Mentalization based treatment of youth on the psychotic spectrum: clinical profiles and outcomes for youth in the ECID.

Introduction: Early intervention may significantly improve the prognosis associated with psychotic disorders in adulthood. Methods: The present study examined the acceptability and effectiveness of a standalone intensive, in-home, mentalization-based treatment (MBT) for extremely high-risk, non-help-seeking youth on the psychotic spectrum [Equipo Clínico de Intervención a Domicilio (ECID), Home Intervention Clinical Team]. Results: Despite previously being unable to participate in treatment, more than 90% of youth engaged and those on the psychotic spectrum demonstrated slightly higher engagement than the general high-risk group (95% and 85%, respectively, X1 = 4.218, p = 0.049). Generalized estimating equation (GEE) models revealed no main group effect on the likelihood of reengaging with school over the first 12 months of treatment (X1 = 1.015, p = 0.314) when controlling for the duration of school absenteeism at intake. Overall, the percentage of school engagement rose from 12 to 55 over this period, more than 40% of the total sample experienced clinically reliable change and an additional 50% appeared clinically stable. No statistically significant difference was observed between the groups in the average change in HoNOSCA total severity score (X1 = 0.249, p = 0.618) or the distribution of youth into categories of clinical change during the first year of treatment (X1 = 0.068, p = 0.795). Discussion: The present findings suggest that a mentalization based intervention may be able to engage extremely high-risk youth in treatment and have clinically meaningful impact on symptom severity and functioning after 12 months.

Ambit-SLN: an Open Source Software Library for Processing of Chemical Objects via SLN Linear Notation.

SLN (SYBYL Line Notation) is the most comprehensive and rich linear notation for representation of chemical objects of various kinds facilitating a wide range of cheminformatics algorithms. Though, it is not the most popular linear notation nowadays, SLN has capabilities for supporting the most challenging tasks of the present day cheminformatics research. We present Ambit-SLN, a new software library for cheminformatics processing of chemical objects via linear notation SLN. Ambit-SLN is developed as a part of the cheminformatics platform AMBIT. It is an open-source tool, distributed under LGPL license, written in Java and based on the Chemistry Development Kit. Ambit-SLN includes a parser for the full SLN syntax of chemical structures and substructure search queries including support for macro and Markush atoms, global and local dictionaries and user defined properties which can be stored and used by the Ambit data model. The Ambit-SLN library includes functionalities for substructure matching based on SLN query strings and utilities for conversion of SLN objects to other chemical formats such as SMILES and SMARTS. The functionality for Markush atom expansion can be used for generation of combinatorial structure sets.

What is behind the closed door? A case illustration of working with social isolation in adolescents using Adaptive Mentalization-Based Integrative Treatment (AMBIT).

BACKGROUND: In this paper, we outline our approach to dealing with complex social isolation by presenting a network treatment approach named Adaptive Mentalization-Based Integrative Treatment (AMBIT). METHOD: We describe the AMBIT approach, what elements it consists of, and we explain how we employed this method in the case of a 17-year-old boy referred to our child and adolescent psychiatric clinic, who isolated himself from the world. RESULTS: We emphasize in which ways the specific network approach pertinent to the AMBIT approach was helpful in this complex case. Furthermore, we describe and reveal our insecurities and doubts related to our interventions and the general treatment process and point to why the AMBIT network approach and the interventions were crucial in this case. DISCUSSION: We argue that the boy could not have been helped out of his social isolation within the conventional child and adolescent psychiatric system without engaging and establishing an integrated professional network from many sectors.

Ambit-GCM: An Open-source Software Tool for Group Contribution Modelling.

Ambit-GCM is a new software tool for group contribution modelling (GCM), developed as a part of the chemoinformatics platform AMBIT. It is an open-source tool distributed under LGPL license, written in Java and based on the Chemistry Development Kit. Ambit-GCM provides an environment for creating models of molecular properties using additive schemes of zero, first or second orders. Ambit-GCM supports a set of local atomic attributes used for dynamic configuration of desired atom descriptions, which are applied to define fragments of different sizes. All defined groups are exhaustively generated for each molecule from a training set of compounds and combined to form the basic set of GCM fragments. Additionally, Ambit-GCM users can define correction factors via custom SMARTS notations or add externally calculated molecular descriptors. A molecular property model is obtained as a sum over all found groups by multiplying each group or correction factor frequency to its corresponding contribution. Multiple linear regression analysis (MLRA) is used for group contributions calculation. Ambit-GCM performs full statistical characterization of the obtained MLRA models via various validation techniques: external tests validation, cross validation, y-scrambling, etc. The software can be optionally used only for molecule fragmentation combined with an external statistical modelling package for further processing. Ambit-GCM example usage and test cases are given.