Biomarkers in Neuropsychiatry: A Prospect for the Twenty-First Century?

Predicting the Cognitive Ability of Young Women Using a New Feature Selection Algorithm

Cognitive abilities are the capabilities to perform mental processes that include executive function, comprehension, decision-making, work performance, and educational attainment. This study aimed to investigate the relationship between several biomarkers and individuals' cognitive ability using various machine learning methods. A total of 144 young women aged between 18 and 24 years old were recruited into the study. Cognitive performance was assessed using a standard questionnaire. A panel of biochemical, hematological, inflammatory, and oxidative stress biomarkers in serum and urine was measured for all participants. A novel combination of feature selection and feature scoring techniques within a hierarchical ensemble structure has been proposed to identify the most effective features in recognizing the importance of various biomarker signatures in cognitive abilities classification. Multiple feature selection methods were employed in conjunction with different classifiers to construct this model. In this manner, using three filter methods, the scores of each feature were considered. The combination of high-scoring features for each filter method was stored as the primary feature subset. A high-accuracy feature subset was selected by using a wrapper method. The collection of highly scored features from each filter method formed the primary feature subset. A wrapper method was also employed to select a feature subset with high accuracy. To ensure robustness and minimize random variations in the feature subset search process, a repeative tenfold cross-validation was conducted. The most frequently recurring features were determined. This iterative step facilitated the identification of an optimal feature subset, effectively reducing the dimensionality of features while maintaining accuracy. Among the 47 extracted factors, serum level of NOx (nitrite ± nitrate), alkaline phosphatase (ALP), and phosphate as well as blood platelet count (PLT) was entered into the model of cognitive abilities with the highest accuracy of approximately 70.9% using a decision tree classifier. Therefore, the serum levels of NOx, ALP, phosphate, and blood PLT count may be important markers of the cognitive abilities in apparently healthy young women. These factors my provide a simple procedure to identify mental abilities and earlier cognitive decline in healthy adults.

16p11.2 deletion mice exhibit compromised fronto-temporal connectivity, GABAergic dysfunction, and enhanced attentional ability

Autism spectrum disorders are more common in males, and have a substantial genetic component. Chromosomal 16p11.2 deletions in particular carry strong genetic risk for autism, yet their neurobiological impact is poorly characterised, particularly at the integrated systems level. Here we show that mice reproducing this deletion (16p11.2 DEL mice) have reduced GABAergic interneuron gene expression (decreased parvalbumin mRNA in orbitofrontal cortex, and male-specific decreases in Gad67 mRNA in parietal and insular cortex and medial septum). Metabolic activity was increased in medial septum, and in its efferent targets: mammillary body and (males only) subiculum. Functional connectivity was altered between orbitofrontal, insular and auditory cortex, and between septum and hippocampus/subiculum. Consistent with this circuit dysfunction, 16p11.2 DEL mice showed reduced prepulse inhibition, but enhanced performance in the continuous performance test of attentional ability. Level 1 autistic individuals show similarly heightened performance in the equivalent human test, also associated with parietal, insular-orbitofrontal and septo-subicular dysfunction. The data implicate cortical and septal GABAergic dysfunction, and resulting connectivity changes, as the cause of pre-attentional and attentional changes in autism.

Recent advancements in biomarker research in schizophrenia: mapping the road from bench to bedside

Schizophrenia (SZ) is a severe progressive neurodegenerative as well as disruptive behavior disorder affecting innumerable people throughout the world. The discovery of potential biomarkers in the clinical scenario would lead to the development of effective methods of diagnosis and would provide an understanding of the prognosis of the disease. Moreover, breakthrough inventions for the treatment and prevention of this mysterious disease could evolve as a result of a thorough understanding of the clinical biomarkers. In this review, we have discussed about specific biomarkers of SZ an emphasis has been laid to delineate (1) diagnostic biomarkers like neuroimmune biomarkers, metabolic biomarkers, oligodendrocyte biomarkers and biomarkers of negative and cognitive symptoms, (2) therapeutic biomarkers like various neurotransmitter systems and (3) prognostic biomarkers. All the biomarkers were evaluated in drug-naïve (at least for 4 weeks) patients in order to achieve a clear comparison between schizophrenic patients and healthy controls. Also, an attempt has been made to elucidate the potential genes which serve as predictors and tools for the determination of biomarkers and would ultimately help in the prevention and treatment of this deadly illness.

Mismatch Negativity and P3a Impairment through Different Phases of Schizophrenia and Their Association with Real-Life Functioning

Impairment in functioning since the onset of psychosis and further deterioration over time is a key aspect of subjects with schizophrenia (SCZ). Mismatch negativity (MMN) and P3a, indices of early attention processing that are often impaired in schizophrenia, might represent optimal electrophysiological candidate biomarkers of illness progression and poor outcome. However, contrasting findings are reported about the relationships between MMN-P3a and functioning. The study aimed to investigate in SCZ the influence of illness duration on MMN-P3a and the relationship of MMN-P3a with functioning. Pitch (p) and duration (d) MMN-P3a were investigated in 117 SCZ and 61 healthy controls (HCs). SCZ were divided into four illness duration groups: ≤ 5, 6 to 13, 14 to 18, and 19 to 32 years. p-MMN and d-MMN amplitude was reduced in SCZ compared to HCs, independently from illness duration, psychopathology, and neurocognitive deficits. p-MMN reduction was associated with lower "Work skills". The p-P3a amplitude was reduced in the SCZ group with longest illness duration compared to HCs. No relationship between P3a and functioning was found. Our results suggested that MMN amplitude reduction might represent a biomarker of poor functioning in SCZ.

Physiological Predictors of Mental Disorders Among Police Officers in Indonesia

Police officers may be at a higher risk of suffering from mental disorders (MDs) compared with other occupations. This study aimed to examine the physiological predictors of MDs among police officers in Indonesia. The mental health status of each participant was examined using a standardized Minnesota Multiphasic Personality Inventory (MMPI) tool and underwent several laboratory examinations including hematology, urine, serology, and electrocardiogram tests. A logistic regression was performed to calculate the odds ratio. Of the 4590 participants, 2.14% were diagnosed with MDs. The study found 19 physiological variables are potential predictors of MDs. In multivariate analysis adjusted for body mass index, sex, and age, the concentration of ketones, protein, and specific gravity in urine and the amount of lymphocyte in the blood are among important predictors of MDs. This suggests that carefully examining the results of urinalysis and hematology tests can help us detect early cases of MDs among police officers.

Drug development post COVID-19 pandemic: toward a better system to meet current and future global health challenges

Introduction: Despite advances in drug research and development, our knowledge of the underlying molecular mechanisms of many diseases remains inadequate. This have led to limited effective medicines for several diseases. To address these challenges, efficient strategies, novel technologies, and policies are urgently needed. The main obstacles in drug discovery and development are the mounting cost, risk, and time frame needed to develop new medicines. Fair pricing and accessibility is another unmet global challenge.Areas covered: Here, the authors cover the pace, risks, cost, and challenges facing drug development processes. Additionally, they introduce disease-associated data which demand global attention and propose solutions to overcome these challenges.Expert opinion: The massive challenges encountered during drug development urgently call for a serious global rethinking of the way this process is done. A partial solution might be if many consortiums of multi-nations, academic institutions, clinicians, pharma companies, and funding agencies gather at different fronts to crowdsource resources, share knowledge and risks. Such an ecosystem can rapidly generate first-in-class molecules that are safe, effective, and affordable. We think that this article represents a wake-up call for the scientific community to immediately reassess the current drug discovery and development procedures.

Finding specificity in structural brain alterations through Bayesian reverse inference

In the field of neuroimaging reverse inferences can lead us to suppose the involvement of cognitive processes from certain patterns of brain activity. However, the same reasoning holds if we substitute "brain activity" with "brain alteration" and "cognitive process" with "brain disorder." The fact that different brain disorders exhibit a high degree of overlap in their patterns of structural alterations makes forward inference-based analyses less suitable for identifying brain areas whose alteration is specific to a certain pathology. In the forward inference-based analyses, in fact, it is impossible to distinguish between areas that are altered by the majority of brain disorders and areas that are specifically affected by certain diseases. To address this issue and allow the identification of highly pathology-specific altered areas we used the Bayes' factor technique, which was employed, as a proof of concept, on voxel-based morphometry data of schizophrenia and Alzheimer's disease. This technique allows to calculate the ratio between the likelihoods of two alternative hypotheses (in our case, that the alteration of the voxel is specific for the brain disorder under scrutiny or that the alteration is not specific). We then performed temporal simulations of the alterations' spread associated with different pathologies. The Bayes' factor values calculated on these simulated data were able to reveal that the areas, which are more specific to a certain disease, are also the ones to be early altered. This study puts forward a new analytical instrument capable of innovating the methodological approach to the investigation of brain pathology.

Multiparametric Assays for Accelerating Early Drug Discovery

Drug discovery campaigns are hampered by substantial attrition rates largely due to a lack of efficacy and safety reasons associated with candidate drugs. This is true in particular for genetically complex diseases, where insufficient knowledge of the modulatory actions of candidate drugs on targets and entire target pathways further adds to the problem of attrition. To better profile compound actions on targets, potential off-targets, and disease-linked pathways, new innovative technologies need to be developed that can elucidate the complex cellular signaling networks in health and disease. Here, we discuss progress in genetically encoded multiparametric assays and mass spectrometry (MS)-based proteomics, which both represent promising toolkits to profile multifactorial actions of drug candidates in disease-relevant cellular systems to promote drug discovery and personalized medicine.

Maternal environmental risk factors and the development of internalizing and externalizing problems in childhood: The complex role of genetic factors

The development of problem behavior in children is associated with exposure to environmental factors, including the maternal environment. Both are influenced by genetic factors, which may also be correlated, that is, environmental risk and problem behavior in children might be influenced by partly the same genetic factors. In addition, environmental and genetic factors could interact with each other increasing the risk of problem behavior in children. To date, limited research investigated these mechanisms in a genome-wide approach. Therefore, the goal of this study was to investigate the association between genetic risk for psychiatric and related traits, as indicated by polygenetic risk scores (PRSs), exposure to previously identified maternal risk factors, and problem behavior in a sample of 1,154 children from the Amsterdam Born Children and their Development study at ages 5-6 and 11-12 years old. The PRSs were derived from genome-wide association studies (GWASs) on schizophrenia, major depressive disorder, neuroticism, and wellbeing. Regression analysis showed that the PRSs were associated with exposure to multiple environmental risk factors, suggesting passive gene-environment correlation. In addition, the PRS based on the schizophrenia GWAS was associated with externalizing behavior problems in children at age 5-6. We did not find any association with problem behavior for the other PRSs. Our results indicate that genetic predispositions for psychiatric disorders and wellbeing are associated with early environmental risk factors for children's problem behavior.

The Role of Biomarkers in Psychiatry

Psychiatric illnesses are cognitive and behavioral disorders of the brain. At present, psychiatric diagnosis is based on DSM-5 criteria. Even if endophenotype specificity for psychiatric disorders is discussed, it is difficult to study and identify psychiatric biomarkers to support diagnosis, prognosis, or clinical response to treatment. This chapter investigates the innovative biomarkers of psychiatric diseases for diagnosis and personalized treatment, in particular post-genomic data and proteomic analyses.

Insulin Resistance in Schizophrenia

Schizophrenia and diabetes have been known to be linked disorders for decades. One reason is due to the fact that a major side effect of antipsychotic medication treatment is metabolic syndrome, which increases the risk of the patients developing type 2 diabetes and cardiovascular disorders. However, signs of metabolic syndrome in schizophrenia patients were identified more than 100 years ago, even before the development of antipsychotic drugs. This suggests that schizophrenia itself predisposes towards diabetes and, in turn, insulin resistance may be a risk factor for the development of schizophrenia. This review summarizes the findings surrounding this issue and places them into context with regards to increasing our understanding of the aetiology of schizophrenia and in support of biomarker and drug discovery efforts.

Stem cell therapy in autism: recent insights

Autism spectrum disorders (ASDs) are characterized by core domains: persistent deficits in social communication and interaction; restricted, repetitive patterns of behavior, interests, or activities. ASDs comprise heterogeneous and complex neurodevelopmental pathologies with well-defined inflammatory conditions and immune system dysfunction. Due to neurobiologic changes underlying ASD development, cell-based therapies have been proposed and applied to ASDs. Indeed, stem cells show specific immunologic properties, which make them promising candidates in ASD treatment. This comprehensive up-to-date review focuses on ASD cellular/molecular abnormalities, potentially useful stem cell types, animal models, and current clinical trials on the use of stem cells in treating autism. Limitations are also discussed.