Association of Demographic and Socioeconomic Indicators With the Use of Wearable Devices Among Children.

Importance: The use of consumer-grade wearable devices for collecting data for biomedical research may be associated with social determinants of health (SDoHs) linked to people's understanding of and willingness to join and remain engaged in remote health studies. Objective: To examine whether demographic and socioeconomic indicators are associated with willingness to join a wearable device study and adherence to wearable data collection in children. Design, Setting, and Participants: This cohort study used wearable device usage data collected from 10 414 participants (aged 11-13 years) at the year-2 follow-up (2018-2020) of the ongoing Adolescent Brain and Cognitive Development (ABCD) Study, performed at 21 sites across the United States. Data were analyzed from November 2021 to July 2022. Main Outcomes and Measures: The 2 primary outcomes were (1) participant retention in the wearable device substudy and (2) total device wear time during the 21-day observation period. Associations between the primary end points and sociodemographic and economic indicators were examined. Results: The mean (SD) age of the 10 414 participants was 12.00 (0.72) years, with 5444 (52.3%) male participants. Overall, 1424 participants (13.7%) were Black; 2048 (19.7%), Hispanic; and 5615 (53.9%) White. Substantial differences were observed between the cohort that participated and shared wearable device data (wearable device cohort [WDC]; 7424 participants [71.3%]) compared with those who did not participate or share data (no wearable device cohort [NWDC]; 2900 participants [28.7%]). Black children were significantly underrepresented (-59%) in the WDC (847 [11.4%]) compared with the NWDC (577 [19.3%]; P < .001). In contrast, White children were overrepresented (+132%) in the WDC (4301 [57.9%]) vs the NWDC (1314 [43.9%]; P < .001). Children from low-income households (<$24 999) were significantly underrepresented in WDC (638 [8.6%]) compared with NWDC (492 [16.5%]; P < .001). Overall, Black children were retained for a substantially shorter duration (16 days; 95% CI, 14-17 days) compared with White children (21 days; 95% CI, 21-21 days; P < .001) in the wearable device substudy. In addition, total device wear time during the observation was notably different between Black vs White children (ß = -43.00 hours; 95% CI, -55.11 to -30.88 hours; P < .001). Conclusions and Relevance: In this cohort study, large-scale wearable device data collected from children showed considerable differences between White and Black children in terms of enrollment and daily wear time. While wearable devices provide an opportunity for real-time, high-frequency contextual monitoring of individuals' health, future studies should account for and address considerable representational bias in wearable data collection associated with demographic and SDoH factors.

LaminaRGeneVis: A Tool to Visualize Gene Expression Across the Laminar Architecture of the Human Neocortex.

The application of RNA sequencing has enabled the characterization of genome-wide gene expression in the human brain, including distinct layers of the neocortex. Neuroanatomically, the molecular patterns that underlie the laminar organization of the neocortex can help link structure to circuitry and function. To advance our understanding of cortical architecture, we created LaminaRGeneVis, a web application that displays across-layer cortical gene expression from multiple datasets. These datasets were collected using bulk, single-nucleus, and spatial RNA sequencing methodologies and were normalized to facilitate comparisons between datasets. The online resource performs single- and multi-gene analyses to provide figures and statistics for user-friendly assessment of laminar gene expression patterns in the adult human neocortex. The web application is available at https://ethanhkim.shinyapps.io/laminargenevis/.

Thrombin Generation in Cardiac Versus Noncardiac Surgical Cohorts.

BACKGROUND: Bleeding can be a significant problem after cardiac surgery. As a result, venous thromboembolism (VTE) or anticoagulation or both following mechanical valve implantation are often delayed in these patients. The calibrated automated thrombin (CAT) generation assay has become the gold standard to evaluate thrombin generation, a critical step in clot formation independent of other hemostatic processes (eg, platelet activation, fibrin cross-linking, and fibrinolysis), and is increasingly used to examine thrombotic and hemorrhagic outcomes. No study has currently used this assay to compare the thrombin generation profiles of cardiac surgical patients to noncardiac surgical patients. We hypothesize that noncardiac patients may be less prone to postoperative changes in thrombin generation. METHODS: A prospective, observational, cohort study was undertaken using blood samples from 50 cardiac and 50 noncardiac surgical patients preoperatively, immediately postoperatively, and on postoperative days 1 to 4. Platelet-poor plasma samples were obtained from patients preoperatively, on arrival to the postanesthesia care unit (PACU) or intensive care unit (ICU), and daily on postoperative days 1 to 4 if patients remained inpatient. Samples were evaluated for CAT measurements. Patient and surgical procedure characteristics were obtained from the electronic medical record. RESULTS: The primary outcome variable, median endogenous thrombin potential (ETP), measured in nanomolar × minutes (nM × min), was decreased 100% in cardiac surgical versus 2% in noncardiac patients (P < .001). All parameters of thrombin generation were similarly depressed. Cardiac (versus noncardiac) surgical type was associated with -76.5% difference of percent change in ETP on multivariable regression analysis (95% confidence interval [CI], -87.4 to -65.5; P value <.001). CONCLUSIONS: Cardiac surgical patients exhibit a profound decrease in thrombin generation postoperatively compared with noncardiac surgical patients evaluated by this study. Hemodilution and coagulation factor depletion likely contribute to this decreased thrombin generation after cardiac surgery.

Evaluation of deep convolutional neural networks for in situ hybridization gene expression image representation.

High resolution in situ hybridization (ISH) images of the brain capture spatial gene expression at cellular resolution. These spatial profiles are key to understanding brain organization at the molecular level. Previously, manual qualitative scoring and informatics pipelines have been applied to ISH images to determine expression intensity and pattern. To better capture the complex patterns of gene expression in the human cerebral cortex, we applied a machine learning approach. We propose gene re-identification as a contrastive learning task to compute representations of ISH images. We train our model on an ISH dataset of ~1,000 genes obtained from postmortem samples from 42 individuals. This model reaches a gene re-identification rate of 38.3%, a 13x improvement over random chance. We find that the learned embeddings predict expression intensity and pattern. To test generalization, we generated embeddings in a second dataset that assayed the expression of 78 genes in 53 individuals. In this set of images, 60.2% of genes are re-identified, suggesting the model is robust. Importantly, this dataset assayed expression in individuals diagnosed with schizophrenia. Gene and donor-specific embeddings from the model predict schizophrenia diagnosis at levels similar to that reached with demographic information. Mutations in the most discriminative gene, Sodium Voltage-Gated Channel Beta Subunit 4 (SCN4B), may help understand cardiovascular associations with schizophrenia and its treatment. We have publicly released our source code, embeddings, and models to spur further application to spatial transcriptomics. In summary, we propose and evaluate gene re-identification as a machine learning task to represent ISH gene expression images.

Dexmedetomidine-Induced Massive Diuresis in a Patient Undergoing Spinal Fusion Surgery: A Case Report and Synthesis of the Literature.

Dexmedetomidine is an α2-adrenergic sedative-hypnotic medication used as an adjunct to general anesthesia. While experimental studies in animals have demonstrated a mild diuretic effect of dexmedetomidine, only recently have case reports described dexmedetomidine-induced diuresis in humans. Interestingly, the majority of such cases have involved patients undergoing spinal fusion surgery. Here, we report a case of a 30-year-old woman undergoing cervical spinal fusion surgery who experienced a massive diuresis starting 30 minutes after receiving dexmedetomidine intravenous infusion. We discuss the differential diagnosis and synthesize the current literature on this rare effect.

Characterization of crude Echis carinatus venom-induced cytotoxicity in HEK 293T cells.

Echis carinatus (saw-scaled viper) produces potent hemorrhagic venom that causes the development of apoptotic and necrotic tissues. In this study, we used polyethyleneimine (PEI) to enhance cellular adherence, and to determine whether the substrate attachment influenced the survival of cells treated with crude E. carinatus venom. Human embryonic kidney (HEK) 293T cells were grown for 18hr in tissue culture plates with or without polyethyleneimine (PEI), and were then stimulated with crude E. carinatus venom for 3 or 12hr. HEK 293T cells grown without PEI displayed a robust oxidative response to corresponding substrate detachment, loss of plasma membrane integrity and decreased cell viability. Cells grown on PEI adsorbed substrates demonstrated prolonged substrate attachment resulting in significantly higher cell viabilities. These observations suggest that the cytotoxicity of crude E. carinatus venom is dependent upon cellular detachment.

Predicting direct protein interactions from affinity purification mass spectrometry data.

BACKGROUND: Affinity purification followed by mass spectrometry identification (AP-MS) is an increasingly popular approach to observe protein-protein interactions (PPI) in vivo. One drawback of AP-MS, however, is that it is prone to detecting indirect interactions mixed with direct physical interactions. Therefore, the ability to distinguish direct interactions from indirect ones is of much interest. RESULTS: We first propose a simple probabilistic model for the interactions captured by AP-MS experiments, under which the problem of separating direct interactions from indirect ones is formulated. Then, given idealized quantitative AP-MS data, we study the problem of identifying the most likely set of direct interactions that produced the observed data. We address this challenging graph theoretical problem by first characterizing signatures that can identify weakly connected nodes as well as dense regions of the network. The rest of the direct PPI network is then inferred using a genetic algorithm.Our algorithm shows good performance on both simulated and biological networks with very high sensitivity and specificity. Then the algorithm is used to predict direct interactions from a set of AP-MS PPI data from yeast, and its performance is measured against a high-quality interaction dataset. CONCLUSIONS: As the sensitivity of AP-MS pipeline improves, the fraction of indirect interactions detected will also increase, thereby making the ability to distinguish them even more desirable. Despite the simplicity of our model for indirect interactions, our method provides a good performance on the test networks.