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Previous pilot studies suggest that sodium benzoate may be a potential cognitive enhancer for patients with Alzheimer's disease (AD), schizophrenia, or late-life depression. Especially for AD treatment, a confirmatory trial with predictive biomarkers is urgently needed. This study aimed to confirm benzoate as a novel treatment for AD and to discover its optimal dose and biomarkers.A 24-week, dose-finding, randomized, double-blind, placebo-controlled trial, with clinical measurements at weeks 0, 8, 16, and 24, was conducted in three major medical centers in Taiwan. Among 154 patients screened for AD, 149 were eligible and randomized to one of the four treatments: (i) benzoate 500 group (fixed 500 mg/day); (ii) benzoate 750 (500 mg/day for the first 4 weeks, 750 mg/day from the 5th week); (iii) benzoate 1000 (500 mg/day for the first 4 weeks, 1000 mg/day from the 5th week); and (iv) placebo. The primary outcome measure was AD assessment scale-cognitive subscale (ADAS-cog).The benzoate 1000 group performed best in improving ADAS-cog (P = 0.026 at week 24), with female advantage. Higher plasma catalase at baseline predicted better outcome. Benzoate receivers tended to have higher catalase and glutathione than placebo recipients after treatment. The four intervention groups showed similar safety profiles.By enhancing two vital endogenous antioxidants, catalase and glutathione, sodium benzoate therapy improved cognition of patients with AD, with higher baseline catalase predicting better response. Supporting the oxidative stress theory, the results show promise for benzoate as a novel treatment for AD.

Background Dysregulation of N-methyl-D-aspartate receptor (NMDAR) neurotransmission has been reported to be implicated in the pathogenesis of Alzheimer’s disease (AD). D-amino acid oxidase (DAO), responsible for degradation of NMDAR-related D-amino acids such as D-serine, regulates NMDAR function. A cross-section study found that serum DAO levels were positively related with the severity of cognitive aging among elderly individuals. This 2-year prospective study aimed to explore the role of DAO levels in predicting the outcome of patients with very early-phase AD, such as mild cognitive impairment (MCI). Methods Fifty-one patients with MCI and 21 healthy individuals were recruited. Serum DAO levels and cognitive function, measured by the AD assessment scale-cognitive subscale and the Mini-Mental Status Examination, were monitored every 6 months. We employed multiple regressions to examine the role of DAO concentration in cognitive decline in the 2-year period. Results From baseline to endpoint (24 months), serum DAO levels increased significantly, and cognitive ability declined according to both cognitive tests in the MCI patients. Among the healthy individuals, DAO concentrations also increased and Mini-Mental Status Examination scores declined; however, AD assessment scale-cognitive subscale scores did not significantly change. Further, DAO levels at both months 12 and 18 were predictive of cognitive impairment at month 24 among the MCI patients. Conclusions To our knowledge, this is the first study to demonstrate that blood DAO levels increased with cognitive deterioration among the MCI patients in a prospective manner. If replicated by future studies, blood DAO concentration may be regarded as a biomarker for monitoring cognitive change in the patients with MCI.

Background It is hypothesized that demographic variables and mRNA expression levels in the N-methyl-D-aspartate receptor (NMDAR) genes can be employed as potential biomarkers to predict bipolar disorder using artificial intelligence and machine learning approaches. Methods To determine bipolar status, we established a logistic ridge regression model resulting from the analysis of age, gender, and mRNA expression levels in 7 NMDAR genes in the blood of 51 bipolar patients and 139 unrelated healthy individuals in the Taiwanese population. The NMDAR genes encompasses COMT, GCAT, NRG1, PSAT1, SHMT2, SLC1A4, and SRR. We also compared our approach with various state-of-the-art algorithms such as support vector machine and C4.5 decision tree. Results The analysis revealed that the mRNA expression levels of COMT, GCAT, NRG1, PSAT1, SHMT2, SLC1A4, and SRR were associated with bipolar disorder. Moreover, the logistic ridge regression model (area under the receiver operating characteristic curve = 0.922) performed maximally among predictive models to infer the complicated relationship between bipolar disorder and biomarkers. Additionally, the results for the age- and gender-matched cohort were similar to those of the unmatched cohort. Limitations The cross-sectional study design limited the predictive value. Conclusion This is the first study demonstrating that the mRNA expression levels in the NMDAR genes may be altered in patients with bipolar disorder, thereby supporting the NMDAR hypothesis of bipolar disorder. The study also indicates that the mRNA expression levels in the NMDAR genes could serve as potential biomarkers to distinguish bipolar patients from healthy controls using artificial intelligence and machine learning approaches.

A growing body of evidence now proposes that machine learning and deep learning techniques can serve as a vital foundation for the pharmacogenomics of antidepressant treatments in patients with major depressive disorder (MDD). In this review, we focus on the latest developments for pharmacogenomics research using machine learning and deep learning approaches together with neuroimaging and multi-omics data. First, we review relevant pharmacogenomics studies that leverage numerous machine learning and deep learning techniques to determine treatment prediction and potential biomarkers for antidepressant treatments in MDD. In addition, we depict some neuroimaging pharmacogenomics studies that utilize various machine learning approaches to predict antidepressant treatment outcomes in MDD based on the integration of research on pharmacogenomics and neuroimaging. Moreover, we summarize the limitations in regard to the past pharmacogenomics studies of antidepressant treatments in MDD. Finally, we outline a discussion of challenges and directions for future research. In light of latest advancements in neuroimaging and multi-omics, various genomic variants and biomarkers associated with antidepressant treatments in MDD are being identified in pharmacogenomics research by employing machine learning and deep learning algorithms.

Early detection and prevention of Alzheimer's disease (AD) is important. The current treatment for early AD is acetylcholine esterase inhibitors (AChEIs); however, the efficacy is poor. Besides, AChEI did not show efficacy in mild cognitive impairment (MCI). Beta-amyloid (A) deposits have been regarded to be highly related to the pathogenesis of AD. However, many clinical trials aiming at the clearance of A deposits failed to improve the cognitive decline of AD, even at its early phase. There should be other important mechanisms unproven in the course of AD and MCI. Feasible biomarkers for the diagnosis and treatment response of AD are lacking to date. The N-methyl-D-aspartate receptor (NMDAR) activation plays an important role in learning and memory. On the other hand, oxidative stress has been regarded to contribute to aging with the assumption that free radicals damage cell constituents and connective tissues. Our recent study found that an NMDAR enhancer, sodium benzoate (the pivotal inhibitor of D-amino acid oxidase [DAAO]), improved the cognitive and global function of patients with early-phase AD. Further, we found that peripheral DAAO levels were higher in patients with MCI and AD than healthy controls. We also found that sodium benzoate was able to change the activity of antioxidant. These pieces of evidence suggest that the NMDAR function is associated with anti-oxidation, and have potential to be biomarkers for the diagnosis and treatment response of AD.

Objective Melatonin has been considered to have an essential role in the pathophysiology of Alzheimer's disease (AD) for its regulatory function on circadian rhythm and interaction with glutamate for the modulation of learning and memory. Previous studies revealed that melatonin levels decreased in patients with AD. However, melatonin supplement didn't show promising efficacy for AD. This study compared trough melatonin levels among elderly people with different severities of cognitive deficits. Methods We enrolled 270 elder individuals (consisting four groups: healthy elderly, amnestic mild cognitive impairment [MCI], mild AD, and moderate-severe AD) in the learning cohort. Trough melatonin levels in plasma were measured using ELISA. Cognitive function was evaluated by Clinical Dementia Rating Scale (CDR) and Mini-Mental State Examination (MMSE). An independent testing cohort, also consisting of four groups, was enrolled for ascertainment. Results In the learning cohort, trough melatonin levels decreased in the MCI group but elevated in the mild and moderate to severe AD groups. Trough melatonin levels were associated with CDR and MMSE in MCI or AD patients significantly. In the testing cohort, the results were similar to those in the learning cohort. Conclusion This study demonstrated that trough melatonin levels in the peripheral blood were decreased in MCI but increased with the severity of AD. The finding supports the trials indicating that melatonin showed efficacy only in MCI but not in AD. Whether trough melatonin level has potential to be a treatment response biomarker for AD, especially its early phase needs further studies.

Major depressive disorder (MDD) is an important cause of disability in the world. Depression has negative influences on a person’s mental and physical health, quality of life, and functioning. The pathophysiology of depression has not yet been confirmed. The traditional monoamine hypothesis of MDD could not explain the unsatisfactory treatment response of antidepressants. Thus, it is necessary to search other probable pathophysiology of MDD. In recent years, the role of glutamate neurotransmission in depression has drawn much attention. The N-methyl-D-aspartate receptor (NMDAR) is a subclass of glutamate receptors and is implicated in the pathogenesis of MDD and other mental disorders. Furthermore, NMDAR ligands, such as ketamine and Dcycloserine, have shown antidepressive effects in several studies. The diagnosis of MDD depends on physician’s subjective evaluation which is often inconsistent. Therefore, reliable objective laboratory biomarkers are essential for more accurate and consistent diagnosis of MDD. In this review, we firstly described the structure and regulation of the NMDAR. We then searched different genes involved in the pathway of glutamatergic neurotransmission and NMDAR, including D-amino acids, glycine, and glutamate. Various related enzymes and transporters that play a role in the modulation of NMDAR neurotransmission were also surveyed. This review aims to investigate NMDAR related metabolism, which may serve as feasible indicators for MDD and may contribute to further exploration of reliable biomarkers for MDD and promote new treatment of depression.

Background Visual learning plays an important role in general populations and patients with schizophrenia. Genetic influences on visual learning remain unknown. Two functional single nucleotide polymorphisms (SNPs), Ser704Cys of the DISC1 gene and M24 (rs1421292) of the G72 gene, are strongly associated with pathogenesis and pathophysiology of schizophrenia. This study examined these two SNPs' effects on visual learning in schizophrenia patients. Methods Two hundred seventy-one patients (mean age, 37.0 years [SD = 9.3]; 159 men) with chronic schizophrenia were genotyped for the DISC1 Ser704Cys and G72 M24 SNPs and assessed for visual learning with Visual Reproduction II (delayed reproduction) of Wechsler Memory Scale - III (WMS-III). For comparison, verbal learning (using Word list II of WMS-III) and attention (by Continuous Performance Test) were also measured. Results The DISC1 Ser carriers excelled DISC1 Cys/Cys homozygotes in visual learning (p=0.004, effect size: 0.43), but not in other cognitive functions. G72 M24 A-allele carriers and G72 M24 T/T homozygotes performed similarly (effect size: 0.07). In SNP-SNP interaction analysis, the patients with Ser carrier_T/T had better visual learning than those with Cys/Cys_T/T (p=0.004, effect size: 0.70) and those with Cys/Cys_A-allele carrier (p=0.003, effect size: 0.65). Education had a positive effect (p=0.007), while negative symptoms had a negative effect (p

Objective Behavioral and psychological symptoms of dementia (BPSD) are associated with poorer prognosis of dementia. A 24-week study demonstrated that sodium benzoate, a D-amino acid oxidase (DAAO) inhibitor, surpassed placebo in improving cognitive function in early-phase Alzheimer's disease; however, benzoate did not excel placebo in another 6-week study on BPSD. The current study examined whether the precision medicine approach was able to identify specific individuals with BPSD who could benefit from benzoate treatment. Methods In the randomized, double-blind, placebo-controlled, 6-week trial, 97 patients with BPSD were allocated to receive 250-1500 mg/day of sodium benzoate or placebo. Cognitive function was measured by the Alzheimer's disease assessment scale-cognitive subscale (ADAS-cog) and behavioral and psychological symptoms were mainly measured by Behavioral Pathology in Alzheimer's Disease Rating Scale (BEHAVE-AD). DAAO level, amino acids (L-serine, D-serine, L-alanine, and D-alanine, glycine), and two antioxidants (catalase, superoxide dismutase) were assayed in peripheral blood. Results After benzoate treatment, DAAO inhibition was correlated with ADAS-cog decrease (p = 0.034), while baseline DAAO level was correlated with baseline BEHAVE-AD score. Multiple linear regression analyses showed that cognitive improvement after benzoate treatment was correlated with DAAO decrease, female gender, younger age, BMI, baseline BPSD severity, and antipsychotic use. Conclusion The finding suggests that sodium benzoate may have potential to benefit cognitive function in a fraction of BPSD patients after 6 weeks of treatment. Of note, the precision medicine approach may be helpful for identifying individuals who could respond to benzoate. More studies are warranted to confirm the preliminary findings. Trial registration The trial was registered online (https://clinicaltrials.gov/ct2/show/NCT02103673).

Bladder cancer (BC) has a high recurrence rate worldwide. The aim of this study was to evaluate the role of fatty acid binding protein 6 (FABP6) in proliferation and migration in human bladder cancer cells. Cell growth was confirmed by MTT and colony formation assay. Western blotting was used to explore protein expressions. Wound healing and Transwell assays were performed to evaluate the migration ability. A xenograft animal model with subcutaneous implantation of BC cells was generated to confirm the tumor progression. Knockdown of FABP6 reduced cell growth in low-grade TSGH-8301 and high-grade T24 cells. Cell cycle blockade was observed with the decrease of CDK2, CDK4, and Ki67 levels in FABP6-knockdown BC cells. Interestingly, knockdown of FBAP6 led to downregulation of autophagic markers and activation of AKT-mTOR signaling. The application of PI3K/AKT inhibitor decreased cell viability mediated by FABP6-knockdown additionally. Moreover, FABP6-knockdown reduced peroxisome proliferator-activated receptor γ and retinoid X receptor α levels but increased p-p65 expression. Knockdown of FABP6 also inhibited BC cell motility with focal adhesive complex reduction. Finally, shFABP6 combined with cisplatin suppressed tumor growth in vivo. These results provide evidence that FABP6 may be a potential target in BC cells progression.

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.

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.

Background Compared with adults with depression in the general population, elderly depressive patients are prone to poor treatment response, more side effects, and early withdrawal with current antidepressants (which principally modulate monoamines). Whether N-methyl-D-aspartate receptor enhancement can benefit treatment of late-life depression deserves study. This study aims to compare sodium benzoate (a D-amino acid oxidase inhibitor and an indirect N-methyl-D-aspartate receptor enhancer), sertraline (a selective serotonin reuptake inhibitor), and placebo in the treatment of late-life depression. Methods In this randomized, double-blind trial, 117 patients with major depressive disorder aged 55 years or older received 8-week treatment of 250–1500 mg/d of sodium benzoate, 25–150 mg/d of sertraline, or placebo in 2 medical centers. The primary outcome measures were Hamilton Depression Rating Scale and Perceived Stress Scale scores. Results Three treatments similarly decreased clinicians-rated Hamilton Depression Rating Scale scores. Compared with placebo, sodium benzoate but not sertraline substantially improved Perceived Stress Scale scores and cognitive function. Sertraline, but not benzoate, significantly reduced self-report Geriatric Depression Scale scores. Benzoate and placebo showed similar safety profiles, while sertraline was more likely to raise low-density lipoprotein than benzoate and placebo. Benzoate-treated patients were less likely to drop out than sertraline or placebo recipients. Conclusions Sertraline can reduce subjective depressive symptoms, while benzoate can decrease perceived stress, improve cognitive function, and enhance treatment adherence in late-life depression patients. The results show promise for D-amino acid oxidase inhibition as a novel approach for perceived stress and cognitive decline among patients with late-life depression. Trial Registration ClinicalTrials.gov Identifier: NCT03414931. Registered January 2016.

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.

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.

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.

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.

Schizophrenia’s pathogenesis remains elusive. Cognitive dysfunction is the endophenotype and outcome predictor of schizophrenia. The LIM and SH3 domain protein (LASP1) protein, a component of CNS synapses and dendritic spines, has been related to the N-methyl-D-aspartate receptor (NMDAR) dysfunction hypothesis and schizophrenia. A single-nucleotide polymorphism (rs979607) in the LASP1 gene promoter region has been also implicated in schizophrenia susceptibility. The aim of this study was to investigate the role of the LASP1 rs979607 polymorphism in the cognitive functions of patients with schizophrenia. Two hundred and ninety-one Han Taiwanese patients with schizophrenia were recruited. Ten cognitive tests and two clinical rating scales were assessed. The scores of cognitive tests were standardized to T-scores. The genotyping of the LASP1 rs979607 polymorphism was performed using TaqMan assay. Among the 291 patients, 85 were C/C homozygotes of rs979607, 141 C/T heterozygotes, and 65 T/T homozygotes, which fitted the Hardy-Weinberg equilibrium. After adjusting age, gender, and education with general linear model, the C/C homozygotes performed better than C/T heterozygotes in overall composite score (p = 0.023), Category Fluency test (representing processing speed and semantic memory) (p = 0.045), and Wechsler Memory Scale (WMS)-III backward Spatial Span test (p = 0.025), albeit without correction for multiple comparisons for the latter two individual tests. To the best of our knowledge, this is the first study suggesting that the genetic variation of LASP1 may be associated with global cognitive function, category verbal fluency, and spatial working memory of patients with schizophrenia. The finding also lends support to the NMDAR dysfunction hypothesis of schizophrenia. More studies with longitudinal designs are warranted.

Background Numerous studies have demonstrated an association between C-reactive protein (CRP) levels and schizophrenia. However, the findings on psychotic severity and cognition remain inconsistent. The relationship between CRP and formal thought disorder in subdomains remains unclear. Methods We enrolled stable patients (defined as those who had no treatment changes during the 4-week period before evaluation) with a diagnosis of schizophrenia or schizoaffective disorder, according to the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. We used the 30-item Thought and Language Disorder (TALD) scale to evaluate thought and language dysfunction over four subscales. We assessed psychotic symptoms using the Positive and Negative Syndrome Scale (PANSS). We collected fasting venous blood and measured plasma CRP levels. Results We enrolled 60 patients with schizophrenia. All patients received TALD and PANSS evaluation, and 33 of them had their CRP levels checked. The multivariate regression analysis indicated that CRP levels were significantly associated with the total score on the TALD (t=2.757, P=0.010) and the TALD Objective Positive subscale (t=2.749, P=0.011), after sex, age, duration of illness (in years), and use of atypical antipsychotics were adjusted for. Additionally, CRP was significantly associated with the PANSS positive subscale (t=2.102, P=0.045). A significantly positive correlation was observed between the total scores on the TALD scale and PANSS (ρ =0.751, P

Major depressive disorder (MDD) is a complex mental disorder. The lack of well-established biomarkers hinders its diagnosis, treatment, and new-drug development. N-methyl-D-aspartate receptor (NMDAR) dysfunction has been implicated in the pathogenesis of MDD. This study examined whether expressions of the NMDAR-related genes are characteristic of MDD.Expressions of NMDAR-related genes including SRR, SHMT2, PSAT1, GCAT, GAD1, SLC1A4, NRG1 and COMT in peripheral WBCs of 110 patients with MDD (25 drug-naïve, 21 drug-free, and 64 medicated patients) and 125 healthy individuals were measured using quantitative PCR.The mRNA expression levels of SRR, PSAT1, GCAT, GAD1, NRG1 and COMT were significantly different among the four groups (all p 0.05). For drug-naïve patients, the ΔΔCT values of SRR, PSAT1, GCAT, GAD1, and NRG1 mRNA expressions were significantly different from those in healthy individuals (all p 0.05). The ROC analysis of the ΔΔCT values of the target genes for differentiating drug-naïve patients from healthy controls showed an excellent sensitivity (0.960) and modest specificity (0.640) (AUC = 0.889). Drug-free and medicated patients obtained less favorable AUC values while compared to healthy controls. The results for the age- and sex-matched cohort were similar to those of the unmatched cohort.This is the first study demonstrating that the peripheral mRNA expression levels of NMDAR-related genes may be altered in patients with MDD, especially drug-naïve individuals. The finding supports the NMDAR hypothesis of depression. Whether mRNA expresssion of NMDAR-related genes could serve as a potential biomarker of MDD deserves further investigations.

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 patients are susceptible to lower bone mineral density (BMD). However, studies exploring the genetic effects are lacking. Genes that affect the activity of antipsychotics may be associated with BMD, particularly in patients receiving long-term antipsychotic treatment. We aimed to explore the relationship between the dopamine receptor D2 (DRD2) gene Taq1A (rs1800497) polymorphism and BMD in chronic schizophrenia patients. We recruited schizophrenia patients (n = 47) and healthy controls (n = 39) from a medical center in Taiwan and collected data that may affect BMD. Patients’ BMD was measured by dual-energy X-ray absorptiometer (DEXA). DRD2 rs1800497 was genotyped through polymerase chain reaction–Restriction Fragment Length Polymorphism (PCR–RFLP). Among all participants, subjects with DRD2 rs1800497(T;T) allele had lower DEXA T score and DEXA Z score compared to those with rs1800497(C;T) and rs1800497(C;C) alleles (p = 0.008, 0.003, respectively). In schizophrenia patients, subjects with rs1800497(T;T) allele also had lower DEXA Z score compared to the other two alleles (p = 0.045). Our findings suggest that individuals with the DRD2 rs1800497(T;T) had lower BMD than those with the rs1800497(C;T) and rs1800497(C;C) genotypes. Therefore, genes should be considered as one of the risk factors of lower BMD.

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.

Supplemental material, Supplementary_Figures for Efficacy and cognitive effect of sarcosine (N-methylglycine) in patients with schizophrenia: A systematic review and meta-analysis of double-blind randomised controlled trials by Chun-Hung Chang, Chieh-Hsin Lin, Chieh-Yu Liu, Shaw-Ji Chen and Hsien-Yuan Lane in Journal of Psychopharmacology

It has been indicated that the interplay between functional outcomes and cognitive functions in schizophrenia is arbitrated by clinical symptoms, where cognitive functions are evaluated by cognitive domains and cognitive tests.To determine which single cognitive domain or test can best predict the overall cognitive function of schizophrenia, we established a bagging ensemble framework resulting from the analysis of factors such as 7 cognitive domain scores and 11 cognitive test scores of 302 schizophrenia patients in the Taiwanese population. We compared our bagging ensemble framework with other state-of-the-art algorithms such as multilayer feedforward neural networks, linear regression, support vector machine, and random forests.The analysis revealed that among the 7 cognitive domains, the speed of processing domain can best predict the overall cognitive function in schizophrenia using our bagging ensemble framework. In addition, among the 11 cognitive tests, the visual learning and memory test can best predict the overall cognitive function in schizophrenia using our bagging ensemble framework. Finally, among the 7 cognitive domains and 11 cognitive tests, the speed of processing domain can best predict the overall cognitive function in schizophrenia using our bagging ensemble framework.The study implicates that the bagging ensemble framework may provide an applicable approach to develop tools for forecasting overall cognitive function in schizophrenia using cognitive domains and/or cognitive tests.