RESUMO
Cisplatin is a known ototoxic chemotherapy drug, causing irreversible hearing loss. Evidence has shown that cisplatin causes inner ear damage as a result of adduct formation, a proinflammatory environment and the generation of reactive oxygen species within the inner ear. The main cochlear targets for cisplatin are commonly known to be the outer hair cells, the stria vascularis and the spiral ganglion neurons. Further evidence has shown that certain transporters can mediate cisplatin influx into the inner ear cells including organic cation transporter 2 (OCT2) and the copper transporter Ctr1. However, the expression profiles for these transporters within inner ear cells are not consistent in the literature, and expression of OCT2 and Ctr1 has also been observed in supporting cells. Organ of Corti supporting cells are essential for hair cell activity and survival. Special interest has been devoted to gap junction expression by these cells as certain mutations have been linked to hearing loss. Interestingly, cisplatin appears to affect connexin expression in the inner ear. While investigations regarding cisplatin-induced hearing loss have been focused mainly on the known targets previously mentioned, the role of supporting cells for cisplatin-induced ototoxicity has been overlooked. In this mini review, we discuss the implications of supporting cells expressing OCT2 and Ctr1 as well as the potential role of gap junctions in cisplatin-induced cytotoxicity.
RESUMO
Artificial intelligence-assisted otologic diagnosis has been of growing interest in the scientific community, where middle and external ear disorders are the most frequent diseases in daily ENT practice. There are some efforts focused on reducing medical errors and enhancing physician capabilities using conventional artificial vision systems. However, approaches with multispectral analysis have not yet been addressed. Tissues of the tympanic membrane possess optical properties that define their characteristics in specific light spectra. This work explores color wavelengths dependence in a model that classifies four middle and external ear conditions: normal, chronic otitis media, otitis media with effusion, and earwax plug. The model is constructed under a computer-aided diagnosis system that uses a convolutional neural network architecture. We trained several models using different single-channel images by taking each color wavelength separately. The results showed that a single green channel model achieves the best overall performance in terms of accuracy (92%), sensitivity (85%), specificity (95%), precision (86%), and F1-score (85%). Our findings can be a suitable alternative for artificial intelligence diagnosis systems compared to the 50% of overall misdiagnosis of a non-specialist physician.
RESUMO
The stress response in cells involves a rapid and transient transcriptional activation of stress genes. It has been shown that Hsp70 limits its own transcriptional activation functioning as a corepressor of heat shock factor 1 (HSF1) during the attenuation of the stress response. Here we show that the transcriptional corepressor CoREST interacts with Hsp70. Through this interaction, CoREST represses both HSF1-dependent and heat shock-dependent transcriptional activation of the hsp70 promoter. In cells expressing short hairpin RNAs directed against CoREST, Hsp70 cannot repress HSF1-dependent transcription. A reduction of CoREST levels also provoked a significant increase of Hsp70 protein levels and an increase of HSF1-dependent transactivation of hsp70 promoter. Via chromatin immunoprecipitation assays we show that CoREST is bound to the hsp70 gene promoter under basal conditions and that its binding increases during heat shock response. In conclusion, we demonstrated that CoREST is a key regulator of the heat shock stress response.