Insomnia Burden among Informal Caregivers of Hospitalized Lung Cancer Patients and Its Influencing Factors / 生物医学与环境科学（英文）

OBJECTIVE@#This study aimed to reveal the insomnia burden and relevant influencing factors among informal caregivers (ICs) of hospitalized patients with lung cancer.@*METHODS@#A cross-sectional study on ICs of hospitalized patients with lung cancer was conducted from December 31, 2020 to December 31, 2021. ICs' burden was assessed using the Caregiver Reaction Assessment (CRA), Hospital Anxiety and Depression Scale (HADS), and Insomnia Severity Index (ISI). Linear and logistic regression models were used to identify the influencing factors.@*RESULTS@#Among 289 ICs of hospitalized patients with lung cancer, 83 (28.72%), 53 (18.34%), and 14 (4.84%) ICs experienced mild, moderate, and severe insomnia, respectively. The scores concerning self-esteem, lack of family support, financial problems, disturbed schedule, and health problems were 4.32 ± 0.53, 2.24 ± 0.79, 2.84 ± 1.14, 3.63 ± 0.77, and 2.44 ± 0.95, respectively. ICs with higher Activities of Daily Living Scale (ADLS) scores were associated with a lower risk of insomnia, with an odd ratio ( OR) and 95% confidence interval ( CI) of 0.940 (0.898-0.983). Among the ICs, female gender ( OR = 2.597), alcohol consumption ( OR = 3.745), underlying medical conditions ( OR = 11.765), long-term caregiving experience ( OR = 37.037), and higher monthly expenses ( OR = 5.714) were associated with a high risk of insomnia.@*CONCLUSION@#Of the hospitalized patients with lung cancer, 51.9% experienced insomnia. Patients' ADL, ICs gender, alcohol consumption, underlying medical conditions, caregiving duration, and monthly expenses were influencing factors. Therefore, prompt screening and early intervention for ICs of patients with lung cancer is necessary.

Exploration of potential therapeutic targets for stroke based on the GEO database.

BACKGROUND: This study aimed to analyze non-coding RNA sequencing results, screen differentially expressed long non-coding RNAs (lncRNAs), and predict lncRNA target genes. It further clarifies the potential functions of lncRNAs, thus exploring potential biomarkers and therapeutic targets for stroke. METHODS: LncRNA sequencing data of blood samples from stroke patients and healthy subjects (GSE102541 and GSE140275) were downloaded from the Gene Expression Omnibus (GEO) database. This study used R software and related R packages to conduct a batch correction and differential analysis of sequencing results. It also screened differentially expressed lncRNAs and visualized the correlations between significantly different lncRNAs. Target genes of differential lncRNAs were predicted by the StarBase database. Gene ontology (GO) functional enrichment analysis of related target genes was performed using the DAVID database. Principal component analysis was performed based on the expression levels of lncRNAs with the most significant differences in stroke blood samples. RESULTS: A total of 239 differentially expressed lncRNAs were screened out in this study, of which 146 were upregulated and 93 were downregulated. According to |log2FC| values from highest to lowest, the top 10 lncRNAs with the most significant differences were selected. The upregulated lncRNAs were LINC02334, TARID, MRGPRF-AS1, CAI2, LINC00189, TUG1, and RNF5P1. The downregulated lncRNAs included AC005180.2, ADAMTS9-AS1, and AC036108.3. TARID was strongly correlated with MRGPRF-AS1. Meanwhile, LINC02334 was strongly correlated with TUG1. CAI2, LINC00189, and RNF5P1 were at the core of the correlation network and may therefore be the critical lncRNAs in stroke pathogenesis. GO functional enrichment results indicated that genes were significantly enriched in muscle contraction, RNA polymerase II promoter transcription regulation, muscle structure composition, focal adhesion, endothelial cell chemotaxis, actin, actin cytoskeleton, actin filament binding, blood lipid regulation, smooth muscle contraction regulation, skeletal muscle cell differentiation, and other functions. Principal component analysis showed that the 10 lncRNAs with significant differences could significantly distinguish stroke blood samples from healthy control blood samples, and could characterize the essential characteristics of stroke. CONCLUSIONS: LINC02334, TARID, MRGPRF-AS1, CAI2, LINC00189, TUG1, RNF5P1, AC005180.2, ADAMTS9-AS1, and AC036108.3 play an essential role in the pathogenesis of stroke, and may be potential therapeutic targets.

Combined Effect of Smoking and Obesity on Coronary Heart Disease Mortality in Male Veterans: A 30-year Cohort Study / 生物医学与环境科学（英文）

Objective@#Evidence is lacking regarding the combined effects of smoking and obesity on mortality from coronary heart disease in male veterans. This study aimed to explore the combined effect of smoking and obesity on coronary heart disease mortality in male veterans in China.@*Methods@#A cohort of 1,268 male veterans from 22 veteran centers in Xi'an (Shaanxi Province, China) were followed up once every 2 years from February 1, 1987 to October 30, 2016. The endpoint was death from any cause. The hazard ratio ( @*Results@#The total follow-up was 24394.21 person-years; each subject was followed up for a mean duration of 19.24 years. By the end of the study, of the 1,268 veterans, 889 had died, 363 were alive, and 16 were lost to follow-up. Cox regression analysis results revealed that current smoking ( @*Conclusion@#Our results suggest that obese veterans who smoke might be an important target population for coronary heart disease mortality control.

Analysis of Factors Influencing Insomnia and Construction of a Prediction Model: A Cross-sectional Survey on Rescuers / 生物医学与环境科学（英文）

Objective@#To determine the factors influencing insomnia and construct early insomnia warning tools for rescuers to informbest practices for early screening and intervention.@*Methods@#Cluster sampling was used to conduct a cross-sectional survey of 1,133 rescuers from one unit in Beijing, China. Logistic regression modeling and R software were used to analyze insomnia-related factors and construct a PRISM model, respectively.@*Results@#The positive rate of insomnia among rescuers was 2.74%. Accounting for participants' age, education, systolic pressure, smoking, per capita family monthly income, psychological resilience, and cognitive emotion regulation, logistic regression analysis revealed that, compared with families with an average monthly income less than 3,000 yuan, the odds ratio ( ) values and the [95% confidence interval ( )] for participants of the following categories were as follows: average monthly family income greater than 5,000 yuan: 2.998 (1.307-6.879), smoking: 4.124 (1.954-8.706), and psychological resilience: 0.960 (0.933-0.988). The ROC curve area of the PRISM model (AUC) = 0.7650, specificity = 0.7169, and sensitivity = 0.7419.@*Conclusion@#Insomnia was related to the participants' per capita family monthly income, smoking habits, and psychological resilience on rescue workers. The PRISM model's good diagnostic value advises its use to screen rescuer early sleep quality. Further, advisable interventions to optimize sleep quality and battle effectiveness include psychological resilience training and smoking cessation.