Factual and counterfactual learning in major adolescent depressive disorder, evidence from an instrumental learning study.

BACKGROUND: The incidence of adolescent depressive disorder is globally skyrocketing in recent decades, albeit the causes and the decision deficits depression incurs has yet to be well-examined. With an instrumental learning task, the aim of the current study is to investigate the extent to which learning behavior deviates from that observed in healthy adolescent controls and track the underlying mechanistic channel for such a deviation. METHODS: We recruited a group of adolescents with major depression and age-matched healthy control subjects to carry out the learning task with either gain or loss outcome and applied a reinforcement learning model that dissociates valence (positive v. negative) of reward prediction error and selection (chosen v. unchosen). RESULTS: The results demonstrated that adolescent depressive patients performed significantly less well than the control group. Learning rates suggested that the optimistic bias that overall characterizes healthy adolescent subjects was absent for the depressive adolescent patients. Moreover, depressed adolescents exhibited an increased pessimistic bias for the counterfactual outcome. Lastly, individual difference analysis suggested that these observed biases, which significantly deviated from that observed in normal controls, were linked with the severity of depressive symoptoms as measured by HAMD scores. CONCLUSIONS: By leveraging an incentivized instrumental learning task with computational modeling within a reinforcement learning framework, the current study reveals a mechanistic decision-making deficit in adolescent depressive disorder. These findings, which have implications for the identification of behavioral markers in depression, could support the clinical evaluation, including both diagnosis and prognosis of this disorder.

A facile and intelligent detection method for diclazuril based on a stable dual emissive Eu3+-dopped metal-organic framework.

Diclazuril (DIC) is a broad-spectrum anti-coccidiosis drug of the triazine class, widely used in poultry farming. The overuse of DIC may lead to its accumulation in animal bodies, which may enter the food chain and threaten human health. In this work, we fabricated a stable Eu3+-doped UiO-66 fluorescence sensor (EuUHIPA-30) for the sensitive detection of DIC. Among 20 veterinary drugs, the fluorescence of EuUHIPA-30 selectively responds to DIC, with a low detection limit (0.19 µM) and fast response (10 s). EuUHIPA-30 is recyclable and can detect DIC in chicken and eggs with good recoveries. Moreover, a smartphone-integrated paper-based sensor enables the instrument-free, rapid, visual, and intelligent detection of DIC in chickens and eggs. This work provides a promising candidate for practical fluorescent DIC sensing in animal-derived food to promote food safety.