[Therapeutic targets of adolescent depression with attenuated symptoms of schizophrenia].

Objective: Identification of therapeutic targets in the treatment of adolescent depression with attenuated symptoms of schizophrenia and assessment of the effectiveness of therapeutic interventions.

Material And Methods: One hundred and twenty-three patients (mean age 19.6±2.

[The relationship between inflammatory markers, fatigue, and effort-based decision-making in patients at clinical high risk for psychosis].

Objective: To evaluate the associations between peripheral levels of inflammatory markers and willingness to exert effort in individuals at clinical high risk for psychosis (CHR-P) and a role of fatigue as a mediator of these associations.

Material And Methods: Forty-nine young men admitted for a depressive episode, who met the criteria for CHR-P, had pre-treatment blood tests to determine levels of C-reactive protein (CRP), tumor necrosis factor-α (TNF-α), and interleukins IL-6, IL-8 and IL-10. After treatment and normalization of their condition, they completed the EEfRT task of willingness to exert physical effort to receive monetary reward and filled out the Fatigue Assessment Scale (FAS-10).

The NCBI Comparative Genome Viewer (CGV) is an interactive visualization tool for the analysis of whole-genome eukaryotic alignments.

We report a new visualization tool for analysis of whole-genome assembly-assembly alignments, the Comparative Genome Viewer (CGV) (https://ncbi.nlm.nih.

[Willingness to expend effort for rewards in individuals at clinical high-risk for psychosis: a relationship with the severity and stability of negative symptoms].

Objective: To identify the deficit in willingness to expend effort and its association with negative symptoms in the high-risk for psychosis (CHR) group.

Material And Methods: The study included young men: 45 patients, who met CHR criteria and were treated for a depressive episode, and 15 controls. All subjects completed a modified version of the Effort Expenditure for Rewards Task (EEfRT).

Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk.

Machine learning approaches using structural magnetic resonance imaging (sMRI) can be informative for disease classification, although their ability to predict psychosis is largely unknown. We created a model with individuals at CHR who developed psychosis later (CHR-PS+) from healthy controls (HCs) that can differentiate each other. We also evaluated whether we could distinguish CHR-PS+ individuals from those who did not develop psychosis later (CHR-PS-) and those with uncertain follow-up status (CHR-UNK).