We examined the potential interactions of anatomic site (head and arms versus trunk and legs) and CSD (CSD versus non-CSD melanoma) as proxy measures of sun exposure on the relationship between MC1R status (any variant) and BRAF-mutant melanoma. No statistically significant interactions were found (data not shown).In a study by Landi et al. (2006), D84E, R142H, and c.86_87insA were defined as ''r'' rather than ''R''. Reclassification of these variants as ''r'' did not significantly change the results (data not shown). For purposes of comparison with cases included by Landi et al. and Fargnoli et al., we repeated all analyses excluding two cases with melanomas on acral skin and four cases with positive or indeterminate germline CDKN2A mutations (Orlow et al., 2007). These reanalyses did not reveal any significant associations (data not shown).Our data do not support a strong association of MC1R variants with BRAF mutations in our North Carolina population. Differences in the findings between studies may be due, in part, to differing frequencies of distinct MC1R variants between study populations, which may not all be similarly associated with BRAF mutation. Another possibility is that the risk-modifying effect of MC1R variants may vary between populations based on unidentified genetic factors. Climate differences, such as ambient sun exposure, could also influence the relationship between MC1R status and BRAF-mutant melanoma. However, despite our study being the largest to date, the sample sizes are relatively modest in all the studies, and further investigation is necessary to clarify the relationship of germline MC1R variants and BRAF-mutant melanomas among different populations.

Artificial intelligence (AI) in the dental field has recently been widely applied to radiographic image analysis, including the diagnosis of dental caries, the identification of periodontal diseases, and the detection of maxillofacial cysts (Heo et al. 2021). Panoramic radiographic images, which have various advantages in terms of diagnosis, are widely used in the dental AI field. Lee and Jeong, 2020. reported the successful identification and classification of dental implant systems in panoramic radiographic images using AI (Lee & Jeong, 2020). Using CBCT as the gold standard, Hiraiwa et al., 2019 reported that AI can identify the different classifications of root morphology in panoramic radiography with high accuracy (Hiraiwa et al. 2019). AI has recently been used to diagnose lesions of the maxillary sinus or mandible in panoramic radiographs. Murata et al. demonstrated the diagnosis of maxillary sinusitis from panoramic radiographic images using AI (Murata et al. 2019). In addition, Lee et al.

Burning mouth syndrome (BMS) is ambiguous and enigmatic oral condition. Sleep disturbance is one of the most prevalent complaints of patients with chronic pain. The aim of this study was to estimate general sleep characteristics and propensity in patients with BMS.Methods: A total of thirty BMS patients and thirty healthy control subjects were investigated. Self-reported measures of sleep quality were conducted using two widely used methods; the Pittsburgh Sleep Quality Index (PSQI) and Epworth Sleepiness Scale (ESS). Data were analyzed with one-way ANOVA , chi-square, Fisher's exact test, Kruskal-Wallis test, Holm method with 95% confidence interval and p<0.05 significant level.Results: BMS patients showed more poor sleepers than those in control subjects in both ESS and PSQI test. BMS patients also showed statistically significant poorer sleep quality compared with control subjects in both test. When BMS group were divided into three groups on the basis of numeric rating scale, the higher score subjects had, the more mean rank they had in the PSQI.Conclusions: BMS patients showed up poor sleep characteristics and propensity than control group, and they also showed the more severe the pain was, the worse the sleep quality was.

Background/purpose The leading symptom of temporomandibular disorders (TMD) is pain, and psychological factors are involved in the persistence of TMD-related pain. Therefore, this study was aimed to analyze the influence of psychological factors on the prognosis of TMD-related pain. Materials and methods The medical records of 486 patients with TMD-related pain were analyzed. Each patient's psychological profile was assessed using the Symptom Checklist-90-Revised (SCL-90-R). Patients were classified into two groups according to a post-treatment numeric rating scale (NRS). Patients with an NRS score of 0 or 1 at the last visit were classified into group G, and those with an NRS score of 2 or greater were classified into group P. Following this, all patients were re-classified into groups N and R according to pain recurrence. Statistical analysis was performed to evaluate differences in the SCL-90-R T scores between the groups. In addition, multiple logistic regression analysis was used to identify psychological factors that affected treatment outcome. Results The patients in groups P and R had higher scores in all subscales of the SCL-90-R than groups G and N, respectively. In particular, somatization (SOM) and psychoticism (PSY) scores showed significant differences between the groups in the treatment outcome. Conclusion A correlation is identified between psychological factors and treatment outcome in patients with TMD-related pain. In particular, patients with elevated SOM and PSY scores are more likely to develop refractory pain, and thus require additional interventions to control this risk.