Comparison of Pulsed and Continuous Wave Diode Laser at 940 nm on the Viability and Migration of Gingival Fibroblasts.

Gingival fibroblasts have critical roles in oral wound healing. Photobiomodulation (PBM) has been shown to promote mucosal healing and is now recommended for managing oncotherapy-associated oral mucositis. This study examined the effects of the emission mode of a 940 nm diode laser on the viability and migration of human gingival fibroblasts. Cells were cultured in a routine growth media and treated with PBM (average power 0.1 W cm-2 , average fluence 3 J cm-2 , every 12 h for six sessions) in one continuous wave and two pulsing settings with 20% and 50% duty cycles. Cell viability was assessed using MTT, and digital imaging quantified cell migration. After 48 and 72 h, all treatment groups had significantly higher viability (n = 6, P < 0.05) compared with the control. The highest viability was seen in the pulsed (20% duty cycle) group at the 72-h time point. PBM improved fibroblast migration in all PBM-treated groups, but differences were not statistically significant (n = 2, P > 0.05). PBM treatments can promote cell viability in both continuous and pulsed modes. Further studies are needed to elucidate the optimal setting for PBM-evoked responses for its rationalized use in promoting specific phases of oral wound healing.

Prediction of Hepatitis disease using ensemble learning methods.

Objective: Hepatitis is one of the chronic diseases that can lead to liver cirrhosis and hepatocellular carcinoma, which cause deaths around the world. Hence, early diagnosis is needed to control, treat, and reduce the effects of this disease. This study's main goal was to compare the performance of traditional and ensemble learning methods for predicting hepatitis B virus (HBV), and hepatitis C virus (HCV). Also, important variables related to HBV and HCV were identified. Methods: This case-control study was conducted in Hamadan Province, in the west of Iran, between 2014 to 2019. It included 534 subjects (267 cases and 267 controls). The bagging, random forest, AdaBoost, and logistic regression were used for predicting HBV and HCV. These methods' performance was evaluated using accuracy. Results: According to the results, the accuracy of bagging, random forest, Adaboost, and logistic regression were 0.65 ± 0.03, 0.66 ± 0.03, 0.62 ± 0.04, and 0.64 ± 0.03, respectively, with random forest showing the best performance for predicting HBV. This method showed that ALT was the most important variable for predicting HBV. The the accuracy of random forest was 0.77±0.03 for predicting HCV. Also, the random forest showed that the order of variable importance has belonged to AST, ALT, and age for predicting HCV. Conclusion: This study showed that random forest performed better than other methods for predicting HBV and HCV.

Prediction the survival of patients with breast cancer using random survival forests for competing risks.

Objectives: Breast cancer (BC) is the most common cause of cancer death in Iranian women. Sometimes death from other causes precludes the event of interest and makes the analysis complicated. The purpose of this study was to identify important prognostic factors associated with survival duration among patients with BC using random survival forests (RSF) model in presence of competing risks. Also, its performance was compared with cause-specific hazard model. Methods: This retrospective cohort study assessed 222 patients with BC who were admitted to Ayatollah Khansari hospital in Arak, a major industrial city and the capital of Markazi province in Iran. The cause-specific Cox proportional hazards and RSF models were employed to determine the important risk factors for survival of the patients. Results: The mean and median survival duration of the patients were 90.71 (95%CI: 83.8-97.6) and 100.73 (95%CI: 89.2-121.5) months, respectively. The cause-specific model indicated that type of surgery and HER2 had statistically significant effects on the risk of death of BC. Moreover, the RSF model identified that HER2 was the most important variable for the event of interest. Conclusion: According to the results of this study, the performance of the RSF model was better than the cause-specific hazard model. Moreover, HER2 was the most important variable for death of BC in both of the models.