Tobacco Quitline utilization compared with cigarette smoking prevalence in Virginia across rurality and Appalachian Status, 2011-2019.

Introduction: While cigarette smoking rates have declined, rural and Appalachian populations in the United States have not seen similar decreases. Quitline programs are promising strategies in reducing disparities in these areas, but research on their usage is limited. Methods: We employed Small Area Estimation on the Virginia Behavioral Risk Factor Surveillance System (2011-2019) to estimate county-level smoking prevalence and utilized The Quit Now Virginia Quitline data (2011-2019) to estimate Quitline users. We analyzed differences in Quitline utilization by rurality and Appalachian status using statistical t-tests. Stepwise regression assessed the absolute estimate of county features, including poverty rate, tobacco retailer density, physician availability, coal mining industry, and tobacco agriculture, on Quitline usage. Results: While the average smoking rate overall was 15.3 %, only 7.4 % of smokers accessed Quitline services from 2011 to 2019. Appalachian regions exhibited higher smoking rates (20.9 %) and lower quitline usage (4.8 %) compared to non-Appalachian areas (14 % smoking prevalence, 8 % quitline usage). Rural regions had higher smoking prevalence (19.0 %) than urban areas (12.9 %), but no significant difference in Quitline utilization (7.6 % vs. 7.2 %, p = 0.7). Stepwise regression revealed counties with more tobacco agriculture had 3.2 % (p = 0.04) lower Quitline utilization. Also, more physicians availability in the county was associated with 3.9 % higher Quitline usage (p = 0.03) and Appalachian counties exhibited a 3.6 % lower Quitline usage rate compared to non-Appalachian counties. Conclusion: A significant gap exists between cigarette smoking prevalence and Quitline utilization, particularly in underserved rural and Appalachian areas, despite no clear barriers to accessing this remote cessation resource. Implication: The study underscores persistent disparities in smoking rates, with rural and Appalachian regions in the United States facing higher smoking prevalence and limited utilization of Quitline services. Despite no clear barriers to access, the gap between smoking prevalence and Quitline usage remains significant, particularly in underserved areas. Tailoring interventions to address regional disparities and factors like tobacco agriculture and physician availability is essential to reduce smoking rates and improve Quitline utilization in these communities.

Stepwise Method and Factor Scoring in Multiple Regression Analysis of Cashmere Production in Liaoning Cashmere Goats.

Liaoning cashmere goat is a well-known local cashmere goat breed in China and even in the world. It is famous for producing cashmere with superior quality and high yield. Cashmere yield, body measurements, and body weight are the primary indicators of cashmere goat breeding, but the correlation between them is not yet clear. Therefore, this study investigated the relationship between certain body measurements, body weight, and cashmere yield in Liaoning cashmere goats using stepwise and factor score analyses in a multiple regression analysis. For this purpose, the body measurements (body slanting length (BSL), body height (BH), chest circumference (CC), pipe circumference (PC), chest depth (CD), chest width (CW), hip breadth (HB), body weight (BW) and cashmere yield (CY)) of 200 (2-year-old) Liaoning cashmere goats were collected. Stepwise analysis of the results showed that body weight had the greatest direct effect on cashmere yield, followed by hip breadth, while chest circumference mainly affected cashmere yield indirectly. The results of factor score analysis showed that the independent variable can be represented by two factors, which explained 49.596% and 12.095% of the total variance, respectively. The factor scores used in the regression analysis explained 75.8% of the total variance in Liaoning cashmere yield. The above studies show that the growth traits of Liaoning cashmere goats are closely related to the cashmere yield. Growth traits should be considered important factors in breed selection, germplasm identification, and rearing.

Gene Networks Driving Genetic Variation in Milk and Cheese-Making Traits of Spanish Assaf Sheep.

Most of the milk produced by sheep is used for the production of high-quality cheese. Consequently, traits related to milk coagulation properties and cheese yield are economically important to the Spanish dairy industry. The present study aims to identify candidate genes and their regulators related to 14 milk and cheese-making traits and to develop a low-density panel of markers that could be used to predict an individual's genetic potential for cheese-making efficiency. In this study, we performed a combination of the classical genome-wide association study (GWAS) with a stepwise regression method and a pleiotropy analysis to determine the best combination of the variants located within the confidence intervals of the potential candidate genes that may explain the greatest genetic variance for milk and cheese-making traits. Two gene networks related to milk and cheese-making traits were created using the genomic relationship matrices built through a stepwise multiple regression approach. Several co-associated genes in these networks are involved in biological processes previously found to be associated with milk synthesis and cheese-making efficiency. The methodology applied in this study enabled the selection of a co-association network comprised of 374 variants located in the surrounding of genes showing a potential influence on milk synthesis and cheese-making efficiency.

A Gene Signature of Survival Prediction for Kidney Renal Cell Carcinoma by Multi-Omic Data Analysis.

Kidney renal cell carcinoma (KIRC), which is the most common subtype of kidney cancer, has a poor prognosis and a high mortality rate. In this study, a multi-omics analysis is performed to build a multi-gene prognosis signature for KIRC. A combination of a DNA methylation analysis and a gene expression data analysis revealed 863 methylated differentially expressed genes (MDEGs). Seven MDEGs (BID, CCNF, DLX4, FAM72D, PYCR1, RUNX1, and TRIP13) were further screened using LASSO Cox regression and integrated into a prognostic risk score model. Then, KIRC patients were divided into high- and low-risk groups. A univariate cox regression analysis revealed a significant association between the high-risk group and a poor prognosis. The time-dependent receiver operating characteristic (ROC) curve shows that the risk group performs well in predicting overall survival. Furthermore, the risk group is contained in the best multivariate model that was obtained by a multivariate stepwise analysis, which further confirms that the risk group can be used as a potential prognostic biomarker. In addition, a nomogram was established for the best multivariate model and shown to perform well in predicting the survival of KIRC patients. In summary, a seven-MDEG signature is a powerful prognosis factor for KIRC patients and may provide useful suggestions for their personalized therapy.