No abstract

The progressive development of seizures in rats by amygdala kindling, which models temporal lobe epilepsy, allows the study of molecular regulators of enduring synaptic changes. Neurotrophins play important roles in synaptic plasticity and neuroprotection. Activin, a member of the transforming growth factor-␤ superfamily of growth and differentiation factors, has recently been added to the list of candidate synaptic regulators. We mapped the induction of activin ␤A mRNA in amygdala and cortex at several stages of seizure development. Strong induction, measured 2 hours after the first stage 2 (partial) seizure, appeared in neurons of the ipsilateral amygdala (confined to the lateral, basal, and posterior cortical nuclei) and insular, piriform, orbital, and infralimbic cortices. Activin ␤A mRNA induction, after the first stage 5 (generalized) seizure, had spread to the contralateral amygdala (same nuclear distribution) and cortex, and the induced labeling covered much of the convexity of neocortex as well as piriform, perirhinal, and entorhinal cortices in a nearly bilaterally symmetrical pattern. This pattern had filled in by the sixth stage 5 seizure. Induced labeling in cortical neurons was confined mainly to layer II. A similar temporal and spatial pattern of increased mRNA expression of brain-derived neurotrophic factor (BDNF) was found in the amygdala and cortex. Activin ␤A and BDNF expression patterns were similar at 1, 2, and 6 hours after the last seizure, subsiding at 24 hours; in contrast, c-fos mRNA induction appeared only at 1 hour throughout cortex and then subsided. In double-label studies, activin ␤A mRNA-positive neurons were also BDNF mRNA positive, and they did not colocalize with GAD67 mRNA (a marker of ␥-aminobutyric acidergic neurons). The data suggest that activin and BDNF transcriptional activities accurately mark excitatory neurons participating in seizure-induced synaptic alterations and may contribute to the enduring changes that underlie the kindled state.

Existing doping detection strategies rely on direct and indirect biochemical measurement methods focused on detecting banned substances, their metabolites, or biomarkers related to their use. However, the goal of doping is to improve performance, and yet evidence from performance data is not considered by these strategies. The emergence of portable sensors for measuring exercise intensities and of player tracking technologies may enable the widespread collection of performance data. How these data should be used for doping detection is an open question. Herein, we review the basis by which performance models could be used for doping detection, followed by critically reviewing the potential of the critical power (CP) model as a prototypical performance model that could be used in this regard. Performance models are mathematical representations of performance data specific to the athlete. Some models feature parameters with physiological interpretations, changes to which may provide clues regarding the specific doping method. The CP model is a simple model of the power-duration curve and features two physiologically interpretable parameters, CP and W′. We argue that the CP model could be useful for doping detection mainly based on the predictable sensitivities of its parameters to ergogenic aids and other performance-enhancing interventions. However, our argument is counterbalanced by the existence of important limitations and unresolved questions that need to be addressed before the model is used for doping detection. We conclude by providing a simple worked example showing how it could be used and propose recommendations for its implementation.