Background: Emotion dysregulation has been implicated in the negative outcomes following trauma exposure. A proposed biomarker of emotion dysregulation, respiratory sinus arrhythmia (RSA), has demonstrated associations with trauma-related phenomena, such as the fear-potentiated startle (FPS) response. The current study aimed to examine the prospective association between emotion dysregulation and RSA and FPS several years following trauma exposure.
Methods: Participants were 131 women exposed to a campus mass shooting on February 14, 2008. Pre-shooting emotion dysregulation was assessed in 2006-2008. Startle response, measured by orbicularis oculi electromyography (EMG), and RSA were gathered during an FPS paradigm conducted from 2012 to 2015.
Results: No significant associations among emotion dysregulation, RSA, and FPS emerged among the full sample. However, emotion dysregulation predicted FPS during both acquisition (r = 0.40, p < 0.05) and extinction (r = 0.57, p < 0.01) among individuals with high resting RSA.
Conclusions: Findings suggest that pre-shooting emotion dysregulation is a potent predictor of FPS several years following potential trauma exposure, and this association varies by RSA level. Results emphasize the importance of examining autonomic regulation in the association between emotion dysregulation and recovery from trauma exposure.
The NIMH Research Domain Criteria (RDoC) initiative aims to describe key dimensional constructs underlying mental function across multiple units of analysis-from genes to observable behaviors-in order to better understand psychopathology. The acute threat ("fear") construct of the RDoC Negative Valence System has been studied extensively from a translational perspective, and is highly pertinent to numerous psychiatric conditions, including anxiety and trauma-related disorders. We examined genetic contributions to the construct of acute threat at two units of analysis within the RDoC framework: (1) neural circuits and (2) physiology. Specifically, we focused on genetic influences on activation patterns of frontolimbic neural circuitry and on startle, skin conductance, and heart rate responses. Research on the heritability of activation in threat-related frontolimbic neural circuitry is lacking, but physiological indicators of acute threat have been found to be moderately heritable (35-50%). Genetic studies of the neural circuitry and physiology of acute threat have almost exclusively relied on the candidate gene method and, as in the broader psychiatric genetics literature, most findings have failed to replicate. The most robust support has been demonstrated for associations between variation in the serotonin transporter (SLC6A4) and catechol-O-methyltransferase (COMT) genes with threat-related neural activation and physiological responses. However, unbiased genome-wide approaches using very large samples are needed for gene discovery, and these can be accomplished with collaborative consortium-based research efforts, such as those of the Psychiatric Genomics Consortium (PGC) and Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium.
The insulin/IGF-1 signalling (IIS) pathway plays an important role in the regulation of larval diapause, the long-lived growth arrest state called dauer arrest, in Caenorhabditis elegans. In this nematode, 40 insulin-like peptides (ILPs) have been identified as putative ligands of the IIS pathway; however, it remains unknown how ILPs modulate larval diapause. Here we show that the secretory polarity of INS-35 and INS-7, which suppress larval diapause, is changed in the intestinal epithelial cells at larval diapause. These ILPs are secreted from the intestine into the body cavity during larval stages. In contrast, they are secreted into the intestinal lumen and degraded during dauer arrest, only to be secreted into the body cavity again when the worms return to developmental growth. The process that determines the secretory polarity of INS-35 and INS-7, thus, has an important role in the modulation of larval diapause.
Two urea transporters, UT-A1 and UT-A3, are expressed in the kidney terminal inner medullary collecting duct (IMCD) and are important for the production of concentrated urine. UT-A1, as the largest isoform of all UT-A urea transporters, has gained much attention and been extensively studied; however, the role and the regulation of UT-A3 are less explored. In this study, we investigated UT-A3 regulation by glycosylation modification. A site-directed mutagenesis verified a single glycosylation site in UT-A3 at Asn279. Loss of the glycosylation reduced forskolin-stimulated UT-A3 cell membrane expression and urea transport activity. UT-A3 has two glycosylation forms, 45 and 65 kDa. Using sugar-specific binding lectins, the UT-A3 glycosylation profile was examined. The 45-kDa form was pulled down by lectin concanavalin A (Con A) and Galant husnivalis lectin (GNL), indicating an immature glycan with a high amount of mannose (Man), whereas the 65-kDa form is a mature glycan composed of acetylglucosamine (GlcNAc) and poly-N-acetyllactosame (poly-LacNAc) that was pulled down by wheat germ agglutinin (WGA) and tomato lectin, respectively. Interestingly, the mature form of UT-A3 glycan contains significant amounts of sialic acid. We explored the enzymes responsible for directing UT-A3 sialylation. Sialyltransferase ST6GalI, but not ST3GalIV, catabolizes UT-A3 α2,6-sialylation. Activation of protein kinase C (PKC) by PDB treatment promoted UT-A3 glycan sialylation and membrane surface expression. The PKC inhibitor chelerythrine blocks ST6GalI-induced UT-A3 sialylation. Increased sialylation by ST6GalI increased UT-A3 protein stability and urea transport activity. Collectively, our study reveals a novel mechanism of UT-A3 regulation by ST6GalI-mediated sialylation modification that may play an important role in kidney urea reabsorption and the urinary concentrating mechanism.
High false alarm rates in the ICU decrease quality of care by slowing staff response times while increasing patient delirium through noise pollution. The 2015 PhysioNet/Computing in Cardiology Challenge provides a set of 1250 multi-parameter ICU data segments associated with critical arrhythmia alarms, and challenges the general research community to address the issue of false alarm suppression using all available signals. Each data segment was 5 minutes long (for real time analysis), ending at the time of the alarm. For retrospective analysis, we provided a further 30 seconds of data after the alarm was triggered. A total of 750 data segments were made available for training and 500 were held back for testing. Each alarm was reviewed by expert annotators, at least two of whom agreed that the alarm was either true or false. Challenge participants were invited to submit a complete, working algorithm to distinguish true from false alarms, and received a score based on their program's performance on the hidden test set. This score was based on the percentage of alarms correct, but with a penalty that weights the suppression of true alarms five times more heavily than acceptance of false alarms. We provided three example entries based on well-known, open source signal processing algorithms, to serve as a basis for comparison and as a starting point for participants to develop their own code. A total of 38 teams submitted a total of 215 entries in this year's Challenge. This editorial reviews the background issues for this challenge, the design of the challenge itself, the key achievements, and the follow-up research generated as a result of the Challenge, published in the concurrent special issue of Physiological Measurement. Additionally we make some recommendations for future changes in the field of patient monitoring as a result of the Challenge.
by
Juan A. Contreras-Vite;
Silvia Cruz-Rangel;
José J. De Jesus-Perez;
Iván A. Arechiga Figueroa;
Aldo A. Rodriguez-Menchaca;
Patricia Perez-Cornejo;
Harrison Hartzell Jr.;
Jorge Arreola
TMEM16A (ANO1), the pore-forming subunit of calcium-activated chloride channels, regulates several physiological and pathophysiological processes such as smooth muscle contraction, cardiac and neuronal excitability, salivary secretion, tumour growth and cancer progression. Gating of TMEM16A is complex because it involves the interplay between increases in intracellular calcium concentration ([Ca 2+ ] i ), membrane depolarization, extracellular Cl − or permeant anions and intracellular protons. Our goal here was to understand how these variables regulate TMEM16A gating and to explain four observations. (a) TMEM16A is activated by voltage in the absence of intracellular Ca 2+ . (b) The Cl − conductance is decreased after reducing extracellular Cl − concentration ([Cl − ] o ). (c) I Cl is regulated by physiological concentrations of [Cl − ] o . (d) In cells dialyzed with 0.2 μM [Ca 2+ ] i , Cl − has a bimodal effect: at [Cl − ] o < 30 mM TMEM16A current activates with a monoexponential time course, but above 30 mM, [Cl − ] o I Cl activation displays fast and slow kinetics. To explain the contribution of V m , Ca 2+ and Cl − to gating, we developed a 12-state Markov chain model. This model explains TMEM16A activation as a sequential, direct, and V m -dependent binding of two Ca 2+ ions coupled to a V m -dependent binding of an external Cl − ion, with V m -dependent transitions between states. Our model predicts that extracellular Cl − does not alter the apparent Ca 2+ affinity of TMEM16A, which we corroborated experimentally. Rather, extracellular Cl − acts by stabilizing the open configuration induced by Ca 2+ and by contributing to the V m dependence of activation.
Purpose: To develop and validate the Pediatric Risk Estimation Score for Children Using Extracorporeal Respiratory Support (Ped-RESCUERS). Ped-RESCUERS is designed to estimate the in-hospital mortality risk for children prior to receiving respiratory extracorporeal membrane oxygenation (ECMO) support. Methods: This study used data from an international registry of patients aged 29 days to less than 18 years who received ECMO support from 2009 to 2014. We divided the registry into development and validation datasets by calendar date. Candidate variables were selected for model inclusion if the variable independently changed the mortality risk by at least 2 % in a Bayesian logistic regression model with in-hospital mortality as the outcome. We characterized the model’s ability to discriminate mortality with the area under curve (AUC) of the receiver operating characteristic. Results: From 2009 to 2014, 2458 non-neonatal children received ECMO for respiratory support, with a mortality rate of 39.8 %. The development dataset contained 1611 children receiving ECMO support from 2009 to 2012. The model included the following variables: pre-ECMO pH, pre-ECMO arterial partial pressure of carbon dioxide, hours of intubation prior to ECMO support, hours of admission at ECMO center prior to ECMO support, ventilator type, mean airway pressure, pre-ECMO use of milrinone, and a diagnosis of pertussis, asthma, bronchiolitis, or malignancy. The validation dataset included 438 children receiving ECMO support from 2013 to 2014. The Ped-RESCUERS model from the development dataset had an AUC of 0.690, and the validation dataset had an AUC of 0.634. Conclusions: Ped-RESCUERS provides a novel measure of pre-ECMO mortality risk. Future studies should seek external validation and improved discrimination of this mortality prediction tool.
Clinical data management systems typically provide caregiver teams with useful information, derived from large, sometimes highly heterogeneous, data sources that are often changing dynamically. Over the last decade there has been a significant surge in interest in using these data sources, from simply reusing the standard clinical databases for event prediction or decision support, to including dynamic and patient-specific information into clinical monitoring and prediction problems. However, in most cases, commercial clinical databases have been designed to document clinical activity for reporting, liability, and billing reasons, rather than for developing new algorithms. With increasing excitement surrounding 'secondary use of medical records' and 'Big Data' analytics, it is important to understand the limitations of current databases and what needs to change in order to enter an era of 'precision medicine.' This review article covers many of the issues involved in the collection and preprocessing of critical care data. The three challenges in critical care are considered: compartmentalization, corruption, and complexity. A range of applications addressing these issues are covered, including the modernization of static acuity scoring; online patient tracking; personalized prediction and risk assessment; artifact detection; state estimation; and incorporation of multimodal data sources such as genomic and free text data.
by
Charles T. Quinn;
Eric P. Smith;
Shahriar Arbabi;
Paramjit K. Khera;
Christopher J. Lindsell;
Omar Niss;
Clinton Joiner;
Robert S. Franco;
Robert M. Cohen
Hemolysis is a key feature of sickle cell anemia (HbSS). Direct quantitation of hemolysis could be used as an objective outcome in clinical trials of new therapeutics for HbSS and would also enable better human studies of the pathogenesis of complications of HbSS that are ostensibly hemolysis-related, such as pulmonary hypertension. However, contemporary human studies in HbSS have used only surrogate markers of hemolysis rather than direct measurements of RBC survival. We directly quantified hemolysis in HbSS by measuring survival of an age cohort of RBCs labeled with a stable isotope, administered orally as 15N-glycine, a metabolic precursor of heme. The atomic excess of 15N in heme extracted from blood was monitored by mass spectrometry over time. We performed 13 labeling experiments in 11 individuals with HbSS. Mean RBC survival was 31.9 days (range 14.1–53.6). Both HbF level, a known determinant of hemolysis, and absolute reticulocyte count (ARC), an index of the marrow’s response to hemolysis, correlated with directly measured RBC survival (r = 0.61, P < 0.002; r = −0.84, P < 0.001). However, commonly used biochemical surrogates of hemolysis (LDH, AST, bilirubin, and plasma free hemoglobin) did not correlate with directly measured RBC survival. These biochemical surrogates should be interpreted cautiously, at best, in clinical trials and human physiologic studies in HbSS. ARC was the best correlate of total hemolysis, but only 70% of the variation in RBC survival was reflected in this marker. If greater accuracy is required in human studies, 15N-glycine RBC labeling can directly and accurately quantify hemolysis. Am. J. Hematol. 91:1195–1201, 2016.
Here, we investigated whether hyperglycemia and/or free fatty acids (palmitate, PAL) affect the expression level of bone morphogenic protein 4 (BMP4), a proatherogenic marker, in endothelial cells and the potential role of BMP4 in diabetic vascular complications. To measure BMP4 expression, human umbilical vein endothelial cells (HUVECs) were exposed to high glucose concentrations and/or PAL for 24 or 72 h, and the effects of these treatments on the expression levels of adhesion molecules and reactive oxygen species (ROS) were examined. BMP4 loss-of-function status was achieved via transfection of a BMP4-specific siRNA. High glucose levels increased BMP4 expression in HUVECs in a dose-dependent manner. PAL potentiated such expression. The levels of adhesion molecules and ROS production increased upon treatment with high glucose and/or PAL, but this effect was negated when BMP4 was knocked down via siRNA. Signaling of BMP4, a proinflammatory and pro-atherogenic cytokine marker, was increased by hyperglycemia and PAL. BMP4 induced the expression of inflammatory adhesion molecules and ROS production. Our work suggests that BMP4 plays a role in atherogenesis induced by high glucose levels and/or PAL.