Effects of nicotine on social cognition, social competence and self-reported stress in schizophrenia patients and healthy controls.

More than 80 % of patients diagnosed with schizophrenia are nicotine-dependent. Self-medication of cognitive deficits and an increased vulnerability to stress are discussed as promoting factors for the development of nicotine dependence. However, the effects of nicotine on social cognition and subjective stress responses in schizophrenia are largely unexplored. A 2 × 2-factorial design (drug × group) was used to investigate the effects of nicotine versus placebo in smoking schizophrenia patients and healthy controls after 24 h of abstinence from smoking. Participants performed a facial affect recognition task and a semi-standardized role-play task, after which social competence and self-reported stress during social interaction were assessed. Data analysis revealed no significant group differences in the facial affect recognition task. During social interaction, healthy controls showed more non-verbal expressions and a lower subjective stress level than schizophrenia patients. There were no significant effects of nicotine in terms of an enhanced recognition of facial affect, more expressive behaviour or reduced subjective stress during social interaction. While schizophrenia patients unexpectedly recognized facial affect not significantly worse than healthy controls, the observed group differences in subjective stress and non-verbal expression during social interaction in the role-play situation are in line with previous findings. Contrary to expectations derived from the self-medication hypothesis, nicotine showed no significant effects on the dependent variables, perhaps because of the dosage used and the delay between the administration of nicotine and the performance of the role-play.

Opportunistic detection of type 2 diabetes using deep learning from frontal chest radiographs.

Deep learning (DL) models can harness electronic health records (EHRs) to predict diseases and extract radiologic findings for diagnosis. With ambulatory chest radiographs (CXRs) frequently ordered, we investigated detecting type 2 diabetes (T2D) by combining radiographic and EHR data using a DL model. Our model, developed from 271,065 CXRs and 160,244 patients, was tested on a prospective dataset of 9,943 CXRs. Here we show the model effectively detected T2D with a ROC AUC of 0.84 and a 16% prevalence. The algorithm flagged 1,381 cases (14%) as suspicious for T2D. External validation at a distinct institution yielded a ROC AUC of 0.77, with 5% of patients subsequently diagnosed with T2D. Explainable AI techniques revealed correlations between specific adiposity measures and high predictivity, suggesting CXRs' potential for enhanced T2D screening.