Health Sciences University of Hokkaido Academic Repository / 北海道医療大学学術リポジトリ
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A Case of Skeletal Class II Malocclusion with Severe Anterior Open Bite Treated with Nonsurgical Orthodontic Therapy
Get PDFSkeletal open bite is a challenging malocclusion characterized by a lack of incisal contact, increased anterior facial height, impaired masticatory function, and esthetic disharmony, all of which can significantly compromise a patient’s quality of life (QOL). Traditionally, non−surgical orthodontic approaches, such as the use of multiloop edgewise archwires, have been employed to extrude anterior teeth or intrude molars. However, such approaches often have limited efficacy in producing substantial skeletal changes. While surgical orthodontic treatment can provide comprehensive correction of both dental and skeletal discrepancies, not all patients are candidates for surgery due to factors such as age, systemic health conditions, or personal preference. Against this backdrop, Temporary Anchorage Devices (TADs), including orthodontic mini−implants and anchorage plates, have gained attention in recent years. These systems provide absolute anchorage, enabling three − dimensional tooth movement—including molar intrusion and incisor retraction—that was previously difficult to achieve without surgical intervention. In particular, the use of skeletal anchorage plates in both the maxilla and mandible offers a promising non−surgical strategy for simultaneously controlling the vertical dimension and improving facial morphology.
This case report describes the non−surgical treatment of a patient with skeletal maxillary protrusion, high mandibular plane angle, and anterior open bite, in which skeletal anchorage plates were placed bilaterally in both jaws to achieve molar intrusion, resulting in favorable clinical outcomes.departmental bulletin pape
Development of a device for real-time measurement and remote monitoring of CO2 concentration for SARS-CoV-2 infection control and implementation of short-term prediction of CO2 concentration trends using machine learning
Get PDFTo help prevent the spread of SARS−CoV−2, we developed a CO2 monitoring system for university lecture halls and clinical training rooms. This system enables real−time measurement, display, and remote monitoring of CO2 concentrations to determine appropriate timing of ventilation. Through continuous monitoring, we found that indoor CO2 levels can be maintained below 1,000 ppm with minimal ventilation when appropriate countermeasures are in place. In contrast, CO2 concentrations in medical settings, where ventilation was sufficient, did not exceed 1,000 ppm. Additionally, we developed a short−term CO2 prediction model using a Random Forest classifier trained on historical environmental data. This model was able to successfully predict CO2 levels exceeding 1,000 ppm approximately five minutes in advance. These results suggest that data−driven ventilation guidance and automated prediction systems can contribute to effective and sustainable infection control in educational and clinical settings.departmental bulletin pape
