Sleep duration in Chinese adolescents: biological, environmental, and behavioral predictors

AbstractObjectiveTo examine sleep duration-related risk factors from multidimensional domains among Chinese adolescents.MethodsA random sample of 4801 adolescents aged 11–20 years participated in a cross-sectional survey. A self-reported questionnaire was used to collect information about the adolescents' sleep behaviors and possible related factors from eight domains.ResultsIn all, 51.0% and 9.8% of adolescents did not achieve optimal sleep duration (defined as <8.0 h per day) on weekdays and on weekends, respectively. According to multivariate logistic regression models, after adjusting for all possible confounders, 17 factors were associated with sleep duration <8 h. Specifically, 13 factors from five domains were linked to physical and psychosocial condition, environment, and behaviors. These factors were overweight/obesity, chronic pain, bedtime anxiety/excitement/depression, bed/room sharing, school starting time earlier than 07:00, cram school learning, more time spent on homework on weekdays, television viewing ≥2 h/day, physical activity <1 h/day, irregular bedtime, and shorter sleep duration of father.ConclusionBiological and psychosocial conditions, sleep environments, school schedules, daily activity and behaviors, and parents' sleep habits significantly may affect adolescents' sleep duration, indicating that the existing chronic sleep loss in adolescents could be, at least partly, intervened by improving adolescents' physical and psychosocial conditions, controlling visual screen exposure, regulating school schedules, improving sleep hygiene and daytime behaviors, and changing parents' sleep habits

Transition from pre-diabetes to diabetes and predictors of risk in Mexican-Americans

Background: No studies have examined risk factors for the transition from pre-diabetes to diabetes in populations with widespread obesity and diabetes. We determined proximal changes and factors affecting the transition among Mexican-Americans with pre-diabetes. Methods: Participants with pre-diabetes (n=285) were recruited from our randomly sampled population-based Cameron County Hispanic Cohort. These participants were followed for an average of 27 months with repeat examination every 3 to 4 months. Metabolic health was defined as having less than 2 metabolic abnormalities (e.g., hypertension, elevated low-density lipoprotein, etc). Diabetes was identified as fasting blood glucose ≥126 mg/dL, glycated hemoglobin ≥6.5% and/or on hypoglycemic medication. Results: Ninety-six of 285 (33.7%) participants transitioned to overt diabetes. The increased risk of diabetes in the metabolically unhealthy varying with follow-up time was 81% (adjusted odds ratio [OR]: 1.81; 95% CI: 1.09–3.02). The risk of diabetes increased 8% for each kg/m2 of increase in body mass index (BMI, OR: 1.08; 95% CI: 1.05–1.11) independent of covariates. Transition to diabetes was accompanied by a mean increase in BMI of 0.28 kg/m2, and deterioration in metabolic health of 9% (OR: 1.09; 95% CI: 1.003–1.18) compared with those who did not transition. Conclusions: Deteriorating metabolic health and/or increasing BMI significantly raises the risk of transitioning from pre-diabetes to diabetes. Transition itself was accompanied by further increase in BMI and deterioration in metabolic health. These data underline the importance of improving metabolic health and avoiding weight gain in pre-diabetes as simple but clear diabetes prevention targets, and emphasize the importance of lifestyle management

CD44: a cancer stem cell marker and therapeutic target in leukemia treatment

CD44 is a ubiquitous leukocyte adhesion molecule involved in cell-cell interaction, cell adhesion, migration, homing and differentiation. CD44 can mediate the interaction between leukemic stem cells and the surrounding extracellular matrix, thereby inducing a cascade of signaling pathways to regulate their various behaviors. In this review, we focus on the impact of CD44s/CD44v as biomarkers in leukemia development and discuss the current research and prospects for CD44-related interventions in clinical application

Metabolic Health Has Greater Impact on Diabetes than Simple Overweight/Obesity in Mexican Americans

To compare the risk for diabetes in each of 4 categories of metabolic health and BMI. Methods. Participants were drawn from the Cameron County Hispanic Cohort, a randomly selected Mexican American cohort in Texas on the US-Mexico border. Subjects were divided into 4 phenotypes according to metabolic health and BMI: metabolically healthy normal weight, metabolically healthy overweight/obese, metabolically unhealthy normal weight, and metabolically unhealthy overweight/obese. Metabolic health was defined as having less than 2 metabolic abnormalities. Overweight/obese status was assessed by BMI higher than 25 kg/m2. Diabetes was defined by the 2010 ADA definition or by being on a diabetic medication. Results. The odds ratio for diabetes risk was 2.25 in the metabolically healthy overweight/obese phenotype (95% CI 1.34, 3.79), 3.78 (1.57, 9.09) in the metabolically unhealthy normal weight phenotype, and 5.39 (3.16, 9.20) in metabolically unhealthy overweight/obese phenotype after adjusting for confounding factors compared with the metabolically healthy normal weight phenotype. Conclusions. Metabolic health had a greater effect on the increased risk for diabetes than overweight/obesity. Greater focus on metabolic health might be a more effective target for prevention and control of diabetes than emphasis on weight loss alone

Circulating microRNAs and life expectancy among identical twins

Human life expectancy is influenced not only by longevity assurance mechanisms and disease susceptibility loci but also by the environment, gene–environment interactions, and chance. MicroRNAs (miRNAs) are a class of small noncoding RNAs closely related to genes. Circulating miRNAs have been shown as promising noninvasive biomarkers in the development of many pathophysiological conditions. However, the concentration of miRNA in the circulation may also be affected by environmental factors. We used a next-generation sequencing platform to assess the association of circulating miRNA with life expectancy, for which deaths are due to all causes independent of genes. In addition, we showed that miRNAs are present in 41-year archived plasma samples, which may be useful for both life expectancy and all-cause mortality risk assessment. Plasma miRNAs from nine identical male twins were profiled using next-generation sequencing. The average absolute difference in the minimum life expectancy was 9.68 years. Intraclass correlation coefficients were above 0.4 for 50% of miRNAs. Comparing deceased twins with their alive co-twin brothers, the concentrations were increased for 34 but decreased for 30 miRNAs. Identical twins discordant in life expectancy were dissimilar in the majority of miRNAs, suggesting that environmental factors are pivotal in miRNAs related to life expectancy

Childhood Cancer Survival in the Highly Vulnerable Population of South Texas: Persistent Challenges for Adolescents and Hispanic Ethnicity

Background: This study examines childhood cancer survival rates and prognostic factors related to survival in the majority Hispanic population of South Texas (STX), whereas most other population studies in childhood cancer survival focus on populations with relatively few Hispanics. Methods: The population-based cohort study used Texas Cancer Registry data (1995-2017) to examine survival and prognostic factors. Results: The 5-year relative survival rate for STX cancer patients diagnosed at 0–19 years was 80.3% for all races/ethnicity. Hispanics had statistically significant lower 5-year relative survival rates than non-Hispanic Whites (NHW) for male and female together diagnosed at age ≥ 5 years. When comparing survival among Hispanics and NHW for the most common cancer, acute lymphocytic leukemia (ALL), the difference was most striking in the 15-19 years age range, with 47.7% Hispanic patients surviving at 5 years compared to 78.4% of NHW counterparts. The multivariable-adjusted analysis showed that males [hazard ratio (HR): 1.13], patients diagnosed at age \u3c 1 year (HR: 1.69), at 10–14 year (HR: 1.42), or at 15–19 years (HR: 1.40), and Hispanics (HR: 1.38) had significantly increased mortality risk compared to the corresponding counterparts for all cancers. Conclusions: STX Hispanics had lower 5-year relative survival than NHW especially for ALL. Male gender, diagnosis at age \u3c 1 year or 10–19 years were also associated with decreased childhood cancer survival. Despite advances in treatment, Hispanics lag significantly behind NHW. Further cohort studies in STX are warranted to identify additional factors affecting survival and to develop interventional strategies

Remote Estimation of Rice Yield With Unmanned Aerial Vehicle (UAV) Data and Spectral Mixture Analysis

The accurate assessment of rice yield is crucially important for China’s food security and sustainable development. Remote sensing (RS), as an emerging technology, is expected to be useful for rice yield estimation especially at regional scales. With the development of unmanned aerial vehicles (UAVs), a novel approach for RS has been provided, and it is possible to acquire high spatio-temporal resolution imagery on a regional scale. Previous reports have shown that the predictive ability of vegetation index (VI) decreased under the influence of panicle emergence during the later stages of rice growth. In this study, a new approach which integrated UAV-based VI and abundance information obtained from spectral mixture analysis (SMA) was established to improve the estimation accuracy of rice yield at heading stage. The six-band image of all studied rice plots was collected by a camera system mounted on an UAV at booting stage and heading stage respectively. And the corresponding ground measured data was also acquired at the same time. The relationship of several widely-used VIs and Rice Yield was tested at these two stages and a relatively weaker correlation between VI and yield was found at heading stage. In order to improve the estimation accuracy of rice yield at heading stage, the plot-level abundance of panicle, leaf and soil, indicating the fraction of different components within the plot, was derived from SMA on the six-band image and in situ endmember spectra collected for different components. The results showed that VI incorporated with abundance information exhibited a better predictive ability for yield than VI alone. And the product of VI and the difference of leaf abundance and panicle abundance was the most accurate index to reliably estimate yield for rice under different nitrogen treatments at heading stage with the coefficient of determination reaching 0.6 and estimation error below 10%

Fractured morphology of femoral head associated with subsequent femoral neck fracture: Injury analyses of 2D and 3D models of femoral head fractures with computed tomography

Background: The injury of femoral head varies among femoral head fractures (FHFs). In addition, the injury degree of the femoral head is a significant predictor of femoral neck fracture (FNF) incidence in patients with FHFs. However, the exact measurement methods have yet been clearly defined based on injury models of FHFs. This study aimed to design a new measurement for the injury degree of the femoral head on 2D and 3D models with computed tomography (CT) images and investigate its association with FHFs with FNF.Methods: A consecutive series of 209 patients with FHFs was assessed regarding patient characteristics, CT images, and rate of FNF. New parameters for injury degree of femoral head, including percentage of maximum defect length (PMDL) in the 2D CT model and percentage of fracture area (PFA) in the 3D CT-reconstruction model, were respectively measured. Four 2D parameters included PMDLs in the coronal, cross-sectional and sagittal plane and average PMDL across all three planes. Reliability tests for all parameters were evaluated in 100 randomly selected patients. The PMDL with better reliability and areas under curves (AUCs) was finally defined as the 2D parameter. Factors associated with FNF were determined by binary logistic regression analysis. The sensitivity, specificity, likelihood ratios, and positive and negative predictive values for different cut-off values of the 2D and 3D parameters were employed to test the diagnostic accuracy for FNF prediction.Results: Intra- and inter-class coefficients for all parameters were ≥0.887. AUCs of all parameters ranged from 0.719 to 0.929 (p &lt; 0.05). The average PMDL across all three planes was defined as the 2D parameter. The results of logistic regression analysis showed that average PMDL across all three planes and PFA were the significant predictors of FNF (p &lt; 0.05). The cutoff values of the average PMDL across all three planes and PFA were 91.65% and 29.68%. The sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, predictive positive value and negative predictive value of 2D (3D) parameters were 91.7% (83.3%), 93.4% (58.4%), 13.8 (2.0), 0.09 (0.29), 45.83% (10.87%), and 99.46% (98.29%).Conclusion: The new measurement on 2D and 3D injury models with CT has been established to assess the fracture risk of femoral neck in patients with FHFs in the clinic practice. 2D and 3D parameters in FHFs were a feasible adjunctive diagnostic tool in identifying FNFs. In addition, this finding might also provide a theoretic basis for the investigation of the convenient digital-model in complex injury analysis

Integrated analysis of single-cell RNA-seq and bulk RNA-seq reveals RNA N6-methyladenosine modification associated with prognosis and drug resistance in acute myeloid leukemia

IntroductionAcute myeloid leukemia (AML) is a type of blood cancer that is identified by the unrestricted growth of immature myeloid cells within the bone marrow. Despite therapeutic advances, AML prognosis remains highly variable, and there is a lack of biomarkers for customizing treatment. RNA N6-methyladenosine (m6A) modification is a reversible and dynamic process that plays a critical role in cancer progression and drug resistance.MethodsTo investigate the m6A modification patterns in AML and their potential clinical significance, we used the AUCell method to describe the m6A modification activity of cells in AML patients based on 23 m6A modification enzymes and further integrated with bulk RNA-seq data.ResultsWe found that m6A modification was more effective in leukemic cells than in immune cells and induced significant changes in gene expression in leukemic cells rather than immune cells. Furthermore, network analysis revealed a correlation between transcription factor activation and the m6A modification status in leukemia cells, while active m6A-modified immune cells exhibited a higher interaction density in their gene regulatory networks. Hierarchical clustering based on m6A-related genes identified three distinct AML subtypes. The immune dysregulation subtype, characterized by RUNX1 mutation and KMT2A copy number variation, was associated with a worse prognosis and exhibited a specific gene expression pattern with high expression level of IGF2BP3 and FMR1, and low expression level of ELAVL1 and YTHDF2. Notably, patients with the immune dysregulation subtype were sensitive to immunotherapy and chemotherapy.DiscussionCollectively, our findings suggest that m6A modification could be a potential therapeutic target for AML, and the identified subtypes could guide personalized therapy