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Abstract Background The research landscape examining social cognition (SC) impairment in patients with major depressive disorders (MDD) and bipolar disorders (BD) is notably scarce. Presently, assessments predominantly rely on static stimuli and self‐reported measures, which may not capture the dynamic dimensions of social cognition. Objectives This study aimed to validate the Chinese version of Movie Assessment of Social Cognition (MASC‐CH) and to investigate whether MDD and BD exhibit distinct patterns of SC impairments, shedding light on potential differences between these two mood disorders. Methods The study encompassed 197 participants, aged 18–65, distributed as follows: 21 BD, 20 MDD, and 156 healthy controls (HC). We focused on examining “cognitive” and “emotional” SC scores and “undermentalizing” and “overmentalizing” error patterns, with nonsocial inference as a control. Additional assessments included the Reading Mind in the Eyes Test (RMET) and the Mayer–Salovey–Caruso Emotional Intelligence Test (MSCEIT). We also explored the association between depression severity (measured by the Hamilton Depressive Rating Scale, HDRS) and distinct SC dimensions between MDD and BD. Results The MASC‐CH exhibited strong validity and reliability for SC assessment. In group comparisons, BD participants scored significantly lower on MASC‐CH, while the MDD group scores were not significantly different from HC. Specifically, BD individuals had notably lower cognitive SC scores and made more undermentalizing and absence of mentalizing errors than MDD and HC. Additionally, a negative correlation between HDRS score and overmentalizing was observed in BD, not in the MDD. Conclusions The findings indicate that depression severity scores in BD were inversely related to MASC‐CH scores. In contrast, this relationship was not observed in the MDD group. These results underscore the importance of SC impairments as distinguishing characteristics of both BD and MDD. It provides valuable insights into the distinct social‐cognitive profiles of both mood disorders.

(1) Background: Early interventions may effectively reduce the risk of mental disorders in individuals with ultra-high risk. Specifying the health needs of individuals with ultra-high risk is crucial before the implementation of successful early intervention. This study aimed to explore the differences in lifestyles, mental risks, and physical indices among individuals with ultra-high risk, patients with schizophrenia, and healthy subjects. (2) Methods: A cross-section design applying seven questionnaires with physical examinations for 144 participants aged 13–45 years old was conducted in this study. The questionnaires included one about personal data, four on mental risks, and two for lifestyles. (3) Results: The individuals with ultra-high risk scored similarly in many dimensions as the patients with schizophrenia, but they displayed lower positive symptoms, lower negative symptoms, lower prodromal symptoms, higher interpersonal deficits, lower nutrition intake, and higher levels of exercise than the patients with schizophrenia. Female individuals with ultra-high risk had lower self-esteem, higher positive symptoms, lower nutrition intake, and higher exercise levels than male ones. (4) Conclusions: The study pinpointed specific health needs with interpersonal deficits, nutrition intake, and physical activity for the individuals with ultra-high risk. Future interventions targeted on improving social function, dietary pattern, and exercise will be beneficial.

Background and aim: We have previously reported that histamine H1 receptor antagonists facilitate electroacupuncture (EA) analgesia in experimental animals. In this pilot study, we sought to determine whether the histamine H1 receptor antagonist dexchlorpheniramine (DCPA) facilitates EA analgesia in healthy human subjects. Experimental procedure: Forty healthy subjects aged 20–30 years were randomly allocated to 1 of 4 groups: (1) sham EA at acupoints Zusanli (ST36) and Yanglingquan (GB34) (sham EA; n = 10); (2) EA at ST36 and GB34 (n = 10); (3) EA at ST36 and GB34 plus low-dose DCPA (2 mg, n = 10); (4) EA at ST36 and GB34 plus high-dose DCPA (4 mg, n = 10). Before and after acupuncture treatment, pain thresholds were determined by transcutaneous electrical stimuli on the glabrous skin of the left upper arm. Results: After the acupuncture session, subjects in the EA plus high-dose DCPA group had a significantly higher pain threshold elevation compared with the other 3 study groups. The change from baseline in pain threshold in the EA plus high-dose DCPA group was significantly greater than the change in pain threshold with EA only, indicating that DCPA 4 mg facilitated EA analgesia. Conclusion: The results suggest that combining H1 receptor antagonist treatment with EA appears to relieve pain to a greater extent compared with EA alone. This study is registered with ClinicalTrials.gov (https://clinicaltrials.gov/), number NCT03805035 (https://clinicaltrials.gov/ct2/show/NCT03805035).

Abstract Acupuncture has been used for treating drug addiction since the 1970s, but little is known about the mechanisms by which acupuncture affects drug cue-induced relapse. The transcription factor delta-FosB (ΔFosB) plays a critical role in behavior and pathology after chronic use of cocaine. ΔFosB regulates glutamate receptor signaling and dendritic spine morphology in animal models. This experimental study compared the effects of electroacupuncture (EA) at acupoints LI4 and LI11 with those of another potentially beneficial intervention, gabapentin (GBP), alone or in combination, on reinstatement of cocaine-induced conditioned place preference (CPP) and levels of ΔFosB and glutamate receptor subunit 2 (GluR2) expression in the nucleus accumbens (NAc). EA at LI4 and LI11 significantly prevented cue-induced cocaine CPP reinstatement, whereas needle insertion without electrical stimulation at these acupoints had no such effect. EA also significantly attenuated cocaine-induced increases in ΔFosB and GluR2 expression in the NAc. Unexpectedly, these effects were reversed when GBP was combined with EA. Treatment with EA at LI4 and LI11 prevented cocaine-induced increases in dendritic spine density in the NAc core and shell. Our results suggest that EA at LI4 and LI11 may prevent cocaine relapse by modulating ΔFosB and GluR2 expression, as well as dendritic spine density.

Abstract The search for susceptibility genes underlying the heterogeneous bipolar disorder has been inconclusive, often with irreproducible results. There is a hope that narrowing the phenotypes will increase the power of genetic analysis. Early-onset bipolar disorder is thought to be a genetically homogeneous subtype with greater symptom severity. We conducted a genome-wide association study (GWAS) for this subtype in bipolar I (BPI) disorder. Study participants included 1779 patients of Han Chinese descent with BPI disorder recruited by the Taiwan Bipolar Consortium. We conducted phenotype assessment using the Chinese version of the Schedules for Clinical Assessment in Neuropsychiatry and prepared a life chart with graphic depiction of lifetime clinical course for each of the BPI patient recruited. The assessment of onset age was based on this life chart with early onset defined as ≤20 years of age. We performed GWAS in a discovery group of 516 early-onset and 790 non-early-onset BPI patients, followed by a replication study in an independent group of 153 early-onset and 320 non-early-onset BPI patients and a meta-analysis with these two groups. The SNP rs11127876, located in the intron of CADM2, showed association with early-onset BPI in the discovery cohort (P = 7.04 × 10−8) and in the test of replication (P = 0.0354). After meta-analysis, this SNP was demonstrated to be a new genetic locus in CADM2 gene associated with early-onset BPI disorder (P = 5.19 × 10−8).

Abstract Genetic variants such as single nucleotide polymorphisms (SNPs) have been suggested as potential molecular biomarkers to predict the functional outcome of psychiatric disorders. To assess the schizophrenia’ functional outcomes such as Quality of Life Scale (QLS) and the Global Assessment of Functioning (GAF), we leveraged a bagging ensemble machine learning method with a feature selection algorithm resulting from the analysis of 11 SNPs (AKT1 rs1130233, COMT rs4680, DISC1 rs821616, DRD3 rs6280, G72 rs1421292, G72 rs2391191, 5-HT2A rs6311, MET rs2237717, MET rs41735, MET rs42336, and TPH2 rs4570625) of 302 schizophrenia patients in the Taiwanese population. We compared our bagging ensemble machine learning algorithm with other state-of-the-art models such as linear regression, support vector machine, multilayer feedforward neural networks, and random forests. The analysis reported that the bagging ensemble algorithm with feature selection outperformed other predictive algorithms to forecast the QLS functional outcome of schizophrenia by using the G72 rs2391191 and MET rs2237717 SNPs. Furthermore, the bagging ensemble algorithm with feature selection surpassed other predictive algorithms to forecast the GAF functional outcome of schizophrenia by using the AKT1 rs1130233 SNP. The study suggests that the bagging ensemble machine learning algorithm with feature selection might present an applicable approach to provide software tools for forecasting the functional outcomes of schizophrenia using molecular biomarkers.

Abstract It has been suggested that the relationship between cognitive function and functional outcome in schizophrenia is mediated by clinical symptoms, while functional outcome is assessed by the Quality of Life Scale (QLS) and the Global Assessment of Functioning (GAF) Scale. To determine the outcome assessed by QLS and GAF, we established a bagging ensemble framework with a feature selection algorithm resulting from the analysis of factors such as 3 clinical symptom scales and 11 cognitive function scores of 302 patients with schizophrenia in the Taiwanese population. We compared our bagging ensemble framework with other state-of-the-art algorithms such as multilayer feedforward neural networks, support vector machine, linear regression, and random forests. The analysis revealed that the bagging ensemble model with feature selection performed best among predictive models in predicting the QLS functional outcome by using 20-item Scale for the Assessment of Negative Symptoms (SANS20) and 17-item Hamilton Depression Rating Scale (HAMD17). Moreover, to predict the GAF outcome, the bagging ensemble model with feature selection performed best among predictive models by using SANS20 and the Positive and Negative Syndrome Scale-Positive (PANSS-Positive) subscale. The study indicates that there are synergistic effects between negative (SANS20) and depressive (HAMD17) symptoms as well as between negative and positive (PANSS-Positive) symptoms in influencing functional outcome of schizophrenia using the bagging ensemble framework with feature selection.

Introduction. Individuals with substance use disorders, particularly those with alcohol use disorder (AUD), have a high risk of suicide. Therefore, identifying risk factors for suicide in these individuals is crucial. Methods. This retrospective study reviewed the medical records of individuals with AUD who participated in an alcohol treatment program in central Taiwan during 2019–2020. We collected data using the Cut down, Annoyed, Guilty, and Eye-opener questionnaire, Alcohol Use Disorders Identification Test (AUDIT), Brief Michigan Alcoholism Screening Test (MAST), Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI), and a suicidal ideation question. Furthermore, we collected information on several related variables, namely, sex, age, marital status, years in school, employment status, family history of alcohol problems, age at first exposure to alcohol, duration of alcohol use, history of alcohol cessation, history of domestic violence, and history of drunk driving. In total, 136 individuals were recruited to participate in this study. Results. The suicidal ideation group had significantly younger participants, a higher proportion of women, a higher proportion of participants with a history of domestic violence, a greater severity of alcohol addiction (based on both AUDIT and MAST scores), higher depression scores, higher anxiety scores, less social support, a lower quality of life (World Health Organization Quality of Life (WHOQOL)), and poorer sleep quality (Pittsburgh Sleep Quality Index, PSQI) compared with the nonsuicidal ideation group. The suicidal ideation score was correlated with the AUDIT score after age, and BDI, BAI, WHOQOL, and PSQI scores were controlled for (P=0.034). Conclusion. Individuals with higher AUDIT scores visiting a clinic for alcohol treatment might have a higher risk of suicidal ideation. Therefore, clinicians should pay close attention to the suicidal ideation problem in this population. Furthermore, appropriate medication or management programs for suicide prevention should be considered.

The aim of the study was to assess the relationship between prolactin levels and sexual dysfunction in patients with schizophrenia who use olanzapine medication. The potential risk factors of hyperprolactinemia and sexual dysfunction were also investigated. Patients with schizophrenia undergoing olanzapine monotherapy were invited to participate in this cross-sectional study. The Arizona Sexual Experiences Scale (ASEX) and the Positive and Negative Syndrome Scale were used to evaluate subjective sexual dysfunction and psychopathology, respectively. Levels of prolactin and metabolic parameters were also measured. In total, 279 participants with schizophrenia were recruited. The overall incidences of hyperprolactinemia, sexual dysfunction, and metabolic syndrome were 51.6, 53.8, and 43.7%, respectively. Higher ASEX scores, higher insulin levels, female sex, and younger age were associated with hyperprolactinemia. Prolactin level was significantly correlated with ASEX score. Elevated prolactin levels, concomitant antidepressant, increased insulin resistance, longer illness duration, and female sex were associated with sexual dysfunction. Female participants recorded higher levels of sexual dysfunction than their male counterparts did, whereas male participants had comparatively lower prolactin levels and lower rates of spousal partnership. Hyperprolactinemia, metabolic syndrome, and sexual dysfunction are prevalent in patients with schizophrenia treated with olanzapine. Clinicians should maintain awareness of these problems and monitor them regularly with their patients.

Methadone maintenance treatment (MMT) remains the cornerstone for the management of opiate abuse. However, MMT can be associated with complex factors, including complications during the tolerance phase, the inability of some patients to maintain treatment effects during the tapering or abstinence phases, and the development of methadone dependence. Previous studies have revealed a sex disparity in MMT efficacy, showing that women undergoing MMT experiencing an increase in psychological symptoms compared with men and suggesting a link between disparate responses and the effects of estrogen signaling on methadone metabolism. More specifically, estradiol levels are positively associated with MMT dosing, and the expression of a single-nucleotide polymorphism (SNP) associated with estrogen receptor (ER) regulation is also associated with MMT dosing. In addition to performing mechanistic dissections of estrogen signaling in the presence of methadone, past studies have also proposed the targeting of estrogen signaling during MMT. The present report provides an overview of the relevant literature regarding sex effects, including differences in sex hormones and their potential impacts on MMT regimens. Moreover, this article provides a pharmacological perspective on the targeting of estrogen signals through the use of selective ER modulators (SERMs) during MMT. Preliminary preclinical experiments were also performed to evaluate the potential effects of targeting estrogen signaling with tamoxifen on methadone metabolism.

Backgrounds: Reduced brain cortical activity over the frontotemporal regions measured by near infrared spectroscopy (NIRS) has been reported in patients with first-episode schizophrenia (FES). This study aimed to differentiate between patients with FES and healthy controls (HCs) on basis of the frontotemporal activity measured by NIRS with a support vector machine (SVM) and deep neural network (DNN) classifier. In addition, we compared the accuracy of performance of SVM and DNN.Methods: In total, 33 FES patients and 34 HCs were recruited. Their brain cortical activities were measured using NIRS while performing letter and category versions of verbal fluency tests (VFTs). The integral and centroid values of brain cortical activity in the bilateral frontotemporal regions during the VFTs were selected as features in SVM and DNN classifier.Results: Compared to HCs, FES patients displayed reduced brain cortical activity over the bilateral frontotemporal regions during both types of VFTs. Regarding the classifier performance, SVM reached an accuracy of 68.6%, sensitivity of 70.1%, and specificity of 64.6%, while DNN reached an accuracy of 79.7%, sensitivity of 88.8%, and specificity of 74.9% in the classification of FES patients and HCs.Conclusions: Compared to findings of previous structural neuroimaging studies, we found that using DNN to measure the NIRS signals during the VFTs to differentiate between FES patients and HCs could achieve a higher accuracy, indicating that NIRS can be used as a potential marker to classify FES patients from HCs. Future additional independent datasets are needed to confirm the validity of our model.

Background: The effects of methadone-induced severe prolongation of the corrected QT interval (QTc) and sudden cardiac death appear unpredictable and sex-dependent. Genetic polymorphisms in the nitric oxide synthase 1 adaptor protein (NOS1AP) have been implicated in QTc prolongation in general populations. We investigated whether common NOS1AP variants interact with methadone in relation to QTc prolongation in patients with heroin dependence. Methods: We genotyped 17 NOS1AP variants spanning the entire gene in heroin-dependent patients who received a 12-lead electrocardiography (ECG) examination both at baseline and during maintenance methadone treatment in Cohort 1 and only during maintenance methadone treatment in Cohort 2. The QT interval was measured automatically by the Marquette 12SL program, and was corrected for heart rate using Bazett’s formula. Results: Cohort 1 consisted of 122 patients (age: 37.65 ± 8.05 years, 84% male, methadone dosage: 42.54 ± 22.17 mg/day), and Cohort 2 comprised of 319 patients (age: 36.9 ± 7.86 years, 82% male, methadone dosage: 26.08 ± 15.84 mg/day), with complete genotyping data for analyses. Before methadone, the QTc intervals increased with increasing age (r = 0.3541, p < 0.001); the age-adjusted QTc showed dose-dependent prolongation in men (r = 0.6320, p < 0.001), but abbreviation in women (r = −0.5348, p = 0.018) in Cohort 1. The pooled genotype-specific analysis of the two cohorts revealed that the QTc interval was significantly shorter in male carriers of the rs164148 AA variant than in male carriers of the reference GG genotype (GG: n = 262, QTc = 423 ± 1.4 ms; AA: n = 10, QTc = 404.1 ± 7 ms, p = 0.009), according to univariate analysis. The QTc remained shorter in male carriers of the rs164148 AA variant compared to GG genotype (423 ± 1.4 ms vs. 405.9 ± 6.9 ms, p = 0.016) in multivariate analysis after adjusting for age and methadone dosage. A cut-off QTc interval of p = 0.2164) in male carriers of the rs164148 variants. Conclusions: Carriers of a common NOS1AP rs164148 AA genotype variant were associated with a shorter QTc interval in men receiving maintenance methadone treatment. This genetic polymorphism attenuates the QTc-prolonging effect by methadone, and thus may explain at least in part the unpredictable and heterogeneous risks for severe QTc prolongation and sudden cardiac death in patients on methadone.

Background: An emotional intelligence (EI) deficit has been noticed in euthymic bipolar spectrum disorder (BD) patients. However, whether this deficit is affected by mood or subtype is unclear.Objectives:The aim of this study was to investigate whether an EI deficit is mood-dependent, and which mood symptoms have more impact on EI in BD.Methods: Two hundred and thirty participants aged between 18 and 65 years old were recruited [130 BD patients (51 bipolar I disorder (BDI) and 79 bipolar II disorder (BDII): 39.2% males; 91 healthy controls (HCs): 48.4% males)]. The Mayer–Salovey–Caruso Emotional Intelligence Test (MSCEIT), which contains experiential and strategic EI ratings, was used to assess social cognition. The Hamilton Depression Rating Scale (HDRS) and the Young's Mania Rating Scale (YMRS) were used for evaluating the severity [HAMD and YMRS scores ≦7 were euthymic (BDeut) and HAMD YMRS sores ≧8 were episodic (BDepi)]. Analyses of covariance (ANCOVA) were performed, with adjustment for background information between the BD patients and HCs.Results: The results showed that, compared to the HCs, the BDeut patients showed no difference in any MSCEIT measures, while the BDepi patients showed lower scores in all MSCEIT measures, except for perceiving emotions. In addition, a main effect of mood state instead of BD subtype was found for the managing emotions branch (p < 0.0007). Regression analyses showed that the duration of illness and HDRS scores were correlated with the scores in the strategic area of the MSCEIT, while age and YMRS scores were more relevant to the scores in the experiential area of the MSCEIT.Conclusion: The results confirm that an EI deficit is mood-dependent in BD patients. In addition, a depressive mood is more related to the strategic EI area, while a manic mood is correlated with the experiential EI area. Understanding the different domains of EI deficits in BD patients may be helpful for developing interventions for BD.

Abstract Objectives Cases of patients with bipolar disorder (BD) having neuropsychological impairment have been reported, although inconsistently. The possibility of comorbidity with anxiety disorder (AD) has been suggested. The association between mood episodes and AD comorbidity on neuropsychological performance is unclear and thus was investigated in the current study. Methods All participants were informed about and agreed to participate in this study. Patients with BD were recruited from outpatient and inpatient settings, and healthy controls (HCs) were recruited as a comparison group. Six hundred and twenty‐eight participants (175 HCs and 453 BD—56 BDI and 397 BDII) were studied based on their current mood episode, namely, depressive (BDd), manic/hypomanic (BDm), mixed (BDmix), and euthymic (BDeu), compared with/without AD comorbidity (164 with AD). Results Compared to HCs, all BD groups had significantly more impaired neuropsychological profiles, but the BDeu group was found to have less impairment in memory and executive function than the episodic BD groups. The percentage of AD comorbidity in BDd, BDm, BDmix, and BDeu was 33.9%, 40.3%, 33.0%, and 35.6%, respectively (χ2 = 1.61, p > .05). The results show that AD plays a different role in neuropsychological impairment across various mood episodes in BD. Conclusion Memory impairment and executive dysfunction may be state‐like cognitive phenotypes and are affected by AD comorbidity during mixed and depressive episodes in BD, while sustained attention deficiencies are more like trait markers, regardless of mood episodes, and persist beyond the course of the illness. The AD comorbidity effect on attentional deficit is greater when suffering from a manic episode.

Cancer drug resistance presents a challenge for precision medicine. Drug-resistant mutations are always emerging. In this study, we explored the relationship between drug-resistant mutations and drug resistance from the perspective of protein structure. By combining data from previously identified drug-resistant mutations and information of protein structure and function, we used machine learning-based methods to build models to predict cancer drug resistance mutations. The performance of our combined model achieved an accuracy of 86%, a Matthews correlation coefficient score of 0.57, and an F1 score of 0.66. We have constructed a fast, reliable method that predicts and investigates cancer drug resistance in a protein structure. Nonetheless, more information is needed concerning drug resistance and, in particular, clarification is needed about the relationships between the drug and the drug resistance mutations in proteins. Highly accurate predictions regarding drug resistance mutations can be helpful for developing new strategies with personalized cancer treatments. Our novel concept, which combines protein structure information, has the potential to elucidate physiological mechanisms of cancer drug resistance.

In the wake of recent advances in artificial intelligence research, precision psychiatry using machine learning techniques represents a new paradigm. The D-amino acid oxidase (DAO) protein and its interaction partner, the D-amino acid oxidase activator (DAOA, also known as G72) protein, have been implicated as two key proteins in the N-methyl-D-aspartate receptor (NMDAR) pathway for schizophrenia. Another potential biomarker in regard to the etiology of schizophrenia is melatonin in the tryptophan catabolic pathway. To develop an ensemble boosting framework with random undersampling for determining disease status of schizophrenia, we established a prediction approach resulting from the analysis of genomic and demographic variables such as DAO levels, G72 levels, melatonin levels, age, and gender of 355 schizophrenia patients and 86 unrelated healthy individuals in the Taiwanese population. We compared our ensemble boosting framework with other state-of-the-art algorithms such as support vector machine, multilayer feedforward neural networks, logistic regression, random forests, naive Bayes, and C4.5 decision tree. The analysis revealed that the ensemble boosting model with random undersampling [area under the receiver operating characteristic curve (AUC) = 0.9242 ± 0.0652; sensitivity = 0.8580 ± 0.0770; specificity = 0.8594 ± 0.0760] performed maximally among predictive models to infer the complicated relationship between schizophrenia disease status and biomarkers. In addition, we identified a causal link between DAO and G72 protein levels in influencing schizophrenia disease status. The study indicates that the ensemble boosting framework with random undersampling may provide a suitable method to establish a tool for distinguishing schizophrenia patients from healthy controls using molecules in the NMDAR and tryptophan catabolic pathways.

Glutathione (GSH) is a major endogenous antioxidant. Several studies have shown GSH redox imbalance and altered GSH levels in Alzheimer’s disease (AD) patients. Early detection is crucial for the outcome of AD. However, whether GSH can serve as a biomarker during the very early-phase of AD, such as mild cognitive impairment (MCI), remains unknown. The current prospective study aimed to examine the longitudinal change in plasma GSH concentration and its influence on cognitive decline in MCI. Overall, 49 patients with MCI and 16 healthy individuals were recruited. Plasma GSH levels and cognitive function, measured by the Mini-Mental Status Examination (MMSE) and Alzheimer’s disease assessment scale-cognitive subscale (ADAS-cog), were monitored every 6 months. We employed multiple regressions to examine the role of GSH level in cognitive decline in the 2 years period. The MCI patients showed significant decline in plasma GSH levels and cognitive function from baseline to endpoint (month 24). In comparison, the healthy individuals’ GSH concentration and cognitive function did not change significantly. Further, both GSH level at baseline and GSH level change from baseline to endpoint significantly influenced cognitive decline among the MCI patients. To our knowledge, this is the first study to demonstrate that both plasma GSH levels and cognitive function declined 2 years later among the MCI patients in a prospective manner. If replicated by future studies, blood GSH concentration may be regarded as a biomarker for monitoring cognitive change in MCI.

Numerous studies over the last several years have shown that d-amino acids, especially d-serine, have been related to brain and neurological disorders. Acknowledged neurological functions of d-amino acids include neurotransmission and learning and memory functions through modulating N-methyl-d-aspartate type glutamate receptors (NMDARs). Aberrant d-amino acids level and polymorphisms of genes related to d-amino acids metabolism are associated with neurodegenerative brain conditions. This review summarizes the roles of d-amino acids and pLG72, also known as d-amino acid oxidase activator, on two neurodegenerative disorders, schizophrenia and Alzheimer’s disease (AD). The scope includes the changes in d-amino acids levels, gene polymorphisms of G72 genomics, and the role of pLG72 on NMDARs and mitochondria in schizophrenia and AD. The clinical diagnostic value of d-amino acids and pLG72 and the therapeutic importance are also reviewed.

Glutamate, a crucial excitatory neurotransmitter, plays a major role in the modulation of schizophrenia’s pathogenesis. New drug developments for schizophrenia have been prompted by the hypoglutamatergic hypothesis of schizophrenia. The cystine/glutamate antiporter system xc− is related to glutamate-release regulation. Patients with schizophrenia were recently discovered to exhibit downregulation of xc− subunits—the solute carrier (SLC) family 3 member 2 and the SLC family 7 member 11. We searched for relevant studies from 1980, when Bannai and Kitamura first identified the protein subunit system xc− in lung fibroblasts, with the aim of compiling the biological, functional, and pharmacological characteristics of antiporter xc−, which consists of several subunits. Some of them can significantly stimulate the human brain through the glutamate pathway. Initially, extracellular cysteine activates neuronal xc−, causing glutamate efflux. Next, excitatory amino acid transporters enhance the unidirectional transportation of glutamate and sodium. These two biochemical pathways are also crucial to the production of glutathione, a protective agent for neural and glial cells and astrocytes. Investigation of the expression of system xc− genes in the peripheral white blood cells of patients with schizophrenia can facilitate better understanding of the mental disorder and future development of novel biomarkers and treatments for schizophrenia. In addition, the findings further support the hypoglutamatergic hypothesis of schizophrenia.

The impacts of menopause on neurodegenerative diseases, especially the changes in steroid hormones, have been well described in cell models, animal models, and humans. However, the therapeutic effects of hormone replacement therapy on postmenopausal women with neurodegenerative diseases remain controversial. The steroid hormones, steroid hormone receptors, and downstream signal pathways in the brain change with aging and contribute to disease progression. Estrogen and progesterone are two steroid hormones which decline in circulation and the brain during menopause. Insulin-like growth factor 1 (IGF-1), which plays an import role in neuroprotection, is rapidly decreased in serum after menopause. Here, we summarize the actions of estrogen, progesterone, and IGF-1 and their signaling pathways in the brain. Since the incidence of Alzheimer’s disease (AD) is higher in women than in men, the associations of steroid hormone changes and AD are emphasized. The signaling pathways and cellular mechanisms for how steroid hormones and IGF-1 provide neuroprotection are also addressed. Finally, the molecular mechanisms of potential estrogen modulation on N-methyl-d-aspartic acid receptors (NMDARs) are also addressed. We provide the viewpoint of why hormone therapy has inconclusive results based on signaling pathways considering their complex response to aging and hormone treatments. Nonetheless, while diagnosable AD may not be treatable by hormone therapy, its preceding stage of mild cognitive impairment may very well be treatable by hormone therapy.

Late-life depression (LLD), compared to depression at a young age, is more likely to have poor prognosis and high risk of progression to dementia. A recent systemic review and meta-analysis of the present antidepressants for LLD showed that the treatment response rate was 48% and the remission rate was only 33.7%, thus implying the need to improve the treatment with other approaches in the future. Recently, agents modulating the glutamatergic system have been tested for mental disorders such as schizophrenia, dementia, and depressive disorder. Ketamine, a noncompetitive NMDA receptor (NMDAR) antagonist, requires more evidence from randomized clinical trials (RCTs) to prove its efficacy and safety in treating LLD. The metabotropic receptors (mGluRs) of the glutamatergic system are family G-protein-coupled receptors, and inhibition of the Group II mGluRs subtypes (mGlu2 and mGlu3) was found to be as effective as ketamine in exerting rapid antidepressant activity in some animal studies. Inflammation has been thought to contribute to depression for a long time. The cytokine levels not only increase with age but also decrease serotonin. Regarding LLD, interleukin 6 (IL-6) and tumor necrosis factor α (TNF-α) released in vivo are likely to contribute to the reduced serotonin level. Brain-derived neurotrophic factor (BDNF), a growth factor and a modulator in the tropomyosin receptor kinase (Trk) family of tyrosine kinase receptors, probably declines quantitatively with age. Recent studies suggest that BDNF/TrkB decrement may contribute to learning deficits and memory impairment. In the process of aging, physiological changes in combination with geriatric diseases such as vascular diseases result in poorer prognosis of LLD in comparison with that of young-age depression. Treatments with present antidepressants have been generally unsatisfactory. Novel treatments such as anti-inflammatory agents or NMDAR agonists/antagonists require more studies in LLD. Last but not least, LLD and dementia, which share common pathways and interrelate reciprocally, are a great concern. If it is possible to enhance the treatment of LDD, dementia can be prevented or delated.

A growing body of evidence currently proposes that deep learning approaches can serve as an essential cornerstone for the diagnosis and prediction of Alzheimer’s disease (AD). In light of the latest advancements in neuroimaging and genomics, numerous deep learning models are being exploited to distinguish AD from normal controls and/or to distinguish AD from mild cognitive impairment in recent research studies. In this review, we focus on the latest developments for AD prediction using deep learning techniques in cooperation with the principles of neuroimaging and genomics. First, we narrate various investigations that make use of deep learning algorithms to establish AD prediction using genomics or neuroimaging data. Particularly, we delineate relevant integrative neuroimaging genomics investigations that leverage deep learning methods to forecast AD on the basis of incorporating both neuroimaging and genomics data. Moreover, we outline the limitations as regards to the recent AD investigations of deep learning with neuroimaging and genomics. Finally, we depict a discussion of challenges and directions for future research. The main novelty of this work is that we summarize the major points of these investigations and scrutinize the similarities and differences among these investigations.

Alzheimer’s disease (AD) is a progressive neurodegenerative disease and accounts for most cases of dementia. The prevalence of AD has increased in the current rapidly aging society and contributes to a heavy burden on families and society. Despite the profound impact of AD, current treatments are unable to achieve satisfactory therapeutic effects or stop the progression of the disease. Finding novel treatments for AD has become urgent. In this paper, we reviewed novel therapeutic approaches in five categories: anti-amyloid therapy, anti-tau therapy, anti-neuroinflammatory therapy, neuroprotective agents including N-methyl-D-aspartate (NMDA) receptor modulators, and brain stimulation. The trend of therapeutic development is shifting from a single pathological target to a more complex mechanism, such as the neuroinflammatory and neurodegenerative processes. While drug repositioning may accelerate pharmacological development, non-pharmacological interventions, especially repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS), also have the potential for clinical application. In the future, it is possible for physicians to choose appropriate interventions individually on the basis of precision medicine.

Matrix-assisted laser desorption ionization/time-of-flight (MALDI-TOF) mass spectrometry is a sensitive analytical tool for characterizing various biomolecules in biofluids. In this study, MALDI-TOF was used to characterize potential plasma biomarkers for distinguishing patients with major depressive disorder (MDD) from patients with schizophrenia and healthy controls. To avoid interference from albumin—the predominant protein in plasma—the plasma samples were pretreated using acid hydrolysis. The results obtained by MALDI-TOF were also validated by electrospray ionization-quadrupole time-of-flight (ESI-QTOF) mass spectrometry. The analytical results were further treated with principal component analysis (PCA), hierarchical clustering analysis (HCA), and receiver operating characteristic (ROC) curve analysis. The statistical analyses showed that MDD patients could be distinguished from schizophrenia patients and healthy controls by the lack of apolipoprotein C1 (Apo C1), which, in fact, was detected in healthy controls and schizophrenia patients. This protein is suggested to be a potential plasma biomarker for distinguishing MDD patients from healthy controls and schizophrenia patients. Since sample preparation for MALDI-TOF is very simple, high-throughput plasma apolipoprotein analysis for clinical purposes is feasible.

Addiction is characterized by drug-craving, compulsive drug-taking, and relapse, and results from the interaction between multiple genetic and environmental factors. Reward pathways play an important role in mediating drug-seeking and drug-taking behaviors, and relapse. The objective of this study was to identify heroin addicts who carry specific genetic variants in their dopaminergic reward systems. A total of 326 heroin-dependent patients undergoing methadone maintenance therapy (MMT) were recruited from the Addiction Center of the China Medical University Hospital. A heroin-use and craving questionnaire was used to evaluate the urge for heroin, the daily or weekly frequency of heroin usage, daily life disturbance, anxiety, and the ability to overcome heroin use. A general linear regression model was used to assess the associations of genetic polymorphisms in one’s dopaminergic reward system with heroin-use and craving scores. Results: The most significant results were obtained for rs2240158 in GRIN3B (p = 0.021), rs3983721 in GRIN3A (p = 0.00326), rs2129575 in TPH2 (p = 0.033), rs6583954 in CYP2C19 (p = 0.033), and rs174699 in COMT (p = 0.036). These were all associated with heroin-using and craving scores with and without adjustments for age, sex, and body mass index. We combined five variants, and the ensuing dose-response effect indicated that heroin-craving scores increased with the numbers of risk alleles (p for trend = 0.0008). These findings will likely help us to understand the genetic mechanism of craving, which will help in predicting the risk of relapse in clinical practice and the potential for therapies to target craving in heroin addiction.

Abstract The gene of protein interacting with C kinase 1 alpha (PICK1) has been implicated in schizophrenia, nevertheless, conflicting results existed. However, its role in cognitive function remains unclear. Besides, cognitive deficits impair the long-term outcome. We explored whether the polymorphisms of PICK1 (rs2076369, rs3952) affected cognitive functions in schizophrenic patients. We analyzed 302 patients and tested the differences of cognitive functions, clinical symptoms between genetic groups. We also used general linear model to analyze the effect of PICK1 genetic polymorphisms on cognitive functions. After adjustment for gender, age, education, the patients with rs2076369 G/T genotype showed better performance than T/T homozygotes in the summary score, global composite score, neurocognitive composite score, category fluency subtest, WAIS-III-Digit Symbol Coding subtest, working memory, WMS-III-Spatial Span (backward) subtest, MSCEIT-managing emotions branch (p = 0.038, 0.025, 0.046, 0.036, 0.025, 0.027, 0.035, 0.028, respectively). G/G homozygotes performed better than T/T in category fluency subtest (p = 0.049). A/A homozygotes of rs3952 performed better than G/G in trail making A subtest (p = 0.048). To our knowledge, this is the first study to indicate that PICK1 polymorphisms may associate with cognitive functions in schizophrenic patients. Further replication studies in healthy controls or other ethnic groups are warranted.

The purpose of this review is to correlate autism with autoimmune dysfunction in the absence of an explanation for the etiology of autism spectrum disorder. The anti-N-methyl-D-aspartate receptor (anti-NMDAR) autoantibody is a typical synaptic protein that can bind to synaptic NMDA glutamate receptors, leading to dysfunctional glutamate neurotransmission in the brain that manifests as psychiatric symptoms (psychosis, hallucinations, and personality changes). Detection of autoantibodies, cytokines, decreased lymphocytes, serum immunoglobulin level imbalance, T-cell mediated immune profile, maternal infection history, and children’s infection history can all be vital biological markers of autoimmune autism. Diagnosing autoimmune encephalitis sooner can increase the effectiveness of curative treatments—such as immune therapy or immune modulatory therapy—that may prevent the long-term consequence of being misdiagnosed with autism spectrum disorder. Glutamate therapy primarily normalizes glutamate neurotransmission and can be a new add-on intervention alongside antipsychotics for treating autoimmune autism.

While the world’s population is aging, the prevalence of dementia and the associated behavioral and psychological symptoms of dementia (BPSD) rises rapidly. BPSD are associated with worsening of cognitive function and poorer prognosis. No pharmacological treatment has been approved to be beneficial for BPSD to date. Dysfunction of the N-methyl-D-aspartate receptor (NMDAR)-related neurotransmission leads to cognitive impairment and behavioral changes, both of which are core symptoms of BPSD. Memantine, an NMDAR partial antagonist, is used to treat moderate to severe Alzheimer’s disease (AD). On the other hand, a D-amino acid oxidase inhibitor improved early-phase AD. Whether to enhance or to attenuate the NMDAR may depend on the phases of dementia. It will be valuable to develop biomarkers indicating the activity of NMDAR, particularly in BPSD. In addition, recent reports suggest that gender difference exists in the treatment of dementia. Selecting subpopulations of patients with BPSD who are prone to improvement with treatment would be important. We reviewed literatures regarding the treatment of BPSD, focusing on the NMDAR-related modulation and precision medicine. Future studies examining the NMDAR modulators with the aid of potential biomarkers to tailor the treatment for individualized patients with BPSD are warranted.

Introduction Obstructive sleep apnea (OSA) is a serious sleep disorder characterized by repetitive episodes of paused or shallow breathing during sleep. Patients with OSA often have excessive daytime sleepiness. The role of cigarette smoking in OSA remains controversial. The aim of this study was to examine the relationship between cigarette smoking and OSA. Methods In this retrospective chart review, we reviewed 18-month sleep laboratory charts in central Taiwan. We collected data regarding sleep, current cigarette smoking status, sex, age, body mass index (BMI), neck circumference, Epworth Sleepiness Scale score, and polysomnographic sleep parameters. In total, 733 subjects were recruited; among these, 151 were smokers and 582 were nonsmokers. Results Smokers had significantly higher apnea–hypopnea index (p

Schizophrenia is a severe mental disorder which leads to functional deterioration. Early detection and intervention are vital for better prognosis. However, the diagnosis of schizophrenia still depends on clinical observation to date. Without reliable biomarkers, schizophrenia is difficult to detect in its early phase. Further, there is no approved medication for prodromal schizophrenia because current antipsychotics fail to show satisfactory efficacy and safety. Therefore, to develop an effective early diagnostic and therapeutic approach for schizophrenia, especially in its prodromal phase, is crucial. Glutamate signaling dysfunction and dysregulation of oxidative stress have been considered to play important roles in schizophrenic prodrome. The N-methyl-D-aspartate receptor (NMDAR) is one of three types of ionotropic glutamate receptors. In this article, we reviewed literature regarding NMDAR hypofunction, oxidative stress, and the linkage between both in prodromal schizophrenia. The efficacy of NMDAR enhancers such as D-amino acid oxidase inhibitor was addressed. Finally, we highlighted potential biomarkers related to NMDAR and oxidative stress regulation, and therefore suggested the strategies of early detection and intervention of prodromal schizophrenia. Future larger-scale studies combining biomarkers and novel drug development for early psychosis are warranted.

The D-amino acid oxidase activator (DAOA, also known as G72) gene is a strong schizophrenia susceptibility gene. Higher G72 protein levels have been implicated in patients with schizophrenia. The current study aimed to differentiate patients with schizophrenia from healthy individuals using G72 single nucleotide polymorphisms (SNPs) and G72 protein levels by leveraging computational artificial intelligence and machine learning tools. A total of 149 subjects with 89 patients with schizophrenia and 60 healthy controls were recruited. Two G72 genotypes (including rs1421292 and rs2391191) and G72 protein levels were measured with the peripheral blood. We utilized three machine learning algorithms (including logistic regression, naive Bayes, and C4.5 decision tree) to build the optimal predictive model for distinguishing schizophrenia patients from healthy controls. The naive Bayes model using two factors, including G72 rs1421292 and G72 protein, appeared to be the best model for disease susceptibility (sensitivity = 0.7969, specificity = 0.9372, area under the receiver operating characteristic curve (AUC) = 0.9356). However, a model integrating G72 rs1421292 only slightly increased the discriminative power than a model with G72 protein alone (sensitivity = 0.7941, specificity = 0.9503, AUC = 0.9324). Among the three models with G72 protein alone, the naive Bayes with G72 protein alone had the best specificity (0.9503), while logistic regression with G72 protein alone was the most sensitive (0.8765). The findings remained similar after adjusting for age and gender. This study suggests that G72 protein alone, without incorporating the two G72 SNPs, may have been suitable enough to identify schizophrenia patients. We also recommend applying both naive Bayes and logistic regression models for the best specificity and sensitivity, respectively. Larger-scale studies are warranted to confirm the findings.

Background: Alzheimer’s disease (AD) is a complex and severe neurodegenerative disease that still lacks effective methods of diagnosis. The current diagnostic methods of AD rely on cognitive tests, imaging techniques and cerebrospinal fluid (CSF) levels of amyloid-β1-42 (Aβ42), total tau protein and hyperphosphorylated tau (p-tau). However, the available methods are expensive and relatively invasive. Artificial intelligence techniques like machine learning tools have being increasingly used in precision diagnosis. Methods: We conducted a meta-analysis to investigate the machine learning and novel biomarkers for the diagnosis of AD. Methods: We searched PubMed, the Cochrane Central Register of Controlled Trials, and the Cochrane Database of Systematic Reviews for reviews and trials that investigated the machine learning and novel biomarkers in diagnosis of AD. Results: In additional to Aβ and tau-related biomarkers, biomarkers according to other mechanisms of AD pathology have been investigated. Neuronal injury biomarker includes neurofiliament light (NFL). Biomarkers about synaptic dysfunction and/or loss includes neurogranin, BACE1, synaptotagmin, SNAP-25, GAP-43, synaptophysin. Biomarkers about neuroinflammation includes sTREM2, and YKL-40. Besides, d-glutamate is one of coagonists at the NMDARs. Several machine learning algorithms including support vector machine, logistic regression, random forest, and naïve Bayes) to build an optimal predictive model to distinguish patients with AD from healthy controls. Conclusions: Our results revealed machine learning with novel biomarkers and multiple variables may increase the sensitivity and specificity in diagnosis of AD. Rapid and cost-effective HPLC for biomarkers and machine learning algorithms may assist physicians in diagnosing AD in outpatient clinics.

Schizophrenia is a form of mental disorder that is behaviorally characterized by abnormal behavior, such as social function deficits or other behaviors that are disconnected from reality. Dysregulation of oxytocin may play a role in regulating the expression of schizophrenia. Given oxytocin’s role in social cognition and behavior, a variety of studies have examined the potential clinical benefits of oxytocin in improving the psychopathology of patients with schizophrenia. In this review, we highlight the evidence for the role of endogenous oxytocin in schizophrenia, from animal models to human studies. We further discuss the potential of oxytocin as a therapeutic agent for schizophrenia and its implication in future treatment.

Alzheimer’s disease (AD), the most common cause of dementia, is a progressive neurodegenerative disease. The number of AD cases has been rapidly growing worldwide. Several the related etiological hypotheses include atypical amyloid β (Aβ) deposition, neurofibrillary tangles of tau proteins inside neurons, disturbed neurotransmission, inflammation, and oxidative stress. During AD progression, aberrations in neurotransmission cause cognitive decline—the main symptom of AD. Here, we review the aberrant neurotransmission systems, including cholinergic, adrenergic, and glutamatergic network, and the interactions among these systems as they pertain to AD. We also discuss the key role of N-methyl-d-aspartate receptor (NMDAR) dysfunction in AD-associated cognitive impairment. Furthermore, we summarize the results of recent studies indicating that increasing glutamatergic neurotransmission through the alteration of NMDARs shows potential for treating cognitive decline in mild cognitive impairment or early stage AD. Future studies on the long-term efficiency of NMDA-enhancing strategies in the treatment of AD are warranted.

A growing body of evidence now suggests that artificial intelligence and machine learning techniques can serve as an indispensable foundation for the process of drug design and discovery. In light of latest advancements in computing technologies, deep learning algorithms are being created during the development of clinically useful drugs for treatment of a number of diseases. In this review, we focus on the latest developments for three particular arenas in drug design and discovery research using deep learning approaches, such as generative adversarial network (GAN) frameworks. Firstly, we review drug design and discovery studies that leverage various GAN techniques to assess one main application such as molecular de novo design in drug design and discovery. In addition, we describe various GAN models to fulfill the dimension reduction task of single-cell data in the preclinical stage of the drug development pipeline. Furthermore, we depict several studies in de novo peptide and protein design using GAN frameworks. Moreover, we outline the limitations in regard to the previous drug design and discovery studies using GAN models. Finally, we present a discussion of directions and challenges for future research.

Background: An increasing number of studies have shown that the brain–gut–microbiota axis may significantly contribute to Alzheimer’s disease (AD) pathogenesis. Moreover, impaired memory and learning involve the dysfunction neurotransmission of glutamate, the agonist of the N-methyl-d-aspartate receptor and a major excitatory neurotransmitter in the brain. This systematic review aimed to summarize the current cutting-edge research on the gut microbiota and glutamate alterations associated with dementia. Methods: PubMed, the Cochrane Collaboration Central Register of Controlled Clinical Trials, and Cochrane Systematic Reviews were reviewed for all studies on glutamate and gut microbiota in dementia published up until Feb 2020. Results: Several pilot studies have reported alterations of gut microbiota and metabolites in AD patients and other forms of dementia. Gut microbiota including Bacteroides vulgatus and Campylobacter jejuni affect glutamate metabolism and decrease the glutamate metabolite 2-keto-glutaramic acid. Meanwhile, gut bacteria with glutamate racemase including Corynebacterium glutamicum, Brevibacterium lactofermentum, and Brevibacterium avium can convert l-glutamate to d-glutamate. N-methyl-d-aspartate glutamate receptor (NMDAR)-enhancing agents have been found to potentially improve cognition in AD or Parkinson’s disease patients. These findings suggest that d-glutamate (d-form glutamate) metabolized by the gut bacteria may influence the glutamate NMDAR and cognitive function in dementia patients. Conclusions: Gut microbiota and glutamate are potential novel interventions to be developed for dementia. Exploring comprehensive cognitive functions in animal and human trials with glutamate-related NMDAR enhancers are warranted to examine d-glutamate signaling efficacy in gut microbiota in patients with AD and other neurodegenerative dementias.

A growing body of evidence now suggests that precision psychiatry, an interdisciplinary field of psychiatry, precision medicine, and pharmacogenomics, serves as an indispensable foundation of medical practices by offering the accurate medication with the accurate dose at the accurate time to patients with psychiatric disorders. In light of the latest advancements in artificial intelligence and machine learning techniques, numerous biomarkers and genetic loci associated with psychiatric diseases and relevant treatments are being discovered in precision psychiatry research by employing neuroimaging and multi-omics. In this review, we focus on the latest developments for precision psychiatry research using artificial intelligence and machine learning approaches, such as deep learning and neural network algorithms, together with multi-omics and neuroimaging data. Firstly, we review precision psychiatry and pharmacogenomics studies that leverage various artificial intelligence and machine learning techniques to assess treatment prediction, prognosis prediction, diagnosis prediction, and the detection of potential biomarkers. In addition, we describe potential biomarkers and genetic loci that have been discovered to be associated with psychiatric diseases and relevant treatments. Moreover, we outline the limitations in regard to the previous precision psychiatry and pharmacogenomics studies. Finally, we present a discussion of directions and challenges for future research.

Brain stimulation techniques can modulate cognitive functions in many neuropsychiatric diseases. Pilot studies have shown promising effects of brain stimulations on Alzheimer's disease (AD). Brain stimulations can be categorized into non-invasive brain stimulation (NIBS) and invasive brain stimulation (IBS). IBS includes deep brain stimulation (DBS), and invasive vagus nerve stimulation (VNS), whereas NIBS includes transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS), electroconvulsive treatment (ECT), magnetic seizure therapy (MST), cranial electrostimulation (CES), and non-invasive VNS. We reviewed the cutting-edge research on these brain stimulation techniques and discussed their therapeutic effects on AD. Both IBS and NIBS may have potential to be developed as novel treatments for AD; however, mixed findings may result from different study designs, patients selection, population, or samples sizes. Therefore, the efficacy of NIBS and IBS in AD remains uncertain, and needs to be further investigated. Moreover, more standardized study designs with larger sample sizes and longitudinal follow-up are warranted for establishing a structural guide for future studies and clinical application.

Background/aimsCognitive impairment, which frequently occurs in patients with schizophrenia, bipolar disorder, Alzheimer’s disease, and Parkinson’s disease, has a significant impact on the daily lives of both patients and their family. Furthermore, since the medications used for cognitive enhancement have limited efficacy, the issue of cognitive enhancement still remains a clinically unsolved challenge.Sampling and methodsWe reviewed the clinical studies (published between 2007 and 2017) that focused on the efficacy of medications used for enhancing cognition in patients with schizophrenia, bipolar disorder, Alzheimer’s disease, and Parkinson’s disease.ResultsAcetylcholinesterase inhibitors and memantine are the standard treatments for Alzheimer’s disease and Parkinson’s disease. Some studies have reported selective cognitive improvement in patients with schizophrenia following galantamine treatment. Newer antipsychotics, including paliperidone, lurasidone, aripiprazole, ziprasidone, and BL-1020, have also been reported to exert cognitive benefits in patients with schizophrenia. Dopaminergic medications were found to improve language function in patients with Parkinson’s disease. However, no beneficial effects on cognitive function were observed with dopamine agonists in patients with schizophrenia. The efficacies of nicotine and its receptor modulators in cognitive improvement remain controversial, with the majority of studies showing that varenicline significantly improved the cognitive function in schizophrenic patients. Several studies have reported that N-methyl-d-aspartate glutamate receptor (NMDAR) enhancers improved the cognitive function in patients with chronic schizophrenia. NMDAR enhancers might also have cognitive benefits in patients with Alzheimer’s disease or Parkinson’s disease. Raloxifene, a selective estrogen receptor modulator, has also been demonstrated to have beneficial effects on attention, processing speed, and memory in female patients with schizophrenia.ConclusionClinical trials with larger sample sizes evaluating comprehensive cognitive domains are warranted to examine the efficacy of medications in cognitive enhancement in patients with schizophrenia, bipolar disorder, Alzheimer’s disease, and Parkinson’s disease.

With ever-increasing elder populations, age-related cognitive decline, which is characterized as a gradual decline in cognitive capacity in the aging process, has turned out to be a mammoth public health concern. Since genetic information has become increasingly important to explore the biological mechanisms of cognitive decline, the search for genetic biomarkers of cognitive aging has received much attention. There is growing evidence that single-nucleotide polymorphisms (SNPs) within the ADAMTS9, BDNF, CASS4, COMT, CR1, DNMT3A, DTNBP1, REST, SRR, TOMM40, circadian clock, and Alzheimer’s diseases-associated genes may contribute to susceptibility to cognitive aging. In this review, we first illustrated evidence of the genetic contribution to disease susceptibility to age-related cognitive decline in recent studies ranging from approaches of candidate genes to genome-wide association studies. We then surveyed a variety of association studies regarding age-related cognitive decline with consideration of gene–gene and gene–environment interactions. Finally, we highlighted their limitations and future directions. In light of advances in precision medicine and multi-omics technologies, future research in genomic medicine promises to lead to innovative ideas that are relevant to disease prevention and novel drugs for cognitive aging.

Major depressive disorder is a severe and complex mental disorder. Impaired neurotransmission and disrupted signalling pathways may influence neuroplasticity, which is involved in the brain dysfunction in depression. Traditional neurobiological theories of depression, such as monoamine hypothesis, cannot fully explain the whole picture of depressive disorders. In this review, we discussed new treatment directions of depression, including modulation of glutamatergic system and noninvasive brain stimulation. Dysfunction of glutamatergic neurotransmission plays an important role in the pathophysiology of depression. Ketamine, an N-methyl-D-aspartate (NMDA) receptor antagonist, has rapid and lasting antidepressive effects in previous studies. In addition to ketamine, other glutamatergic modulators, such as sarcosine, also show potential antidepressant effect in animal models or clinical trials. Noninvasive brain stimulation is another new treatment strategy beyond pharmacotherapy. Growing evidence has demonstrated that superficial brain stimulations, such as transcranial magnetic stimulation, transcranial direct current stimulation, cranial electrotherapy stimulation, and magnetic seizure therapy, can improve depressive symptoms. The antidepressive effect of these brain stimulations may be through modulating neuroplasticity. In conclusion, drugs that modulate neurotransmission via NMDA receptor and noninvasive brain stimulation may provide new directions of treatment for depression. Furthermore, exploring the underlying mechanisms will help in developing novel therapies for depression in the future.

The schizophrenia-related protein G72 plays a unique role in the regulation of D-amino acid oxidase (DAO) in great apes. Several psychiatric diseases, including schizophrenia and bipolar disorder, are linked to overexpression of DAO and G72. Whether G72 plays a positive or negative regulatory role in DAO activity, however, has been controversial. Exploring the molecular basis of the relationship between G72 and DAO is thus important to understand how G72 regulates DAO activity. We performed yeast two-hybrid experiments and determined enzymatic activity to identify potential sites in G72 involved in binding DAO. Our results demonstrate that residues 123–153 and 138–153 in the long isoform of G72 bind to DAO and enhance its activity by 22% and 32%, respectively. A docking exercise indicated that these G72 peptides can interact with loops in DAO that abut the entrance of the tunnel that substrate and cofactor must traverse to reach the active site. We propose that a unique gating mechanism underlies the ability of G72 to increase the activity of DAO. Because upregulation of DAO activity decreases d-serine levels, which may lead to psychiatric abnormalities, our results suggest a molecular mechanism involving interaction between DAO and the C-terminal region of G72 that can regulate N-methyl-d-aspartate receptor-mediated neurotransmission.

Physical activity has benefits for reducing levels of anxiety. However, factors that affect physical activity participation for individuals with anxiety disorders have not been well studied. Here, we aimed to clarify the roles of state and trait anxiety in physical activity participation by examining relationships among seven major study variables in Taiwanese adults with anxiety disorders. Methods: A multi-site, cross-sectional explanatory design was used. Data were collected using one interview and five self-administered questionnaires. The sample included 144 Taiwanese adults diagnosed with anxiety disorders. Results: State and trait anxiety were significantly correlated with most of the study variables. Physical activity participation by subjects with anxiety disorders was significantly correlated with state anxiety, benefits of activity, self-efficacy for activity, and social support for activity. When age, sex, and education were controlled in the analysis, state anxiety was associated significantly and negatively with physical activity, benefits of activity, and self-efficacy for activity, and was correlated positively with barriers to activity. Trait anxiety was found to be correlated significantly and negatively with benefits of activity and self-efficacy for activity, and correlated positively with barriers to activity. Conclusion: State anxiety demonstrated greater power than trait anxiety in its relationship with physical activity. These findings suggest that clinical mental health professionals should consider state anxiety when encouraging Taiwanese adults with anxiety disorders to engage in physical activity.

Methadone is a widely used substitution therapy for opioid addiction. Large inter-individual variability has been observed in methadone maintenance dosages and P-glycoprotein (P-gp) was considered to be one of the major contributors. To investigate the mechanism of P-gp's interaction with methadone, as well as the effect of genetic variants on the interaction, Flp-In™-293 cells stably transfected with various genotypes of human P-gp were established in the present study. The RNA and protein expression levels of human P-gp were confirmed by real-time quantitative RT-PCR and western blot, respectively. Utilizing rhodamine 123 efflux assay and calcein-AM uptake study, methadone was demonstrated to be an inhibitor of wild-type human P-gp via non-competitive kinetic (IC50 = 2.17±0.10 µM), while the variant-type human P-gp, P-gp with 1236T-2677T-3435T genotype and P-gp with 1236T-2677A-3435T genotype, showed less inhibition potency (IC50 = 2.97±0.09 µM and 4.43±1.10 µM, respectively) via uncompetitive kinetics. Methadone also stimulated P-gp ATPase and inhibited verapamil-stimulated P-gp ATPase activity under therapeutic concentrations. These results may provide a possible explanation for higher methadone dosage requirements in patients carrying variant-type of P-gp and revealed the possible drug-drug interactions in patients who receive concomitant drugs which are also P-gp substrates.

BackgroundFacial emotion perception is a major social skill, but its molecular signal pathway remains unclear. The MET/AKT cascade affects neurodevelopment in general populations and face recognition in patients with autism. This study explores the possible role of MET/AKT cascade in facial emotion perception.MethodsOne hundred and eighty two unrelated healthy volunteers (82 men and 100 women) were recruited. Four single nucleotide polymorphisms (SNP) of MET (rs2237717, rs41735, rs42336, and rs1858830) and AKT rs1130233 were genotyped and tested for their effects on facial emotion perception. Facial emotion perception was assessed by the face task of Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT). Thorough neurocognitive functions were also assessed.ResultsRegarding MET rs2237717, individuals with the CT genotype performed better in facial emotion perception than those with TT (p = 0.016 by ANOVA, 0.018 by general linear regression model [GLM] to control for age, gender, and education duration), and showed no difference with those with CC. Carriers with the most common MET CGA haplotype (frequency = 50.5%) performed better than non-carriers of CGA in facial emotion perception (p = 0.018, df = 1, F = 5.69, p = 0.009 by GLM). In MET rs2237717/AKT rs1130233 interaction, the C carrier/G carrier group showed better facial emotion perception than those with the TT/AA genotype (p = 0.035 by ANOVA, 0.015 by GLM), even when neurocognitive functions were controlled (p = 0.046 by GLM).ConclusionsTo our knowledge, this is the first study to suggest that genetic factors can affect performance of facial emotion perception. The findings indicate that MET variances and MET/AKT interaction may affect facial emotion perception, implicating that the MET/AKT cascade plays a significant role in facial emotion perception. Further replication studies are needed.