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Background. Liver hepatocellular carcinoma (LIHC) is the predominant type of liver cancer, and its treatment still faces great challenges presently. Mitochondrial inner membrane protein MPV17 is reported to be involved in multiple biological activities of cancers. Here, we seek to investigate the specific role and functions of MPV17 in LIHC progression. Methods. Firstly, MPV17 expressions in various tumors and corresponding normal samples and LIHC groups with various clinical features were analyzed, respectively. Next, the relationship between MPV17 expression and LIHC survival was analyzed and verified by AUC curves. Besides, differentially expressed genes (DEGs) for LIHC were screened from TCGA and then analyzed by GO and KEGG. Then, MPV17 was analyzed by prognostic model, Cox analysis, predictive nomogram, pathway correlation, and immunoassay. Finally, the functions of MPV17 were determined by CCK-8 and Tranwell assays. Results. In most tumors, MPV17 expression was higher than that in the normal group, and it was related to LIHC clinical features. In the LIHC survival analysis, highly expressed MPV17 was associated with a poor prognosis. Besides, 314 upregulated and 193 downregulated DEGs are mainly involved in the TNF signaling pathway and tyrosine metabolism. Through prognostic model, Cox analysis, and predictive nomogram, MPV17 had the prognostic value for LIHC. Gene-pathway correlation analysis showed that MPV17 had the strongest correlation with the G2M_checkpoint pathway. In an immunoassay, MPV17 had a strong correlation with many immune cells. Functional assays showed that MPV17 reduction in LIHC cells could inhibit cell invasion, migration, and proliferation. Conclusion. MPV17, as a tumor promoter, could be a new biomarker for LIHC diagnosis and prognosis and probably shed new light on the exploration of LIHC therapies.
Given the rising burden of palliative care and the limited human resources for its facilitation in China, volunteers are becoming increasingly indispensable. In particular, there is a high demand for volunteers who can serve as spiritual caregivers. However, a volunteer’s ability to provide good spiritual care in a palliative setting may be influenced by their attitude toward palliative care. To uncover the current state of spiritual caregiving in palliative settings in China and insights into best practices for its improvement, this study measured spiritual care competence and identified its influencing factors and explored its relationship with attitudes toward palliative care among volunteers. Notably, this study is the first to consider spiritual care competence alongside attitudes toward palliative care.
A descriptive cross-sectional study using online survey methods was conducted with 385 volunteers in Shanghai, China. Data were collected using a structured questionnaire.
Volunteers demonstrated relatively low levels of spiritual care competence (58.50 ± 10.92). Statistically significant correlations were found between spiritual care competence and the following variables: age, educational background, marital status, religious beliefs, occupational status, and relevant training and practical experience. Attitude toward palliative care significantly correlated with spiritual care competence (r = 0.49, p < 0.001).
Significance of results
To continually improve volunteers’ spiritual care competence, diversified education and training programs about spiritual care should be designed for different kinds of volunteers; moreover, because attitude toward palliative care significantly impacted spiritual care competence, such programs should encourage positive attitudes toward palliative care.
Few studies examined the association of energy, macronutrients and food consumption at dinner v. breakfast with hypercholesterolaemia. A total of 27 911 participants from the National Health and Nutrition Examination Survey (2003–2016) were included in the cross-sectional study. Energy, macronutrients and food consumption at breakfast, dinner and the difference at dinner v. breakfast (Δratio) were calculated. Multiple logistic regression models and substitution effects of foods at dinner with breakfast were also performed. After adjustment for potential covariates, compared with the lowest quintile, participants in the highest quintile of Δratio in terms of energy had a higher risk of prevalent hypercholesterolaemia (ORΔratio of energy 1·16, 95 % CI (1·01, 1·33)) mainly due to Δratio of low-quality carbohydrates and plant protein (ORΔratio of low-quality carbohydrates 1·19; 95 % CI (1·05, 1·35)); ORΔratio of plant protein 1·13; 95 % CI (1·01, 1·28)). ΔAdded sugars and Δnuts were associated with hypercholesterolaemia (ORΔadded sugars 1·01; 95 % CI (1·00, 1·02)); ORΔnuts 1·08; 95 % CI (1·01, 1·16)). Furthermore, the substitution of added sugars, nuts and processed meat at dinner with breakfast could reduce the OR of hypercholesterolaemia. This study indicated that among US adults, overconsumption of energy, macronutrients including low-quality carbohydrates and plant protein at dinner than breakfast was significantly associated with a higher risk of prevalent hypercholesterolaemia. The replacing of added sugar, nuts and processed meat at dinner with breakfast reduced the risk of prevalent hypercholesterolaemia. This study emphasised the importance of meal timing in the prevention of hypercholesterolaemia.
Our objective is to forecast the number of coronavirus disease 2019 (COVID-19) cases in the state of Maryland, United States, using transfer function time series (TS) models based on a Social Distancing Index (SDI) and determine how their parameters relate to the pandemic mechanics.
A moving window of 2 mo was used to train the transfer function TS model that was then tested on the next week data. After accounting for a secular trend and weekly cycle of the SDI, a high correlation was documented between it and the daily caseload 9 days later. Similar patterns were also observed on the daily COVID-19 cases and incorporated in our models.
In most cases, the proposed models provide a reasonable performance that was, on average, moderately better than that delivered by TS models based only on previous observations. The model coefficients associated with the SDI were statistically significant for most of the training/test sets.
Our proposed models that incorporate SDI can forecast the number of COVID-19 cases in a region. Their parameters have real-life interpretations and, hence, can help understand the inner workings of the epidemic. The methods detailed here can help local health governments and other agencies adjust their response to the epidemic.
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