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Background
Previous research identified an association between work‐family conflict and musculoskeletal pain. This study explores how the work‐life interface might affect pain experienced by residential aged care staff.Methods
A cross‐sectional survey of 426 employees in residential aged care was analyzed to assess the impacts of workplace hazards, work‐family conflict, and work‐life balance on self‐reported musculoskeletal pain.Results
Work‐family conflict acts as a mediator of the relationships between workplace hazards and the total number of body regions at which musculoskeletal pain was experienced. Work‐life balance only acts as a mediator for particular hazards and only if work‐family conflict is not taken into account.Conclusions
Addressing work‐life interaction, and in particular work‐family conflict, warrants further investigation as a legitimate means through which musculoskeletal disorder risk can be reduced. Policies and practices to improve work‐life interaction and reduce work‐family conflict should be considered as integral components of musculoskeletal disorder risk management strategies.Background
Gene expression signatures have proven to be useful tools in many cancers to identify distinct subtypes of disease based on molecular features that drive pathogenesis, and to aid in predicting clinical outcomes. However, there are no current signatures for kidney cancer that are applicable in a clinical setting.Objective
To generate a signature biomarker for the clear cell renal cell carcinoma (ccRCC) good risk (ccA) and poor risk (ccB) subtype classification that could be readily applied to clinical samples to develop an integrated model for biologically defined risk stratification.Design, setting, and participants
A set of 72 ccRCC sample standards was used to develop a 34-gene classifier (ClearCode34) for assigning ccRCC tumors to subtypes. The classifier was applied to RNA-sequencing data from 380 nonmetastatic ccRCC samples from the Cancer Genome Atlas (TCGA), and to 157 formalin-fixed clinical samples collected at the University of North Carolina.Outcome measurements and statistical analysis
Kaplan-Meier analyses were performed on the individual cohorts to calculate recurrence-free survival (RFS), cancer-specific survival (CSS), and overall survival (OS). Training and test sets were randomly selected from the combined cohorts to assemble a risk prediction model for disease recurrence.Results and limitations
The subtypes were significantly associated with RFS (p < 0.01), CSS (p < 0.01), and OS (p < 0.01). Hazard ratios for subtype classification were similar to those of stage and grade in association with recurrence risk, and remained significant in multivariate analyses. An integrated molecular/clinical model for RFS to assign patients to risk groups was able to accurately predict CSS above established, clinical risk-prediction algorithms.Conclusions
The ClearCode34-based model provides prognostic stratification that improves upon established algorithms to assess risk for recurrence and death for nonmetastatic ccRCC patients.Patient summary
We developed a 34-gene subtype predictor to classify clear cell renal cell carcinoma tumors according to ccA or ccB subtypes and built a subtype-inclusive model to analyze patient survival outcomes. 相似文献This study aimed to identify if individuals with mild to severe alpha-1 antitrypsin deficiency (AATD) are at higher risk for developing obstructive sleep apnea (OSA) than the general population.
MethodsA seven-question sleep apnea risk assessment questionnaire, STOP-BAG, was applied to 2338 participant responses from the Alpha-1 Coded Testing Study (ACT) and 4638 participant responses from the Kentucky Behavioral Risk Factor Survey (KyBRFS). Propensity scores were generated from a logistic regression model using continuous variables of age and body mass index (BMI). STOP-BAG scores were analyzed using chi-square analysis on this matched cohort to assess OSA risk in AATD.
ResultsSelf-reported OSA was higher in the KyBRFS cohort (14.5%) than in individuals with mild or severe AATD (11.2%) (p?=?0.012). However, a higher percentage of the AATD cohort met clinically meaningful thresholds for STOP-BAG scores?≥?5 (22.7%) than the KyBRFS cohort (13.0%) (p?=?0.001). These differences persisted despite 1:1 propensity score matching on age and BMI to account for differences in baseline characteristics. No statistically significant difference in OSA risk between AATD genotypes was found.
ConclusionAATD appears to have higher risk for OSA than the general population. The 11.2% prevalence of diagnosed OSA in the AATD population is much lower than symptom scores would predict. Further studies are needed to validate the possibility that elastin loss is involved in OSA pathogenesis.
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