Laser Applications in Regenerative Endodontics: A Review.

Introduction: Developing regenerative endodontic treatment (RET) is an exciting approach to managing immature permanent teeth with pulp necrosis. RET is usually performed in two clinical steps: disinfection (first step) and tissue engineering (second step). Recently, laser therapy has entered the field of RET. This study aimed to provide an overview of the literature that employed laser therapy for root regeneration. Methods: A comprehensive search was performed on four databases, including PubMed, Web of Science, Scopus, and Google Scholar. The searched keywords were laser, regenerative endodontics, immature permanent teeth, and dental pulp necrosis, and related English-published articles were included up to October 2023. Results: Thirteen studies utilized a laser for RET. In the first step of RET, both high-power and low-level lasers (through photodynamic therapy [PDT]) may be applied for canal disinfection. In contrast, regenerative procedures in the second step of RET are just accelerated by low-power lasers (biostimulation). The literature does not support the benefit of laser-assisted irrigation in improving the clinical success of RET. There is some evidence that laser-assisted disinfection with a diode laser may provide comparable results to triple antibiotic paste in reducing bacterial counts in root canals while providing slightly better clinical and radiographic outcomes. PDT may be an effective and suitable adjunct to conventional disinfection methods in immature, necrotic teeth. Conclusion: Low-power lasers may be beneficial tools for improving the results of regenerative endodontics through chemical disinfection in the first step (PDT) or by biostimulation in the second step of RET.

Machine learning algorithms to uncover risk factors of breast cancer: insights from a large case-control study.

Introduction: This large case-control study explored the application of machine learning models to identify risk factors for primary invasive incident breast cancer (BC) in the Iranian population. This study serves as a bridge toward improved BC prevention, early detection, and management through the identification of modifiable and unmodifiable risk factors. Methods: The dataset includes 1,009 cases and 1,009 controls, with comprehensive data on lifestyle, health-behavior, reproductive and sociodemographic factors. Different machine learning models, namely Random Forest (RF), Neural Networks (NN), Bootstrap Aggregating Classification and Regression Trees (Bagged CART), and Extreme Gradient Boosting Tree (XGBoost), were employed to analyze the data. Results: The findings highlight the significance of a chest X-ray history, deliberate weight loss, abortion history, and post-menopausal status as predictors. Factors such as second-hand smoking, lower education, menarche age (>14), occupation (employed), first delivery age (18-23), and breastfeeding duration (>42 months) were also identified as important predictors in multiple models. The RF model exhibited the highest Area Under the Curve (AUC) value of 0.9, as indicated by the Receiver Operating Characteristic (ROC) curve. Following closely was the Bagged CART model with an AUC of 0.89, while the XGBoost model achieved a slightly lower AUC of 0.78. In contrast, the NN model demonstrated the lowest AUC of 0.74. On the other hand, the RF model achieved an accuracy of 83.9% and a Kappa coefficient of 67.8% and the XGBoost, achieved a lower accuracy of 82.5% and a lower Kappa coefficient of 0.6. Conclusion: This study could be beneficial for targeted preventive measures according to the main risk factors for BC among high-risk women.

Socioeconomic Disparities in Iranians' Preferences toward Dental Care Services: A Population-Based Survey.

Background and Aims: Patients' perspectives and preferences are considered an essential influencing factor for healthcare utilization. This study is one of the first to investigate patient preference for dental services across socioeconomic and demographic indicators in Iran. Materials and Methods: This cross-sectional study was conducted through telephone interviews with adult residents in Mashhad and Kerman cities. A representative sample was selected by stratified random sampling. A valid structured questionnaire was used for data collection, including people's preference toward dental care services in terms of dental settings, choosing a general or specialist dentist, prevention or treatment, and the preferable gender of the dentist. Factors potentially associated with preferences included gender, age, educational level, job, monthly income, house size, family number, insurance coverage, dental insurance, type of insurance, and social class in the city were investigated. Results: 1475 individuals participated in the study [response rate 63%]. Our findings showed higher preferences for private offices (50.6%), specialist dentists (76.2%), treatment services (40.8%), and no specific gender preference for the dentist (60.6%). Their preferences were significantly influenced by age range, social class, insurance status, dental insurance, and type of insurance. Income, household size, level of education, and job were not statistically significant with none of the preferences. Conclusions: Socioeconomic and demographic factors' correlation with people's preferences was observed. Efforts are needed to promote preventive care demand in deprived regions. Moreover, increasing financial resources allocated to preventive care and covering preventive dental services in insurance plans are recommended.