Insufficient Sleep and Poor Sleep Quality Completely Mediate the Relationship between Financial Stress and Dietary Risk among Higher Education Students

The coronavirus disease 2019 (COVID-19) pandemic worsened financial stress for higher education students in the U.S. Financial stress is associated with poor dietary behaviors; however, factors that might influence this relationship are not well characterized. The present cross-sectional study investigated the associations between financial stress and dietary intake and dietary risk scores among higher education students (undergraduate and graduate students) in the U.S. and examined whether poor sleep quality and short sleep duration mediated the relationship between financial stress and dietary risk score. Validated tools were used to assess financial stress, sleep quality, sleep duration, dietary intake, and dietary risk. A total of 1280 students from three large U.S. universities completed the study. Results indicated that higher financial stress was associated with lower vegetable, fruit, fiber, and calcium intake, higher added sugar intake from sugar sweetened beverages, and higher dietary risk score. Further, the positive relationship between financial stress and dietary risk score was completely mediated by poor sleep quality among students who reported poor sleep quality and by short sleep duration among students who slept less than 7 h per night. These findings suggest that students might benefit from both financial management training and sleep education services to reduce undesirable dietary behaviors

Gender Differences in the Relationships between Perceived Stress, Eating Behaviors, Sleep, Dietary Risk, and Body Mass Index

Background: Obesity is a growing epidemic among university students, and the high levels of stress reported by this population could contribute to this issue. Singular relationships between perceived stress; engagement in restrained, uncontrolled, and emotional eating; sleep; dietary risk; and body mass index (BMI) have been reported in the current body of literature; however, these constructs interact with each other, and the complex relationships among them are infrequently examined. Therefore, the aim of the present study was to explore the complex relationships between these constructs using mediation and moderation analyses stratified by gender. Methods: A cross-sectional study, enrolling university students from the United States (U.S.), the Netherlands, South Korea, Malaysia, Ireland, Ghana, and China, was conducted between October 2020 and January 2021 during the COVID-19 pandemic. Perceived stress; maladaptive eating behaviors including restrained, uncontrolled, and emotional eating; sleep duration and quality; dietary risk; and BMI were assessed using validated questionnaires, which were distributed through an online platform. Results: A total of 1392 students completed the online survey (379 male, 973 female, and 40 who self-identified as “other”). Uncontrolled and emotional eating mediated the relationship between perceived stress and dietary risk for both males and females; higher sleep quality weakened this relationship among female students but not males. Emotional eating mediated the relationship between perceived stress and BMI for both males and females, but higher sleep quality weakened this relationship only among females. Conclusions: Our findings suggest that students in higher education are likely to benefit from interventions to reduce uncontrolled and emotional eating. Programs that improve sleep quality, especially during highly stressful periods, may be helpful

Вихретоковый анизотропный термоэлектрический первичный преобразователь лучистого потока

Представлена оригинальная конструкция первичного преобразователя лучистого потока, который может служить основой для создания приемника неселективного излучения с повышенной чувствительностью

Relationships between Dairy and Calcium Intake and Mental Health Measures of Higher Education Students in the United States: Outcomes from Moderation Analyses

Background: The prevalence of mental health concerns among university students in the United States (U.S.) continues to increase, while current treatments, including medication and counseling, present shortcomings. Higher dairy and calcium intakes are associated with protective effects on mental health; however, previous studies have focused on investigating singular relationships between dairy and calcium intakes and mental health measures. A more complex exploration of these relationships is warranted to better examine whether increasing dairy and calcium intakes could serve as an intervention to improve mental health. The present study sought to further characterize the relationships between dairy and calcium intake, perceived stress, and a variety of mental health measures using linear regression and moderation analyses. Methods: The present cross-sectional study involved students studying at three large U.S. universities, and data collection occurred from April to May 2020 when students were learning remotely due to the COVID-19 pandemic. An online survey comprising validated tools was distributed among students to assess dairy and calcium intake, perceived stress, anxiety, negative and positive moods, rumination, and resilience, sleep quality and duration, dietary risk, and physical activity. Results: A total of 1233 students completed the study. Higher dairy and calcium intake was coincident with lower perceived stress and higher positive mood scores, while higher calcium intake was also coincident with lower anxiety, rumination, and higher resilience scores. Additionally, as calcium intake increased, the relationship between perceived stress and anxiety and the relationship between perceived stress and negative mood weakened. Dairy intake did not have this effect. Conclusions: Based on the results, and considering that calcium is a shortfall nutrient, universities should consider initiating programs and public health campaigns to promote dairy and calcium intake among this population

Low-level arsenic exposure:Nutritional and dietary predictors in first-grade Uruguayan children

Arsenic exposure in children is a public health concern but is understudied in relation to the predictors, and effects of low-level exposure. We examined the extent and dietary predictors of exposure to inorganic arsenic in 5–8 year old children from Montevideo, Uruguay. Children were recruited at school; 357 were enrolled, 328 collected morning urine samples, and 317 had two 24-hour dietary recalls. Urinary arsenic metabolites, i.e. inorganic arsenic (iAs), methylarsonic acid (MMA), and dimethylarsinic acid (DMA), were measured using high-performance liquid chromatography with hydride generation and inductively coupled plasma mass spectrometry (HPLC-HG-ICP-MS), and the sum concentration (U-As) used for exposure assessment. Proportions of arsenic metabolites (%iAs, %MMA and %DMA) in urine were modelled in OLS regressions as functions of food groups, dietary patterns, nutrient intake, and nutritional status. Exposure to arsenic was low (median U-As: 9.9 µg/L) and household water (water As: median 0.45 µg/L) was not a major contributor to exposure. Children with higher consumption of rice had higher U-As but lower %iAs, %MMA, and higher %DMA. Children with higher meat consumption had lower %iAs and higher %DMA. Higher scores on ”nutrient dense” dietary pattern were related to lower %iAs and %MMA, and higher %DMA. Higher intake of dietary folate was associated with lower %MMA and higher %DMA. Overweight children had lower %MMA and higher %DMA than normal-weight children. In summary, rice was an important predictor of exposure to inorganic arsenic and DMA. Higher meat and folate consumption, diet rich in green leafy and red-orange vegetables and eggs, and higher BMI contributed to higher arsenic methylation capacity

Insufficient Sleep and Poor Sleep Quality Completely Mediate the Relationship between Financial Stress and Dietary Risk among Higher Education Students

The coronavirus disease 2019 (COVID-19) pandemic worsened financial stress for higher education students in the U.S. Financial stress is associated with poor dietary behaviors; however, factors that might influence this relationship are not well characterized. The present cross-sectional study investigated the associations between financial stress and dietary intake and dietary risk scores among higher education students (undergraduate and graduate students) in the U.S. and examined whether poor sleep quality and short sleep duration mediated the relationship between financial stress and dietary risk score. Validated tools were used to assess financial stress, sleep quality, sleep duration, dietary intake, and dietary risk. A total of 1280 students from three large U.S. universities completed the study. Results indicated that higher financial stress was associated with lower vegetable, fruit, fiber, and calcium intake, higher added sugar intake from sugar sweetened beverages, and higher dietary risk score. Further, the positive relationship between financial stress and dietary risk score was completely mediated by poor sleep quality among students who reported poor sleep quality and by short sleep duration among students who slept less than 7 h per night. These findings suggest that students might benefit from both financial management training and sleep education services to reduce undesirable dietary behaviors

Multiple Clinical Manifestations and Diagnostic Challenges of Incontinentia Pigmenti—12 Years' Experience in 1 Medical Center

Incontinentia pigmenti (IP) is a rare X-linked dominant disorder that involves ectodermal tissues of multiple systems. Previous reports are few in Taiwan. To contribute toward better understanding of IP, we describe and discuss the clinical features of cases that were diagnosed in a medical center during the past 12 years. Methods: The medical records of all patients with IP between July 1995 and June 2007 were reviewed retrospectively. The demographics, physical findings, pathology reports, molecular study reports, eosinophil counts and outcome were recorded. Results: A total of 4 patients, 3 female and 1 male neonate, who met the criteria for the diagnosis of IP were enrolled. Among these cases, 3 were not diagnosed with IP at initial presentation but were regarded to have infectious diseases. A definite family history of 3 consecutive generations was proved not only by clinical manifestations but also by molecular study in 1 patient. The patient also had retinal and vitreous body hemorrhage, which rapidly progressed to retinal detachment of the right eye in 2 months. Another patient presenting with stage III hyperpigmentation at birth had an extremely rare finding of left foot deformity. The male patient had unilateral and localized vesicular lesions over his left thigh. Conclusion: Diagnosis of IP is difficult in the neonatal period. Referral to experienced specialists is necessary. Multiple clinical characteristics of IP and rapid progression of ophthalmologic manifestations can be demonstrated through our study. Furthermore, 3 of the 4 cases in our study are the very first reports in Taiwan

Mortality prediction in patients with isolated moderate and severe traumatic brain injury using machine learning models.

BACKGROUND:The purpose of this study was to build a model of machine learning (ML) for the prediction of mortality in patients with isolated moderate and severe traumatic brain injury (TBI). METHODS:Hospitalized adult patients registered in the Trauma Registry System between January 2009 and December 2015 were enrolled in this study. Only patients with an Abbreviated Injury Scale (AIS) score ≥ 3 points related to head injuries were included in this study. A total of 1734 (1564 survival and 170 non-survival) and 325 (293 survival and 32 non-survival) patients were included in the training and test sets, respectively. RESULTS:Using demographics and injury characteristics, as well as patient laboratory data, predictive tools (e.g., logistic regression [LR], support vector machine [SVM], decision tree [DT], naive Bayes [NB], and artificial neural networks [ANN]) were used to determine the mortality of individual patients. The predictive performance was evaluated by accuracy, sensitivity, and specificity, as well as by area under the curve (AUC) measures of receiver operator characteristic curves. In the training set, all five ML models had a specificity of more than 90% and all ML models (except the NB) achieved an accuracy of more than 90%. Among them, the ANN had the highest sensitivity (80.59%) in mortality prediction. Regarding performance, the ANN had the highest AUC (0.968), followed by the LR (0.942), SVM (0.935), NB (0.908), and DT (0.872). In the test set, the ANN had the highest sensitivity (84.38%) in mortality prediction, followed by the SVM (65.63%), LR (59.38%), NB (59.38%), and DT (43.75%). CONCLUSIONS:The ANN model provided the best prediction of mortality for patients with isolated moderate and severe TBI