No abstract

The ability to infer the authenticity of other’s emotional expressions is a social cognitive process taking place in all human interactions. Although the neurocognitive correlates of authenticity recognition have been probed, its potential recruitment of the peripheral autonomic nervous system is not known. In this work, we asked participants to rate the authenticity of authentic and acted laughs and cries, while simultaneously recording their pupil size, taken as proxy of cognitive effort and arousal. We report, for the first time, that acted laughs elicited higher pupil dilation than authentic ones and, reversely, authentic cries elicited higher pupil dilation than acted ones. We tentatively suggest the lack of authenticity in others’ laughs elicits increased pupil dilation through demanding higher cognitive effort; and that, reversely, authenticity in cries increases pupil dilation, through eliciting higher emotional arousal. We also show authentic vocalizations and laughs (i.e. main effects of authenticity and emotion) to be perceived as more authentic, arousing and contagious than acted vocalizations and cries, respectively. In conclusion, we show new evidence that the recognition of emotional authenticity can be manifested at the level of the autonomic nervous system in humans. Notwithstanding, given its novelty, further independent research is warranted to ascertain its psychological meaning.

IntroductionPsychosis is usually preceded by a prodromal phase in which patients are clinically identified as being at in an “At Risk Mental State” (ARMS). A few studies have demonstrated the feasibility of predicting psychosis transition from an ARMS using structural magnetic resonance imaging (sMRI) data and machine learning (ML) methods. However, the reliability of these findings is unclear due to possible sampling bias. Moreover, the value of genetic and environmental data in predicting transition to psychosis from an ARMS is yet to be explored.MethodsIn this study we aimed to predict transition to psychosis from an ARMS using a combination of ML, sMRI, genome-wide genotypes, and environmental risk factors as predictors, in a sample drawn from a pool of 246 ARMS subjects (60 of whom later transitioned to psychosis). First, the modality-specific values in predicting transition to psychosis were evaluated using several: (a) feature types; (b) feature manipulation strategies; (c) ML algorithms; (d) cross-validation strategies, as well as sample balancing and bootstrapping. Subsequently, the modalities whose at least 60% of the classification models showed an balanced accuracy (BAC) statistically better than chance level were included in a multimodal classification model.Results and discussionResults showed that none of the modalities alone, i.e., neuroimaging, genetic or environmental data, could predict psychosis from an ARMS statistically better than chance and, as such, no multimodal classification model was trained/tested. These results suggest that the value of structural MRI data and genome-wide genotypes in predicting psychosis from an ARMS, which has been fostered by previous evidence, should be reconsidered.

Obesity is nowadays a serious worldwide public health problem. In order to give patients a better quality of live, there are now medical and surgical treatments that may be offered to those patients. Since 2005, the Hospital de Santarém has been doing bariatric surgery. From 77 procedures with laparoscopic adjustable gastric banding, there were three cases of migration. The authors propose a new procedure to deal with migrations, according to a classification for band migration, as an alternative to a removal by laparoscopy or laparotomy. Let loose technique is a procedure that can be used in patients with band migration beyond the angle of Treitz and without other associated complications. It is consist of removing the port under local anaesthesia, leaving the insufflations' tube, hospitalization for monitoring the patient and awaiting for the elimination of the band with faeces. It's less costly and with minimal risks for patient. The first results are satisfactory and encouraging.

Predicting gene expression from genotyped data is valuable for studying inaccessible tissues such as the brain. Herein we present eGenScore, a polygenic/poly-variation method, and compare it with PrediXcan, a method based on regularized linear regression using elastic nets. While both methods have the same purpose of predicting gene expression based on genotype, they carry important methodological differences. We compared the performance of expression quantitative trait loci (eQTL) models to predict gene expression in the frontal cortex, comparing across these frameworks (eGenScore vs. PrediXcan) and training datasets (BrainEAC, which is brain-specific, vs. GTEx, which has data across multiple tissues). In addition to internal five-fold cross-validation, we externally validated the gene expression models using the CommonMind Consortium database. Our results showed that (1) PrediXcan outperforms eGenScore regardless of the training database used; and (2) when using PrediXcan, the performance of the eQTL models in frontal cortex is higher when trained with GTEx than with BrainEAC.