Easonhess8572

The goal of this study was to develop a Monte Carlo (MC)-based analytical model that can predict the in-room ambient dose equivalent from a Mevion gantry-mounted passively scattered proton system. The Mevion S250 and treatment vault were simulated using the MCNPX MC code. The results of the in-room neutron dose measurements, using an FHT 762 WENDI-II detector, were employed to benchmark the MC-derived values. After tuning the MCNPX MC code, for the same beam delivery parameters, the code was used to calculate the neutron spectra and ambient dose equivalent in the vault and at varying angles from the isocenter. Then, based on the calculations, an analytical model was reconstructed and data were fitted to derive the model parameters at 95% confidence intervals (CI). The MCNPX codes were tuned to within about 19% of the measured values for most of the measurements in the vault. For the maze, up to 0.08 mSv Gy-1 discrepancies were found between the experimental measurements and MCNPX calculated results. The analytical model showed up to 18% discrepancy for distances between 100 and 600 cm from the isocenter compared to the MC calculations. The model may underestimate the neutron ambient dose equivalent up to 21% for distances less than 100 cm from the isocenter. The proposed analytical model can be used to estimate the contribution of the secondary neutron dose from the Mevion S250 for the design of local shielding inside the proton therapy treatment vault.

Opioids are known to contribute to central sleep apnea (CSA), but the influence of nonopioid central nervous system active medications (CNSAMs) on CSA remains unclear. In light of the hypothesized impact of nonopioid CNSAMs on respiration, we examined the relationships between the use of opioids only, nonopioid CNSAMs alone, and their combination with CSA.

Among all adults who underwent polysomnography testing at the University of Michigan's sleep laboratory between 2013 and 2018 (n = 10,606), we identified 212 CSA cases and randomly selected 300 controls. Participants were classified into four groups based on their medication use opioids alone, nonopioid CNSAMs only, their combination, and a reference group, including those who did not use any of these medications. We defined CSA as a binary outcome and as a continuous variable using central apnea index data. Logistic and linear regression were used to examine associations between medication use, CSA diagnosis, and central apnea index.

Study participants included 58% men, and mean age was 50 (± 14 standard deviation years. Nearly half of the study participants did not use opioids or nonopioid CNSAMs, 6% used opioids alone, 27% nonopioid CNSAMs alone, and 16% used a combination of these medications. In adjusted analyses, opioids-only users had a nearly twofold increase in CSA odds, whereas those who used a combination of opioids and nonopioid CNSAMs had fivefold higher odds of CSA relative to the reference group. In contrast, the use of nonopioid CNSAMs alone had protective associations with CSA.

This report showed increased odds of CSA, particularly among patients with sleep complaints who were prescribed opioids in combination with nonopioid CNSAMs compared with those who did not use any of these medications.

This report showed increased odds of CSA, particularly among patients with sleep complaints who were prescribed opioids in combination with nonopioid CNSAMs compared with those who did not use any of these medications.

The diagnosis of a nightmare disorder is based on clinically significant distress caused by the nightmares, eg, sleep or mood disturbances during the day. learn more The question what factors might be associated with nightmare distress in addition to nightmares frequency is not well studied.

Overall, 1,474 persons (893 women, 581 men) completed an online survey. Nightmare distress was measured with the Nightmare Distress Questionnaire.

The findings indicated that nightmare distress, measured by the Nightmare Distress Questionnaire, correlated with a variety of factors in addition to nightmare frequency neuroticism, female sex, low education, extraversion, low agreeableness, and sensation seeking. Moreover, the percentage of replicative trauma-related nightmares was also associated with higher nightmare distress.

A large variety of factors are associated with nightmare distress, a finding that is of clinical importance. The construct harm avoidance, however, was not helpful in explaining interindividual differences in nightmare distress. Furthermore, the relationship between nightmare distress and other factors, eg, education or agreeableness, is not yet understood.

A large variety of factors are associated with nightmare distress, a finding that is of clinical importance. The construct harm avoidance, however, was not helpful in explaining interindividual differences in nightmare distress. Furthermore, the relationship between nightmare distress and other factors, eg, education or agreeableness, is not yet understood.

Polysomnography is the gold standard in identifying sleep stages; however, there are discrepancies in how technicians use the standards. Because organizing meetings to evaluate this discrepancy and/or reach a consensus among multiple sleep centers is time-consuming, we developed an artificial intelligence system to efficiently evaluate the reliability and consistency of sleep scoring and hence the sleep center quality.

An interpretable machine learning algorithm was used to evaluate the interrater reliability (IRR) of sleep stage annotation among sleep centers. The artificial intelligence system was trained to learn raters from 1 hospital and was applied to patients from the same or other hospitals. The results were compared with the experts' annotation to determine IRR. Intracenter and intercenter assessments were conducted on 679 patients without sleep apnea from 6 sleep centers in Taiwan. Centers with potential quality issues were identified by the estimated IRR.

In the intracenter assessment, the median accuracy ranged from 80.3%-83.3%, with the exception of 1 hospital, which had an accuracy of 72.3%. In the intercenter assessment, the median accuracy ranged from 75.7%-83.3% when the 1 hospital was excluded from testing and training. The performance of the proposed method was higher for the N2, awake, and REM sleep stages than for the N1 and N3 stages. The significant IRR discrepancy of the 1 hospital suggested a quality issue. This quality issue was confirmed by the physicians in charge of the 1 hospital.

The proposed artificial intelligence system proved effective in assessing IRR and hence the sleep center quality.

The proposed artificial intelligence system proved effective in assessing IRR and hence the sleep center quality.