Stokholmmarker3180

Research has consistently shown high levels of post-traumatic stress disorder (PTSD) in correctional settings. We aimed to compare the prevalences of trauma exposure, subthreshold PTSD, and full PTSD in incarcerated people with those observed in the general population. We used the Mini-International Neuropsychiatric Interview to screen for psychiatric disorders among men upon admission to jail (N = 630) and non-incarcerated men living in the same geographic area (the northern district of France; N = 5793). We utilized a multinomial regression model to assess the association between admission to jail and the prevalences of trauma exposure, subthreshold PTSD, and full PTSD. We employed logistic regression models to verify the interaction between admission to jail and PTSD status on the presence of psychiatric comorbidities. Full PTSD was overrepresented among men in jail after adjustment for all covariates (OR [95% CI] = 3.49 [1.55-7.85], p = 0.002). The association between PTSD status and the presence of at least one psychiatric comorbidity was also more important upon admission to jail than in the general population. Admission to jail was not associated with a higher prevalence of trauma exposure (OR [95% CI] = 1.12 [0.85-1.46], p = 0.419) or subthreshold PTSD (OR [95% CI] = 1.17 [0.81-1.68], p = 0.413). These results suggest higher prevalence rates of full PTSD and psychiatric comorbidities associated with PTSD symptoms in incarcerated people than in the general population. The provision of trauma-focused interventions tailored to these clinical specificities should be considered for the jail population.Influences of pesticide exposures on the clinical expression of children with ASD not known. The aim of this study was to analyze the associations between early residential proximity to agricultural crops, proxy of exposure to pesticides, and adaptive behaviors in children with ASD. Children with ASD were recruited within the Etude Longitudinale de l'Enfant avec Autisme (ELENA) French cohort. Adaptive behaviors were assessed with the second edition of the Vineland Adaptive Behavior Scales (VABS-II). Baseline subscores in communication, daily living skills and socialization were considered. Residential exposure to agricultural crops was estimated by crops acreage within a 1000m radius around homes. We ran multiple linear regression models to investigate the associations between exposures to agricultural crops during the pregnancy (n = 183), the first two years of life (n = 193) and adaptive behaviors in children with ASD. The mean (SD) age of children at the inclusion in the ELENA cohort was 6.1 (3.5) years, 39% of them presented an intellectual disability (ID). The mean communication score was 73.0 (15.8). On average, the crop acreage covered 29(27)% of the acreage formed by the 1000m radius around homes. Each increase of 20% in the crop acreage was associated with a significant decrease in communication score of the VABS-II in children without ID for the pregnancy (β = -2.21, 95%CI 4.16 to -0.27) and the first two years of life (β = -1.90, 95%CI 3.68 to -0.11) periods. No association was found in children with ID. This study opens perspectives for future works to better understand ASD phenotypes.

Sleep disturbance is a core feature of bipolar disorder; hence, sleep must be accurately assessed in patients with bipolar disorder. Subjective sleep assessment tools such as sleep diary and questionnaires are often used clinically for assessing sleep in these patients. However, the insight into whether these tools are as accurate as objective tools, such as actigraphy, remains controversial.

This cross-sectional study included 164 outpatients with a diagnosis of bipolar disorder, including patients who had euthymic and residual symptomatic periods. Objective sleep assessment was conducted prospectively using actigraphy for 7 consecutive days, whereas subjective sleep assessment was conducted prospectively using a sleep diary.

The correlations were high and moderate between sleep diary and actigraphy when assessing the total sleep time and sleep onset latency, respectively (r=0.81 and 0.47). These correlations remained significant after correction for multiple testing (both p<0.001) and in both euthymic and residual symptomatic states (total sleep time r=0.86 and 0.77; sleep onset latency r=0.51 and 0.40, respectively). The median (interquartile ranges) of the percentage difference (sleep diary parameters minus actigraphy parameters divided by actigraphy parameter) in the total sleep time was relatively small (6.2% [-0.2% to 13.6%]).

Total sleep time assessment using a sleep diary could be clinically useful in the absence of actigraphy or polysomnography.

Total sleep time assessment using a sleep diary could be clinically useful in the absence of actigraphy or polysomnography.Forecasting in the medical domain is critical to the quality of decisions made by physicians, patients, and health planners. Modeling is one of the most important components of decision support systems, which are frequently used to simulate and analyze under-studied systems in order to make more appropriate decisions in medical science. In the medical modeling literature, various approaches with varying structures and characteristics have been proposed to cover a wide range of application categories and domains. Regardless of the differences between modeling approaches, all of them aim to maximize the accuracy or reliability of the results in order to achieve the most generalizable model and, as a result, a higher level of profitability decisions. Despite the theoretical significance and practical impact of reliability on generalizability, particularly in high-risk decisions and applications, a significant number of models in the fields of medical forecasting, classification, and time series prediction have bcy room and ICU. According to empirical findings, the reliability-based strategy outperformed the accuracy-based strategy in causal forecasting cases by 2.26%, classification cases by 13.49%, and time series prediction cases by 3.08%. Furthermore, compared to similar accuracy-based models, the reliability-based models can generate a 6.28% improvement. As a result, they can be considered an appropriate alternative to traditional accuracy-based models for medical decision support systems modeling purposes.

Robust differentiation between infarcted and normal tissue is important for clinical diagnosis and precision medicine. Selleckchem MI-503 The aim of this work is to investigate the radiomic features and to develop a machine learning algorithm for the differentiation of myocardial infarction (MI) and viable tissues/normal cases in the left ventricular myocardium on non-contrast Cine Cardiac Magnetic Resonance (Cine-CMR) images.

Seventy-two patients (52 with MI and 20 healthy control patients) were enrolled in this study. MR imaging was performed on a 1.5T MRI using the following parameters TR=43.35ms, TE=1.22ms, flip angle=65°, temporal resolution of 30-40ms. N4 bias field correction algorithm was applied to correct the inhomogeneity of images. All images were segmented and verified simultaneously by two cardiac imaging experts in consensus. Subsequently, features extraction was performed within the whole left ventricular myocardium (3D volume) in end-diastolic volume phase. Re-sampling to 1×1×1mm

voxels was performed for egression (AUC=0.93±0.03, Accuracy=0.86±0.05, Recall=0.87±0.1, Precision=0.93±0.03 and F1 Score=0.90±0.04) and SVM (AUC=0.92±0.05, Accuracy=0.85±0.04, Recall=0.92±0.01, Precision=0.88±0.04 and F1 Score=0.90±0.02) yielded optimal performance as the best machine learning algorithm for this radiomics analysis.

This study demonstrated that using radiomics analysis on non-contrast Cine-CMR images enables to accurately detect MI, which could potentially be used as an alternative diagnostic method for Late Gadolinium Enhancement Cardiac Magnetic Resonance (LGE-CMR).

This study demonstrated that using radiomics analysis on non-contrast Cine-CMR images enables to accurately detect MI, which could potentially be used as an alternative diagnostic method for Late Gadolinium Enhancement Cardiac Magnetic Resonance (LGE-CMR).

Ventilatory pacing by electrical stimulation of the phrenic nerve has many advantages compared to mechanical ventilation. However, commercially available respiratory pacing devices operate in an open-loop fashion, which require manual adjustment of stimulation parameters for a given patient. Here, we report the model development of a closed-loop respiratory pacemaker, which can automatically adapt to various pathological ventilation conditions and metabolic demands.

To assist the model design, we have personalized a computational lung model, which incorporates the mechanics of ventilation and gas exchange. The model can respond to the device stimulation where the gas exchange model provides biofeedback signals to the device. We use a pacing device model with a proportional integral (PI) controller to illustrate our approach.

The closed-loop adaptive pacing model can provide superior treatment compared to open-loop operation. The adaptive pacing stimuli can maintain physiological oxygen levels in the blood under various simulated breathing disorders and metabolic demands.

We demonstrate that the respiratory pacing devices with the biofeedback can adapt to individual needs, while the lung model can be used to validate and parametrize the device.

The closed-loop model-based framework paves the way towards an individualized and autonomous respiratory pacing device development.

The closed-loop model-based framework paves the way towards an individualized and autonomous respiratory pacing device development.Since December 2019, the COVID-19 outbreak has resulted in countless deaths and has harmed all facets of human existence. COVID-19 has been designated an epidemic by the World Health Organization (WHO), which has placed a tremendous burden on nearly all countries, especially those with weak health systems. However, Deep Learning (DL) has been applied in several applications and many types of detection applications in the medical field, including thyroid diagnosis, lung nodule recognition, fetal localization, and detection of diabetic retinopathy. Furthermore, various clinical imaging sources, like Magnetic Resonance Imaging (MRI), X-ray, and Computed Tomography (CT), make DL a perfect technique to tackle the epidemic of COVID-19. Inspired by this fact, a considerable amount of research has been done. A Systematic Literature Review (SLR) has been used in this study to discover, assess, and integrate findings from relevant studies. DL techniques used in COVID-19 have also been categorized into seven main distinct categories as Long Short Term Memory Networks (LSTM), Self-Organizing Maps (SOMs), Conventional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), Autoencoders, and hybrid approaches. Then, the state-of-the-art studies connected to DL techniques and applications for health problems with COVID-19 have been highlighted. Moreover, many issues and problems associated with DL implementation for COVID-19 have been addressed, which are anticipated to stimulate more investigations to control the prevalence and disaster control in the future. According to the findings, most papers are assessed using characteristics such as accuracy, delay, robustness, and scalability. Meanwhile, other features are underutilized, such as security and convergence time. Python is also the most commonly used language in papers, accounting for 75% of the time. According to the investigation, 37.83% of applications have identified chest CT/chest X-ray images for patients.