken (70 individuals) breeds from other researches, to analyze the genetic structure, selection signatures, and effects of selective breeding within QYM and its 3 strains (QYMA, QYMB, and QYMC). Population structure analysis revealed an independent QYM cluster, which exhibited distinct from other breeds, with each of its 3 strains displaying distinct clustering patterns. Linkage disequilibrium analysis highlighted QYMB's notably slower decay rate, potentially influenced by selection pressure from various production indicators. Examination of selection signatures uncovered genes and genetic mechanisms associated with genomic changes resulting from extensive selective breeding within the QYM and its strains. Intriguingly, diacylglycerol kinase beta (DGKB) and catenin alpha 2 (CTNNA2) were identified as commonly selected genes across the 3 QYM strains, linked to energy metabolism, muscle development, and fat metabolism. Our research validates the substantial impact of selective breeding on QYM and its strains, concurrently identifying genomic regions and signaling pathways associated with their distinctive characters. This research also establishes a fundamental framework for advancing yellow-feathered broiler breeding strategies.

Radiomics based on preoperative rectal cancer MRI to predict the metachronous liver metastasis.

PURPOSE: At present, there are few effective method to predict metachronous liver metastasis (MLM) from rectal cancer. We aim to investigate the efficacy of radiomics based on multiparametric MRI of first diagnosed rectal cancer in predicting MLM from rectal cancer. METHODS: From 301 consecutive histopathologically confirmed rectal cancer patients, 130 patients who have no distant metastasis detected at the time of diagnosis were enrolled and divided into MLM group (n = 49) and non-MLM group (n = 81) according to whether liver metastasis be detected later than 6 month after the first diagnosis of rectal cancer within 3 years' follow-up. The 130 patients were divided into a training set (n = 91) and a testing set (n = 39) at a ratio of 7:3 by stratified sampling using SPSS 24.0 software. The DWI model, HD T2WI model, and DWI + HD T2WI model were constructed respectively. The best performing model was selected and combined with the screened clinical features (including non-radiomics MRI features) to construct a fusion model. The testing set was used to evaluate the performance of the models, and the area under the curve (AUC) of receiver operating characteristics (ROC) was calculated for both the training set and the testing set. RESULTS: The AUC of the DWI + HD T2WI model in the testing set was higher than that of the DWI or the HD T2 model alone with statistically significance (P < 0.05). The screened clinical features were extramural vascular invasion (EMVI), T and N stages in MRI (mrT, mrN), and the distance from the lower edge of the tumor to the anal verge. The AUC of the fusion model in the testing set was 0.911. Decision curves and nomogram also showed that the fusion model had excellent clinical performance. CONCLUSION: The fusion model of primary rectal cancer MRI based radiomics combing clinical features can effectively predict MLM from rectal cancer, which may assist clinicians in formulating individualized monitoring and treatment plans.