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Given the increased prevalence of SC use in our country and around the world, dermatologists should continue to familiarize themselves with the common mucocutaneous markers of this substance use. Awareness of signs of SCs use will facilitate earlier diagnose, intervention, and directed treatment.

The porphyrias are a heterogeneous group of disorders characterized by abnormal heme biosynthesis. Although all subtypes are rare, acute intermittent porphyria (AIP) is the most common form of the neuroporphyrias. Abdominal pain, peripheral neuropathy, and changes in cognitive function are the classical triad of an acute porphyric attack but the variability of the symptoms may interfere with the diagnosis of AIP. Delayed diagnosis and treatment of acute porphyric attacks, however, can be fatal or may cause long-term or permanent neurological damage. We hereby report a case of 45-year-old male with suicide attempt because of his psychotic symptoms during an AIP attack.

BackgroundAlcohol use disorder is characterized by perseverative alcohol use despite negative consequences. This hallmark feature of addiction potentially relates to impairments in behavioral flexibility, which can be measured by probabilistic reversal learning (PRL) paradigms. We here aimed to examine the cognitive mechanisms underlying impaired PRL task performance in patients with alcohol use disorder (AUDP) using computational models of reinforcement learning.MethodsTwenty-eight early abstinent AUDP and 27 healthy controls (HC) performed an extensive PRL paradigm. We compared conventional behavioral variables of choices (perseveration; correct responses) between groups. Moreover, we fitted Bayesian computational models to the task data to compare differences in latent cognitive variables including reward and punishment learning and choice consistency between groups.ResultsAUDP and HC did not significantly differ with regard to direct perseveration rates after reversals. However, AUDP made overall less correct responses and specifically showed decreased win–stay behavior compared to HC. Interestingly, AUDP showed premature switching after no or little negative feedback but elevated proneness to stay when accumulation of negative feedback would make switching a more optimal option. Computational modeling revealed that AUDP compared to HC showed enhanced learning from punishment, a tendency to learn less from positive feedback and lower choice consistency.ConclusionOur data do not support the assumption that AUDP are characterized by increased perseveration behavior. Instead our findings provide evidence that enhanced negative reinforcement and decreased non-drug-related reward learning as well as diminished choice consistency underlie dysfunctional choice behavior in AUDP.

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