This study is the first to demonstrate that macrophage migration inhibitory factor (MIF), an immune system ‘inflammatory’ cytokine that is released by the developing otocyst, plays a role in regulating early innervation of the mouse and chick inner ear. We demonstrate that MIF is a major bioactive component of the previously uncharacterized otocyst-derived factor, which directs initial neurite outgrowth from the statoacoustic ganglion (SAG) to the developing inner ear. Recombinant MIF acts as a neurotrophin in promoting both SAG directional neurite outgrowth and neuronal survival and is expressed in both the developing and mature inner ear of chick and mouse. A MIF receptor, CD74, is found on both embryonic SAG neurons and adult mouse spiral ganglion neurons. Mif knockout mice are hearing impaired and demonstrate altered innervation to the organ of Corti, as well as fewer sensory hair cells. Furthermore, mouse embryonic stem cells become neuron-like when exposed to picomolar levels of MIF, suggesting the general importance of this cytokine in neural development.
With the advent of the era of big data, the information society inevitably intersects and integrates with everyone's life. Compared with the traditional selfstatement scale for psychological measurement, big data network information has advantages such as excellent ecological validity. In this paper, we use the consumption data and access control data of college students in a university as the data source and the employment situation as the target variable to identify and predict the loneliness of college students. In the context of the current COVID-19 epidemic, which is generally in quarantine, this paper provides a realistic basis for this study. By extracting the features from the data, we address the limitations of the machine learning modeling approach for the autonomous identification and prediction of loneliness symptoms and propose further development prospects. This paper provides a practical basis for this research. By extracting features from the data information, we propose the limitations of the machine learning modeling approach for the autonomous identification and prediction of symptoms of loneliness, and propose the future development of this approach.
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