Diagnosing Alzheimer's Disease Specifically and Sensitively With pLG72 and Cystine/Glutamate Antiporter SLC7A11 AS Blood Biomarkers.

BACKGROUND: Reliable blood biomarkers for Alzheimer's disease (AD) have been lacking. The D-amino acids oxidase modulator (named pLG72) modulates glutamate N-methyl-D-aspartate receptor activity. The cystine/glutamate antiporter contains a SLC7A11 subunit, which mediates glutamate release. This study aimed to determine the accuracy of pLG72 protein and SLC7A11 mRNA in diagnosing AD. METHODS: This study enrolled 130 healthy controls and 109 unmatched AD patients; among them, 40 controls and 70 patients were selected to match by age. We measured their pLG72 protein in plasma and SLC7A11 mRNA in white blood cells. RESULTS: AD patients had markedly higher pLG72 levels and SLC7A11 mRNA ΔCT values than healthy controls (in both unmatched and matched cohorts; all 4 P values <.001). The receiver operating characteristics analysis in the unmatched cohorts demonstrated that the pLG72 level had a high specificity (0.900) at the optimal cutoff value of 2.3285, the ΔCT of SLC7A11 mRNA displayed an excellent sensitivity (0.954) at the cutoff of 12.185, and the combined value of pLG72 and SLC7A11 ΔCT determined a favorable area under the curve (AUC) (0.882) at the cutoff of 21.721. The AUC of the combined value surpassed that of either biomarker. The specificity, sensitivity, and AUC of the matched cohort were like those of the unmatched cohort. CONCLUSIONS: The findings suggest that pLG72 protein and SLC7A11 mRNA can distinguish AD patients from healthy controls with excellent specificity and sensitivity, respectively. The combination of pLG72 and SLC7A11 yields better AUC than either, suggesting the superiority of simultaneously measuring both biomarkers in identifying AD patients.

Elucidating the Mechanisms of Sodium Benzoate in Alzheimer Disease: Insights from Quantitative Proteomics Analysis of Serum Samples.

BACKGROUND: N-methyl-D-aspartate receptors (NMDARs) are crucial components of brain function involved in memory and neurotransmission. Sodium benzoate is a promising NMDAR enhancer and has been proven to be a novel, safe, and efficient therapy for patients with Alzheimer disease (AD). However, in addition to the role of sodium benzoate as an NMDA enhancer, other mechanisms of sodium benzoate in treating AD are still unclear. To elucidate the potential mechanisms of sodium benzoate in Alzheimer disease, this study employed label-free quantitative proteomics to analyze serum samples from AD cohorts with and without sodium benzoate treatment. METHODS: The serum proteins from each patient were separated into 24 fractions using an immobilized pH gradient, digested with trypsin, and then subjected to nanoLC‒MS/MS to analyze the proteome of all patients. The nanoLC‒MS/MS data were obtained with a label-free quantitative proteomic approach. Proteins with fold changes were analyzed with STRING and Cytoscape to find key protein networks/processes and hub proteins. RESULTS: Our analysis identified 861 and 927 protein groups in the benzoate treatment cohort and the placebo cohort, respectively. The results demonstrated that sodium benzoate had the most significant effect on the complement and coagulation cascade pathways, amyloidosis disease, immune responses, and lipid metabolic processes. Moreover, Transthyretin, Fibrinogen alpha chain, Haptoglobin, Apolipoprotein B-100, Fibrinogen beta chain, Apolipoprotein E, and Alpha-1-acid glycoprotein 1 were identified as hub proteins in the protein‒protein interaction networks. CONCLUSIONS: These findings suggest that sodium benzoate may exert its influence on important pathways associated with AD, thus contributing to the improvement in the pathogenesis of the disease.

Differential Impacts of Endogenous Antioxidants on Clinical Symptoms and Cognitive Function in Acute and Chronic Schizophrenia Patients.

BACKGROUND: Impaired antioxidant defense is implicated in the pathophysiology of schizophrenia, and superoxide dismutase (SOD), catalase (CAT), and glutathione (GSH) are 3 first-line endogenous antioxidants. Various cognitive functions decline differently during the schizophrenia course. The characteristic roles of the 3 antioxidants in clinical and cognitive profiles in acute and chronic phases of schizophrenia require study. METHODS: We recruited 311 patients with schizophrenia, including 92 acutely exacerbated patients who had been off antipsychotics for at least 2 weeks and 219 chronic patients who had been stable on medication for at least 2 months. Blood SOD, CAT, and GSH levels; clinical symptoms; and 9 cognitive test scores were measured. RESULTS: Blood CAT levels were higher in the acute patients than in the chronic patients, whereas SOD and GSH levels were similar to one another. Higher CAT levels were correlated with less positive symptoms, better working memory and problem solving in the acute phase, and less negative symptoms, less general psychopathology, better global assessment of function, and better cognitive function (in speed of processing, attention, problem solving) in the chronic period. Higher SOD levels were correlated with better global assessment of function in the acute phase and better speed of processing, working memory, and verbal learning and memory in the chronic period. GSH influenced neither clinical nor cognitive manifestations. CONCLUSIONS: This study showed that blood CAT affected different clinical and cognitive domains between acute and chronic stages of schizophrenia, SOD influenced cognitive functions in chronic state, but GSH affected none. Further studies are needed to explore the underlying mechanisms.

Endogenous antioxidants predicted outcome and increased after treatment: A benzoate dose-finding, randomized, double-blind, placebo-controlled trial for Alzheimer's disease.

AIM: 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. METHODS: 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). RESULTS: 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. CONCLUSIONS: 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.

Blood D-Amino Acid Oxidase Levels Increased With Cognitive Decline Among People With Mild Cognitive Impairment: A Two-Year Prospective Study.

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.

Effects of Sodium Benzoate, a D-Amino Acid Oxidase Inhibitor, on Perceived Stress and Cognitive Function Among Patients With Late-Life Depression: A Randomized, Double-Blind, Sertraline- and Placebo-Controlled Trial.

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.

De Novo Peptide and Protein Design Using Generative Adversarial Networks: An Update.

Nowadays, machine learning and deep learning approaches are widely utilized for generative chemistry and computer-aided drug design and discovery such as de novo peptide and protein design, where target-specific peptide-based/protein-based therapeutics have been suggested to cause fewer adverse effects than the traditional small-molecule drugs. In light of current advancements in deep learning techniques, generative adversarial network (GAN) algorithms are being leveraged to a wide variety of applications in the process of generative chemistry and computer-aided drug design and discovery. In this review, we focus on the up-to-date developments for de novo peptide and protein design research using GAN algorithms in the interdisciplinary fields of generative chemistry, machine learning, deep learning, and computer-aided drug design and discovery. First, we present various studies that investigate GAN algorithms to fulfill the task of de novo peptide and protein design in the drug development pipeline. In addition, we summarize the drawbacks with respect to the previous studies in de novo peptide and protein design using GAN algorithms. Finally, we depict a discussion of open challenges and emerging problems for future research.

Fatty Acid Binding Protein 6 Inhibition Decreases Cell Cycle Progression, Migration and Autophagy in Bladder Cancers.

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.

Brain Activity of Benzoate, a D-Amino Acid Oxidase Inhibitor, in Patients With Mild Cognitive Impairment in a Randomized, Double-Blind, Placebo Controlled Clinical Trial.

BACKGROUND: Current anti-dementia drugs cannot benefit mild cognitive impairment (MCI). Sodium benzoate (a D-amino acid oxidase [DAO] inhibitor) has been found to improve the cognitive function of patients with early-phase Alzheimer's disease (mild Alzheimer's disease or MCI). However, its effect on brain function remains unknown. This study aimed to evaluate the influence of benzoate on functional magnetic resonance imaging in patients with amnestic MCI. METHODS: This was a 24-week, randomized, double-blind, placebo-controlled trial that enrolled 21 patients with amnestic MCI and allocated them randomly to either of 2 treatment groups: (1) benzoate group (250-1500 mg/d), or (2) placebo group. We assessed the patients' working memory, verbal learning and memory, and resting-state functional magnetic resonance imaging and regional homogeneity (ReHo) maps at baseline and endpoint. RESULTS: Resting-state ReHo decreased in right orbitofrontal cortex after benzoate treatment but did not change after placebo. Moreover, after benzoate treatment, the change in working memory was positively correlated with the change in ReHo in right precentral gyrus and right middle occipital gyrus; and the change in verbal learning and memory was positively correlated with the change in ReHo in left precuneus. In contrast, after placebo treatment, the change in working memory or in verbal learning and memory was not correlated with the change in ReHo in any brain region. CONCLUSION: The current study is the first to our knowledge to demonstrate that a DAO inhibitor, sodium benzoate herein, can alter brain activity as well as cognitive functions in individuals with MCI. The preliminary finding lends supports for DAO inhibition as a novel approach for early dementing processes.

Deep Learning with Neuroimaging and Genomics in Alzheimer's Disease.

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.

Involvement of Cholinergic, Adrenergic, and Glutamatergic Network Modulation with Cognitive Dysfunction in Alzheimer's Disease.

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 ß (Aß) 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.

Novel Therapeutic Approaches for Alzheimer's Disease: An Updated Review.

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.

From Menopause to Neurodegeneration-Molecular Basis and Potential Therapy.

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.

Molecular Basis of Late-Life Depression.

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.

d-Amino Acids and pLG72 in Alzheimer's Disease and Schizophrenia.

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.

Cystine/Glutamate Antiporter in Schizophrenia: From Molecular Mechanism to Novel Biomarker and Treatment.

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.

Machine Learning and Novel Biomarkers for the Diagnosis of Alzheimer's Disease.

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-ß1-42 (Aß42), 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 Aß 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.

Characterization of Potential Protein Biomarkers for Major Depressive Disorder Using Matrix-Assisted Laser Desorption Ionization/Time-of-Flight Mass Spectrometry.

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.

Precision Psychiatry Applications with Pharmacogenomics: Artificial Intelligence and Machine Learning Approaches.

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.

d-glutamate and Gut Microbiota in Alzheimer's Disease.

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.