Risk Factors for Pediatric Intracranial Neoplasms in the Kids' Inpatient Database.

INTRODUCTION: In children and adolescents, brain and central nervous system (CNS) tumors are the leading types of cancers. Past studies have found differing rates of intracranial cancers among races and identified additional cancer risk factors. This study aimed to see if these differences can be substantiated with further investigation of the latest version (2019) of the Kids' Inpatient Database (KID). METHODS: A total of 7,818 pediatric patients <21 years old in KID with ICD-10 codes consisting of malignant neoplasms of the brain, brainstem, and cerebral meninges (C700, C709-C719) were queried. Modifiable risk factors evaluated include: hospital region, insurance type, hospital city size, the average income of patient zip code, and location/teaching status of a hospital. Non-modifiable risk factors were race and sex at birth. Dependent variables were tested in Excel and GraphPad Prism 9 using a χ2 test with Yates' continuity correction and Tukey's one-way and two-way ANOVAs. RESULTS: Mortality rates of females (2.88%) compared to males (1.99%) were significant (p < 0.05). Mortality was (4.17%) in black patients compared to (1.68%) for white (p < 0.0001), Hispanic mortality (2.95%) compared to white (p < 0.01), and mortality of Asian/Pacific Islander (3.86%) compared to white (p < 0.01). Black patients had significantly higher mortality than white, Hispanic, Asian/Pacific Islander, Native American, and other races overall (p < 0.01). There was no significant difference in the mortality rates between children's hospitals and large hospitals for any race. After accounting for patient race, mortality was still not significantly different for patients with Medicaid insurance compared to non-Medicaid insurance types. Of the children treated at children's hospitals, the most transferred in from outside hospitals were Native American (20.00%) followed by Asian/Pacific Islander (15.09%) then Hispanic patients (13.67%). A significant difference between races was also seen regarding length of stay (p < 0.001) and number of charges (p < 0.001). CONCLUSION: These findings confirm prior studies suggesting gender and race are significant factors in mortality rates for children with intracranial neoplasms. However, the findings do not identify the root causes of these discrepancies but may serve as an impetus for clinicians, healthcare administrators, and governmental leaders to improve national resource allocation to better care for pediatric patients with intracranial neoplasms.

The Day-Level Association Between Child Care Attendance and 24-Hour Movement Behaviors in Preschool-Aged Children.

BACKGROUND: Twenty-four hour movement behaviors (ie, physical activity [PA], screen time [ST], and sleep) are associated with children's health outcomes. Identifying day-level contextual factors, such as child care, that positively influence children's movement behaviors may help identify potential intervention targets, like improving access to child care programs. This study aimed to examine the between- and within-person effects of child care on preschoolers' 24-hour movement behaviors. METHODS: Children (N = 74, 4.7 [0.9] y, 48.9% girls, 63.3% White) wore an Axivity AX3 accelerometer on their nondominant wrist 24 hours per day for 14 days to measure PA and sleep. Parents completed surveys each night about their child's ST and child care attendance that day. Linear mixed effects models predicted day-level 24-hour movement behaviors from hours spent in child care. RESULTS: Children spent an average of 5.0 (2.9) hours per day in child care. For every additional hour of child care above their average, children had 0.3 hours (95% CI, -0.3 to -0.2) less ST that day. Between-person effects showed that compared with children who attended fewer overall hours of child care, children who attended more hours had less overall ST (B = -0.2 h; 95% CI, -0.4 to 0.0). Child care was not significantly associated with PA or sleep. CONCLUSIONS: Child care attendance was not associated with 24-hour PA or sleep; however, it was associated with less ST. More research utilizing objective measures of ST and more robust measures of daily schedules or structure is necessary to better understand how existing infrastructure may influence preschool-aged children's 24-hour movement behaviors. In addition, future research should consider how access to child care may influence child care attendance.

Development and Calibration of a PATCH Device for Monitoring Children's Heart Rate and Acceleration.

INTRODUCTION: Current wearables that collect heart rate and acceleration were not designed for children and/or do not allow access to raw signals, making them fundamentally unverifiable. This study describes the creation and calibration of an open-source multichannel platform (PATCH) designed to measure heart rate and acceleration in children ages 3-8 yr. METHODS: Children (N = 63; mean age, 6.3 yr) participated in a 45-min protocol ranging in intensities from sedentary to vigorous activity. Actiheart-5 was used as a comparison measure. We calculated mean bias, mean absolute error (MAE) mean absolute percent error (MA%E), Pearson correlations, and Lin's concordance correlation coefficient (CCC). RESULTS: Mean bias between PATCH and Actiheart heart rate was 2.26 bpm, MAE was 6.67 bpm, and M%E was 5.99%. The correlation between PATCH and Actiheart heart rate was 0.89, and CCC was 0.88. For acceleration, mean bias was 1.16 mg and MAE was 12.24 mg. The correlation between PATCH and Actiheart was 0.96, and CCC was 0.95. CONCLUSIONS: The PATCH demonstrated clinically acceptable accuracies to measure heart rate and acceleration compared with a research-grade device.

A Device Agnostic Approach to Predict Children's Activity from Consumer Wearable Accelerometer Data: A Proof-of-Concept Study.

INTRODUCTION: This study examined the potential of a device agnostic approach for predicting physical activity from consumer wearable accelerometry compared with a research-grade accelerometry. METHODS: Seventy-five 5- to 12-year-olds (58% male, 63% White) participated in a 60-min protocol. Children wore wrist-placed consumer wearables (Apple Watch Series 7 and Garmin Vivoactive 4) and a research-grade device (ActiGraph GT9X) concurrently with an indirect calorimeter (COSMED K5). Activity intensities (i.e., inactive, light, moderate-to-vigorous physical activity) were estimated via indirect calorimetry (criterion), and the Hildebrand thresholds were applied to the raw accelerometer data from the consumer wearables and research-grade device. Epoch-by-epoch (e.g., weighted sensitivity, specificity) and discrepancy (e.g., mean bias, absolute error) analyses evaluated agreement between accelerometry-derived and criterion estimates. Equivalence testing evaluated the equivalence of estimates produced by the consumer wearables and ActiGraph. RESULTS: Estimates produced by the raw accelerometry data from ActiGraph, Apple, and Garmin produced similar criterion agreement with weighted sensitivity = 68.2% (95% confidence interval (CI), 67.1%-69.3%), 73.0% (95% CI, 71.8%-74.3%), and 66.6% (95% CI, 65.7%-67.5%), respectively, and weighted specificity = 84.4% (95% CI, 83.6%-85.2%), 82.0% (95% CI, 80.6%-83.4%), and 75.3% (95% CI, 74.7%-75.9%), respectively. Apple Watch produced the lowest mean bias (inactive, -4.0 ± 4.5; light activity, 2.1 ± 4.0) and absolute error (inactive, 4.9 ± 3.4; light activity, 3.6 ± 2.7) for inactive and light physical activity minutes. For moderate-to-vigorous physical activity, ActiGraph produced the lowest mean bias (1.0 ± 2.9) and absolute error (2.8 ± 2.4). No ActiGraph and consumer wearable device estimates were statistically significantly equivalent. CONCLUSIONS: Raw accelerometry estimated inactive and light activity from wrist-placed consumer wearables performed similarly to, if not better than, a research-grade device, when compared with indirect calorimetry. This proof-of-concept study highlights the potential of device-agnostic methods for quantifying physical activity intensity via consumer wearables.

Understanding Accelerated Summer Body Mass Index Gain by Tracking Changes in Children's Height, Weight, and Body Mass Index Throughout the Year.

Background: Drivers of summer body mass index (BMI) gain in children remain unclear. The Circadian and Circannual Rhythm Model (CCRM) posits summer BMI gain is biologically driven, while the Structured Days Hypothesis (SDH) proposes it is driven by reduced structure. Objectives: Identify the mechanisms driving children's seasonal BMI gain through the CCRM and SDH. Methods: Children's (N = 147, mean age = 8.2 years) height and weight were measured monthly during the school year, and once in summer (July-August). BMI z-score (zBMI) was calculated using CDC growth charts. Behaviors were measured once per season. Mixed methods regression estimated monthly percent change in children's height (%HΔ), weight (%WΔ), and monthly zBMI for school year vs. summer vacation, seasonally, and during school months with no breaks vs. school months with a break ≥1 week. Results: School year vs. summer vacation analyses showed accelerations in children's %WΔ (Δ = 0.9, Standard Error (SE) = 0.1 vs. Δ = 1.4, SE = 0.1) and zBMI (Δ = -0.01, SE = 0.01 vs. Δ = 0.04, SE = 0.3) during summer vacation, but %HΔ remained relatively constant during summer vacation compared with school (Δ = 0.3, SE = 0.0 vs. Δ = 0.4, SE = 0.1). Seasonal analyses showed summer had the greatest %WΔ (Δ = 1.8, SE = 0.4) and zBMI change (Δ = 0.05, SE = 0.03) while %HΔ was relatively constant across seasons. Compared with school months without a break, months with a break showed higher %WΔ (Δ = 0.7, SE = 0.1 vs. Δ = 1.6, SE = 0.2) and zBMI change (Δ = -0.03, SE = 0.01 vs. Δ = 0.04, SE = 0.01), but %HΔ was constant (Δ = 0.4, SE = 0.0 vs. Δ = 0.3, SE = 0.1). Fluctuations in sleep timing and screen time may explain these changes. Conclusions: Evidence for both the CCRM and SDH was identified but the SDH may more fully explain BMI gain. Interventions targeting consistent sleep and reduced screen time during breaks from school may be warranted no matter the season.

What does it mean to use the mean? The impact of different data handling strategies on the proportion of children classified as meeting 24-hr movement guidelines and associations with overweight and obesity.

Background: Despite the widespread endorsement of 24-hour movement guidelines (physical activity, sleep, screentime) for youth, no standardized processes for categorizing guideline achievement exists. The purpose of this study was to illustrate the impact of different data handling strategies on the proportion of children meeting 24-hour movement guidelines (24hrG) and associations with overweight and obesity. Methods: A subset of 524 children (ages 5-12yrs) with complete 24-hour behavior measures on at least 10 days was used to compare the impact of data handling strategies on estimates of meeting 24hrG. Physical activity and sleep were measured via accelerometry. Screentime was measured via parent self-report. Comparison of meeting 24hrG were made using 1) average of behaviors across all days (AVG-24hr), 2) classifying each day and evaluating the percentage meeting 24hrG from 10-100% of their measured days (DAYS-24hr), and 3) the average of a random sample of 4 days across 10 iterations (RAND-24hr). A second subset of children (N=475) with height and weight data was used to explore the influence of each data handling strategy on children meeting guidelines and the odds of overweight/obesity via logistic regression. Results: Classification for AVG-24hr resulted in 14.7% of participants meeting 24hrG. Classification for DAYS-24hr resulted in 63.5% meeting 24hrG on 10% of measured days with <1% meeting 24hrG on 100% of days. Classification for RAND-24hr resulted in 15.9% of participants meeting 24hrG. Across 10 iterations, 63.6% of participants never met 24hrG regardless of the days sampled, 3.4% always met 24hrG, with the remaining 33.0% classified as meeting 24hrG for at least one of the 10 random iterations of days. Using AVG-24hr as a strategy, meeting all three guidelines associated with lower odds of having overweight obesity (OR=0.38, p<0.05). The RAND-24hr strategy produced a range of odds from 0.27 to 0.56. Using the criteria of needing to meet 24hrG on 100% of days, meeting all three guidelines associated with the lowest odds of having overweight and obesity as well (OR=0.04, p<0.05). Conclusions: Varying estimates of meeting the 24hrG and the odds of overweight and obesity results from different data handling strategies and days sampled.

Validation of hypermethylated DNA regions found in colorectal cancers as potential aging-independent biomarkers of precancerous colorectal lesions.

BACKGROUND: We previously identified 16,772 colorectal cancer-associated hypermethylated DNA regions that were also detectable in precancerous colorectal lesions (preCRCs) and unrelated to normal mucosal aging. We have now conducted a study to validate 990 of these differentially methylated DNA regions (DMRs) in a new series of preCRCs. METHODS: We used targeted bisulfite sequencing to validate these 990 potential biomarkers in 59 preCRC tissue samples (41 conventional adenomas, 18 sessile serrated lesions), each with a patient-matched normal mucosal sample. Based on differential DNA methylation tests, a panel of candidate DMRs was chosen on a subset of our cohort and then validated on the remaining part of our cohort and two publicly available datasets with respect to their stratifying potential between preCRCs and normal mucosa. RESULTS: Strong statistical significance for the difference in methylation levels was observed across the full set of 990 investigated DMRs. From these, a selected candidate panel of 30 DMRs correctly identified 58/59 tumors (area under the receiver operating curve: 0.998). CONCLUSIONS: These validated DNA hypermethylation markers can be exploited to develop more accurate noninvasive colorectal tumor screening assays.

Evaluation of a device-agnostic approach to predict sleep from raw accelerometry data collected by Apple Watch Series 7, Garmin Vivoactive 4, and ActiGraph GT9X Link in children with sleep disruptions.

GOAL AND AIMS: Evaluate the performance of a sleep scoring algorithm applied to raw accelerometry data collected from research-grade and consumer wearable actigraphy devices against polysomnography. FOCUS METHOD/TECHNOLOGY: Automatic sleep/wake classification using the Sadeh algorithm applied to raw accelerometry data from ActiGraph GT9X Link, Apple Watch Series 7, and Garmin Vivoactive 4. REFERENCE METHOD/TECHNOLOGY: Standard manual PSG sleep scoring. SAMPLE: Fifty children with disrupted sleep (M = 8.5 years, range = 5-12 years, 42% Black, 64% male). DESIGN: Participants underwent to single night lab polysomnography while wearing ActiGraph, Apple, and Garmin devices. CORE ANALYTICS: Discrepancy and epoch-by-epoch analyses for sleep/wake classification (devices vs. polysomnography). ADDITIONAL ANALYTICS AND EXPLORATORY ANALYSES: Equivalence testing for sleep/wake classification (research-grade actigraphy vs. commercial devices). CORE OUTCOMES: Compared to polysomnography, accuracy, sensitivity, and specificity were 85.5, 87.4, and 76.8, respectively, for Actigraph; 83.7, 85.2, and 75.8, respectively, for Garmin; and 84.6, 86.2, and 77.2, respectively, for Apple. The magnitude and trend of bias for total sleep time, sleep efficiency, sleep onset latency, and wake after sleep were similar between the research and consumer wearable devices. IMPORTANT ADDITIONAL OUTCOMES: Equivalence testing indicated that total sleep time and sleep efficiency estimates from the research and consumer wearable devices were statistically significantly equivalent. CORE CONCLUSION: This study demonstrates that raw acceleration data from consumer wearable devices has the potential to be harnessed to predict sleep in children. While further work is needed, this strategy could overcome current limitations related to proprietary algorithms for predicting sleep in consumer wearable devices.

A Sliding Scale Signal Quality Metric of Photoplethysmography Applicable to Measuring Heart Rate across Clinical Contexts with Chest Mounting as a Case Study.

Photoplethysmography (PPG) signal quality as a proxy for accuracy in heart rate (HR) measurement is useful in various public health contexts, ranging from short-term clinical diagnostics to free-living health behavior surveillance studies that inform public health policy. Each context has a different tolerance for acceptable signal quality, and it is reductive to expect a single threshold to meet the needs across all contexts. In this study, we propose two different metrics as sliding scales of PPG signal quality and assess their association with accuracy of HR measures compared to a ground truth electrocardiogram (ECG) measurement. METHODS: We used two publicly available PPG datasets (BUT PPG and Troika) to test if our signal quality metrics could identify poor signal quality compared to gold standard visual inspection. To aid interpretation of the sliding scale metrics, we used ROC curves and Kappa values to calculate guideline cut points and evaluate agreement, respectively. We then used the Troika dataset and an original dataset of PPG data collected from the chest to examine the association between continuous metrics of signal quality and HR accuracy. PPG-based HR estimates were compared with reference HR estimates using the mean absolute error (MAE) and the root-mean-square error (RMSE). Point biserial correlations were used to examine the association between binary signal quality and HR error metrics (MAE and RMSE). RESULTS: ROC analysis from the BUT PPG data revealed that the AUC was 0.758 (95% CI 0.624 to 0.892) for signal quality metrics of STD-width and 0.741 (95% CI 0.589 to 0.883) for self-consistency. There was a significant correlation between criterion poor signal quality and signal quality metrics in both Troika and originally collected data. Signal quality was highly correlated with HR accuracy (MAE and RMSE, respectively) between PPG and ground truth ECG. CONCLUSION: This proof-of-concept work demonstrates an effective approach for assessing signal quality and demonstrates the effect of poor signal quality on HR measurement. Our continuous signal quality metrics allow estimations of uncertainties in other emergent metrics, such as energy expenditure that relies on multiple independent biometrics. This open-source approach increases the availability and applicability of our work in public health settings.

Single vesicle analysis reveals the release of tetraspanin positive extracellular vesicles into circulation with high intensity intermittent exercise.

Small extracellular vesicles (sEVs) are released from all cell types and participate in the intercellular exchange of proteins, lipids, metabolites and nucleic acids. Proteomic, flow cytometry and nanoparticle tracking analyses suggest sEVs are released into circulation with exercise. However, interpretation of these data may be influenced by sources of bias introduced by different analytical approaches. Seven healthy participants carried out a high intensity intermittent training (HIIT) cycle protocol consisting of 4 × 30 s at a work-rate corresponding to 200% of individual max power (watts) interspersed by 4.5 min of active recovery. EDTA-treated blood was collected before and immediately after the final effort. Platelet-poor (PPP) and platelet-free (PFP) plasma was derived by one or two centrifugal spins at 2500 g, respectively (15 min, room temperature). Platelets were counted on an automated haemocytometer. Plasma samples were assessed with the Exoview R100 platform, which immobilises sEVs expressing common tetraspanin markers CD9, CD63, CD81 and CD41a on microfluidic chips and with the aid of fluorescence imaging, counts their abundance at a single sEV resolution, importantly, without a pre-isolation step. There was a lower number of platelets in the PFP than PPP, which was associated with a lower number of CD9, CD63 and CD41a positive sEVs. HIIT induced an increase in fluorescence counts in CD9, CD63 and CD81 positive sEVs in both PPP and PFP. These data support the concept that sEVs are released into circulation with exercise. Furthermore, platelet-free plasma is the preferred, representative analyte to study sEV dynamics and phenotype during exercise. KEY POINTS: Small extracellular vesicles (sEV) are nano-sized particles containing protein, metabolites, lipid and RNA that can be transferred from cell to cell. Previous findings implicate that sEVs are released into circulation with exhaustive, aerobic exercise, but since there is no gold standard method to isolate sEVs, these findings may be subject to bias introduced by different approaches. Here, we use a novel method to immobilise and image sEVs, at single-vesicle resolution, to show sEVs are released into circulation with high intensity intermittent exercise. Since platelet depletion of plasma results in a reduction in sEVs, platelet-free plasma is the preferred analyte to examine sEV dynamics and phenotype in the context of exercise.

Psychological predictors of health anxiety and pain in ambulatory presentations in a hospital emergency department.

BACKGROUND: Health anxiety in attendees of out-patient medical clinics is well established; however, there has been a lack of research into health anxiety within emergency settings. AIMS: This study explored the prevalence of health anxiety in ambulatory presentations in a tertiary emergency department (ED) as well as the factors associated with pain and health anxiety in this setting. METHOD: A cross-sectional questionnaire design was used to gather data from adult ED ambulatory attendees across a 4-day sampling period to assess psychological and physical health variables. Number of attendances to ED over the previous 12 months was accessed through healthcare records. RESULTS: Of the final sample (n = 106), 77% were white British, 54% were male, and 14% presented with severe health anxiety as measured by the Short Health Anxiety Inventory (≥18). Participants with pre-existing health conditions had significantly higher levels of health anxiety (M = 12.36, SE = 1.59) compared with those without (M = 7.79, SE = 0.66). Stepwise multiple regression analyses identified anxiety sensitivity and pain catastrophizing as significant independent predictors of health anxiety, explaining 51% of the variance in health anxiety. Pain catastrophizing was also a significant independent predictor of pain level, accounting for 20% of the variance. CONCLUSION: This study provides insight into the prevalence of health anxiety in ED ambulatory presentations and key psychological predictors of health anxiety and pain. This has implications for treatment in an ED setting whereby patients may benefit from referral to medical psychology or mental health services.

Accuracy and Precision of Opportunistic Measures of Body Composition from the Tanita DC-430U.

Background: It is essential to quantify the accuracy and precision of bioelectrical impedance (BIA)-estimated percent body fat (%BF) to better interpret community-based research findings that utilize opportunistic measures. Methods: Study 1 measured the accuracy of a new dual-frequency foot-to-foot BIA device (Tanita DC-430U) compared with dual-energy X-ray absorptiometry (DXA) among healthy elementary school-aged children (N = 50). Study 2 examined the precision of BIA %BF estimates within and between days among children and adults (N = 38). Results: Regarding accuracy, Tanita DC-430U underestimated %BF by 8.0 percentage points compared with DXA (20.6% vs. 28.5%), but correctly ranked children in terms of %BF. Differences in %BF between BIA and DXA were driven by lower BIA-estimated fat mass (7.8 kg vs. 9.9 kg, p < 0.05) and higher BIA-estimated fat-free mass (25.3 kg vs. 24.1 kg, p < 0.05). The absolute agreement between BIA and DXA for estimated %BF was moderate (concordance correlation coefficients = 0.53). Regarding precision, measures taken at the same time, but on different days (root mean square standard deviation [RMSD] = 0.42-0.74) were more precise than the measures taken at different times within a single day (RMSD = 1.04-1.10). Conclusion: The Tanita DC-430U substantially underestimated %BF compared with DXA, highlighting the need to assess accuracy of new BIA devices when they are introduced to the market. Opportunistic measures of %BF estimates were most precise when taken at consistent times and in the morning, but may be utilized throughout the day with an understanding of within- and between-day variability.

Advances in the Development of Prodrugs as Selective Modulators of Estrogen Receptors.

Due to the complexity of estrogen signaling mediated by estrogen receptors (ERs) in a variety of biological environments, there is great interest in the identification and optimization of selective estrogen receptor ligands. Prodrugs that can be activated in specific environments allow for tissue selectivity. Therefore, there have been recent advances in the development of prodrugs for ERs that can be released through enzymatic reactions, chemical reactions (eg, oxidation by reactive oxygen species or reduction by ascorbic acid), or light-mediated processes. In addition, researchers have linked ER ligands to additional drugs for selective cellular targeting. In this review, we highlight the compounds that have been generated and their potential uses in disease states such as breast cancer, inflammation, and menopause.

Acute ECG changes in a woman presenting to coronary care with fluctuating consciousness.

We present the case of a 67-year-old woman brought into the coronary care unit (CCU) with a suspected ST-segment elevation myocardial infarction (STEMI) due to lateral ST-segment elevation on her 12-lead electrocardiogram (ECG) and a significant troponin rise, but no reported chest pain and a fluctuating consciousness level. Whilst in CCU, she deteriorated further with a reduction in consciousness and sluggish pupillary reflexes, warranting urgent computed tomography (CT) of her brain, which confirmed extensive subarachnoid haemorrhage (SAH) with early evidence of hydrocephalus. She was therefore transferred to the local tertiary neurosurgical centre for endovascular coiling. ECG changes alongside a raised troponin are not uncommon findings in SAH and clinicians should exercise vigilance and consider urgent brain imaging in the absence of chest pain and presence of neurological deficit, to prevent adverse events from unnecessary antiplatelet or anticoagulant therapy, and invasive coronary angiography. SAH is a medical emergency and prompt recognition and referral for neurosurgical intervention is integral for optimal patient outcome.

Feasibility of Measuring Screen Time, Activity, and Context Among Families With Preschoolers: Intensive Longitudinal Pilot Study.

BACKGROUND: Digital media has made screen time more available across multiple contexts, but our understanding of the ways children and families use digital media has lagged behind the rapid adoption of this technology. OBJECTIVE: This study evaluated the feasibility of an intensive longitudinal data collection protocol to objectively measure digital media use, physical activity, sleep, sedentary behavior, and socioemotional context among caregiver-child dyads. This paper also describes preliminary convergent validity of ecological momentary assessment (EMA) measures and preliminary agreement between caregiver self-reported phone use and phone use collected from passive mobile sensing. METHODS: Caregivers and their preschool-aged child (3-5 years) were recruited to complete a 30-day assessment protocol. Within 30-days, caregivers completed 7 days of EMA to measure child behavior problems and caregiver stress. Caregivers and children wore an Axivity AX3 (Newcastle Upon Tyne) accelerometer to assess physical activity, sedentary behavior, and sleep. Phone use was assessed via passive mobile sensing; we used Chronicle for Android users and screenshots of iOS screen time metrics for iOS users. Participants were invited to complete a second 14-day protocol approximately 3-12 months after their first assessment. We used Pearson correlations to examine preliminary convergent validity between validated questionnaire measures of caregiver psychological functioning, child behavior, and EMA items. Root mean square errors were computed to examine the preliminary agreement between caregiver self-reported phone use and objective phone use. RESULTS: Of 110 consenting participants, 105 completed all protocols (105/110, 95.5% retention rate). Compliance was defined a priori as completing ≥70%-75% of each protocol task. There were high compliance rates for passive mobile sensing for both Android (38/40, 95%) and iOS (64/65, 98%). EMA compliance was high (105/105, 100%), but fewer caregivers and children were compliant with accelerometry (62/99, 63% and 40/100, 40%, respectively). Average daily phone use was 383.4 (SD 157.0) minutes for Android users and 354.7 (SD 137.6) minutes for iOS users. There was poor agreement between objective and caregiver self-reported phone use; root mean square errors were 157.1 and 81.4 for Android and iOS users, respectively. Among families who completed the first assessment, 91 re-enrolled to complete the protocol a second time, approximately 7 months later (91/105, 86.7% retention rate). CONCLUSIONS: It is feasible to collect intensive longitudinal data on objective digital media use simultaneously with accelerometry and EMA from an economically and racially diverse sample of families with preschool-aged children. The high compliance and retention of the study sample are encouraging signs that these methods of intensive longitudinal data collection can be completed in a longitudinal cohort study. The lack of agreement between self-reported and objectively measured mobile phone use highlights the need for additional research using objective methods to measure digital media use. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-36240.

Measuring Microtemporal Processes Underlying Preschoolers' Screen Use and Behavioral Health: Protocol for the Tots and Tech Study.

BACKGROUND: Excessive screen time is associated with poor health and behavioral outcomes in children. However, research on screen time use has been hindered by methodological limitations, including retrospective reports of usual screen time and lack of momentary etiologic processes occurring within each day. OBJECTIVE: This study is designed to assess the feasibility and utility of a comprehensive multibehavior protocol to measure the digital media use and screen time context among a racially and economically diverse sample of preschoolers and their families. This paper describes the recruitment, data collection, and analytical protocols for the Tots and Tech study. METHODS: The Tots and Tech study is a longitudinal, observational study of 100 dyads: caregivers and their preschool-age children (aged 3-5 years). Both caregivers and children will wear an Axivity AX3 accelerometer (Axivity Ltd) for 30 days to assess their physical activity, sedentary behavior, and sleep. Caregivers will complete ecological momentary assessments (EMAs) for 1 week to measure child behavioral problems, caregiver stress, and child screen time. RESULTS: The Tots and Tech study was funded in March 2020. This study maintains rolling recruitment, with each dyad on their own assessment schedule, depending on the time of enrollment. Enrollment was scheduled to take place between September 2020 and May 2022. We aim to enroll 100 caregiver-child dyads. The Tots and Tech outcome paper is expected to be published in 2022. CONCLUSIONS: The Tots and Tech study attempts to overcome previous methodological limitations by using objective measures of screen time, physical activity, sedentary behavior, and sleep behaviors with contextual factors measured by EMA. The results will be used to evaluate the feasibility and utility of a comprehensive multibehavior protocol using objective measures of mobile screen time and accelerometry in conjunction with EMA among caregiver-child dyads. Future observational and intervention studies will be able to use this study protocol to better measure screen time and its context. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/36240.

Urinary Exosomal MicroRNAs as Biomarkers for Obesity-Associated Chronic Kidney Disease.

The early detection of chronic kidney disease (CKD) is key to reducing the burden of disease and rising costs of care. This need has spurred interest in finding new biomarkers for CKD. Ideal bi-omarkers for CKD should be: easy to measure; stable; reliably detected, even when interfering substances are present; site-specific based on the type of injury (tubules vs. glomeruli); and its changes in concentration should correlate with disease risk or outcome. Currently, no single can-didate biomarker fulfills these criteria effectively, and the mechanisms underlying kidney fibrosis are not fully understood; however, there is growing evidence in support of microRNA-mediated pro-cesses. Specifically, urinary exosomal microRNAs may serve as biomarkers for kidney fibrosis. In-creasing incidences of obesity and the recognition of obesity-associated CKD have increased interest in the interplay of obesity and CKD. In this review, we provide: (1) an overview of the current scope of CKD biomarkers within obese individuals to elucidate the genetic pathways unique to obesi-ty-related CKD; (2) a review of microRNA expression in obese individuals with kidney fibrosis in the presence of comorbidities, such as diabetes mellitus and hypertension; (3) a review of thera-peutic processes, such as diet and exercise, that may influence miR-expression in obesity-associated CKD; (4) a review of the technical aspects of urinary exosome isolation; and (5) future areas of research.

MCC Gene Silencing Is a CpG Island Methylator Phenotype-Associated Factor That Predisposes Colon Cancer Cells to Irinotecan and Olaparib.

Chemotherapy is a mainstay of colorectal cancer treatment, and often involves a combination drug regime. CpG island methylator phenotype (CIMP)-positive tumors are potentially more responsive to the topoisomerase-inhibitor irinotecan. The mechanistic basis of the increased sensitivity of CIMP cancers to irinotecan is poorly understood. Mutated in Colorectal Cancer (MCC) is emerging as a multifunctional tumor suppressor gene in colorectal and liver cancers, and has been implicated in drug responsiveness. Here, we found that CIMP tumors undergo MCC loss almost exclusively via promoter hypermethylation rather than copy number variation or mutations. A subset of cancers display hypomethylation which is also associated with low MCC expression, particularly in rectal cancer, where CIMP is rare. MCC knockdown or deletion was found to sensitize cells to SN38 (the active metabolite of irinotecan) or the PARP-inhibitor Olaparib. A synergistic effect on cell death was evident when these drugs were used concurrently. The improved SN38/irinotecan efficacy was accompanied by the down-regulation of DNA repair genes. Thus, differential methylation of MCC is potentially a valuable biomarker to identify colorectal cancers suitable for irinotecan therapy, possibly in combination with PARP inhibitors.

Impact of the COVID-19 pandemic on elementary schoolers' physical activity, sleep, screen time and diet: A quasi-experimental interrupted time series study.

BACKGROUND: COVID-19 school closures pose a threat to children's wellbeing, but no COVID-19-related studies have assessed children's behaviours over multiple years . OBJECTIVE: To examine children's obesogenic behaviours during spring and summer of the COVID-19 pandemic compared to previous data collected from the same children during the same calendar period in the 2 years prior. METHODS: Physical activity and sleep data were collected via Fitbit Charge-2 in 231 children (7-12 years) over 6 weeks during spring and summer over 3 years. Parents reported their child's screen time and dietary intake via a survey on 2-3 random days/week. RESULTS: Children's behaviours worsened at a greater rate following the pandemic onset compared to pre-pandemic trends. During pandemic spring, sedentary behaviour increased (+79 min; 95% CI = 60.6, 97.1) and MVPA decreased (-10 min, 95% CI = -18.2, -1.1) compared to change in previous springs (2018-2019). Sleep timing shifted later (+124 min; 95% CI = 112.9, 135.5). Screen time (+97 min, 95% CI = 79.0, 115.4) and dietary intake increased (healthy: +0.3 foods, 95% CI = 0.2, 0.5; unhealthy: +1.2 foods, 95% CI = 1.0, 1.5). Similar patterns were observed during summer. CONCLUSIONS: Compared to pre-pandemic measures, children's PA, sedentary behaviour, sleep, screen time, and diet were adversely altered during the COVID-19 pandemic. This may ultimately exacerbate childhood obesity.