Modeling Metabolic Exhaustion of the Auditory System
[Technical Report, Final Report]
L3 APPLIED TECHNOLOGIES, INC.
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This report describes the results of the two-year project sponsored by the US Army Medical Research and Development Command USAMRDC to develop a metabolic exhaustion ME model of the auditory system to account for the effect of complex noise exposures on dose accumulation for the development of auditory damage risk criteria. Using chinchilla auditory temporary threshold shift TTS data from exposure to complex noise, a ME model was developed that predicts the effects of complex noise exposure on TTS. The complex impulse noise was composed of multiple impulses of unequal intensities and unequal inter-pulse intervalsIPI, and in some cases including the presence of high-intensity background noise. The chinchilla ME model was validated with data comparison and provided an understanding of the mechanism of injury by damage accumulation inside the cochlea. The chinchilla model was adapted to construct the human ME model using cochlear tissue stiffness data measured from chinchilla and human cadaver ears. The ME model is based on a mechanistic description of the energy deficit occurring within the outer hair cells OHC inside the cochlea as a result of complex noise exposure. The straining energy of the OHC caused by the noise exposure exceeding a threshold leads to increase in metabolic demand while supply is insufficient that can cause damage. An electroacoustic model of the cochlea including the OHC was developed that calculates the OHC energy deficit OHC-ED as the integrated difference between the rate of work of stress within the OHC and available power supply. The OHC-ED was correlated to TTS data to establish the dose-response curve for complex noise exposure. The major findings of this study are summarized as follows. There was a good correlation between the OHC-ED predictions and TTS data from chinchillas. The dose-response curve was established with a good fit and relative tight confidence band, considering the small data sample size.
- Anatomy and Physiology
- Medicine and Medical Research