High-intensity noise can cause permanent hearing loss; however, short-duration medium-intensity noise only induces a temporary threshold shift (TTS) and damages synapses formed by inner hair cells (IHCs) and spiral ganglion nerves. Synaptopathy is generally thought to be caused by glutamate excitotoxicity. In this study, we investigated the expression levels of vesicle transporter protein 3 (Vglut3), responsible for the release of glutamate; glutamate/aspartate transporter protein (GLAST), responsible for the uptake of glutamate; and Na+/K+-ATPase α1 coupled with GLAST, in the process of synaptopathy in the cochlea. The results of the auditory brainstem response (ABR) and CtBP2 immunofluorescence revealed that synaptopathy was induced on day 30 after 100 dB SPL noise exposure in C57BL/6J mice. We found that GLAST and Na+/K+-ATPase α1 were co-localized in the cochlea, mainly in the stria vascularis, spiral ligament, and spiral ganglion cells. Furthermore, Vglut3, GLAST, and Na+/K+-ATPase α1 expression were disrupted after noise exposure. These results indicate that disruption of glutamate release and uptake-related protein expression may exacerbate the occurrence of synaptopathy.

Fatigue results from a series of physiological and psychological changes due to continuous energy consumption. It can affect the physiological states of operators, thereby reducing their labor capacity. Fatigue can also reduce efficiency and, in serious cases, cause severe accidents. In addition, it can trigger pathological-related changes. By establishing appropriate methods to closely monitor the fatigue status of personnel and relieve the fatigue on time, operation-related injuries can be reduced. Existing fatigue detection methods mostly include subjective methods, such as fatigue scales, or those involving the use of professional instruments, which are more demanding for operators and cannot detect fatigue levels in real time. Speech contains information that can be used as acoustic biomarkers to monitor physiological and psychological statuses. In this study, we constructed a fatigue model based on the method of sleep deprivation by collecting various physiological indexes, such as P300 and glucocorticoid level in saliva, as well as fatigue questionnaires filled by 15 participants under different fatigue procedures and graded the fatigue levels accordingly. We then extracted the speech features at different instances and constructed a model to match the speech features and the degree of fatigue using a machine learning algorithm. Thus, we established a method to rapidly judge the degree of fatigue based on speech. The accuracy of the judgment based on unitary voice could reach 94%, whereas that based on long speech could reach 81%. Our fatigue detection method based on acoustic information can easily and rapidly determine the fatigue levels of the participants. This method can operate in real time and is non-invasive and efficient. Moreover, it can be combined with the advantages of information technology and big data to expand its applicability.