When large engineered tissue structures are used to achieve tissue regeneration, formation of vasculature is an essential process.

Multiple myeloma (MM) is characterized by a neoplastic proliferation of plasma cells primarily in the bone marrow. Bisphosphonates (BP) are used as supportive therapy in the management of MM. This study aimed to analyze the incidence, risk factors, and clinical outcomes of medication-related necrosis of the jaw (MRONJ) in MM patients. One hundred thirty MM patients who had previous dental evaluations were retrospectively reviewed. Based on several findings, we applied the staging and treatment strategies on MRONJ. We analyzed gender, age, type of BP, incidence, and local etiological factors and assessed the relationship between these factors and the clinical findings at the first oral examination. MRONJ was found in nine male patients (6.9%). The mean patient age was 62.2 years. The median BP administration time was 19 months. Seven patients were treated with a combination of IV zoledronate and pamidronate, and two patients received single-agent therapy. The lesions were predominantly located in the mandible (n = 8), and the most common predisposing dental factor was a history of prior extraction (n = 6). Half of the MRONJ were related to diseases found on the initial dental screen. Patients with MRONJ were treated with infection control and antibiotic therapy. When comparing between the MRONJ stage and each factor (sign, location, etiologic factor, BP type, treatment, and outcome), there were no significant differences between stages, except for between the stage and sign (with or without purulence). For prevention of MRONJ, we recommend routine dental examinations and treatment prior to starting BP therapy.

Socioeconomic status (SES) is associated with systemic disease and influences oral and general health. Several studies have found inequalities associated with oral health and SES. We examined the relationship between tooth loss and SES in Korean adults using data from the 2012–2013 Korean National Health and Nutrition Examination Survey. Methods: A total of 7,005 participants were included in this study. Subjects were divided into two groups depending on their total number of natural teeth: <20 and ≥20. Next, participants were divided into quartiles depending on household income and educational level. Multivariate logistic regression was used to obtain odds ratios (OR) for remaining teeth according to income and education levels. As income and education levels increased, subjects were more likely to have ≥20 remaining teeth (p-value and p-value for trend <0.001), brush their teeth more than three times per day, use extra oral products, and have regular oral-health checkups (all p<0.001). The odds of having ≥20 remaining teeth increased with increases in income and education, after adjusting for all covariates (OR = 1.493 for income Q3, OR = 1.571 for income Q4; OR = 1.763 for 10–12 years education, OR = 2.189 for ≥13years education). Subjects with higher SES had more remaining teeth than subjects with lower SES. Preserving remaining teeth should be encouraged in subjects with low SES by promoting good oral-health behavior and encouraging more oral-health checkups.

Background: Accurate age estimation is vital for clinical and forensic purposes. With the rapid advancement of artificial intelligence(AI) technologies, traditional methods relying on tooth development, while reliable, can be enhanced by leveraging deep learning, particularly neural networks. This study evaluated the efficiency of an AI model by applying the entire panoramic image for age estimation. The outcome performances were analyzed through supervised learning (SL) models. Methods: Total of 27,877 dental panorama images from 5 to 90 years of age were classified by 2 types of grouping. In type 1 they were classified by each age and in type 2, applying heuristic grouping, the age over 20 years were classified by every 5 years. Wide ResNet (WRN) and DenseNet (DN) were used for supervised learning. In addition, the analysis with ± 3 years of deviation in both types were performed. Results: For the DN model, while the type 1 grouping achieved an accuracy of 0.1016 and F1 score of 0.058, the type 2 achieved an accuracy of 0.3146 and F1 score of 0.2027. Incorporating ± 3years of deviation, the accuracy of type 1 and 2 were 0.281, 0.7323 respectively; and the F1 score were 0.1768, 0.6583 respectively. For the WRN model, while the type 1 grouping achieved an accuracy of 0.1041 and F1 score of 0.0599, the type 2 achieved an accuracy of 0.3182 and F1 score of 0.2071. Incorporating ± 3years of deviation, the accuracy of type 1 and 2 were 0.2716, 0.7323 respectively; and the F1 score were 0.1709, 0.6437 respectively. Conclusions: The application of entire panorama image data for supervised with classification by heuristics grouping with ± 3years of deviation for supervised learning models and demonstrated satisfactory outcome for the age estimation.

During the ongoing coronavirus disease (COVID-19) pandemic, hospitals have strengthened their guidelines on infection prevention and control (IPC), and a rigorous adherence to these guidelines is crucial. An infection control surveillance-working group (ICS-WG) and infection control coordinators (ICCs) team were created to monitor the IPC practices of the healthcare workers (HCWs) in a regional hospital in Korea. This study analyzed the surveillance results and aimed to identify what IPC practices needed improvement. During phase 1 (March to April 2020), the ICS-WG performed random audits, recorded incidences of improper IPC practices, and provided advice to the violators. During phase 2 (April to July), the ICCs inspected the hospital units and proposed practical ideas about IPC. The surveillance and proposals targeted the following practices: patient screening, usage of personal protective equipment (PPE), hand and respiratory hygiene, equipment reprocessing, environmental cleaning, management of medical waste, and social distancing. In phase 1, of the 127 violations observed, most (32.3%) corresponded to hand and respiratory hygiene. In phase 2, the highest proportion of violation per category was observed in the management of medical waste (37.8%); among these, a higher proportion of violation (71.4%) was observed in the collection of medical waste. Of the 106 proposals made by the ICCs, the most addressed practice was patient screening (28.3%). No case of nosocomial infection was reported during the study period. Adherence to proper hand and respiratory hygiene was inadequate at the early stage of the COVID-19 pandemic. The results indicate that more attention and further training are needed for the management of medical waste, particularly medical waste collection, and that continuous upgrading of the strategies for patient screening is essential. These results will be useful in helping other healthcare facilities to establish their IPC strategies.

Objectives: The aim of the study was to evaluate the efficacy of traditional supervised learning (SL) and semi-supervised learning (SSL) in the classification of mandibular third molars (Mn3s) on panoramic images. The simplicity of preprocessing step and the outcome of the performance of SL and SSL were analyzed. Methods: Total 1625 Mn3s cropped images from 1000 panoramic images were labeled for classifications of the depth of impaction (D class), spatial relation with adjacent second molar (S class), and relationship with inferior alveolar nerve canal (N class). For the SL model, WideResNet (WRN) was applicated and for the SSL model, LaplaceNet (LN) was utilized. Results: In the WRN model, 300 labeled images for D and S classes, and 360 labeled images for N class were used for training and validation. In the LN model, only 40 labeled images for D, S, and N classes were used for learning. The F1 score were 0.87, 0.87, and 0.83 in WRN model, 0.84, 0.94, and 0.80 for D class, S class, and N class in the LN model, respectively. Conclusions: These results confirmed that the LN model applied as SSL, even utilizing a small number of labeled images, demonstrated the satisfactory of the prediction accuracy similar to that of the WRN model as SL.