Comparison of combined application treatment with one-visit varnish treatments in an orthodontic population

Objective: To evaluate the effect of chlorhexidine-thymol varnish alone, its combination with chlorhexidine-fluo - ride containing dentifrice and fluoride varnish on oral hygiene and caries prevention in orthodontic patients. Study design: Sixty patients, aged 12-18, with orthodontic fixed appliances were randomly assigned into three groups as follows: Group 1 (n=20): 1% chlorhexidine and 1% thymol varnish (Cervitec ® Plus); Group 2 (n=20): Cervitec ® Plus+ 0.2% chlorhexidine and 0.2% sodium fluoride (900 ppm fluoride) (Cervitec ® Gel)); and Group 3 (n=20): 0.1% fluoride varnish (Fluor Protector ® ). Mutans streptococci (MS), lactobacilli (LB) levels, buffering capacity (BC), visible plaque index (VPI), and gingival bleeding index (GBI) scores were evaluated at four stages: T 0 , before orthodontic bonding; T 1 , one week after orthodontic bonding; T 2 , one week; and T 3 , four weeks after the first application, respectively. Inter and intra group comparisons were made by the Kruskal-Wallis, Mann- Whitney U, Friedman and Wilcoxon Signed-Rank tests with Bonferroni step-down correction ( P< 0.017). Results: Significantly lower MS and LB levels were found in Group 2 than Group 1 (T 2 ) and 3 (T 2 , T 3 ) ( P< 0.017). Groups 1-2 (T 2 ) showed significantly higher BC ( P< 0.017) and lower VPI and GBI ( P< 0.017) scores compared with Group 3. Decreased MS levels at T 2 ( P< 0.017) and T 3 ( P> 0.017) were found in Group1-2 compared with T 0 . Significantly lower LB levels were recorded in Group 2 at T 2 compared with T0 ( P< 0.017) while no significant differences were seen in Group 1 and 3 ( P> 0.017). Conclusions: Addition of Cervitec ® Plus+Cervitec ® Gel combination to the standard oral hygiene regimen may be beneficial for orthodontic patients for maintaining oral health by reducing bacterial colonisation and gingivitis

Hybrid deep feature generation for appropriate face mask use detection

Mask usage is one of the most important precautions to limit the spread of COVID-19. Therefore, hygiene rules enforce the correct use of face coverings. Automated mask usage classification might be used to improve compliance monitoring. This study deals with the problem of inappropriate mask use. To address that problem, 2075 face mask usage images were collected. The individual images were labeled as either mask, no masked, or improper mask. Based on these labels, the following three cases were created: Case 1: mask versus no mask versus improper mask, Case 2: mask versus no mask + improper mask, and Case 3: mask versus no mask. This data was used to train and test a hybrid deep feature-based masked face classification model. The presented method comprises of three primary stages: (i) pre-trained ResNet101 and DenseNet201 were used as feature generators; each of these generators extracted 1000 features from an image; (ii) the most discriminative features were selected using an improved RelieF selector; and (iii) the chosen features were used to train and test a support vector machine classifier. That resulting model attained 95.95%, 97.49%, and 100.0% classification accuracy rates on Case 1, Case 2, and Case 3, respectively. Having achieved these high accuracy values indicates that the proposed model is fit for a practical trial to detect appropriate face mask use in real time

A Hand-Modeled Feature Extraction-Based Learning Network to Detect Grasps Using sEMG Signal

Recently, deep models have been very popular because they achieve excellent performance with many classification problems. Deep networks have high computational complexities and require specific hardware. To overcome this problem (without decreasing classification ability), a hand-modeled feature selection method is proposed in this paper. A new shape-based local feature extractor is presented which uses the geometric shape of the frustum. By using a frustum pattern, textural features are generated. Moreover, statistical features have been extracted in this model. Textures and statistics features are fused, and a hybrid feature extraction phase is obtained; these features are low-level. To generate high level features, tunable Q factor wavelet transform (TQWT) is used. The presented hybrid feature generator creates 154 feature vectors; hence, it is named Frustum154. In the multilevel feature creation phase, this model can select the appropriate feature vectors automatically and create the final feature vector by merging the appropriate feature vectors. Iterative neighborhood component analysis (INCA) chooses the best feature vector, and shallow classifiers are then used. Frustum154 has been tested on three basic hand-movement sEMG datasets. Hand-movement sEMG datasets are commonly used in biomedical engineering, but there are some problems in this area. The presented models generally required one dataset to achieve high classification ability. In this work, three sEMG datasets have been used to test the performance of Frustum154. The presented model is self-organized and selects the most informative subbands and features automatically. It achieved 98.89%, 94.94%, and 95.30% classification accuracies using shallow classifiers, indicating that Frustum154 can improve classification accuracy

Developing quantitative experimental model systems to study drug resistance

It is widely known that secondary resistance inevitably leads to treatment failure through Darwinian evolution. Therefore, quantifying the clonal evolution using experimental model systems can hold a great promise in designing evolutionarily informed therapies, and thus, in predicting drug response. In this talk, I present our recently developed strategy that contributed to the understanding of collateral drug sensitivity with its direct link to clonal evolution to overcome the drug resistance in non-small cell lung cancer cell line model system. Additionally, I also present a similar approach that has been developed to delineate chemotherapy induced clonal alterations in colorectal cancer cell line model systems. More specifically, high-complexity cellular barcoding allowed us the identification of the resistance that was ultimately driven by the presence and emergence of multiple pre-existing and de novo resistant clones, respectively. Overall, our work highlights evolutionary tradeoffs and provides an opportunity to exploit the tumour vulnerability

Effect of different surface treatments on the repair bond strength of resin composites with titanium

Ates, Sabit Melih/0000-0001-7137-2096WOS: 000480836200001The purpose of this study was to evaluate the effect of different surface treatment combinations on the bonding of composite resins to NiCr and titanium alloys after thermal cycling. Square-shaped specimens (10 mm x 10 mm x 2 mm) were made from NiCr and titanium alloys. the specimens were divided into 6 pretreatment groups (n = 11): (1) machined titanium (control, no treatment); (2) CoJet sand application; (3) grinding with a diamond bur; (4) metal primer application; (5) CoJet sand + metal primer application; and (6) grinding with a diamond bur + metal primer application. the surface roughness of the mechanically treated specimens (control, grinding, CoJet sand) was evaluated. the surface morphology of both metals and elemental composition were examined with SEM and EDS. the composite resin was applied to the specimens. Shear bond strength (SBS) was tested after thermal cycling (5000 cycles, 5 degrees C to 55 degrees C). Failure modes were determined. the data were analyzed using the Shapiro-Wilk test, two-way ANOVA and post hoc Fisher's LSD test (p = .05). For titanium specimens, the grinding + metal primer exhibited higher values than the other groups, and all groups showed higher SBS values than the control group. Combined use of CoJet sand, grinding with a diamond bur, and metal primer application would be useful for enhancing the bond strength of composite resin to titanium. the grinding of the NiCr surface with a diamond bur is the only method that could improve the bond strength of a composite resin compared to the other methods

InCR: Inception and concatenation residual block-based deep learning network for damaged building detection using remote sensing images

In February 2023, Turkey experienced a series of earthquakes that caused significant damage to buildings and affected many people. Detecting building damage quickly is crucial for helping earthquake victims, and we believe machine learning models offer a promising solution. In our research, we introduce a new, lightweight deep-learning model capable of accurately classifying damaged buildings in remote-sensing datasets.Our main goal is to create an automated damage detection system using a novel deep-learning model. We started by collecting a new dataset with two categories: damaged and undamaged buildings. Then, we developed a unique convolutional neural network (CNN) called the inception and concatenation residual (InCR) deep learning network, which incorporates concatenation-based residual blocks and inception blocks to improve performance.We trained our InCR model on the newly collected dataset and used it to extract features from images using global average pooling. To refine these features and select the most informative ones, we applied iterative neighborhood component analysis (INCA). Finally, we classified the refined features using commonly used shallow classifiers.To evaluate our method, we used tenfold cross-validation (10-fold CV) with eight classifiers. The results showed that all classifiers achieved classification accuracies higher than 98 %. This demonstrates that our proposed InCR model is a viable option for CNNs and can be used to create an accurate automated damage detection application.Our research presents a unique solution to the challenge of automated damage detection after earthquakes, showing promising results that highlight the potential of our approach

CGP17Pat: Automated Schizophrenia Detection Based on a Cyclic Group of Prime Order Patterns Using EEG Signals

Background and Purpose: Machine learning models have been used to diagnose schizophrenia. The main purpose of this research is to introduce an effective schizophrenia hand-modeled classification method. Method: A public electroencephalogram (EEG) signal data set was used in this work, and an automated schizophrenia detection model is presented using a cyclic group of prime order with a modulo 17 operator. Therefore, the presented feature extractor was named as the cyclic group of prime order pattern, CGP17Pat. Using the proposed CGP17Pat, a new multilevel feature extraction model is presented. To choose a highly distinctive feature, iterative neighborhood component analysis (INCA) was used, and these features were classified using k-nearest neighbors (kNN) with the 10-fold cross-validation and leave-one-subject-out (LOSO) validation techniques. Finally, iterative hard majority voting was employed in the last phase to obtain channel-wise results, and the general results were calculated. Results: The presented CGP17Pat-based EEG classification model attained 99.91% accuracy employing 10-fold cross-validation and 84.33% accuracy using the LOSO strategy. Conclusions: The findings and results depicted the high classification ability of the presented cryptologic pattern for the data set used