METHODS: The RNA expression profiles and corresponding clinical data of HCC were retrieved from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and International Cancer Genome Consortium (ICGC). Univariate Cox regression analysis was performed to identify the relevant prognostic genes, and Lasso Cox regression analysis was employed to calculate the risk score. The relationship between the risk score and clinicopathological characteristics, immune cell infiltration, and immune checkpoint expression was analyzed.
RESULTS: A prognostic risk model for HCC was constructed from the identified CDRGs and patients were subgrouped based on risk score. High-risk patients for HCC exhibited a significantly lower overall survival (OS) rate than the low-risk patients. In addition, the receiver operating characteristic (ROC) curve demonstrated the predictive ability of the risk score. Patients in the high-risk group exhibited lower immune cell infiltration and higher expression levels of immune checkpoint molecules.
CONCLUSIONS: The cell death-related signature established herein provides a valuable predictive tool for survival and holds promise as a potential therapeutic biomarker for HCC.
METHODS: A total of 2231 higher vocational students from Shandong Province were surveyed by means of Academic Self-efficacy Questionnaire, Meaning in Life Questionnaire, and Test Anxiety Scale.
RESULTS: There were significant negative correlations among academic self-efficacy, sense of life meaning, and test anxiety. Fear of failure was positively correlated with test anxiety. Sense of life meaning and fear of failure played a mediating role in the relationship between academic self-efficacy and test anxiety. The chain mediating effect was significant only in the female group, not in the male group. In contrast, academic self-efficacy indirectly predicted test anxiety by the independent mediating effect of sense of life meaning or fear of failure in the male group.
CONCLUSION: Academic self-efficacy may influence test anxiety through the independent mediating effect of sense of life meaning, fear of failure, and the chain mediating effect, and there is a gender difference in these effects.
OBJECTIVE: This study aimed to synthesize current literature on digital solutions for CVD prevention, identify the key components of effective digital interventions, and highlight critical research gaps to inform the development of sustainable strategies for CVD prevention.
METHODS: Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we conducted a comprehensive search in Web of Science, Scopus, and PubMed to identify original English-language studies published between January 2000 and May 2024 that examined primary or secondary CVD prevention through digital solutions. The exclusion criteria included: telephone-only interventions, abstract-only publications, methodology-focused studies without primary data, studies without participants or specific groups, and studies with no follow-up period. The literature search used the string with terms like "digital health," "mHealth," "mobile health," "text message," "short message service," "SMS," "prevention," "prevent," "cardiovascular disease," "CVD," etc. Study bias was assessed using the RoB 2 (Cochrane Collaboration) and the ROBINS-I tool (Cochrane Collaboration). Data on prevention components, prevention types, study design, population, intervention, follow-up duration, personnel, and delivery settings were extracted.
RESULTS: A total of 2871 studies were identified through the search. After excluding ineligible studies, 30 studies remained, including 24 randomized controlled trials. The reviewed digital solutions for CVD prevention focused on baseline assessment (29/30, 97%), physical activity counseling (18/30, 60%), tobacco cessation (14/30, 47%), blood pressure management (13/30, 43%), and medication adherence (10/30, 33%). The technologies used were categorized into 3 types, smartphones and wearables (16/30, 53%), email and SMS communications (12/30, 40%), and websites or web portals (3/30, 10%). The majority of the study outcomes addressed blood pressure (14/30, 47%), exercise capacity (12/30, 40%), weight (12/30, 40%), and lipid profile (11/30, 37%), while fewer focused on nicotine dependence (9/30, 30%), medication use (8/30, 27%), quality of life (7/30, 23%), dietary habits (5/30, 17%), intervention adherence (4/30, 13%), waist circumference (4/30, 13%), and blood glucose levels (2/30, 7%).
CONCLUSIONS: Digital solutions can address challenges in traditional CVD prevention by improving preventive behaviors and monitoring health indicators. However, most evaluated interventions have focused on medication use, quality of life, dietary habits, adherence, and waist circumference. Further studies are needed to assess the long-term impact of more comprehensive interventions on key cardiovascular outcomes.
OBJECTIVE: We aimed to investigate the correlation between social media and eHealth literacy in older individuals and answer four research questions: (1) What are the specific social media behaviors (including general use behaviors and health behaviors) of older adults on social media? (2) How do these behaviors impact their eHealth literacy? (3) How does eHealth literacy influence older adults' social media behaviors? and (4) What factors influence older adults' use of social media for health-related purposes?
METHODS: Using predetermined keywords and inclusion criteria, we searched Scopus, Web of Science, and PubMed databases for English-language journal articles published from 2000 to 2024, following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) principles. The initial search identified 1591 publications, and after removing duplicates, 48.21% (767/1591) of publications remained. Ultimately, 1% (16/1591) of studies met the inclusion criteria. A research question-driven manual qualitative thematic analysis was conducted, guided by the categorization of social media use behaviors, the definition of eHealth literacy, and the social-ecological model to provide direction for coding and thematic analysis. In addition, attention was given to identifying unanticipated behaviors or phenomena during the coding process, and these were subsequently incorporated into the analytical framework.
RESULTS: The results indicated that older adults' general social media use behaviors are primarily characterized by social media preferences, with 2 subthemes identified. Their social media health behaviors revealed 5 main themes and 14 subthemes. Among the primary themes, health information behavior appeared most frequently (12/16, 75%), followed by self-management (8/16, 50%). Other themes included health decision-making (4/16, 25%), telemedicine (3/16, 19%), and health interventions (2/16, 13%). Cross-thematic analysis confirmed that older adults' social media use behaviors and their eHealth literacy had a reciprocal relationship. Finally, the study revealed that the use of social media to improve eHealth literacy among older adults was influenced by individual, interpersonal, institutional or organizational, and social factors.
CONCLUSIONS: The reciprocal relationship between older adults' social media use and eHealth literacy highlights the importance of establishing a long-term positive mechanism that mutually reinforces social media health practices and eHealth literacy. Based on the findings, this review proposes key directions for efforts to achieve this goal: (1) leveraging postpandemic momentum to enhance eHealth literacy among older adults through social media, (2) reconsidering the dimensions of eHealth literacy among older adults in the context of Web 2.0, (3) actively developing age-friendly integrated social media health service platforms, (4) optimizing social media for engaging and reliable health information for older adults, and (5) integrating social support systems to foster lifelong eHealth learning for older adults.