The object of the experiment was to verify whether cannabidiol (CBD) reduces the anxiety provoked by delta 9-THC in normal volunteers, and whether this effect occurs by a general block of the action of delta 9-THC or by a specific anxiolytic effect. Appropriate measurements and scales were utilized and the eight volunteers received, the following treatments in a double-blind procedure: 0.5 mg/kg delta 9-THC, 1 mg/kg CBD, a mixture containing 0.5 mg/kg delta 9-THC and 1 mg/kg CBD and placebo and diazepam (10 mg) as controls. Each volunteer received the treatments in a different sequence. It was verified that CBD blocks the anxiety provoked by delta 9-THC, however this effect also extended to marihuana-like effects and to other subjective alterations induced by delta 9-THC. This antagonism does not appear to be caused by a general block of delta 9-THC effects, since no change was detected in the pulse-rate measurements. Several further effects were observed typical of CBD and of an opposite nature to those of delta 9-THC. These results suggest that the effects of CBD, as opposed to those of delta 9-THC, might be involved in the antagonism of effects between the two cannabinoids.

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The purpose of the study was to evaluate neuropsychologically adolescents who use ayahuasca in a religious context. A battery of neuropsychological tests was administered to adolescents who use ayahuasca. These subjects were compared to a matched control group of adolescents who did not use ayahuasca. The controls were matched with regards to sex, age, and education. The neuropsychological battery included tests of speeded attention, visual search, sequencing, psychomotor speed, verbal and visual abilities, memory, and mental flexibility. The statistical results for subjects from matched controls on neuropsychological measures were computed using independent t-tests. Overall, statistical findings suggested that there was no significant difference between the two groups on neuropsychological measures. Even though, the data overall supports that there was not a difference between ayahuasca users and matched controls on neuropsychological measures, further studies are necessary to support these findings.

The interaction of Δ⁹-tetrahydrocannabinol (Δ⁹-THC) and cannabinol (CBN) was studied in man. Five male volunteers were given placebo, 50 mg CBN, 25 mg Δ⁹-THC, 12.5 mg Δ⁹-THC + 25 mg CBN, and 25 mg Δ⁹-THC + 50 mg CBN (orally). Administrations were spaced 1 week apart. With physiological measures, Δ⁹-THC produced an increase in heart rate while CBN did not. When combined, no change of the Δ⁹-THC effect occurred. No changes occurred on the electrocardiogram, blood pressure, or body temperature. With psychophysical measures no changes occurred in pain thresholds or skin sensitivity as a function of drug treatment. In time estimates of the passage of 1 minute, Δ⁹ -THC alone produced underestimates of the passage of 1 minute and CBN alone had no effect. In combination the two drugs had a tendency to produce significant overestimates and underestimates of the passage of 1 minute. On a 66-item adjective-pair drug reaction scale, the volunteers reported feeling drugged, drunk, dizzy, and drowsy under the Δ⁹ -THC condition, but not under the CBN condition. With combined drug treatment, volunteers reported feeling more drugged, drunk, dizzy, and drowsy than under the Δ⁹-THC condition alone. None of the drug treatments produced significant changes on other items which included items on perception, emotion, cognition and sociability. It appears that CBN increases the effect of Δ⁹-THC on some aspects of physiological and psychological processes, but that these effects are small and cannot account for the greater potency which has been reported when plant material is used.

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Clinical decision support systems are useful tools for assisting physicians to diagnose complex illnesses. Schizophrenia is a complex, heterogeneous and incapacitating mental disorder that should be detected as early as possible to avoid a most serious outcome. These artificial intelligence systems might be useful in the early detection of schizophrenia disorder. The objective of the present study was to describe the development of such a clinical decision support system for the diagnosis of schizophrenia spectrum disorders (SADDESQ). The development of this system is described in four stages: knowledge acquisition, knowledge organization, the development of a computerassisted model, and the evaluation of the system's performance. The knowledge was extracted from an expert through open interviews. These interviews aimed to explore the expert's diagnostic decisionmaking process for the diagnosis of schizophrenia. A graph methodology was employed to identify the elements involved in the reasoning process. Knowledge was first organized and modeled by means of algorithms and then transferred to a computational model created by the covering approach. The performance assessment involved the comparison of the diagnoses of 38 clinical vignettes between an expert and the SADDESQ. The results showed a relatively low rate of misclassification (18-34%) and a good performance by SADDESQ in the diagnosis of schizophrenia, with an accuracy of 66-82%. The accuracy was higher when schizophreniform disorder was considered as the presence of schizophrenia disorder. Although these results are preliminary, the SADDESQ has exhibited a satisfactory performance, which needs to be further evaluated within a clinical setting.