METHODS: Thirty Wistar rats were used in the study. A defect was created in each animal's femur using a low-speed diamond bur. In the control group, the bone was then treated with polyethylene glycol (PEG). In one of the other groups, the bone was treated with hydroxyapatite, and in the other, with ellagic acid-hydroxyapatite. The femur was biopsied 7 days after the procedure and again 14 days after the procedure, and an indirect immunohistochemical (IHC) examination was performed for TNF-α, IL-10, BMP-4, and OPN expression.
RESULTS: The ellagic acid-hydroxyapatite decreased TNF-α expression in the bone tissue after 7 days and again after 14 days (p
Aim: To produce an accurate model of BCVA changes of postpterygium surgery according to its morphological characteristics by using the machine learning technique. Methodology. A retrospective of the secondary dataset of 93 samples of pterygium patients with different pterygium attributes was used and imported into four different machine learning algorithms in RapidMiner software to predict the improvement of BCVA after pterygium surgery.
Results: The performance of four machine learning techniques were evaluated, and it showed the support vector machine (SVM) model had the highest average accuracy (94.44% ± 5.86%), specificity (100%), and sensitivity (92.14% ± 8.33%).
Conclusion: Machine learning algorithms can produce a highly accurate postsurgery classification model of BCVA changes using pterygium characteristics.
Results: Participants in the RPG and CG reported a statistically significant reduction in knee pain and stiffness (p ≤ 0.05) within the group. The reduction in the scores of knee pain was higher in participants in the RPG than that in participants in the CG (p=0.001). Additionally, participants in the RPG reported greater satisfaction (p=0.001) and higher self-reported exercise adherence (p=0.010) and coordinator-reported exercise adherence (p=0.046) than the participants in the CG.
Conclusion: Short-term effects of the LLRP appear to reduce knee pain and stiffness only, but not physical function and BMI.
Results: This study showed that only 25.2% of our respondents were aware of glaucoma and it is associated with ethnicity, religion, education, and household income. Besides, among those who were aware, they fall into the group of poor knowledge of glaucoma. On the other hand, the knowledge of glaucoma was associated with occupation and the awareness of glaucoma by definition. The validated questionnaire was distributed and the data were analyzed by SPSS software using t-test, one-way ANOVA, and chi-square test.
Conclusion: Awareness and knowledge of glaucoma in this population is low. These findings suggest that there is a need for an efficient information and education strategy to be designed and conducted to increase the awareness and knowledge of glaucoma so that early detection can be made and effective management of individuals with this condition can be delivered.