AIMS: In the present study, we investigated the effects of mitragynine on spatial learning and synaptic transmission in the CA1 region of the hippocampus.
METHODS: Male Sprague Dawley rats received daily (for 12 days) training sessions in the Morris water maze, with each session followed by treatment either with mitragynine (1, 5, or 10 mg/kg; intraperitoneally), morphine (5 mg/kg; intraperitoneally) or a vehicle. In the second experiment, we recorded field excitatory postsynaptic potentials in the hippocampal CA1 area in anesthetized rats and assessed the effects of mitragynine on baseline synaptic transmission, paired-pulse facilitation, and long-term potentiation. Gene expression of major memory- and addiction-related genes was investigated and the effects of mitragynine on Ca2+ influx was also examined in cultured primary neurons from E16-E18 rats.
RESULTS/OUTCOMES: Escape latency results indicate that animals treated with mitragynine displayed a slower rate of acquisition as compared to their control counterparts. Further, mitragynine treatment significantly reduced the amplitude of baseline (i.e. non-potentiated) field excitatory postsynaptic potentials and resulted in a minor suppression of long-term potentiation in CA1. Bdnf and αCaMKII mRNA expressions in the brain were not affected and Ca2+ influx elicited by glutamate application was inhibited in neurons pre-treated with mitragynine.
CONCLUSIONS/INTERPRETATION: These data suggest that high doses of mitragynine (5 and 10 mg/kg) cause memory deficits, possibly via inhibition of Ca2+ influx and disruption of hippocampal synaptic transmission and long-term potentiation induction.
MATERIALS AND METHODS: In this study, mapping of the physiology curriculum of three batches of BDS programme was conducted retrospectively. The components of the curriculum used for mapping were expected learning outcomes, curriculum content, learning opportunities, assessments and learning resources. The data were gathered by reviewing office records.
RESULTS: Descriptive analysis of the data revealed reasonable alignment between the curriculum content and questions asked in examinations for all three batches. It was found that all the expected learning outcomes were addressed in the curriculum and assessed in different assessments. Moreover, the study revealed that the physiology curriculum was contributing to majority of the programme outcomes. Nevertheless, the study could identify some gaps in the curriculum, as well.
CONCLUSION: This study revealed that majority of the components of the curriculum were linked and contributed to attaining the expected learning outcomes. It also showed that curriculum mapping was feasible and could be used as a tool to evaluate the curriculum.
METHODS: A search of four databases was conducted: Web of Science, PubMed, Dimensions, and Scopus for research papers dated between January 2016 and September 2021. The search keywords are 'data mining', 'machine learning' in combination with 'suicidal behaviour', 'suicide', 'suicide attempt', 'suicidal ideation', 'suicide plan' and 'self-harm'. The studies that used machine learning techniques were synthesized according to the countries of the articles, sample description, sample size, classification tasks, number of features used to develop the models, types of machine learning techniques, and evaluation of performance metrics.
RESULTS: Thirty-five empirical articles met the criteria to be included in the current review. We provide a general overview of machine learning techniques, examine the feature categories, describe methodological challenges, and suggest areas for improvement and research directions. Ensemble prediction models have been shown to be more accurate and useful than single prediction models.
CONCLUSIONS: Machine learning has great potential for improving estimates of future suicidal behaviour and monitoring changes in risk over time. Further research can address important challenges and potential opportunities that may contribute to significant advances in suicide prediction.
OBJECTIVE: The aim of this proof-of-concept study was to evaluate whether combining population pharmacokinetic and machine learning approaches could provide a more accurate prediction of the clearance of renally eliminated drugs in individual neonates.
METHODS: Six drugs that are primarily eliminated by the kidneys were selected (vancomycin, latamoxef, cefepime, azlocillin, ceftazidime, and amoxicillin) as 'proof of concept' compounds. Individual estimates of clearance obtained from population pharmacokinetic models were used as reference clearances, and diverse machine learning methods and nested cross-validation were adopted and evaluated against these reference clearances. The predictive performance of these combined methods was compared with the performance of two other predictive methods: a covariate-based maturation model and a postmenstrual age and body weight scaling model. Relative error was used to evaluate the different methods.
RESULTS: The extra tree regressor was selected as the best-fit machine learning method. Using the combined method, more than 95% of predictions for all six drugs had a relative error of < 50% and the mean relative error was reduced by an average of 44.3% and 71.3% compared with the other two predictive methods.
CONCLUSION: A combined population pharmacokinetic and machine learning approach provided improved predictions of individual clearances of renally cleared drugs in neonates. For a new patient treated in clinical practice, individual clearance can be predicted a priori using our model code combined with demographic data.