ABSTRACT
In rare cases, antiresoprtive medications for osteoporosis cause osteonecrosis. Necrotic bone lesions localized at the jaw after the use of antiresorptive medications is named medicationrelated osteonecrosis of the jaw (MRONJ). Although some cases with MRONJ affect the paranasal sinus, inferior turbinate necrosis have not been reported in South Korea yet. Here, we report a case of inferior turbinate osteonecrosis that developed after denosumab had been used.
ABSTRACT
Background@#and PurposeResponses to oral appliances (OAs) in obstructive sleep apnea (OSA) vary, and have not been fully evaluated in Korean patients. In this study we aimed to determine the efficacy of OAs for the first-line treatment of Korean patients with moderate or severe OSA. @*Methods@#This multicenter prospective observational study included 45 patients with moderate or severe OSA that had been newly diagnosed between March 2017 and May 2018 and who underwent OA treatment for 1 month. Questionnaires were completed and polysomnography (PSG) was performed before and after OA treatment. The primary outcome measures were improvement in the absolute apnea-hypopnea index (AHI) and the percentage reduction in the AHI. The secondary outcomes were improvements in the questionnaire scores related to sleep-associated symptoms and PSG parameters. @*Results@#The patients were aged 47.4±12.1 years (mean±SD), only two of them were female, and their AHI at baseline was 29.7±10.9/h. After OA treatment the AHI had reduced by 63.9±25.8%, with the reduction was similar between the patients with moderate OSA and those with severe OSA. Overall 31.1% of the patients achieved a normal AHI (<5/h), and 64.4% had an AHI of ≤10/h after the treatment. The body mass index (BMI) was the most reliable factor for predicting the percentage reduction in the AHI. The OAs also improved the sleep architecture and subjective sleep-related symptoms. @*Conclusions@#The OAs were effective in patients with moderate or severe OSA. The OAs reduced the mean AHI to 63.9% of the baseline value, and this reduction was influenced by the BMI.
ABSTRACT
PURPOSE: This study was conducted to develop and validate an individualized prediction model for automated detection of acquired taxane resistance (ATR). MATERIALS AND METHODS: Penalized regression, combinedwith an individualized pathway score algorithm,was applied to construct a predictive model using publically available genomic cohorts of ATR and intrinsic taxane resistance (ITR). To develop a model with enhanced generalizability, we merged multiple ATR studies then updated the learning parameter via robust cross-study validation. RESULTS: For internal cross-study validation, the ATR model produced a perfect performance with an overall area under the receiver operating curve (AUROC) of 1.000 with an area under the precision-recall curve (AUPRC) of 1.000, a Brier score of 0.007, a sensitivity and a specificity of 100%. The model showed an excellent performance on two independent blind ATR cohorts (overall AUROC of 0.940, AUPRC of 0.940, a Brier score of 0.127). When we applied our algorithm to two large-scale pharmacogenomic resources for ITR, the Cancer Genome Project (CGP) and the Cancer Cell Line Encyclopedia (CCLE), an overall ITR cross-study AUROC was 0.70, which is a far better accuracy than an almost random level reported by previous studies. Furthermore, this model had a high transferability on blind ATR cohorts with an AUROC of 0.69, suggesting that general predictive features may be at work across both ITR and ATR. CONCLUSION: We successfully constructed a multi-study–derived personalized prediction model for ATR with excellent accuracy, generalizability, and transferability.