Deep graph neural network-based prediction of acute suicidal ideation in young adults.
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ABSTRACT:
SUBMITTER: Choi KS
PROVIDER: S-EPMC8338980 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:A molecule's geometry, also known as conformation, is one of a molecule's most important properties, determining the reactions it participates in, the bonds it forms, and the interactions it has with other molecules. Conventional conformation generation methods minimize hand-designed molecular force field energy functions that are often not well correlated with the true energy function of a molecule observed in nature. They generate geometrically diverse sets of conformations, some of which are very similar to the lowest-energy conformations and others of which are very different. In this paper, we propose a conditional deep generative graph neural network that learns an energy function by directly learning to generate molecular conformations that are energetically favorable and more likely to be observed experimentally in data-driven manner. On three large-scale datasets containing small molecules, we show that our method generates a set of conformations that on average is far more likely to be close to the corresponding reference conformations than are those obtained from conventional force field methods. Our method maintains geometrical diversity by generating conformations that are not too similar to each other, and is also computationally faster. We also show that our method can be used to provide initial coordinates for conventional force field methods. On one of the evaluated datasets we show that this combination allows us to combine the best of both methods, yielding generated conformations that are on average close to reference conformations with some very similar to reference conformations.
| S-EPMC6938476 | biostudies-literature
Project description:BackgroundSuicide is one of the leading causes of death for college students. The predictors of suicidal ideation among college students are inconsistent and few studies have systematically investigated psychological symptoms of college students to predict suicide. Therefore, this study aims to develop a suicidal ideation prediction model and explore important predictors of suicidal ideation among college students in China.MethodsWe recruited 1,500 college students of Sichuan University and followed up for 4 years. Demographic information, behavioral and psychological information of the participants were collected using computer-based questionnaires. The Radial Basis Function Neural Network (RBFNN) method was used to develop three suicidal ideation risk prediction models and to identify important predictive factors for suicidal ideation among college students.ResultsThe incidence of suicidal ideation among college students in the last 12 months ranged from 3.00 to 4.07%. The prediction accuracies of all the three models were over 91.7%. The area under curve scores were up to 0.96. Previous suicidal ideation and poor subjective sleep quality were the most robust predictors. Poor self-rated mental health has also been identified to be an important predictor. Paranoid symptom, internet addiction, poor self-rated physical health, poor self-rated overall health, emotional abuse, low average annual household income per person and heavy study pressure were potential predictors for suicidal ideation.ConclusionsThe study suggested that the RBFNN method was accurate in predicting suicidal ideation. And students who have ever had previous suicidal ideation and poor sleep quality should be paid consistent attention to.
| S-EPMC9751327 | biostudies-literature
Project description:This study aimed to determine the prevalence of suicidal ideation among young and middle-aged adults, and explore the risk factors that affect suicidal ideation.A descriptive study design was used for secondary data analysis. A total sample of 5,214 was drawn from two waves (2012-2013) of the 7th Korea Health Panel (KHP) survey. The KHP data were collected by a well-trained interviewer using the face-to-face method during home visits as well as self-report method. Descriptive statistics of frequency, percentage, chi-square test, and logistic regression analysis were performed using SPSS 22.0.The prevalence of suicidal ideation in young and middle-aged adults was 4.4% and 5.6%, respectively. For young adults, suicidal ideation risk was higher among those with low income or heavy drinking habits. In middle-aged adults, low income, poor perceived health status, negative perception of peer-compared health status, and negative social perspective were the major risk factors.There is considerable risk of suicidal ideation in adulthood. Opportunities for increased income, avoidance of heavy drinking, and the construction of positive subjective health status and social perspective should be considered in suicide prevention interventions for Korean young and middle-aged adults.
| S-EPMC5525562 | biostudies-other
Project description:To examine reciprocal associations between substance use (cigarette smoking, use of alcohol, marijuana, and other illegal drugs) and suicidal ideation among adolescents and young adults (aged 11-21 at wave 1; aged 24-32 at wave 4).Four waves public-use Add Health data were used in the analysis (N=3342). Respondents were surveyed in 1995, 1996, 2001-2002, and 2008-2009. Current regular smoking, past-year alcohol use, past-year marijuana use, and ever use of other illegal drugs as well as past-year suicidal ideation were measured at the four waves (1995, 1996, 2001-2002, and 2008-2009). Fixed effects models with lagged dependent variables were modeled to test unidirectional associations between substance use and suicidal ideation, and nonrecursive models with feedback loops combining correlated fixed factors were conducted to examine reciprocal relations between each substance use and suicidal ideation, respectively.After adjusting for the latent time-invariant effects and lagged effects of dependent variables, the unidirectional associations from substance use to suicidal ideation were consistently significant, and vice versa. Nonrecursive model results showed that use of cigarette or alcohol increased risk of suicidal ideation, while suicidal ideation was not associated with cigarette or alcohol use. Reversely, drug use (marijuana and other drugs) did not increase risk of suicidal ideation, but suicidal ideation increased risk of illicit drug use.The results suggest that relations between substance use and suicidal ideation are unidirectional, with cigarette or alcohol use increasing risk of suicidal ideation and suicidal ideation increasing risk of illicit drug use.
| S-EPMC4129651 | biostudies-literature
Project description:The selection of coarse-grained (CG) mapping operators is a critical step for CG molecular dynamics (MD) simulation. It is still an open question about what is optimal for this choice and there is a need for theory. The current state-of-the art method is mapping operators manually selected by experts. In this work, we demonstrate an automated approach by viewing this problem as supervised learning where we seek to reproduce the mapping operators produced by experts. We present a graph neural network based CG mapping predictor called Deep Supervised Graph Partitioning Model (DSGPM) that treats mapping operators as a graph segmentation problem. DSGPM is trained on a novel dataset, Human-annotated Mappings (HAM), consisting of 1180 molecules with expert annotated mapping operators. HAM can be used to facilitate further research in this area. Our model uses a novel metric learning objective to produce high-quality atomic features that are used in spectral clustering. The results show that the DSGPM outperforms state-of-the-art methods in the field of graph segmentation. Finally, we find that predicted CG mapping operators indeed result in good CG MD models when used in simulation.
| S-EPMC8161155 | biostudies-literature
Project description:ImportanceMental health problems are common during the transition from adolescence to young adulthood. Although perceived social support and mental health problems have been shown to be concurrently associated, longitudinal studies are lacking to document the directionality of this association, especially in emerging adulthood (late teens to late 20s).ObjectiveTo test whether social support in emerging adulthood protects against later depression, anxiety, and suicidal ideation and suicide attempts after adjusting for a range of confounders, including prior mental health problems and family characteristics.Design, setting, and participantsThis population-based cohort study included 1174 participants from the Quebec Longitudinal Study of Child Development. Participants underwent yearly or biennial assessment (starting from age 5 months to age 20 years). Data were collected from March 16, 1998, through June 1, 2018.Main outcomes and measuresSelf-reported perceived social support was measured at age 19 years using the 10-item Social Provision Scale. Mental health problems, including depressive and anxiety symptoms as well as suicidal ideation and attempts, were measured at age 20 years. Social support and mental health problem raw scores were converted to z-scores to ease interpretation. Depressive and anxiety symptoms were categorized using validated cutoffs to determine clinical significance.ResultsThe study consisted of 1174 participants (574 female [48.89%] and 600 male [51.11%] individuals). Emerging adults with higher levels of perceived social support at age 19 years reported fewer mental health problems 1 year later, even after adjusting for a range of mental health problems in adolescence at ages 15 and 17 years (eg, depressive and anxiety symptoms and suicidal ideation and attempts) and family characteristics (eg, socioeconomic status and family functioning and structure). Higher perceived social support was associated with fewer symptoms of depression (β = -0.23; 95% CI, -0.26 to -0.18; P = <.001 and odds ratio [OR], 0.53; 95% CI, 0.42-0.66 for severe depression) and anxiety (β = -0.10; 95% CI, -0.15 to -0.04; P < .001 and OR, 0.78; 95% CI, 0.62-0.98 for severe anxiety). Higher perceived social support was associated with a lower risk for suicide-related outcomes (OR, 0.59 [95% CI, 0.50-0.70] for suicidal ideation and OR, 0.60 [95% CI, 0.46-0.79] for suicide attempts).Conclusions and relevanceIn this cohort study, emerging adults who perceived higher levels of social support reported experiencing fewer mental health problems 1 year later. These findings suggest that perceived social support may protect against mental health problems during the transition into adulthood, even in those who experience mental health problems in adolescence. Leveraging social support in prevention and treatment options may protect against mental health symptoms during this transition period.
| S-EPMC7718598 | biostudies-literature
Project description: Not available
| S-EPMC11341278 | biostudies-literature
Project description:We present a supervised learning approach to predict the products of organic reactions given their reactants, reagents, and solvent(s). The prediction task is factored into two stages comparable to manual expert approaches: considering possible sites of reactivity and evaluating their relative likelihoods. By training on hundreds of thousands of reaction precedents covering a broad range of reaction types from the patent literature, the neural model makes informed predictions of chemical reactivity. The model predicts the major product correctly over 85% of the time requiring around 100 ms per example, a significantly higher accuracy than achieved by previous machine learning approaches, and performs on par with expert chemists with years of formal training. We gain additional insight into predictions via the design of the neural model, revealing an understanding of chemistry qualitatively consistent with manual approaches.
| S-EPMC6335848 | biostudies-other
Project description:BACKGROUND:Research on the neural correlates associated with risk for suicidal ideation (SI) has been limited, particularly in one increasingly at-risk group-adolescents. Previous research with adolescents indicates that poor emotion regulation skills are linked with SI, but these studies have not previously examined neural activation in service of emotion regulation between those with and without SI histories. METHODS:Here we examine whether SI is associated with neural responses during an emotion regulation functional magnetic resonance imaging task in a group of adolescents (N = 49) 13 to 20 years of age (mean = 16.95). RESULTS:While there were no differences between youths with and without SI in self-reported emotional responses to negative pictures, youths with SI activated the dorsolateral prefrontal cortex more than youths without SI on trials in which they attempted to regulate their emotional responses compared with trials in which they passively viewed negative pictures. In contrast, during passive viewing of negative stimuli, youths with SI activated the dorsolateral prefrontal cortex, temporoparietal junction, and cerebellum less than same-age control subjects. CONCLUSIONS:These findings were robust to control subjects for depression and adversity exposure and are consistent with the idea that youths with SI have disrupted emotion regulation, potentially related to differences in recruitment of top-down control regions. In contrast, youths without SI activated regions implicated in emotion regulation even when not directed to effortfully control their emotional response. This is the first study to examine neural function during emotion regulation as a potential neural correlate of risk for SI in adolescents.
| S-EPMC5851479 | biostudies-literature
Project description:PurposeMetabolic and bariatric surgery (MBS) is increasingly used in adolescents. The aim was to explore symptoms of depression and anxiety in young adults over 5 years' follow-up after undergoing MBS.MethodsBeck Depression Inventory-2 and the Beck Anxiety Inventory were used to assess symptoms of depression and anxiety in 62 patients 1, 2, and 5 years after having Roux-en-Y gastric bypass at 13-18 years of age. Mental health, eating-related problems, and weight outcomes were tested for association with suicidal ideation at the 5-year follow-up.ResultsAt the 5-year follow-up, the mean score for depression was 11.4 (± 12.4), indicating minimal symptoms of depression. The mean score for anxiety was 12.82 (± 11.50), indicating mild anxiety symptoms. Still, several participants reported moderate or severe symptoms of depression (26%) and anxiety (32%). Women reported more symptoms than men (P = 0.03 and 0.04). No significant changes were found in self-reported symptoms of depression and anxiety between the 1-year and the 5-year follow-up (P = 0.367 and 0.934). Suicidal ideation was reported by 16% at the 5-year follow-up. Participants reporting suicidal ideation had lost significantly less excess weight than participants without suicidal ideation (P = 0.009).ConclusionFive years after adolescent MBS, a substantial minority still struggles with mental health issues, and women are more burdened than men. Our results indicate an association between less optimal weight loss and suicidal ideation 5 years after MBS. The findings emphasize the importance of offering long-term follow-up and mental health treatment several years after MBS.Level of evidenceLevel III, cohort study.Clinical trial registrationThe study is registered with ClinicalTrials.gov (NCT00289705). First posted February 10, 2006.
| S-EPMC8062317 | biostudies-literature