D-serine and D-amino acid oxidase levels in patients with schizophrenia spectrum disorders in the first episode and 6-month follow-up.

BACKGROUND: D-serine and the D-amino acid oxidase (DAO) enzyme, which breaks down d-amino acids, may be involved in the pathophysiology of schizophrenia by affecting the N-methyl-D-aspartate (NMDA) receptor. The exact role of D-serine and DAO, as well as the consequences of increased DAO activity in patients with schizophrenia, remain unclear. We aimed to investigate D-serine and DAO levels in patients with first-episode schizophrenia spectrum disorders before treatment and after six months of treatment. METHOD: Comparisons for the serum levels of D-serine and DAO were made between 81 healthy controls and 89 patients with first-episode schizophrenia spectrum disorders without a history of treatment. Further comparisons were made after 6 months for changes in these levels in the 41 patients in follow-up. The Positive and Negative Syndrome Scale (PANNS), Calgary Scale for Depression in Schizophrenia (CDSS), Montreal Cognitive Assessment Scale (MoCA), Global Assessment Scale (GAS), and Clinical Global Impression Scale (CGI) were used to evaluate the symptom severity and functionality. Secondary results included comparisons related to antipsychotic equivalent doses. RESULTS: Before treatment, patients had significantly lower levels of D-serine, DAO, and D-serine/DAO ratio compared to healthy individuals (p < 0.001; p < 0.001; p = 0.004). DAO and D-serine levels of the patients were higher after six months of treatment (p = 0.025; p = 0.001). There was correlation of DAO levels with antipsychotic dosage and with PANSS negative and total subscale scores (rho = 0.421, p = 0.01; rho = 0.280, p = 0.008; rho = 0.371, p = 0.000). No correlation was found between serum D-serine level, DAO level, and the D-serine/DAO ratio with cognitive function. CONCLUSIONS: The results suggest that D-serine and DAO may play a role that is sensitive to treatment effects in schizophrenia spectrum disorders. To gain a more comprehensive understanding of the impact antipsychotic drugs have on NMDA receptor dysfunction, there is a requirement for studies that directly evaluates the activity of the DAO enzyme.

The Impact of Coping Strategies on Suicide Attempts and Suicidal Ideation in Bipolar Disorder.

ABSTRACT: The stress coping strategies of patients with bipolar disorder (BD) may affect their suicide risk. We examined coping behaviors and impact of coping strategies and clinical characteristics on suicide attempts and lifetime suicidal ideation in patients with BD I, compared with a healthy control group. We recruited 185 euthymic patients with BD and 94 healthy controls. Participants completed the Coping Orientation to Problems Experienced Inventory. Suicide attempt prevalence in patients with BD was around 34%, and frequency of lifetime suicide ideation was around 60%. Binary logistic regression analysis revealed greater use of behavioral disengagement and religious coping strategies among patients with BD, compared with controls. Patients with previous suicide attempts presented a more severe illness course, notably early onset, with more depressive and mixed episodes and a more dysfunctional coping style than nonsuicidal patients. Behavioral interventions can target avoidant coping behavior, such as denial, especially in patients with suicide attempts.

Frequency and Correlates of Acute Dystonic Reactions After Antipsychotic Initiation in 441 Children and Adolescents.

Objective: To determine the incidence of acute dystonic reactions (ADRs) and risk factors for ADRs in children and adolescents treated with antipsychotics. Methods: This was a retrospective chart review-based cohort study of consecutive patients who attended a university hospital's child and adolescent psychiatry department between 2015 and 2017 and who were treated with antipsychotics and had at least two follow-up visits. Results: Thirty of 441 patients (6.8%) 4-19 years of age who were treated with antipsychotics for conduct disorders (21.5%), attention-deficit/hyperactivity disorder (13.2%) and, irritability and aggression that accompanied intellectual disability (12.9%) and followed for 99.5 ± 223.3 (median: 34) days developed ADRs. ADRs developed in 11/391 patients (2.8%) treated with one antipsychotic and 19/50 patients (38.0%) treated with two antipsychotics (p < 0.001). In patients treated with one antipsychotic that developed ADRs, the time to ADRs was 4.0 ± 4.0 days after antipsychotic initiation and 2.7 ± 2.4 days after an increase in the antipsychotic dose. The time to ADRs in those treated with two antipsychotics was 3.0 ± 2.3 days after the addition of the second antipsychotic and 1.6 ± 0.8 days after a dose increase in the second antipsychotic. The incidence of ADRs during antipsychotic monotherapy was 10.5% with first-generation antipsychotics (FGAs) and 2.2% with second-generation antipsychotics (SGAs; p = 0.037). The antipsychotic was changed due to ADRs in 12/30 (40.0%) of ADR cases. Independent factors associated with ADRs were antipsychotic polypharmacy (p < 0.0001), inpatient treatment (p = 0.013), FGA use (p = 0.015), and diagnoses of schizophrenia (p = 0.039) or bipolar disorder (p < 0.0001). Conclusion: SGAs and low-potency FGA monotherapy in children and adolescents were associated with a relatively low ADR risk, whereas high- and mid-potency FGAs were associated with a high risk. Independent predictors of ADRs were antipsychotic polypharmacy, inpatient treatment, FGAs, and schizophrenia or bipolar disorder diagnoses, which may be related to more aggressive antipsychotic dosing.

Adverse Drug Event Detection from Electronic Health Records Using Hierarchical Recurrent Neural Networks with Dual-Level Embedding.

INTRODUCTION: Adverse drug event (ADE) detection is a vital step towards effective pharmacovigilance and prevention of future incidents caused by potentially harmful ADEs. The electronic health records (EHRs) of patients in hospitals contain valuable information regarding ADEs and hence are an important source for detecting ADE signals. However, EHR texts tend to be noisy. Yet applying off-the-shelf tools for EHR text preprocessing jeopardizes the subsequent ADE detection performance, which depends on a well tokenized text input. OBJECTIVE: In this paper, we report our experience with the NLP Challenges for Detecting Medication and Adverse Drug Events from Electronic Health Records (MADE1.0), which aims to promote deep innovations on this subject. In particular, we have developed rule-based sentence and word tokenization techniques to deal with the noise in the EHR text. METHODS: We propose a detection methodology by adapting a three-layered, deep learning architecture of (1) recurrent neural network [bi-directional long short-term memory (Bi-LSTM)] for character-level word representation to encode the morphological features of the medical terminology, (2) Bi-LSTM for capturing the contextual information of each word within a sentence, and (3) conditional random fields for the final label prediction by also considering the surrounding words. We experiment with different word embedding methods commonly used in word-level classification tasks and demonstrate the impact of an integrated usage of both domain-specific and general-purpose pre-trained word embedding for detecting ADEs from EHRs. RESULTS: Our system was ranked first for the named entity recognition task in the MADE1.0 challenge, with a micro-averaged F1-score of 0.8290 (official score). CONCLUSION: Our results indicate that the integration of two widely used sequence labeling techniques that complement each other along with dual-level embedding (character level and word level) to represent words in the input layer results in a deep learning architecture that achieves excellent information extraction accuracy for EHR notes.