Methods: Thirty patients (25 females, 5 males; mean age, 22.66 ± 3.27 years) who presented with moderate crowding of the upper labial segment and underwent extraction-based fixed appliance treatment were recruited. They were randomly allocated to receive adjunctive therapy with MOPs (n = 15) or treatment with fixed appliances only (control group; n = 15). EARR was measured from long-cone periapical radiographs taken at the start and the sixth month of treatment. A correction factor for the enlargement difference was used to calculate EARR. Data were analyzed with descriptive statistics and repeated-measures analysis of variance.
Results: The mean root lengths of 168 teeth were measured and showed no statistically significant difference (p > 0.05) after six months of fixed appliance treatment in the MOP (mean difference [MD] = 0.13 mm; 95% confidence interval [CI] = -0.10-0.35) and control group (MD = 0.14 mm; 95% CI = -0.10-0.37). Most of the roots in the MOP and control groups (42.86% and 52.38%, respectively) showed only mild resorption. Less than 8% of the roots in both groups (7.14% in the MOP group and 4.76% in the control group) showed moderate resorption.
Conclusions: Acceleration of orthodontic tooth movement with adjunctive MOPs therapy during the alignment phase does not exacerbate EARR in patients with moderate crowding of the upper labial segment in comparison with controls.
Methods: This study employed a qualitative design. Semi-structured interviews (n = 20) were conducted with key opinion leaders from 14 countries. The participants were predominantly members of the International COVID-19 and Cancer Taskforce, who convened in March 2020 to address delivery of cancer care in the context of the pandemic. The Framework Method was employed to analyse the positive changes of the pandemic with corresponding challenges to their maintenance post-pandemic.
Results: Ten themes of positive changes were identified which included: value in cancer care, digital communication, convenience, inclusivity and cooperation, decentralisation of cancer care, acceleration of policy change, human interactions, hygiene practices, health awareness and promotion and systems improvement. Impediments to the scale-up of these positive changes included resource disparities and variation in legal frameworks across regions. Barriers were largely attributed to behaviours and attitudes of stakeholders.
Conclusion: The COVID-19 pandemic has led to important value-based innovations and changes for better cancer care across different health systems. The challenges to maintaining/implementing these changes vary by setting. Efforts are needed to implement improved elements of care that evolved during the pandemic.
METHODOLOGY: The test was conducted for two different road conditions, tarmac and dirt roads. HAV exposure was measured using a Brüel & Kjær Type 3649 vibration analyzer, which is capable of recording HAV exposures from steering wheels. The data was analyzed using I-kaz Vibro to determine the HAV values in relation to varying speeds of a truck and to determine the degree of data scattering for HAV data signals.
RESULTS: Based on the results obtained, HAV experienced by drivers can be determined using the daily vibration exposure A(8), I-kaz Vibro coefficient (Ƶ(v)(∞)), and the I-kaz Vibro display. The I-kaz Vibro displays also showed greater scatterings, indicating that the values of Ƶ(v)(∞) and A(8) were increasing. Prediction of HAV exposure was done using the developed regression model and graphical representations of Ƶ(v)(∞). The results of the regression model showed that Ƶ(v)(∞) increased when the vehicle speed and HAV exposure increased.
DISCUSSION: For model validation, predicted and measured noise exposures were compared, and high coefficient of correlation (R(2)) values were obtained, indicating that good agreement was obtained between them. By using the developed regression model, we can easily predict HAV exposure from steering wheels for HAV exposure monitoring.