The mean number of treatments administered was 10.5. The most commonly reported side effects were headache (75%) and memory problems (65%). One patient experienced tardive seizures. There were no deaths or serious injuries. Clinical response was not predicted by age, sex, or clinical features (all P > .05). These data suggest that ECT is a safe and effective treatment for children and adolescents with certain severe psychiatric illnesses. ECT outcomes and side effects were similar to those reported in adults, particularly for patients aged 15-18 years, for whom there are the most data.These data suggest that ECT is a safe and effective treatment for children and adolescents with certain severe psychiatric illnesses. ECT outcomes and side effects were similar to those reported in adults, particularly for patients aged 15-18 years, for whom there are the most data. To assess the relationship between short- and longer-term retention in outpatient substance use disorder (SUD) treatment and pharmacotherapy for comorbid attention-deficit/hyperactivity disorder (ADHD). In this retrospective cohort study conducted in a single addiction psychiatry clinic, electronic health record data from July 14, 2014, through January 15, 2020, were queried for clinical ADHD diagnosis (DSM-5 criteria), ADHD pharmacotherapy, treatment duration, demographic variables, comorbid psychiatric and SUD diagnoses, and buprenorphine therapy. Individuals with ADHD (n = 203) were grouped by ADHD pharmacotherapy status (171 receiving medication compared to 32 receiving none). Kaplan-Meier and Cox proportional hazards regression analyses were performed and assessed for significance. ADHD was clinically diagnosed in 9.4% of outpatients and was associated with younger age, comorbid cocaine use, and private insurance (P < .001). Individuals receiving no ADHD pharmacotherapy were younger than those rnference. Further studies addressing unmeasured covariates and associated risks of treatment in adults with ADHD and SUD are necessary. Fatty acids (FAs) are involved in the functioning of biological systems previously associated with suicidal behavior (eg, monoamine signaling and the immune system). We sought to determine (1) whether observed FA levels in a sample of military suicide decedents and living matched controls were consistent with latent classes having distinctive FA profiles and (2) whether those latent classes were associated with suicide and mental health diagnoses. Serum samples from 800 US military suicide decedents who died between 2002 and 2008 and 800 demographically matched living controls were selected at random from a large military serum repository and assayed for 22 different FAs. A latent class cluster analysis was performed using values of 6 FAs previously individually associated with suicide. Once the latent classes were identified, they were compared in terms of suicide decedent proportion, demographic variables, estimated FA enzyme activity, diagnoses, and mental health care usage. A 6-latent class solution best characterized the dataset. Suicide decedents were less likely to belong to 2 of the classes and more likely to belong to 3 of the classes. The low-decedent classes differed from the high-decedent classes on 9 FAs and on estimated indices of activity for 3 FA enzymes 140, 240, 181 n-9, 241 n-9, 225 n-3, 226 n-3, 202 n-6, 204 n-6, 225 n-6, elongation of very long chain fatty acids protein 1 (ELOVL1), ELOVL6, and Δ9 desaturase. The FA profiles of the latent classes were consistent with biological abnormalities previously associated with suicidal behavior. This study suggests the utility of methods that simultaneously examine multiple FAs when trying to understand their relationship with suicide and psychiatric illness.This study suggests the utility of methods that simultaneously examine multiple FAs when trying to understand their relationship with suicide and psychiatric illness. Homicide-suicide is an extremely heterogeneous and rare form of lethal violence. In an effort to capture this heterogeneity to enhance research and prevention efforts, typologies have been developed from literature reviews or geographically limited samples. The purpose of the present study was to develop the first empirically derived typology of homicide-suicide decedents, using a large, geographically diverse sample. Data were used from the Centers for Disease Control and Prevention's National Violent Death Reporting System from 2003 to 2015 across 27 states. Homicide-suicide decedents were included if they were ≥ 18 years of age, they were the only victim and suspect involved, they had a known relationship with the victim(s), and the circumstances surrounding the event were known. There were 2,447 decedents that met study criteria. Unsupervised machine learning was used to classify decedents by precipitating circumstances and victim types. Eight homicide-suicide subtypes were identified and cross-valisubtypes, known mental health problems vary across subtypes. Suicide is a priority health problem. Suicide assessment depends on imperfect clinician assessment with minimal ability to predict the risk of suicide. Machine learning/deep learning provides an opportunity to detect an individual at risk of suicide to a greater extent than clinician assessment. The present study aimed to use deep learning of structural magnetic resonance imaging (MRI) to create an algorithm for detecting suicidal ideation and suicidal attempts. We recruited 4 groups comprising a total of 186 participants 33 depressive patients with suicide attempt (SA), 41 depressive patients with suicidal ideation (SI), 54 depressive patients without suicidal thoughts (DP), and 58 healthy controls (HCs). The confirmation of depressive disorder, SA and SI was based on psychiatrists' diagnosis and Mini-International Neuropsychiatric Interview (MINI) interviews. In the generalized q-sampling imaging (GQI) dataset, indices of generalized fractional anisotropy (GFA), the isotropic value of the orientation di levels of suicide risk, from depression to suicidal ideation and attempted suicide. see more Further studies from different populations, larger sample sizes, and prospective follow-up studies are warranted to confirm the utility of deep learning methods for suicide prevention and intervention.