Biomarker identification in psychiatric disorders: from neuroscience to clinical practice

Aptamer Technologies in Neuroscience, Neuro-Diagnostics and Neuro-Medicine Development

Aptamers developed using in vitro Systematic Evolution of Ligands by Exponential Enrichment (SELEX) technology are single-stranded nucleic acids 10-100 nucleotides in length. Their targets, often with specificity and high affinity, range from ions and small molecules to proteins and other biological molecules as well as larger systems, including cells, tissues, and animals. Aptamers often rival conventional antibodies with improved performance, due to aptamers' unique biophysical and biochemical properties, including small size, synthetic accessibility, facile modification, low production cost, and low immunogenicity. Therefore, there is sustained interest in engineering and adapting aptamers for many applications, including diagnostics and therapeutics. Recently, aptamers have shown promise as early diagnostic biomarkers and in precision medicine for neurodegenerative and neurological diseases. Here, we critically review neuro-targeting aptamers and their potential applications in neuroscience research, neuro-diagnostics, and neuro-medicine. We also discuss challenges that must be overcome, including delivery across the blood-brain barrier, increased affinity, and improved in vivo stability and in vivo pharmacokinetic properties.

Support Vector Machine-Based Schizophrenia Classification Using Morphological Information from Amygdaloid and Hippocampal Subregions

Structural changes in the hippocampus and amygdala have been demonstrated in schizophrenia patients. However, whether morphological information from these subcortical regions could be used by machine learning algorithms for schizophrenia classification were unknown. The aim of this study was to use volume of the amygdaloid and hippocampal subregions for schizophrenia classification. The dataset consisted of 57 patients with schizophrenia and 69 healthy controls. The volume of 26 hippocampal and 20 amygdaloid subregions were extracted from T1 structural MRI images. Sequential backward elimination (SBE) algorithm was used for feature selection, and a linear support vector machine (SVM) classifier was configured to explore the feasibility of hippocampal and amygdaloid subregions in the classification of schizophrenia. The proposed SBE-SVM model achieved a classification accuracy of 81.75% on 57 patients and 69 healthy controls, with a sensitivity of 84.21% and a specificity of 81.16%. AUC was 0.8241 (p < 0.001 tested with 1000-times permutation). The results demonstrated evidence of hippocampal and amygdaloid structural changes in schizophrenia patients, and also suggested that morphological features from the amygdaloid and hippocampal subregions could be used by machine learning algorithms for the classification of schizophrenia.

An analysis of plasma reveals proteins in the acute phase response pathway to be candidate diagnostic biomarkers for depression

Globally, depression is one of the most serious debilitating psychiatric mental disorders. In this study, we validated the expression levels of fibrinogen alpha (FGA), fibrinogen beta (FGB), fibrinogen gamma (FGG), Complement factor B (CFB) and serpin family D member 1(SERPIND1) in the acute phase response signaling pathway in plasma samples using enzyme-linked immunosorbent assay (ELISA).Then illuminate the roles of FGA, FGB, FGG, CFB, SERPIND1 in depression using microarray data. Gene expression dataset GSE98793 was downloaded from the Gene Expression Omnibus database. There were 128 whole blood samples included 64 patients with major depressed patients and 64 healthy controls. Differentially expressed genes (DEGs) were identified, and then protein-protein interaction (PPI) network was constructed to screen crucial genes associated with FGA, FGB, FGG, CFB and SERPIND1. Moreover, gene ontology (GO) biological processes analyses was performed. The ELISA data showed that the expression levels of FGA, FGB, FGG, CFB and SERPIND1 were up-regulated in depressed patients. The enriched GO terms were predominantly associated with the biological processes including more genes were inflammation related. The PPI network was found these five genes interacted with 11 genes. FGA, FGB, FGG, CFB and SERPIND1 may be important in the pathogenesis of depression.

Increased serum G72 protein levels in patients with schizophrenia: a potential candidate biomarker

OBJECTIVE

The product of the G72 gene is an activator of d-amino acid oxidase and has been suggested to play a role in the pathogenesis of schizophrenia. Increased G72 protein levels may be associated with disturbed glutamatergic transmission and increased reactive oxygen species. Only one pilot study by Lin et al. has investigated the potential role of serum G72 protein levels as a biomarker for schizophrenia. In this study, we aimed to compare serum G72 protein levels between patients with schizophrenia and healthy controls, and to retest the results of the previous pilot study. Materials and methods In total, 107 patients with a diagnosis of schizophrenia according to the inclusion and exclusion criteria and 60 age-sex-matched healthy controls were included in the study. The groups were compared regarding serum G72 protein levels.

RESULTS

The mean serum G72 protein values were 495.90±152.03 pg/ml in the schizophrenia group and 346.10±102.08 pg/ml in the healthy control group. The mean serum G72 protein level was significantly increased in the schizophrenia group compared with the healthy control group (t=-3.89, p<0.001). A receiver operating characteristics analysis was performed to compare the schizophrenia and healthy control groups. It was determined that the cut-off value was 141.51 pg/ml with a sensitivity of 0.991 and a specificity of 0.821.

CONCLUSION

We suggest that serum G72 protein levels may represent a candidate biomarker for schizophrenia and have confirmed the results of the previous preliminary study. Additional studies with larger sample sizes and the inclusion of first episode schizophrenia patients are required to clarify the reliability and validity of serum G72 protein levels as a biomarker for schizophrenia.

Personalized medicine in psychiatry

BACKGROUND

Personalized medicine is a model in which a patient's unique clinical, genetic, and environmental characteristics are the basis for treatment and prevention. Aim, method, and results: This review aims to describe the current tools, phenomenological features, clinical risk factors, and biomarkers used to provide personalized medicine. Furthermore, this study describes the target areas in which they can be applied including diagnostics, treatment selection and response, assessment of risk of side-effects, and prevention.

DISCUSSION AND CONCLUSION

Personalized medicine in psychiatry is challenged by the current taxonomy, where the diagnostic categories are broad and great biological heterogeneity exists within each category. There is, thus, a gap between the current advanced research prospects and clinical practice, and the current taxonomy is, thus, a poor basis for biological research. The discussion proposes possible solutions to narrow this gap and to move psychiatric research forward towards personalized medicine.

Epilepsy, Antiepileptic Drugs, and Aggression: An Evidence-Based Review

Antiepileptic drugs (AEDs) have many benefits but also many side effects, including aggression, agitation, and irritability, in some patients with epilepsy. This article offers a comprehensive summary of current understanding of aggressive behaviors in patients with epilepsy, including an evidence-based review of aggression during AED treatment. Aggression is seen in a minority of people with epilepsy. It is rarely seizure related but is interictal, sometimes occurring as part of complex psychiatric and behavioral comorbidities, and it is sometimes associated with AED treatment. We review the common neurotransmitter systems and brain regions implicated in both epilepsy and aggression, including the GABA, glutamate, serotonin, dopamine, and noradrenaline systems and the hippocampus, amygdala, prefrontal cortex, anterior cingulate cortex, and temporal lobes. Few controlled clinical studies have used behavioral measures to specifically examine aggression with AEDs, and most evidence comes from adverse event reporting from clinical and observational studies. A systematic approach was used to identify relevant publications, and we present a comprehensive, evidence-based summary of available data surrounding aggression-related behaviors with each of the currently available AEDs in both adults and in children/adolescents with epilepsy. A psychiatric history and history of a propensity toward aggression/anger should routinely be sought from patients, family members, and carers; its presence does not preclude the use of any specific AEDs, but those most likely to be implicated in these behaviors should be used with caution in such cases.