214 research outputs found
Chaos in symmetric phase oscillator networks
Copyright © 2011 American Physical SocietyThis is the final version. Available from the American Physical Society via the DOI in this recordPhase-coupled oscillators serve as paradigmatic models of networks of weakly interacting oscillatory units in physics and biology. The order parameter which quantifies synchronization so far has been found to be chaotic only in systems with inhomogeneities. Here we show that even symmetric systems of identical oscillators may not only exhibit chaotic dynamics, but also chaotically fluctuating order parameters. Our findings imply that neither inhomogeneities nor amplitude variations are necessary to obtain chaos; i.e., nonlinear interactions of phases give rise to the necessary instabilities
Polygenic risk scoring to assess genetic overlap and protective factors influencing posttraumatic stress, depression, and chronic pain after motor vehicle collision trauma
Posttraumatic stress (PTS), depressive symptoms (DS), and musculoskeletal pain (MSP) are common sequelae of trauma exposure. Although these adverse posttraumatic neuropsychiatric sequelae (APNS) are often studied separately, clinical comorbidity is high. In a cohort of European American motor vehicle collision (MVC) trauma survivors (n = 781), substantial PTS (≥33, IES-R), DS (≥26, CES-D), and MSP (≥4, 0-10 NRS) were identified via a 6-month survey. Genetic risk was estimated using polygenic risk scores (PRSs) calculated from the largest available GWAS datasets of PTSD, MDD, and back pain. We then assessed comorbidity and genetic risk influence for developing chronic PTS, DS, and MSP after MVC. Secondary analyses explored whether common social determinants of health ameliorate genetic vulnerability. We found that 6 months after MVC, nearly half 357/781 (46%) of the participants had substantial PTS, DS, and/or MSP, and overlap was common (PTS + MSP (23%), DS + MSP (18%), PTS + DS (12%)). Genetic risk predicted post-MVC outcomes. PTSD-PRSs predicted PTS and DS (R2 = 2.21% and 2.77%, padj \u3c 0.01), MDD-PRSs predicted DS and MSP (R2 = 1.89%, padj \u3c 0.01) and 0.79%, padj \u3c 0.05), and back pain-PRS predicted MSP (R2 = 1.49%, padj \u3c 0.01). Individuals in the highest quintile of PTSD-PRSs had 2.8 and 3.5 times the odds of developing PTS and DS vs. the lowest quintile (95% CI = 1.39-5.75 and 1.58-7.76). Among these high-risk individuals, those living in non-disadvantaged neighborhoods and with college education had 47% (p = 0.048) and 52% (p = 0.04) less risk of developing PTS, and those with high social support had 60% (p = 0.008) less risk of developing DS. Overall, genetic factors influence the risk of APNS after MVC, genetic risk of distinct APNS are overlapping, and specific social determinants greatly augment genetic risk of APNS development after MVC
Author Correction: Defining the r factor for post-trauma resilience and its neural predictors
Author Correction for "Defining the r factor for post-trauma resilience and its neural predictors
A prospective examination of sex differences in posttraumatic autonomic functioning
Full author list omitted for brevity. For the full list of authors, see article.Background: Cross-sectional studies have found that individuals with posttraumatic stress disorder (PTSD) exhibit deficits in autonomic functioning. While PTSD rates are twice as high in women compared to men, sex differences in autonomic functioning are relatively unknown among trauma-exposed populations. The current study used a prospective design to examine sex differences in posttraumatic autonomic functioning. Methods: 192 participants were recruited from emergency departments following trauma exposure (Mean age = 35.88, 68.2% female). Skin conductance was measured in the emergency department; fear conditioning was completed two weeks later and included measures of blood pressure (BP), heart rate (HR), and high frequency heart rate variability (HF-HRV). PTSD symptoms were assessed 8 weeks after trauma. Results: 2-week systolic BP was significantly higher in men, while 2-week HR was significantly higher in women, and a sex by PTSD interaction suggested that women who developed PTSD demonstrated the highest HR levels. Two-week HF-HRV was significantly lower in women, and a sex by PTSD interaction suggested that women with PTSD demonstrated the lowest HF-HRV levels. Skin conductance response in the emergency department was associated with 2-week HR and HF-HRV only among women who developed PTSD. Conclusions: Our results indicate that there are notable sex differences in autonomic functioning among trauma-exposed individuals. Differences in sympathetic biomarkers (BP and HR) may have implications for cardiovascular disease risk given that sympathetic arousal is a mechanism implicated in this risk among PTSD populations. Future research examining differential pathways between PTSD and cardiovascular risk among men versus women is warranted
Does Decreased Access to Emergency Departments Affect Patient Outcomes? Analysis of AMI Population 1996-2005
We analyze whether decreased emergency department access (measured by increased driving time to the nearest ED) results in adverse patient outcomes or changes in the patient health profile for patients suffering from acute myocardial infarction. Data sources include 100% Medicare Provider Analysis and Review, AHA hospital annual surveys, Medicare hospital cost reports, and longitude and latitude information for 1995-2005. We define four ED access change categories and estimate a zip codes fixed-effects regression models on the following AMI outcomes: time-specific mortality rates, age, and probability of PTCA on the day of admission. We find a small increase in 30-day to 1-year mortality rates among patients in communities that experience 30-minute increases in driving time, we find a substantial increase in long-term mortality rates, a shift to younger ages (suggesting that the older ones die en route) and a higher probability of immediate PTCA. Most of the adverse effects disappear after the initial three-year transition window.
Immunoregulatory Mechanisms Evaluated by Quantitation of Immunoglobulin-Secreting Cells in Man
Development and Validation of a Model to Predict Posttraumatic Stress Disorder and Major Depression After a Motor Vehicle Collision
Full author list omitted for brevity. For the full list of authors, see article.Importance: A substantial proportion of the 40 million people in the US who present to emergency departments (EDs) each year after traumatic events develop posttraumatic stress disorder (PTSD) or major depressive episode (MDE). Accurately identifying patients at high risk in the ED would facilitate the targeting of preventive interventions. Objectives: To develop and validate a prediction tool based on ED reports after a motor vehicle collision to predict PTSD or MDE 3 months later. Design, Setting, and Participants: The Advancing Understanding of Recovery After Trauma (AURORA) study is a longitudinal study that examined adverse posttraumatic neuropsychiatric sequalae among patients who presented to 28 US urban EDs in the immediate aftermath of a traumatic experience. Enrollment began on September 25, 2017. The 1003 patients considered in this diagnostic/prognostic report completed 3-month assessments by January 31, 2020. Each patient received a baseline ED assessment along with follow-up self-report surveys 2 weeks, 8 weeks, and 3 months later. An ensemble machine learning method was used to predict 3-month PTSD or MDE from baseline information. Data analysis was performed from November 1, 2020, to May 31, 2021. Main Outcomes and Measures: The PTSD Checklist for DSM-5 was used to assess PTSD and the Patient Reported Outcomes Measurement Information System Depression Short-Form 8b to assess MDE. Results: A total of 1003 patients (median [interquartile range] age, 34.5 [24-43] years; 715 [weighted 67.9%] female; 100 [weighted 10.7%] Hispanic, 537 [weighted 52.7%] non-Hispanic Black, 324 [weighted 32.2%] non-Hispanic White, and 42 [weighted 4.4%] of non-Hispanic other race or ethnicity were included in this study. A total of 274 patients (weighted 26.6%) met criteria for 3-month PTSD or MDE. An ensemble machine learning model restricted to 30 predictors estimated in a training sample (patients from the Northeast or Midwest) had good prediction accuracy (mean [SE] area under the curve [AUC], 0.815 [0.031]) and calibration (mean [SE] integrated calibration index, 0.040 [0.002]; mean [SE] expected calibration error, 0.039 [0.002]) in an independent test sample (patients from the South). Patients in the top 30% of predicted risk accounted for 65% of all 3-month PTSD or MDE, with a mean (SE) positive predictive value of 58.2% (6.4%) among these patients at high risk. The model had good consistency across regions of the country in terms of both AUC (mean [SE], 0.789 [0.025] using the Northeast as the test sample and 0.809 [0.023] using the Midwest as the test sample) and calibration (mean [SE] integrated calibration index, 0.048 [0.003] using the Northeast as the test sample and 0.024 [0.001] using the Midwest as the test sample; mean [SE] expected calibration error, 0.034 [0.003] using the Northeast as the test sample and 0.025 [0.001] using the Midwest as the test sample). The most important predictors in terms of Shapley Additive Explanations values were symptoms of anxiety sensitivity and depressive disposition, psychological distress in the 30 days before motor vehicle collision, and peritraumatic psychosomatic symptoms. Conclusions and Relevance: The results of this study suggest that a short set of questions feasible to administer in an ED can predict 3-month PTSD or MDE with good AUC, calibration, and geographic consistency. Patients at high risk can be identified in the ED for targeting if cost-effective preventive interventions are developed
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