Reducing Employee Turnover Intentions in Tourism and Hospitality Sector: The Mediating Effect of Quality of Work Life and Intrinsic Motivation.

Employee turnover causes various organizational disruptions, including economic and social loss and a deficit in organizational knowledge-skill inventory. Considering different forms of organizational disruptions associated with employee turnover, the contemporary literature on organizational sciences has shown serious concern in dealing with the challenge of employee turnover. However, shockingly, the employee turnover rate in the tourism and hospitality sector has been reported to be critically high even at a global level. Moreover, considering the customer-facing nature of this industry, employee turnover has more consequences for the tourism and hospitality sector compared to other segments of the economy. Past literature has acknowledged the role of employee-related corporate social responsibility (ERCSR) activities of an organization in influencing employee behavior. However, a critical knowledge gap in this domain still exists. That is, most of the prior studies tested the impact of ERCSR on positive employee behavior and did not test how ERCSR engagement in an organization may reduce employee turnover intentions, especially in a hospitality context. To fill this knowledge gap, this study aimed to investigate the relationship between ERCSR and employee turnover intentions in a hospitality sector of a developing country. Additionally, the mediating roles of quality of work life and intrinsic motivation were also tested in the above-proposed relationship. The hotel employees were the respondents in this survey who provided their responses related to the study variables on a self-administered questionnaire (n = 278). A hypothetical model was developed and analyzed with the help of the structural equation modeling technique. The results confirmed that ERCSR orientation of a hotel organization significantly reduces the turnover intentions of employees, whereas both quality of work life and intrinsic motivation buffered this association by producing mediating effects. These findings have different theoretical and practical implications, among which the most important implication is to realize the key role of ERCSR in reducing employees' turnover intentions in a hospitality context. Various other implications are discussed in detail.

Combining genetic risk score with artificial neural network to predict the efficacy of folic acid therapy to hyperhomocysteinemia.

Artificial neural network (ANN) is the main tool to dig data and was inspired by the human brain and nervous system. Several studies clarified its application in medicine. However, none has applied ANN to predict the efficacy of folic acid treatment to Hyperhomocysteinemia (HHcy). The efficacy has been proved to associate with both genetic and environmental factors while previous studies just focused on the latter one. The explained variance genetic risk score (EV-GRS) had better power and could represent the effect of genetic architectures. Our aim was to add EV-GRS into environmental factors to establish ANN to predict the efficacy of folic acid therapy to HHcy. We performed the prospective cohort research enrolling 638 HHcy patients. The multilayer perception algorithm was applied to construct ANN. To evaluate the effect of ANN, we also established logistic regression (LR) model to compare with ANN. According to our results, EV-GRS was statistically associated with the efficacy no matter analyzed as a continuous variable (OR = 3.301, 95%CI 1.954-5.576, P < 0.001) or category variable (OR = 3.870, 95%CI 2.092-7.159, P < 0.001). In our ANN model, the accuracy was 84.78%, the Youden's index was 0.7073 and the AUC was 0.938. These indexes above indicated higher power. When compared with LR, the AUC, accuracy, and Youden's index of the ANN model (84.78%, 0.938, 0.7073) were all slightly higher than the LR model (83.33% 0.910, 0.6687). Therefore, clinical application of the ANN model may be able to better predict the folic acid efficacy to HHcy than the traditional LR model. When testing two models in the validation set, we got the same conclusion. This study appears to be the first one to establish the ANN model which added EV-GRS into environmental factors to predict the efficacy of folic acid to HHcy. This model would be able to offer clinicians a new method to make decisions and individual therapeutic plans.

Elevated transcriptional levels of aldolase A (ALDOA) associates with cell cycle-related genes in patients with NSCLC and several solid tumors.

BACKGROUND: Aldolase A (ALDOA) is one of the glycolytic enzymes primarily found in the developing embryo and adult muscle. Recently, a new role of ALDOA in several cancers has been proposed. However, the underlying mechanism remains obscure and inconsistent. In this study, we tried to investigate ALDOA-associated (AA) genes using available microarray datasets to help elucidating the role of ALDOA in cancer. RESULTS: In the dataset of patients with non-small-cell lung cancer (NSCLC, E-GEOD-19188), 3448 differentially expressed genes (DEGs) including ALDOA were identified, in which 710 AA genes were found to be positively associated with ALDOA. Then according to correlation coefficients between each pair of AA genes, ALDOA-associated gene co-expression network (GCN) was constructed including 182 nodes and 1619 edges. 11 clusters out of GCN were detected by ClusterOne plugin in Cytoscape, and only 3 of them have more than three nodes. These three clusters were functionally enriched. A great number of genes (43/79, 54.4%) in the biggest cluster (Cluster 1) primarily involved in biological process like cell cycle process (Pa = 6.76E-26), mitotic cell cycle (Pa = 4.09E-19), DNA repair (Pa = 1.13E-04), M phase of meiotic cell cycle (Pa = 0.006), positive regulation of ubiquitin-protein ligase activity during mitotic cell cycle (Pa = 0.014). AA genes with highest degree and betweenness were considered as hub genes of GCN, namely CDC20, MELK, PTTG1, CCNB2, CDC45, CCNB1, TK1 and PSMB2, which could distinguish cancer from normal controls with ALDOA. Their positive association with ALDOA remained after removing the effect of HK2 and PKM, the two rate limiting enzymes in glycolysis. Further, knocking down ALDOA blocked breast cancer cells in the G0/G1 phase under minimized glycolysis. All suggested that ALDOA might affect cell cycle progression independent of glycolysis. RT-qPCR detection confirmed the relationship of ALDOA with CDC45 and CCNB2 in breast tumors. High expression of the hub genes indicated poor outcome in NSCLC. ALDOA could improve their predictive power. CONCLUSIONS: ALDOA could contribute to the progress of cancer, at least partially through its association with genes relevant to cell cycle independent of glycolysis. AA genes plus ALDOA represent a potential new signature for development and prognosis in several cancers.