Quantification of 5-HT1A and 5-HT2A receptor Binding in Depressed Suicide Attempters and Non-Attempters

Objective: To determine serotonin system abnormalities related to major depression or previous suicidal behavior.

Methods: [¹¹C]WAY100635, [¹⁸F]altanserin and positron emission tomography were used to compare 5-HT_1A and 5-HT_2A binding in MDD patients divided into eight past suicide attempters (>4yrs prior to scanning) and eight lifetime non-attempters, and both groups were compared to eight healthy volunteers.

Results: The two receptor types differed in binding pattern across brain regions from each other, but there were no differences in binding between healthy volunteers and the two depressed groups or between depressed suicide attempters and non-attempters. No effects of depression severity or lifetime aggression were observed for either receptor.

Conclusion: Limitations of this study include small sample size and absence of high lethality suicide attempts in the depressed attempter group. No trait-like binding correlations with past suicide attempt or current depression were observed. Given the heterogeneity of nonfatal suicidal behavior, a larger sample study emphasizing higher lethality suicide attempts may find the serotonin biological phenotype seen in suicide decedents.     

A Multi-Scale DNN Algorithm for Nonlinear Elliptic Equations with Multiple Scales

Algorithms based on deep neural networks (DNNs) have attracted increasing attention from the scientific computing community. DNN based algorithms are easy to implement, natural for nonlinear problems, and have shown great potential to overcome the curse of dimensionality. In this work, we utilize the multi-scale DNN-based algorithm (MscaleDNN) proposed by Liu, Cai and Xu (2020) to solve multi-scale elliptic problems with possible nonlinearity, for example, the p-Laplacian problem. We improve the MscaleDNN algorithm by a smooth and localized activation function. Several numerical examples of multi-scale elliptic problems with separable or non-separable scales in low-dimensional and high-dimensional Euclidean spaces are used to demonstrate the effectiveness and accuracy of the MscaleDNN numerical scheme.     

Prevalence and Correlates of Mental Health Problems in Norwegian Peacekeepers 18–38 Years Postdeployment

Peacekeeping missions involve experiences that may impact the mental health of participating soldiers. However, research on the long-term mental health consequences of peacekeeping is sparse. The present study aimed to find the prevalence of mental health problems (MHPs), possible MHP predictors, and associations between predictors and MHPs in Norwegian peacekeepers 18–38 years after deployment to a United Nations peacekeeping mission. We used data from a cross-sectional, postdeployment survey of Norwegian peacekeepers who served in Lebanon between 1978 and 1998 (N = 10,605). Participants were assessed for posttraumatic stress disorder (PTSD); anxiety; depression; insomnia; alcohol misuse; drug misuse; and exposure to pre-, peri-, and postdeployment stressors. Logistic regressions were executed to explore key variables associated with MHPs. Total MHP prevalence was 15.1%, 95% CI [14.4, 15.8]. The estimates for specific disorders were 0.1% for drug misuse, 3.4% for alcohol misuse, 4.0% for depression, 6.2% for PTSD, 6.4% for anxiety, and 9.3% for insomnia. Postdeployment stressors, OR = 1.91, 95% CI [1.79, 2.04]; employment status, OR = 1.41, 95% CI [1.33, 1.48]; and traumatic exposure during deployment, OR = 1.11, 95% CI [1.09, 1.12], were positively related to PTSD, χ2(17, N = 8,568) = 1,791.299, p < .001. Similar patterns were found for the other MHPs. Considering that most participants (84.9%) reported low symptom levels, our findings challenge the widespread public perception that most peacekeepers have MHPs. Moreover, our results indicate that future peacekeepers should be prepared for challenges they may face not only during deployment but also in the years following their homecoming.     

The development and validation of the Score for the Prediction of Postoperative Respiratory Complications (SPORC-2) to predict the requirement for early postoperative tracheal re-intubation: a hospital registry study

Postoperative pulmonary complications are associated with an increase in mortality, morbidity and healthcare utilisation. The Agency for Healthcare Research and Quality recommends risk assessment for postoperative respiratory complications in patients undergoing surgery. In this hospital registry study of adult patients undergoing non-cardiac surgery between 2005 and 2017 at two independent healthcare networks, a prediction instrument for early postoperative tracheal re-intubation was developed and externally validated. This was based on the development of the Score for Prediction Of Postoperative Respiratory Complications. For predictor selection, stepwise backward logistic regression and bootstrap resampling were applied. Development and validation cohorts were represented by 90,893 patients at Partners Healthcare and 67,046 patients at Beth Israel Deaconess Medical Center, of whom 699 (0.8%) and 587 (0.9%) patients, respectively, had their tracheas re-intubated. In addition to five pre-operative predictors identified in the Score for Prediction Of Postoperative Respiratory Complications, the final model included seven additional intra-operative predictors: early post-tracheal intubation desaturation; prolonged duration of surgery; high fraction of inspired oxygen; high vasopressor dose; blood transfusion; the absence of volatile anaesthetic use; and the absence of lung-protective ventilation. The area under the receiver operating characteristic curve for the new score was significantly greater than that of the original Score for Prediction Of Postoperative Respiratory Complications (0.84 [95%CI 0.82–0.85] vs. 0.76 [95%CI 0.75–0.78], respectively; p < 0.001). This may allow clinicians to develop and implement strategies to decrease the risk of early postoperative tracheal re-intubation.