Disparities in Hospital Services Utilization Among Patients with Mental Health Issues: A Statewide Example Examining Insurance Status and Race Factors from 1999-2010

There exist many disconnects between the mental and general health care sectors. However, a goal of the Affordable Care Act (ACA) of 2010 is to change this by improving insurance access and the intersection of mental and general health care. As insurance status intersects with race, the present study examines how race, insurance status, and hospital mental health services utilization differ across groups within the state of New Jersey. The present study aims to determine trends in hospital mental health care utilization by insurance status and race from 1999 to 2010. The rate of self-pay for mental health disorders in the Black population was significantly higher than the rate for Whites and Asians during this period. However, though Asian mental health utilization increased the most over the 11-year period, the Asian population had the slowest growth in self-pay rates. ANOVA tests demonstrated significant differences in the rate of self-pay mental health cases between race groups (

Estimating good discrete partitions from observed data: symbolic false nearest neighbors

A symbolic analysis of observed time series data requires making a discrete partition of a continuous state space containing observations of the dynamics. A particular kind of partition, called ``generating'', preserves all dynamical information of a deterministic map in the symbolic representation, but such partitions are not obvious beyond one dimension, and existing methods to find them require significant knowledge of the dynamical evolution operator or the spectrum of unstable periodic orbits. We introduce a statistic and algorithm to refine empirical partitions for symbolic state reconstruction. This method optimizes an essential property of a generating partition: avoiding topological degeneracies. It requires only the observed time series and is sensible even in the presence of noise when no truly generating partition is possible. Because of its resemblance to a geometrical statistic frequently used for reconstructing valid time-delay embeddings, we call the algorithm ``symbolic false nearest neighbors''

Navigator channel adaptation to reconstruct three dimensional heart volumes from two dimensional radiotherapy planning data

BACKGROUND: Biologically-based models that utilize 3D radiation dosimetry data to estimate the risk of late cardiac effects could have significant utility for planning radiotherapy in young patients. A major challenge arises from having only 2D treatment planning data for patients with long-term follow-up. In this study, we evaluate the accuracy of an advanced deformable image registration (DIR) and navigator channels (NC) adaptation technique to reconstruct 3D heart volumes from 2D radiotherapy planning images for Hodgkin's Lymphoma (HL) patients. METHODS: Planning CT images were obtained for 50 HL patients who underwent mediastinal radiotherapy. Twelve image sets (6 male, 6 female) were used to construct a male and a female population heart model, which was registered to 23 HL "Reference" patients' CT images using a DIR algorithm, MORFEUS. This generated a series of population-to-Reference patient specific 3D deformation maps. The technique was independently tested on 15 additional "Test" patients by reconstructing their 3D heart volumes using 2D digitally reconstructed radiographs (DRR). The technique involved: 1) identifying a matching Reference patient for each Test patient using thorax measurements, 2) placement of six NCs on matching Reference and Test patients' DRRs to capture differences in significant heart curvatures, 3) adapting the population-to-Reference patient-specific deformation maps to generate population-to-Test patient-specific deformation maps using linear and bilinear interpolation methods, 4) applying population-to-Test patient specific deformation to the population model to reconstruct Test-patient specific 3D heart models. The percentage volume overlap between the NC-adapted reconstruction and actual Test patient's true heart volume was calculated using the Dice coefficient. RESULTS: The average Dice coefficient expressed as a percentage between the NC-adapted and actual Test model was 89.4 ± 2.8%. The modified NC adaptation technique made significant improvements to the population deformation heart models (p = 0.01). As standard evaluation, the residual Dice error after adaptation was comparable to the volumetric differences observed in free-breathing heart volumes (p = 0.62). CONCLUSIONS: The reconstruction technique described generates accurate 3D heart models from limited 2D planning data. This development could potentially be used to retrospectively calculate delivered dose to the heart for historically treated patients and thereby provide a better understanding of late radiation-related cardiac effects

Visual Diagnosis: Pearling: a case study

We present the case of a patient who attempted to perform a type of body modification known as "pearling" or "genital beading" while in prison. This patient unfortunately caused severe trauma to his penis, requiring surgical intervention. Photographs of the traumatic injuries are presented

Noninvasive assessment of coronary vasodilation using cardiovascular magnetic resonance in patients at high risk for coronary artery disease

Abstract Background Impaired coronary vasodilation to both endothelial-dependent and endothelial-independent stimuli have been associated with atherosclerosis. Direct measurement of coronary vasodilation using x-ray angiography or intravascular ultrasound is invasive and, thus, not appropriate for asymptomatic patients or for serial follow-up. In this study, high-resolution coronary cardiovascular magnetic resonance (CMR) was used to investigate the vasodilatory response to nitroglycerine (NTG) of asymptomatic patients at high risk for CAD. Methods A total of 46 asymptomatic subjects were studied: 13 high-risk patients [8 with diabetes mellitus (DM), 5 with end stage renal disease (ESRD)] and 33 age-matched controls. Long-axis and cross-sectional coronary artery images were acquired pre- and 5 minutes post-sublingual NTG using a sub-mm-resolution multi-slice spiral coronary CMR sequence. Coronary cross sectional area (CSA) was measured on pre- and post-NTG images and % coronary vasodilation was calculated. Results Patients with DM and ESRD had impaired coronary vasodilation to NTG compared to age-matched controls (17.8 ± 7.3% vs. 25.6 ± 7.1%, p = 0.002). This remained significant for ESRD patients alone (14.8 ± 7.7% vs. 25.6 ± 7.1%; p = 0.003) and for DM patients alone (19.8 ± 6.3% vs. 25.6 ± 7.1%; p = 0.049), with a non-significant trend toward greater impairment in the ESRD vs. DM patients (14.8 ± 7.7% vs. 19.8 ± 6.3%; p = 0.23). Conclusion Noninvasive coronary CMR demonstrates impairment of coronary vasodilation to NTG in high-risk patients with DM and ESRD. This may provide a functional indicator of subclinical atherosclerosis and warrants clinical follow up to determine prognostic significance.</p

Integration of Biofunctional Molecules into 3D-Printed Polymeric Micro-/Nanostructures

Three-dimensional printing at the micro-/nanoscale represents a new challenge in research and development to achieve direct printing down to nanometre-sized objects. Here, FluidFM, a combination of microfluidics with atomic force microscopy, offers attractive options to fabricate hierarchical polymer structures at different scales. However, little is known about the effect of the substrate on the printed structures and the integration of (bio)functional groups into the polymer inks. In this study, we printed micro-/nanostructures on surfaces with different wetting properties, and integrated molecules with different functional groups (rhodamine as a fluorescent label and biotin as a binding tag for proteins) into the base polymer ink. The substrate wetting properties strongly affected the printing results, in that the lateral feature sizes increased with increasing substrate hydrophilicity. Overall, ink modification only caused minor changes in the stiffness of the printed structures. This shows the generality of the approach, as significant changes in the mechanical properties on chemical functionalization could be confounders in bioapplications. The retained functionality of the obtained structures after UV curing was demonstrated by selective binding of streptavidin to the printed structures. The ability to incorporate binding tags to achieve specific interactions between relevant proteins and the fabricated micro-/nanostructures, without compromising the mechanical properties, paves a way for numerous bio and sensing applications. Additional flexibility is obtained by tuning the substrate properties for feature size control, and the option to obtain functionalized printed structures without post-processing procedures will contribute to the development of 3D printing for biological applications, using FluidFM and similar dispensing techniques

Enhancing Crop Yield Prediction Utilizing Machine Learning on Satellite-Based Vegetation Health Indices

Accurate crop yield forecasting is essential in the food industry’s decision-making process, where vegetation condition index (VCI) and thermal condition index (TCI) coupled with machine learning (ML) algorithms play crucial roles. The drawback, however, is that a one-fits-all prediction model is often employed over an entire region without considering subregional VCI and TCI’s spatial variability resulting from environmental and climatic factors. Furthermore, when using nonlinear ML, redundant VCI/TCI data present additional challenges that adversely affect the models’ output. This study proposes a framework that (i) employs higher-order spatial independent component analysis (sICA), and (ii), exploits a combination of the principal component analysis (PCA) and ML (i.e., PCA-ML combination) to deal with the two challenges in order to enhance crop yield prediction accuracy. The proposed framework consolidates common VCI/TCI spatial variability into their respective subregions, using Vietnam as an example. Compared to the one-fits-all approach, subregional rice yield forecasting models over Vietnam improved by an average level of 20% up to 60%. PCA-ML combination outperformed ML-only by an average of 18.5% up to 45%. The framework generates rice yield predictions 1 to 2 months ahead of the harvest with an average of 5% error, displaying its reliability

Predictors of fatigue severity in early systemic sclerosis: a prospective longitudinal study of the GENISOS cohort.

ObjectivesLongitudinal studies examining the baseline predictors of fatigue in SSc have not been reported. Our objectives were to examine the course of fatigue severity over time and to identify baseline clinical, demographic, and psychosocial predictors of sequentially obtained fatigue scores in early SSc. We also examined baseline predictors of change in fatigue severity over time.MethodsWe analyzed 1090 longitudinal Fatigue Severity Scale (FSS) scores belonging to 256 patients who were enrolled in the Genetics versus Environment in Scleroderma Outcomes Study (GENISOS). Predictive significance of baseline variables for sequentially obtained FSS scores was examined with generalized linear mixed models. Predictors of change in FSS over time were examined by adding an interaction term between the baseline variable and time-in-study to the model.ResultsThe patients' mean age was 48.6 years, 47% were Caucasians, and 59% had diffuse cutaneous involvement. The mean disease duration at enrollment was 2.5 years. The FSS was obtained at enrollment and follow-up visits (mean follow-up time = 3.8 years). Average baseline FSS score was 4.7(±0.96). The FSS was relatively stable and did not show a consistent trend for change over time (p = 0.221). In a multivariable model of objective clinical variables, higher Medsger Gastrointestinal (p = 0.006) and Joint (p = 0.024) Severity Indices, and anti-U1-RNP antibodies (p = 0.024) were independent predictors of higher FSS. In the final model, ineffective coping skills captured by higher Illness Behavior Questionnaire scores (p&lt;0.001), higher self-reported pain (p = 0.006), and higher Medsger Gastrointestinal Severity Index (p = 0.009) at enrollment were independent predictors of higher longitudinal FSS scores. Baseline DLco % predicted was the only independent variable that significantly predicted a change in FSS scores over time (p = 0.013), with lower DLco levels predicting an increase in FSS over time.ConclusionsThis study identified potentially modifiable clinical and psychological factors that predict longitudinal fatigue severity in early SSc