AI Article Synopsis

  • - The study introduces a method for real-time monitoring of hydrogen peroxide and pH changes in rat stroke models using fiber-optic technology, allowing researchers to better understand the effects of ischemia on the brain.
  • - By utilizing advanced fluorescent protein sensors and reconnectable fiber probes, the framework enables detailed, multi-site analysis of oxidative stress and acidosis during stroke events, which are critical markers of the condition.
  • - The approach improves the accuracy of measurements by providing enhanced background noise reduction, making the results of in vivo stroke studies more reliable and statistically significant across different animal models.

Article Abstract

We present an experimental framework and methodology for in vivo studies on rat stroke models that enable a real-time fiber-optic recording of stroke-induced hydrogen peroxide and pH transients in ischemia-affected brain areas. Arrays of reconnectable implantable fiber probes combined with advanced optogenetic fluorescent protein sensors are shown to enable a quantitative multisite time-resolved study of oxidative-stress and acidosis buildup dynamics as the key markers, correlates and possible drivers of ischemic stroke. The fiber probes designed for this work provide a wavelength-multiplex forward-propagation channel for a spatially localized, dual-pathway excitation of genetically encoded fluorescence-protein sensors along with a back-propagation channel for the fluorescence return from optically driven fluorescence sensors. We show that the spectral analysis of the fiber-probe-collected fluorescence return provides means for a high-fidelity autofluorescence background subtraction, thus enhancing the sensitivity of real-time detection of stroke-induced transients and significantly reducing measurement uncertainties in in vivo acute-stroke studies as inherently statistical experiments operating with outcomes of multiply repeated measurements on large populations of individually variable animal stroke models.

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Source
http://dx.doi.org/10.1002/jbio.202200050DOI Listing

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