Effect of metacognitive interpersonal therapy on brain structural connectivity in borderline personality disorder: Results from the CLIMAMITHE randomized clinical trial.

Background: Recently, we showed that Metacognitive Interpersonal Therapy (MIT) is effective in improving clinical symptoms in borderline personality disorder (BPD). Here, we investigated whether the effect of MIT on clinical features is associated to microstructural changes in brain circuits supporting core BPD symptoms.

Methods: Forty-seven BPD were randomized to MIT or structured clinical management, and underwent a clinical assessment and diffusion-weighted imaging before and after the intervention.

Cryosectioning-enabled super-resolution microscopy for studying nuclear architecture at the single protein level.

DNA-PAINT combined with total Internal Reflection Fluorescence (TIRF) microscopy enables the highest localization precisions, down to single nanometers in thin biological samples, due to TIRF's unique method for optical sectioning and attaining high contrast. However, most cellular targets elude the accessible TIRF range close to the cover glass and thus require alternative imaging conditions, affecting resolution and image quality. Here, we address this limitation by applying ultrathin physical cryosectioning in combination with DNA-PAINT.

Metacognitive interpersonal therapy in borderline personality disorder: Clinical and neuroimaging outcomes from the CLIMAMITHE study-A randomized clinical trial.

Different psychotherapeutic approaches demonstrated their efficacy but the possible neurobiological mechanism underlying the effect of psychotherapy in borderline personality disorder (BPD) patients is poorly investigated. We assessed the effects of metacognitive interpersonal therapy (MIT) on BPD features and other dimensions compared to structured clinical management (SCM). We also assessed changes in amygdala activation by viewing emotional pictures after psychotherapy.

PRECOGx: exploring GPCR signaling mechanisms with deep protein representations.

In this study we show that protein language models can encode structural and functional information of GPCR sequences that can be used to predict their signaling and functional repertoire. We used the ESM1b protein embeddings as features and the binding information known from publicly available studies to develop PRECOGx, a machine learning predictor to explore GPCR interactions with G protein and β-arrestin, which we made available through a new webserver (https://precogx.bioinfolab.