We attempted to explore the association between metformin use and hearing loss in in a large-scale study. This retrospective multicenter cohort study assessed the data of patients with type 2 diabetes mellitus (DM) aged over 40 years using the Observational Health Data Science and Informatics open-source software and the Common Data Model database from 1 January 2002 to 31 December 2019. Each participant was selected using the ICD-10-CM diagnosis code E11 for type 2 DM with sensorineural hearing loss. The participants were divided into metformin and non-metformin users. The outcome measure was the first occurrence of hearing loss after the diagnosis of DM as measured by the CDM cohort study. A total of 80,596 patients, including 46,152 metformin users and 34,444 non-metformin users from three hospitals were assessed. After calibration, we compared the risk of hearing loss using Kaplan–Meier curves, and found significant differences between the groups. The calibrated hazard ratio in the three hospitals (0.79 [95% confidence interval, 0.57–1.12]) was summarized. These findings suggest that the probability of hearing loss-free survival in the metformin user group is higher than that in the non-metformin user group.

Our study investigated the embryo-ototoxic effects of deodorant2 (DA2) on zebrafish embryos, which serve as valuable model organisms due to genetic and physiological similarities to humans. We focused on understanding DA2’s impact on zebrafish hair cells, which are vital for sensory perception and balance regulation. DA2, provided by the Ministry of Environment, Republic of Korea, was used at 460 μg/mL in dimethyl sulfoxide (DMSO), with a 0.43% DMSO solvent control group. Three experiments, each using 10 zebrafish specimens from each group, showed an initial 13% hair cell count reduction in the DA2-exposed group. Subsequent experiments demonstrated reductions of 37% and 22%, each with one mortality case. Statistical analysis revealed a significant 24% hair cell count reduction in the DA2-exposed group. We also assessed DA2’s impact on zebrafish behavior. Although not statistically significant, differences in distances traveled (0.33–0.39, 95% confidence interval: −0.46–1.1, p = 0.2033) and latencies (−0.016–0.018, 95% confidence interval: −0.052–0.021, p = 0.1917) hinted at negative effects. These results highlight DA2’s ototoxic properties affecting zebrafish auditory systems and behavior. Further investigation into DA2’s effects on aquatic organisms and potential mitigation strategies are essential. These findings contribute to understanding DA2’s safety profile, benefiting aquatic ecosystems and human health assessments.

Background: Chronic otitis media affects approximately 2% of the global population, causing significant hearing loss and diminishing the quality of life. However, there is a lack of studies focusing on outcome prediction for otitis media patients undergoing canal-wall-down mastoidectomy. Methods: This study proposes a recovery prediction model for chronic otitis media patients undergoing canal-wall-down mastoidectomy, utilizing data from 298 patients treated at Korea University Ansan Hospital between March 2007 and August 2020. Various machine learning techniques, including logistic regression, decision tree, random forest, support vector machine (SVM), extreme gradient boosting (XGBoost), and light gradient boosting machine (light GBM), were employed. Results: The light GBM model achieved a predictive value (PPV) of 0.6945, the decision tree algorithm showed a sensitivity of 0.7574 and an F1 score of 0.6751, and the light GBM algorithm demonstrated the highest AUC-ROC values of 0.7749 for each model. XGBoost had the most efficient PR-AUC curve, with a value of 0.7196. Conclusions: This study presents the first predictive model for chronic otitis media patients undergoing canal-wall-down mastoidectomy. The findings underscore the potential of machine learning techniques in predicting hearing recovery outcomes in this population, offering valuable insights for personalized treatment strategies and improving patient care.

Background: This retrospective, cross-sectional study aimed to assess the functional hearing capacity of individuals with Chronic Otitis Media (COM) using prediction modeling techniques and the Hearing Handicap Inventory for the Elderly (HHIE) questionnaire. This study investigated the potential of predictive models to identify hearing levels in patients with COM. Methods: We comprehensively examined 289 individuals diagnosed with COM, of whom 136 reported tinnitus and 143 did not. This study involved a detailed analysis of various patient characteristics and HHIE questionnaire results. Logistic and Random Forest models were employed and compared based on key performance metrics. Results: The logistic model demonstrated a slightly higher accuracy (73.56%), area under the curve (AUC; 0.73), Kappa value (0.45), and F1 score (0.78) than the Random Forest model. These findings suggest the superior predictive performance of the logistic model in identifying hearing levels in patients with COM. Conclusions: Although the AUC for the logistic regression did not meet the benchmark, this study highlights the potential for enhanced reliability and improved performance metrics using a larger dataset. The integration of prediction modeling techniques and the HHIE questionnaire shows promise for achieving greater diagnostic accuracy and refining intervention strategies for individuals with COM.