Machine learning-based longitudinal prediction for GJB2-related sensorineural hearing loss.

BACKGROUND: Recessive GJB2 variants, the most common genetic cause of hearing loss, may contribute to progressive sensorineural hearing loss (SNHL). The aim of this study is to build a realistic predictive model for GJB2-related SNHL using machine learning to enable personalized medical planning for timely intervention. METHOD: Patients with SNHL with confirmed biallelic GJB2 variants in a nationwide cohort between 2005 and 2022 were included. Different data preprocessing protocols and computational algorithms were combined to construct a prediction model. We randomly divided the dataset into training, validation, and test sets at a ratio of 72:8:20, and repeated this process ten times to obtain an average result. The performance of the models was evaluated using the mean absolute error (MAE), which refers to the discrepancy between the predicted and actual hearing thresholds. RESULTS: We enrolled 449 patients with 2184 audiograms available for deep learning analysis. SNHL progression was identified in all models and was independent of age, sex, and genotype. The average hearing progression rate was 0.61 dB HL per year. The best MAE for linear regression, multilayer perceptron, long short-term memory, and attention model were 4.42, 4.38, 4.34, and 4.76 dB HL, respectively. The long short-term memory model performed best with an average MAE of 4.34 dB HL and acceptable accuracy for up to 4 years. CONCLUSIONS: We have developed a prognostic model that uses machine learning to approximate realistic hearing progression in GJB2-related SNHL, allowing for the design of individualized medical plans, such as recommending the optimal follow-up interval for this population.

Suppression of Migration and Invasion by 4-Carbomethoxyl-10-Epigyrosanoldie E from the Cultured Soft Coral Sinularia sandensis through the MAPKs Pathway on Oral Cancer Cells.

The primary reason for cancer-related fatalities is metastasis. The compound 4-carbomethoxyl-10-epigyrosanoldie E, derived from the Sinularia sandensis soft coral species grown in cultures, exhibits properties that counteract inflammation. Moreover, it has been observed to trigger both apoptosis and autophagy within cancerous cells. This research focuses on examining the inhibitory impact of 4-carbomethoxyl-10-epigyrosanoldie E on the migration and invasion processes in Cal-27 and Ca9-22 oral cancer cell lines. To assess how this compound affects cell migration and invasion, the Boyden chamber assay was employed. Furthermore, Western blot analysis was utilized to explore the underlying molecular mechanisms. In a dose-dependent manner, 4-carbomethoxyl-10-epigyrosanoldie E notably decreased the levels of matrix metalloproteinase-2 (MMP-2) and MMP-9, along with urokinase-type plasminogen activator (uPA), in both Cal-27 and Ca9-22 cell lines. Conversely, it elevated the concentrations of tissue inhibitors of metalloproteinases-1 (TIMP-1) and TIMP-2. In addition, the treatment with this compound led to the inhibition of phosphorylation in extracellular signal-regulated kinase (ERK), p38, and c-Jun N-terminal kinase (JNK). It also curtailed the expression of several key proteins including focal adhesion kinase (FAK), protein kinase C (PKC), growth factor receptor-bound protein 2 (GRB2), Rac, Ras, Rho A, mitogen-activated protein kinase kinase kinase 3 (MEKK3), and mitogen-activated protein kinase kinase 7 (MKK7). Furthermore, the expression levels of IQ-domain GTPase-activating protein 1 (IQGAP1) and zonula occludens-1 (ZO-1) were significantly reduced by the compound. The ability of 4-carbomethoxyl-10-epigyrosanoldie E to inhibit the migration and invasion of Cal-27 and Ca9-22 oral cancer cells was observed to be dose dependent. This inhibitory effect is primarily attributed to the suppression of MMP-2 and MMP-9 expression, as well as the downregulation of the mitogen-activated protein kinase (MAPK) signaling pathway.