Evidence-based effectiveness of Ozone therapy in the treatment for oral lichen planus - A systematic review.

Oral lichen planus is a common, chronic mucocutaneous condition of uncertain origin. Early treatment of OLP can dramatically reduce the risk of further development, which in turn reduces the risk of developing cancer. Numerous methods can be used to treat OLP. Since the significance of ozone in treating this disease is still uncertain. This systematic review was conducted based on english databases, including PUBMED, SCOPUS, Embase, Ovid, and Journal of Web up to July 2022. We used the search phrases "ozone," "ozone in the treatment of oral lichen planus," "oral lichen planus," and "ozone therapy." Finally, five papers were selected for qualitative analysis. This review included a total of five papers, four of which were clinical trials and one was a longitudinal study. All studies included the erosive form of OLP, also ozone therapy was applied to patients who did not respond to conventional treatment. Ozone showed significant therapeutic effects in terms of reduction in pain and size of the lesion. The signs and symptoms associated with OLP such as burning sensation, lesion size, and scarring all considerably improved with ozone therapy.

Stereomicroscopic evaluation of sealing ability of four different root canal sealers: an in-vitro study.

AIM: To compare and evaluate the sealing ability of four different commercially available sealers to provide seal against the dye penetration test using a stereomicroscope-an in-vitro study. MATERIAL/METHOD: 80 extracted single rooted mandibular premolar with single canal were used in this study. The samples were divided in 4 groups (20 in each) based on sealer. Group I (Diaproseal), Group II (apexit Plus), Group III (MTA Fillapex) and Group IV (Bio-C). The samples were analyzed using a stereomicroscope and data analysis was done with one-way Anova And post hoc Tukey's test. RESULT: The mean dye penetration score was 1.2400 ± 0.778 mm for Group I. 2.6000 ± 0.897 mm for Group II, 4.2000 ± 0.923 mm for Group III and 4.225 ± 2.055 mm for Group IV. One-way Anova analysis shows that intergroup comparison was statistically significant between the four groups. The post hoc Tukey's test reveals that the difference was statistically non-significant between group III and group IV. CONCLUSION: It was concluded that between the four groups the Group I (Diaproseal) showed the least dye penetration followed by Group II (Apexit Pus), Group III (MTA Fillapex) and then Group IV (Bio-C), where there was no significant difference between the Group III (MTA Fillapex) and Group IV (Bio-C).

Combining biophysical parameters with thermal and RGB indices using machine learning models for predicting yield in yellow rust affected wheat crop.

Evaluating crop health and forecasting yields in the early stages are crucial for effective crop and market management during periods of biotic stress for both farmers and policymakers. Field experiments were conducted during 2017-18 and 2018-19 with objective to evaluate the effect of yellow rust on various biophysical parameters of 24 wheat cultivars, with varying levels of resistance to yellow rust and to develop machine learning (ML) models with improved accuracy for predicting yield by integrating thermal and RGB indices with crucial plant biophysical parameters. Results revealed that as the level of rust increased, so did the canopy temperature and there was a significant decrease in crop photosynthesis, transpiration, stomatal conductance, leaf area index, membrane stability index, relative leaf water content, and normalized difference vegetation index due to rust, and the reductions were directly correlated with levels of rust severity. The yield reduction in moderate resistant, low resistant and susceptible cultivars as compared to resistant cultivars, varied from 15.9-16.9%, 28.6-34.4% and 59-61.1%, respectively. The ML models were able to provide relatively accurate early yield estimates, with the accuracy increasing as the harvest approached. The yield prediction performance of the different ML models varied with the stage of the crop growth. Based on the validation output of different ML models, Cubist, PLS, and SpikeSlab models were found to be effective in predicting the wheat yield at an early stage (55-60 days after sowing) of crop growth. The KNN, Cubist, SLR, RF, SpikeSlab, XGB, GPR and PLS models were proved to be more useful in predicting the crop yield at the middle stage (70 days after sowing) of the crop, while RF, SpikeSlab, KNN, Cubist, ELNET, GPR, SLR, XGB and MARS models were found good to predict the crop yield at late stage (80 days after sowing). The study quantified the impact of different levels of rust severity on crop biophysical parameters and demonstrated the usefulness of remote sensing and biophysical parameters data integration using machine-learning models for early yield prediction under biotically stressed conditions.