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Abstract Our goal is to explore quantitative motor features in critically ill patients with severe brain injury (SBI). We hypothesized that computational decoding of these features would yield information on underlying neurological states and outcomes. Using wearable microsensors placed on all extremities, we recorded a median 24.1 (IQR: 22.8–25.1) hours of high-frequency accelerometry data per patient from a prospective cohort (n = 69) admitted to the ICU with SBI. Models were trained using time-, frequency-, and wavelet-domain features and levels of responsiveness and outcome as labels. The two primary tasks were detection of levels of responsiveness, assessed by motor sub-score of the Glasgow Coma Scale (GCSm), and prediction of functional outcome at discharge, measured with the Glasgow Outcome Scale–Extended (GOSE). Detection models achieved significant (AUC: 0.70 [95% CI: 0.53–0.85]) and consistent (observation windows: 12 min–9 h) discrimination of SBI patients capable of purposeful movement (GCSm > 4). Prediction models accurately discriminated patients of upper moderate disability or better (GOSE > 5) with 2–6 h of observation (AUC: 0.82 [95% CI: 0.75–0.90]). Results suggest that time series analysis of motor activity yields clinically relevant insights on underlying functional states and short-term outcomes in patients with SBI.

Abstract Traumatic brain injury (TBI) is a leading neurological cause of death and disability across the world. Early characterization of TBI severity could provide a window for therapeutic intervention and contribute to improved outcome. We hypothesized that granular electronic health record data available in the first 24 h following admission to the intensive care unit (ICU) can be used to differentiate outcomes at discharge. Working from two ICU datasets we focused on patients with a primary admission diagnosis of TBI whose length of stay in ICU was ≥ 24 h (N = 1689 and 127). Features derived from clinical, laboratory, medication, and physiological time series data in the first 24 h after ICU admission were used to train elastic-net regularized Generalized Linear Models for the prediction of mortality and neurological function at ICU discharge. Model discrimination, determined by area under the receiver operating characteristic curve (AUC) analysis, was 0.903 and 0.874 for mortality and neurological function, respectively. Model performance was successfully validated in an external dataset (AUC 0.958 and 0.878 for mortality and neurological function, respectively). These results demonstrate that computational analysis of data routinely collected in the first 24 h after admission accurately and reliably predict discharge outcomes in ICU stratum TBI patients.

Abstract Coronavirus SARS-COV-2 infections continue to spread across the world, yet effective large-scale disease detection and prediction remain limited. COVID Control: A Johns Hopkins University Study, is a novel syndromic surveillance approach, which collects body temperature and COVID-like illness (CLI) symptoms across the US using a smartphone app and applies spatio-temporal clustering techniques and cross-correlation analysis to create maps of abnormal symptomatology incidence that are made publicly available. The results of the cross-correlation analysis identify optimal temporal lags between symptoms and a range of COVID-19 outcomes, with new taste/smell loss showing the highest correlations. We also identified temporal clusters of change in taste/smell entries and confirmed COVID-19 incidence in Baltimore City and County. Further, we utilized an extended simulated dataset to showcase our analytics in Maryland. The resulting clusters can serve as indicators of emerging COVID-19 outbreaks, and support syndromic surveillance as an early warning system for disease prevention and control.

Introduction There is little consensus and high heterogeneity on the optimal set of relevant clinical outcomes in research studies regarding extubation in neurocritical care patients with brain injury undergoing mechanical ventilation. The aims of this study are to: (1) develop a core outcome set (COS) and (2) reach consensus on a hierarchical composite endpoint for such studies.Methods and analysis The study will include a broadly representative, international panel of stakeholders with research and clinical expertise in this field and will involve four stages: (1) a scoping review to generate an initial list of outcomes represented in the literature, (2) an investigator meeting to review the outcomes for inclusion in the Delphi surveys, (3) four rounds of online Delphi consensus-building surveys and (4) online consensus meetings to finalise the COS and hierarchical composite endpoint.Ethics and dissemination This study received ethical approval from the French Society of Anesthesia and Critical Care Medicine Institutional Review Board (SFAR CERAR-IRB 00010254‐2023-029). The study results will be disseminated through communication to stakeholders, publication in a peer-reviewed journal, and presentations at conferences.Trial registration number This study is registered with the Core Outcome Measures in Effectiveness Trials (COMET) Initiative.

When a patient is admitted to the intensive care unit (ICU) after a traumatic brain injury (TBI), an early prognosis is essential for baseline risk adjustment and shared decision making. TBI outcomes are commonly categorised by the Glasgow Outcome Scale-Extended (GOSE) into eight, ordered levels of functional recovery at 6 months after injury. Existing ICU prognostic models predict binary outcomes at a certain threshold of GOSE (e.g., prediction of survival [GOSE > 1]). We aimed to develop ordinal prediction models that concurrently predict probabilities of each GOSE score. From a prospective cohort (n = 1,550, 65 centres) in the ICU stratum of the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) patient dataset, we extracted all clinical information within 24 hours of ICU admission (1,151 predictors) and 6-month GOSE scores. We analysed the effect of two design elements on ordinal model performance: (1) the baseline predictor set, ranging from a concise set of ten validated predictors to a token-embedded representation of all possible predictors, and (2) the modelling strategy, from ordinal logistic regression to multinomial deep learning. With repeated k-fold cross-validation, we found that expanding the baseline predictor set significantly improved ordinal prediction performance while increasing analytical complexity did not. Half of these gains could be achieved with the addition of eight high-impact predictors to the concise set. At best, ordinal models achieved 0.76 (95% CI: 0.74-0.77) ordinal discrimination ability (ordinal c-index) and 57% (95% CI: 54%- 60%) explanation of ordinal variation in 6-month GOSE (Somers' Dxy). Model performance and the effect of expanding the predictor set decreased at higher GOSE thresholds, indicating the difficulty of predicting better functional outcomes shortly after ICU admission. Our results motivate the search for informative predictors that improve confidence in prognosis of higher GOSE and the development of ordinal dynamic prediction models.

Marginal deficiency of vitamin B-6 is common among segments of the population worldwide. Because pyridoxal 5'-phosphate (PLP) serves as a coenzyme in the metabolism of amino acids, carbohydrates, organic acids, and neurotransmitters, as well as in aspects of one-carbon metabolism, vitamin B-6 deficiency could have many effects. Healthy men and women (age: 20-40 y; n = 23) were fed a 2-day controlled, nutritionally adequate diet followed by a 28-day low-vitamin B-6 diet (

The disruption of cholesterol homeostasis leads to an increase in cholesterol levels which results in the development of cardiovascular disease. Mitogen Inducible Gene 6 (Mig-6) is an immediate early response gene that can be induced by various mitogens, stresses, and hormones. To identify the metabolic role of Mig-6 in the liver, we conditionally ablated Mig-6 in the liver using the Albumin-Cre mouse model (Alb(cre/+)Mig-6(f/f); Mig-6(d/d)). Mig-6(d/d) mice exhibit hepatomegaly and fatty liver. Serum levels of total, LDL, and HDL cholesterol and hepatic lipid were significantly increased in the Mig-6(d/d) mice. The daily excretion of fecal bile acids was significantly decreased in the Mig-6(d/d) mice. DNA microarray analysis of mRNA isolated from the livers of these mice showed alterations in genes that regulate lipid metabolism, bile acid, and cholesterol synthesis, while the expression of genes that regulate biliary excretion of bile acid and triglyceride synthesis showed no difference in the Mig-6(d/d) mice compared to Mig-6(f/f) controls. These results indicate that Mig-6 plays an important role in cholesterol homeostasis and bile acid synthesis. Mice with liver specific conditional ablation of Mig-6 develop hepatomegaly and increased intrahepatic lipid and provide a novel model system to investigate the genetic and molecular events involved in the regulation of cholesterol homeostasis and bile acid synthesis. Defining the molecular mechanisms by which Mig-6 regulates cholesterol homeostasis will provide new insights into the development of more effective ways for the treatment and prevention of cardiovascular disease.

To determine if caloric restriction (CR) would cause changes in plasma metabolic intermediates in response to a mixed meal, suggestive of changes in the capacity to adapt fuel oxidation to fuel availability or metabolic flexibility, and to determine how any such changes relate to insulin sensitivity (S(I)).Forty-six volunteers were randomized to a weight maintenance diet (Control), 25% CR, or 12.5% CR plus 12.5% energy deficit from structured aerobic exercise (CR+EX), or a liquid calorie diet (890 kcal/d until 15% reduction in body weight)for six months. Fasting and postprandial plasma samples were obtained at baseline, three, and six months. A targeted mass spectrometry-based platform was used to measure concentrations of individual free fatty acids (FFA), amino acids (AA), and acylcarnitines (AC). S(I) was measured with an intravenous glucose tolerance test.Over three and six months, there were significantly larger differences in fasting-to-postprandial (FPP) concentrations of medium and long chain AC (byproducts of FA oxidation) in the CR relative to Control and a tendency for the same in CR+EX (CR-3 month P = 0.02; CR-6 month P = 0.002; CR+EX-3 month P = 0.09; CR+EX-6 month P = 0.08). After three months of CR, there was a trend towards a larger difference in FPP FFA concentrations (P = 0.07; CR-3 month P = 0.08). Time-varying differences in FPP concentrations of AC and AA were independently related to time-varying S(I) (P

Obesity has reached epidemic proportions worldwide. Several animal models of obesity exist, but studies are lacking that compare traditional lard-based high fat diets (HFD) to "Cafeteria diets" (CAF) consisting of nutrient poor human junk food. Our previous work demonstrated the rapid and severe obesogenic and inflammatory consequences of CAF compared to HFD including rapid weight gain, markers of Metabolic Syndrome, multi-tissue lipid accumulation, and dramatic inflammation. To identify potential mediators of CAF-induced obesity and Metabolic Syndrome, we used metabolomic analysis to profile serum, muscle, and white adipose from rats fed CAF, HFD, or standard control diets. Principle component analysis identified elevations in clusters of fatty acids and acylcarnitines. These increases in metabolites were associated with systemic mitochondrial dysfunction that paralleled weight gain, physiologic measures of Metabolic Syndrome, and tissue inflammation in CAF-fed rats. Spearman pairwise correlations between metabolites, physiologic, and histologic findings revealed strong correlations between elevated markers of inflammation in CAF-fed animals, measured as crown like structures in adipose, and specifically the pro-inflammatory saturated fatty acids and oxidation intermediates laurate and lauroyl carnitine. Treatment of bone marrow-derived macrophages with lauroyl carnitine polarized macrophages towards the M1 pro-inflammatory phenotype through downregulation of AMPK and secretion of pro-inflammatory cytokines. Results presented herein demonstrate that compared to a traditional HFD model, the CAF diet provides a robust model for diet-induced human obesity, which models Metabolic Syndrome-related mitochondrial dysfunction in serum, muscle, and adipose, along with pro-inflammatory metabolite alterations. These data also suggest that modifying the availability or metabolism of saturated fatty acids may limit the inflammation associated with obesity leading to Metabolic Syndrome.