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Objective The aim of the study was to develop a checklist for mental health clinicians to predict and manage suicidality.Methods A literature review of the risk and protective factors for suicide was conducted to develop a checklist for evaluating suicidality.Results The fixed risk factors included sex (male), age (older individuals), history of childhood adversity, and a family history of suicide. Changeable risk factors included marital status (single), economic status (poverty), physical illness, history of psychiatric hospitalization, and history of suicide attempts. Recent discharge from a mental hospital and a recent history of suicide attempts were also included. Manageable risk factors included depression (history and current), alcohol problems (frequent drinking and alcohol abuse), hopelessness, agitation, impulsivity, impaired reality testing, and command hallucinations. Protective factors included responsibility to family, social support, moral objections to suicide, religiosity, motivation to get treatment, ability to cope with stress, and a healthy lifestyle. A final score was assigned based on the sum of the risk and protective factor scores.Conclusion We believe that the development of this checklist will help mental health clinicians to better assess those at risk for suicidal behavior. Further studies are necessary to validate the checklist.

BackgroundThe role of childhood abuse and serum brain-derived neurotrophic factor (BDNF) levels in suicidal behaviour is controversial.AimsWe aimed to investigate the individual and interactive effects of the childhood abuse and serum BDNF on suicidal behaviour before and after pharmacologic treatment in patients with depressive disorders.MethodAt baseline, reported childhood emotional, physical and sexual abuse were ascertained and serum BDNF levels were measured in 1094 patients with depressive disorder, 884 of whom were followed during a 1-year period of stepwise pharmacotherapy. Suicidal behaviours evaluated at baseline were previous suicide attempt and baseline suicide severity, and suicidal behaviours evaluated at follow-up were increased suicide severity and fatal/non-fatal suicide attempt. Individual and interactive associations of any childhood abuse and serum BDNF levels with four types of suicidal behaviours were analysed using logistic regression models, after adjusting relevant covariates.ResultsIndividual associations of childhood abuse were significant only with previous suicide attempt, and no significant individual associations were found for serum BDNF with any suicide outcome. However, the presence of both childhood abuse and lower serum BDNF levels was associated with the highest prevalence/incidence of all four suicidal behaviours, with significant interactions for baseline suicide severity and fatal/non-fatal suicide attempt during follow-up.ConclusionsSynergistic interactive effects of child abuse and serum BDNF levels on suicidal behaviours were found before and after pharmacologic treatment in patients with depressive disorders. Information combining childhood abuse and serum BDNF levels could improve predictions of suicidal behaviour in patients with depressive disorders.

Accurately calculating the vehicle load acting on a bridge at any one time is crucial to determining the integrity and safety of the bridge. To ensure this integrity and safety, information on the types, characteristics, and load of vehicles that regularly cross the bridge is very important in terms of its structural adequacy and maintenance. In this study, the vehicle load that a bridge will be subjected to was estimated using the reaction force response at the support. To estimate this response to the reaction force, a vertical displacement sensor, developed based on Fiber Bragg Grating (FBG), was applied to the Eradi Quake System (EQS), a commercially available bridge bearing. This vertical displacement sensor can measure the vertical load and has the advantage of being easy to attach and detach. To verify the performance and accuracy of this sensor, this study conducted numerical analysis and vehicle loading tests. It found that the vehicle load can be estimated from the reaction force response, as measured by the vertical displacement sensor on the bridge.

Objectives. Dopamine receptor D2 gene (DRD2) and glucocorticoid receptor gene (NR3C1) are implicated in the development of psychosis. We investigated methylation levels of DRD2 and NR3C1 in peripheral blood of patients with recent-onset (RO) psychosis using bisulfite pyrosequencing as well as its association with childhood trauma and rumination. Methods. In all, 51 individuals with RO psychosis and 47 healthy controls were recruited. DNA methylation levels in the targeted regions of two genes were analyzed and compared. Childhood trauma and rumination were evaluated using the Early Trauma Inventory Self-Report Short Form (ETI-SF) and Brooding Scale (BS), respectively. Correlations between the scores of the ETI-SF and BS and methylation levels were explored. Results. For DRD2, we found no significant differences between groups in terms of methylation level or association with childhood trauma or rumination. For NR3C1, we found a trend level significance for average value of all CpG sites and significant hypermethylation or hypomethylation at specific sites. There was also a significant positive correlation between the methylation level at the CpG8 site of NR3C1 exon 1F and negative symptom subscale score of the PANSS (PANSS-N). Conclusion. Epigenetic alterations of NR3C1 are associated with the pathophysiology of psychosis. Further epigenetic studies will elucidate the molecular mechanisms underpinning the pathophysiology of psychosis.

Contains fulltext : 249144.pdf (Publisher’s version ) (Closed access) BACKGROUND: Treatment with the aminoglycoside antibiotic gentamicin can be associated with severe adverse effects, including renal Ca(2+) wasting. The underlying mechanism is unknown but it has been proposed to involve activation of the Ca(2+)-sensing receptor (CaSR) in the thick ascending limb, which would increase expression of claudin-14 (CLDN14) and limit Ca(2+) reabsorption. However, no direct evidence for this hypothesis has been presented. METHODS: We studied the effect of gentamicin in vivo using mouse models with impaired Ca(2+) reabsorption in the proximal tubule and the thick ascending limb. We used a Cldn14 promoter luciferase reporter assay to study CaSR activation and investigated the effect of gentamicin on activity of the distal nephron Ca(2+) channel transient receptor potential vanilloid 5 (TRPV5), as determined by patch clamp in HEK293 cells. RESULTS: Gentamicin increased urinary Ca(2+) excretion in wild-type mice after acute and chronic administration. This calciuretic effect was unaltered in mice with genetic CaSR overactivation and was present in furosemide-treated animals, whereas the calciuretic effect in Cldn14(-/-) mice and mice with impaired proximal tubular Ca(2+) reabsorption (claudin-2 [CLDN2]-deficient Cldn2(-/-) mice) was equivalent to that of wild-type mice. In vitro, gentamicin failed to activate the CaSR. In contrast, patch clamp analysis revealed that gentamicin strongly inhibited rabbit and human TRPV5 activity and chronic gentamicin administration downregulated distal nephron Ca(2+) transporters. CONCLUSIONS: Gentamicin does not cause hypercalciuria via activation of the CaSR-CLDN14 pathway or by interfering with proximal tubular CLDN2-dependent Ca(2+) reabsorption. Instead, gentamicin blocks distal Ca(2+) reabsorption by direct inhibition of the Ca(2+) channel TRPV5. These findings offer new insights into Ca(2+) wasting in patients treated with gentamicin.

Forecasting blood glucose (BG) values for patients can help prevent hypoglycemia and hyperglycemia events in advance. To this end, this study proposes an intelligent ensemble deep learning system to predict BG values in 15, 30, and 60 min prediction horizons (PHs) based on historical BG values collected via continuous glucose monitoring devices as an endogenous factor and carbohydrate intake and insulin administration information (times) as exogenous factors. Although there are numerous deep learning algorithms available, this study applied five algorithms, namely, recurrent neural network (RNN), which is optimized for sequence data (e.g., time-series), and RNN-based algorithms (e.g., long short-term memory (LSTM), stacked LSTM, bidirectional LSTM, and gated recurrent unit). Then, a genetic algorithm (GA) was applied to the five prediction models to optimize their weights through ensemble techniques and to yield (output) the final predicted BG values. The performance of the proposed model was compared to that of the autoregressive integrated moving average (ARIMA) model as a baseline. The results show that the proposed model significantly outperforms the baseline in terms of the root mean square error (RMSE) and continuous glucose error grid analysis. For the valid 29 diabetic patients for the multivariate models, the RMSE was 11.08 ( ± 3.19), 19.25 ( ± 5.28), and 31.30 ( ± 8.81) mg/DL for 15, 30, and 60 min PH, respectively. When the same data were applied to univariate models, the RMSE was 11.28 ( ± 3.34), 19.99 ( ± 5.59), and 33.13 ( ± 9.27) mg/DL for 15, 30, and 60 min PH, respectively. Both the univariate and multivariate models showed a statistically significant difference compared with the baseline at a 5% statistical significance level. Instead of using a model with a single algorithm, applying a GA based on each output of a model with multiple algorithms was found to play a significant role in improving model performance.

Additional file 1. Implementation of competing methods.

To promote recovery in psychosis, targeting modifiable factors related to recovery is critical. Using more strict definition of full recovery, we examined predictors for recovery in patients with early stage schizophrenia spectrum disorders (SSD) followed up to 6.5 years. The target subjects were 375 patients with early stage SSD who had been over at least 1-year after registration and evaluated. The criteria for full recovery were having the score of the Positive and Negative Syndrome Scale (PANSS) 8-item ≤ 2 and adequate functional recovery for at least 1-year. We performed univariate Cox and stepwise Cox regression in both total and acute patients. In stepwise Cox regression, several independent predictors for recovery, i.e., negative symptoms of the PANSS, duration of untreated psychosis (DUP) and non-professional job were identified in patients with early stage SSD. In acute patients, other factors such as professional job and subjective well-being under neuroleptics were more important. The present study identified independent predictors for recovery modifiable by various psychosocial intervention and early intervention services. Moreover, it highlights the need of providing different treatment strategies depending on clinical status.