Summary:Because heat shock proteins (HSPs) have an important protective function against ischemia/anoxia in mammalian brain, the authors investigated the expression of Hsp72 and Hsc73 in the anoxia-surviving turtle brain. Unlike the mammalian brain, high levels of Hsp72 were found in the normoxic turtle brain. Hsp72 levels were significantly increased by 4 hours of anoxia, remained constant until 8 hours, and then decreased to baseline at 12 hours. By contrast, Hsc73 was progressively increased throughout 12 hours of anoxia. This differential expression suggests different protective roles: Hsp72 in the initial downregulatory transition phase, and Hsc73 in maintaining neural network integrity during the long-term hypometabolic phase.

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Voltage-dependent potassium channels (Kv channels) are important determinants of brain electrical activity. Hypoxia may be an important modifier, because several voltage-gated K+ channels are reversibly blocked by acute hypoxia and are thought to act as oxygen sensors. Here we show, using the anoxia-tolerant turtle brain (Trachemys scripta) as a model, that brain Kv1 channel transcription is reversibly regulated by oxygen supply. We found that in turtle brains exposed to 4-h anoxia Kv1 transcripts were reduced to 18.5% of normoxic levels. Kv1 channel mRNA levels were restored to normal within 4 h of subsequent reoxygenation. Our results provide clear evidence that brain Kv channel expression is sensitive to oxygen supply and indicate an important mechanism that matches brain activity to oxygen supply.

Many physical processes have distributions of times between events that have non-normalizable, power law probability density functions ͑PDF's͒. The moments of such distributions are not defined. We found that the PDF's of the times between events of ventricular tachyarrhythmia ͑rapid heart rate͒ and premature ventricular contractions have a power law form indicative of a non-normalizable distribution, and that the timing between these events cannot be meaningfully characterized by the mean frequency of such events. The Hurst analysis showed that there were self-similar correlations in the data. These results indicate that the physical processes that disrupt the normal rhythm of the heart produce a fractal pattern in the timing between these events. It also suggests that the mean and the variance of the frequency of these events may not be good measures to assess the status of patients with these arrhythmias and determine the effectiveness of therapeutic procedures.

We used to think that a good measurement is characterized by its mean and variance and that a good theory is characterized by its ability to predict the values measured in an experiment. The properties of nonlinear systems called fractals and chaos have now taught us that this isn't necessarily true. Data from fractal systems extend over many scales and so cannot be characterized by a single characteristic average number. Data from chaotic systems do not repeat the same time series of values, even if they are started very close to the same initial conditions. This means that a valid mathematical model will not be able to predict the values of the time series.