Effect of repeated firing on the topographical, optical, and mechanical properties of fully crystallized lithium silicate-based ceramics.

STATEMENT OF PROBLEM: The influence of different firing protocols on the topographical, optical, and mechanical properties of fully crystallized computer-aided design and computer-aided manufacturing (CAD-CAM) lithium silicate-based glass-ceramics (LSCs) for dental restorations remains unclear. PURPOSE: The purpose of this in vitro study was to investigate the effect of different firing regimens on the surface roughness, gloss, Martens hardness, indentation modulus, biaxial flexural strength, and crystalline structure of fully crystallized CAD-CAM LSCs and the effect of their interposition on the irradiance of a light-polymerization unit. MATERIAL AND METHODS: Three fully crystallized CAD-CAM LSC blocks were evaluated (N=150): lithium disilicate (Initial LiSi Blocks; LS), zirconia-reinforced silicate (Celtra Duo; CD), and lithium aluminum disilicate (CEREC Tessera; CT). Specimens were allocated to 5 subgroups according to their firing protocol. LSC roughness (Sa) was measured with an optical profilometer, and gloss (GU) was detected with a gloss meter. Martens hardness (HM) and indentation modulus (EIT) data were obtained from a hardness testing machine. The irradiance of a light-polymerization unit and transmittance of LSCs were measured with an instrument (Managing Accurate Resin Curing-Light Collector; BlueLight analytics, Inc) subsequent to ceramic interposition. Crystalline phases were analyzed by X-ray diffraction, and biaxial flexural strength (σ) was determined by the ball-on-3-ball method in a universal testing machine followed by Weibull analysis to calculate characteristic strength (σ0) and Weibull modulus (m). Two-way ANOVA and Tukey HSD post hoc tests (α=.05) were used to analyze the data. RESULTS: Statistically significant differences were found among different treatment groups based on Sa, GU, HM, and EIT values (P<.001). Delivered irradiance was significantly reduced following CT (P<.01) and glazed LSC (P<.005) interposition. CD displayed highest biaxial flexural strength and reliability after 1 firing cycle (σ=568.2 MPa, m=16.8) CONCLUSIONS: The type of material and firing regimens had a significant effect on the topographical, optical, and mechanical properties of fully crystallized CAD-CAM LSCs. Glazing significantly reduced delivered irradiance, Martens hardness, and biaxial flexural strength.

Exam evaluation in prosthodontics across preclinical and clinical years from students' perspective: A cross-sectional study.

INTRODUCTION: The purpose of this study was to explore the students' perceptions and performance in prosthodontics theory exam. METHODS: A cross-sectional descriptive study was conducted on 560 (80.82%) students of different levels (third, fourth and fifth years) to explore their opinions and performance with regard to a number of issues on a prosthodontics theory exam (exam evaluation, exam preparation, exam material, exam timing). Demographic data were also collected. Descriptive statistics were generated and Chi-square test, independent sample t-test, ANOVA test and Pearson's correlation coefficient were used to examine the associations between different variables. The significance level was set at p < .05. RESULTS: Students' responses regarding exam evaluation was influenced by their gender, study level, high-school Grade Point Average (GPA) and undergraduate cumulative GPA. Perceived exam difficulty was significantly affected by gender (p = .03) and study level (p < .001), and negatively correlated to both high-school GPA (p < .001) and university GPA (p = .03). The vast majority (88.2%) depended on lecture hand-outs and lecture notes for study. Exam material and preparation were not significantly affected by any of the demographic variables with most respondents (76.8%) thinking that the lectures blended with prosthodontics laboratories/clinics would improve their understanding of the exam material. The suggested best time to conduct the exam was early afternoon (31.6%). Student performance was significantly affected by the study level (p < .001) and cumulative GPA (p < .001) with significant positive correlation between the high-school GPA and the mark in the exam (r = .29, p < .001) and by the amount of time students spent for exam preparation (p < .001). Those students who reported using textbooks to prepare for the exam got significantly higher marks (66.1 ± 8.7) compared to the students who did not (62.8 ± 9.7) (p = .03). CONCLUSIONS: Course level, GPA and gender were identified as the most influential factors in different aspects of exam evaluation and students' performance. Regular study and use of textbooks were demonstrated to improve academic performance. Additional orientation and guidance relating to the exam (especially for third year students) would be welcomed, as would alternate teaching methods such as small group discussions or study groups.

A Real Time Method for Distinguishing COVID-19 Utilizing 2D-CNN and Transfer Learning.

Rapid identification of COVID-19 can assist in making decisions for effective treatment and epidemic prevention. The PCR-based test is expert-dependent, is time-consuming, and has limited sensitivity. By inspecting Chest R-ray (CXR) images, COVID-19, pneumonia, and other lung infections can be detected in real time. The current, state-of-the-art literature suggests that deep learning (DL) is highly advantageous in automatic disease classification utilizing the CXR images. The goal of this study is to develop models by employing DL models for identifying COVID-19 and other lung disorders more efficiently. For this study, a dataset of 18,564 CXR images with seven disease categories was created from multiple publicly available sources. Four DL architectures including the proposed CNN model and pretrained VGG-16, VGG-19, and Inception-v3 models were applied to identify healthy and six lung diseases (fibrosis, lung opacity, viral pneumonia, bacterial pneumonia, COVID-19, and tuberculosis). Accuracy, precision, recall, f1 score, area under the curve (AUC), and testing time were used to evaluate the performance of these four models. The results demonstrated that the proposed CNN model outperformed all other DL models employed for a seven-class classification with an accuracy of 93.15% and average values for precision, recall, f1-score, and AUC of 0.9343, 0.9443, 0.9386, and 0.9939. The CNN model equally performed well when other multiclass classifications including normal and COVID-19 as the common classes were considered, yielding accuracy values of 98%, 97.49%, 97.81%, 96%, and 96.75% for two, three, four, five, and six classes, respectively. The proposed model can also identify COVID-19 with shorter training and testing times compared to other transfer learning models.

3D-printed nanocomposite denture base resin: The effect of incorporating TiO2 nanoparticles on the growth of Candida albicans.

PURPOSE: To develop a biocompatible denture base resin/TiO2 nanocomposite material with antifungal characteristics that is suitable for 3D-printing denture bases. MATERIALS AND METHODS: TiO2 nanoparticles (NPs) with a 0.10, 0.25, 0.50, and 0.75 weight percent (wt.%) were incorporated into a commercially available 3D-printed resin material. The resulting nanocomposite material was analyzed using Lactate dehydrogenase (LDH) and AlamarBlue (AB) assays for biocompatibility testing with human gingival fibroblasts (HGF). The composite material was also tested for its antifungal efficacy against Candida albicans. Fourier transform infrared (FTIR) and Energy Dispersive X-ray Spectroscopy (EDX) mapping were conducted to assess the surface coating and the dispersion of the NPs. RESULTS: LDH and AB assays confirmed the biocompatibility of the material showing cell proliferation at a rate of nearly 100% at day 10, with a cytotoxicity of less than 13% of the cells at day 10. The concentrations of 0.10, 0.25, and 0.50 wt.% caused a significant reduction (p < 0.05) in the number of candida cells attached to the surface of the specimens (p < 0.05), while 0.75 wt.% did not show any significant difference compared to the control (no TiO2 NPs) (p > 0.05). FTIR and EDX analysis confirmed the presence of TiO2 NPs within the nanocomposite material with a homogenous dispersion for 0.10 and 0.25 wt.% groups and an aggregation of the NPs within the material at higher concentrations. CONCLUSION: The addition of TiO2 NPs into 3D-printed denture base resin proved to have an antifungal effect against Candida albicans. The resultant nanocomposite material was a biocompatible material with HGFs and was successfully used for 3D printing.

Parallel CNN-ELM: A multiclass classification of chest X-ray images to identify seventeen lung diseases including COVID-19.

Numerous epidemic lung diseases such as COVID-19, tuberculosis (TB), and pneumonia have spread over the world, killing millions of people. Medical specialists have experienced challenges in correctly identifying these diseases due to their subtle differences in Chest X-ray images (CXR). To assist the medical experts, this study proposed a computer-aided lung illness identification method based on the CXR images. For the first time, 17 different forms of lung disorders were considered and the study was divided into six trials with each containing two, two, three, four, fourteen, and seventeen different forms of lung disorders. The proposed framework combined robust feature extraction capabilities of a lightweight parallel convolutional neural network (CNN) with the classification abilities of the extreme learning machine algorithm named CNN-ELM. An optimistic accuracy of 90.92% and an area under the curve (AUC) of 96.93% was achieved when 17 classes were classified side by side. It also accurately identified COVID-19 and TB with 99.37% and 99.98% accuracy, respectively, in 0.996 microseconds for a single image. Additionally, the current results also demonstrated that the framework could outperform the existing state-of-the-art (SOTA) models. On top of that, a secondary conclusion drawn from this study was that the prospective framework retained its effectiveness over a range of real-world environments, including balanced-unbalanced or large-small datasets, large multiclass or simple binary class, and high- or low-resolution images. A prototype Android App was also developed to establish the potential of the framework in real-life implementation.

An Ensemble Approach for the Prediction of Diabetes Mellitus Using a Soft Voting Classifier with an Explainable AI.

Diabetes is a chronic disease that continues to be a primary and worldwide health concern since the health of the entire population has been affected by it. Over the years, many academics have attempted to develop a reliable diabetes prediction model using machine learning (ML) algorithms. However, these research investigations have had a minimal impact on clinical practice as the current studies focus mainly on improving the performance of complicated ML models while ignoring their explainability to clinical situations. Therefore, the physicians find it difficult to understand these models and rarely trust them for clinical use. In this study, a carefully constructed, efficient, and interpretable diabetes detection method using an explainable AI has been proposed. The Pima Indian diabetes dataset was used, containing a total of 768 instances where 268 are diabetic, and 500 cases are non-diabetic with several diabetic attributes. Here, six machine learning algorithms (artificial neural network (ANN), random forest (RF), support vector machine (SVM), logistic regression (LR), AdaBoost, XGBoost) have been used along with an ensemble classifier to diagnose the diabetes disease. For each machine learning model, global and local explanations have been produced using the Shapley additive explanations (SHAP), which are represented in different types of graphs to help physicians in understanding the model predictions. The balanced accuracy of the developed weighted ensemble model was 90% with a F1 score of 89% using a five-fold cross-validation (CV). The median values were used for the imputation of the missing values and the synthetic minority oversampling technique (SMOTETomek) was used to balance the classes of the dataset. The proposed approach can improve the clinical understanding of a diabetes diagnosis and help in taking necessary action at the very early stages of the disease.

Explainable Transformer-Based Deep Learning Model for the Detection of Malaria Parasites from Blood Cell Images.

Malaria is a life-threatening disease caused by female anopheles mosquito bites. Various plasmodium parasites spread in the victim's blood cells and keep their life in a critical situation. If not treated at the early stage, malaria can cause even death. Microscopy is a familiar process for diagnosing malaria, collecting the victim's blood samples, and counting the parasite and red blood cells. However, the microscopy process is time-consuming and can produce an erroneous result in some cases. With the recent success of machine learning and deep learning in medical diagnosis, it is quite possible to minimize diagnosis costs and improve overall detection accuracy compared with the traditional microscopy method. This paper proposes a multiheaded attention-based transformer model to diagnose the malaria parasite from blood cell images. To demonstrate the effectiveness of the proposed model, the gradient-weighted class activation map (Grad-CAM) technique was implemented to identify which parts of an image the proposed model paid much more attention to compared with the remaining parts by generating a heatmap image. The proposed model achieved a testing accuracy, precision, recall, f1-score, and AUC score of 96.41%, 96.99%, 95.88%, 96.44%, and 99.11%, respectively, for the original malaria parasite dataset and 99.25%, 99.08%, 99.42%, 99.25%, and 99.99%, respectively, for the modified dataset. Various hyperparameters were also finetuned to obtain optimum results, which were also compared with state-of-the-art (SOTA) methods for malaria parasite detection, and the proposed method outperformed the existing methods.

COVID-19 Detection from Chest X-ray Images Using Feature Fusion and Deep Learning.

Currently, COVID-19 is considered to be the most dangerous and deadly disease for the human body caused by the novel coronavirus. In December 2019, the coronavirus spread rapidly around the world, thought to be originated from Wuhan in China and is responsible for a large number of deaths. Earlier detection of the COVID-19 through accurate diagnosis, particularly for the cases with no obvious symptoms, may decrease the patient's death rate. Chest X-ray images are primarily used for the diagnosis of this disease. This research has proposed a machine vision approach to detect COVID-19 from the chest X-ray images. The features extracted by the histogram-oriented gradient (HOG) and convolutional neural network (CNN) from X-ray images were fused to develop the classification model through training by CNN (VGGNet). Modified anisotropic diffusion filtering (MADF) technique was employed for better edge preservation and reduced noise from the images. A watershed segmentation algorithm was used in order to mark the significant fracture region in the input X-ray images. The testing stage considered generalized data for performance evaluation of the model. Cross-validation analysis revealed that a 5-fold strategy could successfully impair the overfitting problem. This proposed feature fusion using the deep learning technique assured a satisfactory performance in terms of identifying COVID-19 compared to the immediate, relevant works with a testing accuracy of 99.49%, specificity of 95.7% and sensitivity of 93.65%. When compared to other classification techniques, such as ANN, KNN, and SVM, the CNN technique used in this study showed better classification performance. K-fold cross-validation demonstrated that the proposed feature fusion technique (98.36%) provided higher accuracy than the individual feature extraction methods, such as HOG (87.34%) or CNN (93.64%).

Selecting drill size for post space preparation based on final endodontic radiographs: An in vitro study.

BACKGROUND: For placement of intraradicular posts the intracanal filling material has to be removed. If drills are employed for this purpose, extra widening of the canal, incomplete cracks or root perforation are probable when inappropriate size of drill is used. OBJECTIVE: This in vitro study assessed the efficacy of radiographs taken after completion of root canal therapy in selecting the appropriate-sized Peeso reamer for post space preparation. METHODS: Canals of 53 extracted maxillary and mandibular teeth of different types were cleaned and shaped. Then with acrylic resin 3-dimensional model of the intracanal space of each tooth was fabricated. Next, all canals were filled with gutta-percha and teeth were radiographed buccolingually. Based on these radiographs two observers selected a Peeso reamer that best matched each canal's diameter. The diameter of the selected Peeso reamer was compared to the diameter of the corresponding resin model of each canal by two independent observers and the difference was measured. The data were analyzed by paired sample t-test using SPSS version 22. RESULTS: The diameter of the selected Peeso reamers ranged from 0.21 mm smaller to 0.12 mm larger than the diameter of intracanal spaces. The difference between reamer and resin model was less than 0.1 mm in 75% of the cases. CONCLUSION: The result of this study suggests that post-operation endodontic radiographs are reliable means for selecting a size of Peeso reamer that does not encroach on dentinal wall during removal of intracanal filling material and post space preparation.

Impact of Artificial Aging on the Physical and Mechanical Characteristics of Denture Base Materials Fabricated via 3D Printing.

Three-dimensional (3D) printing is becoming more prevalent in the dental sector due to its potential to save time for dental practitioners, streamline fabrication processes, enhance precision and consistency in fabricating prosthetic models, and offer cost-effective solutions. However, the effect of aging in artificial saliva of this type of material has not been explored. To assess the physical and mechanical properties of the two types of 3D-printed materials before and after being subjected to artificial saliva, a total of 219 acrylic resin specimens were produced. These specimens were made with two types of 3D-printed materials, namely, NextDent (ND) and Formlabs (FLs), and a Schottlander heat-cured (HC) resin material that was used as a control. Water sorption and solubility specimens (n = 5) were tested after three months of storage in artificial saliva. Moreover, the Vickers hardness, Martens hardness, flexural strength/modulus, and impact strength were evaluated both under dry conditions and after three months of storage in artificial saliva. The degree of conversion (DC), elemental analysis, and filler content were also investigated. The ANOVA showed that 3D-printed resins had significantly greater sorption than the control group (p < 0.05). However, the flexural strength values of the 3D-printed materials were significantly greater (p < 0.05) than those of the heat-cured material. The DC of the 3D-printed resins was lower than that of the control group, but the difference was not significant (p > 0.05). The 3D-printed materials contained significantly more filler than the control (p < 0.05). Moreover, the artificial saliva had a significant effect on the Vickers hardness for all tested groups and on the Martens hardness for the control group only (p < 0.05). Compared with conventional heat-cured materials, 3D-printed denture base materials demonstrated relatively poorer performance in terms of sorption, solubility, and DC but exhibited either comparable or superior mechanical properties. The aging process also influenced the Vickers and Martens' hardness. The strength of the 3D-printed materials was in compliance with ISO recommendations, and the materials could be used alongside conventional heat-cured materials.

Effects of printing orientation and artificial ageing on martens hardness and indentation modulus of 3D printed restorative resin materials.

BACKGROUND: Three-dimensional (3D) printing is increasingly used to fabricate dental restorations due to its enhanced precision, consistency and time and cost-saving advantages. The properties of 3D-printed resin materials can be influenced by the chosen printing orientation which can impact the mechanical characteristics of the final products. PURPOSE: The objective of this study was to evaluate the influence of printing orientation and artificial ageing on the Martens hardness (HM) and indentation modulus (EIT) of 3D-printed definitive and temporary dental restorative resins. METHODS: Disk specimens (20 mm diameter × 2 mm height) were additively manufactured in three printing orientations (0°, 45°, 90°) using five 3D-printable resins: VarseoSmile Crownplus (VCP), Crowntec (CT), Nextdent C&B MFH (ND), Dima C&B temp (DT), and GC temp print (GC). The specimens were printed using a DLP 3D-printer (ASIGA MAX UV), while LavaTM Ultimate (LU) and Telio CAD (TC) served as milled control materials. Martens hardness (HM) and indentation modulus (EIT) were tested both before and after storage in distilled water and artificial saliva for 1, 30, and 90 days at 37 °C. RESULTS: 90° printed specimens exhibited higher HM than the other orientations at certain time points, but no significant differences were observed in HM and EIT between orientations for all 3D-printed materials after 90 days of ageing in both aging media. LU milled control material exhibited the highest HM and EIT among the tested materials, while TC, the other milled control, showed similar values to the 3D printed resins. CT and VCP (definitive resins) and ND displayed higher Martens parameters compared to DT and GC (temporary resins). The hardness of the 3D-printed materials was significantly impacted by artificial ageing compared to the controls, with ND having the least hardness reduction percentage amongst all 3D-printed materials. The hardness reduction percentage in distilled water and artificial saliva was similar for all materials except for TC, where higher reduction was noted in artificial saliva. SIGNIFICANCE: The used 3D printed resins cannot yet be considered viable alternatives to milled materials intended for definitive restorations but are preferable for use as temporary restorations.

Effect of surface treatments on optical, topographical and mechanical properties of CAD/CAM reinforced lithium silicate glass ceramics.

OBJECTIVES: To investigate the effect of different surface treatments on optical, topographical and mechanical properties of CAD/CAM lithium silicate-based glass ceramics (LSC's) and their combined effect on the output of a light curing unit (LCU). METHODS: Four CAD/CAM LSC's were investigated: Lithium Disilicate (Emax CAD; EC), Zirconia-reinforced silicates (Vita Suprinity; VS and Celtra Duo;CD) and Lithium Aluminum Disilicate (CEREC Tessera; CT). Ceramic specimens (n = 240) were divided into six subgroups according to their surface treatment: (a) Control, (b) Hydrofluoric acid (HF) 5%, (c) HF 5% + Neutralizing agent (N), (d) HF 9%, (e) HF 9% +N and (f) Self-etching ceramic primer (SEP). Irradiance, power and radiant exposure of a LCU were measured with MARC-LC following ceramic specimen interposition. Direct light transmission (T%) and absorbance (Abs%) of the specimens were measured with UV-Vis spectrophotometry. Roughness (Sa, Sq) and wettability (θ°) were measured with optical profilometry and sessile drop profile analysis, respectively. Biaxial flexural strength (σ) of the ceramic specimens was measured by the ball-on-three-balls method and ceramic specimens were examined microscopically. Statistical analyses was performed by two-way ANOVA followed by post hoc multiple comparisons (α = 0.05). RESULTS: Acid neutralization decreased T% and increased Abs% in all LSC's and highest T% was exhibited with VS. Neutralized EC, VS and CD displayed higher Sa in HF9, while neutralized CT displayed higher Sa in HF5. Self-etch primer significantly reduced θ° (p < 0.001). σ was observed in the followed ascending order: HF9 +N < HF9 < HF5 +N < HF5 < SEP < Control for all LSC's. SIGNIFICANCE: Optical, topographical and mechanical properties of the CAD/CAM ceramic blocks were strongly dependent on the type of surface treatment. Results of neutralization post-etching indicate promising potential for future investigations.

Metronidazole Potentiation by Panax Ginseng and Symphytum officinale: A New Strategy for P. gingivalis Infection Control.

The important periodontal disease pathogen Porphyromonas gingivalis produces thick biofilms that increase its pathogenicity. Finding natural antimicrobial agents is crucial because of the rise in antibiotic resistance. The purpose of this study was to determine if plant extracts such as Symphytum officinale (S) and Panax Ginseng (G) were effective against P. gingivalis separately and in combination with a common antibiotic, metronidazole (F). Six different dilutions were produced using the plant extracts in different concentrations and antibiotics separately and in combination with F, G, and S using the two-fold serial dilution technique. To evaluate the effects of these substances, biofilm inhibition experiments were conducted. Plaque samples were collected from periodontitis patients to isolate P. gingivalis, and a standard strain of P. gingivalis (ATCC 33277) was purchased. Additionally, Acylated Homoserine Lactones (AHLs) detection was carried out to look for any activity that would interfere with quorum sensing. GraphPad Prism was used for statistical analysis with a p-value < 0.05. The combinations of Symphytum officinale and metronidazole (S+F) showed the maximum effectiveness in biofilm inhibition (98.7%), which was slightly better than G+F (98.2%), with substantial variations in biofilm inhibition levels in different treatment regimes. Notably, the patient isolate was more active than the standard strain. Additionally, the plant extracts and their combinations at particular dilutions had notable inhibitory effects on the generation of AHL (p < 0.05). The study highlights the possibility of Symphytum officinale and Panax Ginseng as effective treatments for P. gingivalis biofilm and AHLs, both on their own and in combination with metronidazole. These organic substances may open the door to cutting-edge methods of treating periodontal disorders.

Biological properties of polycaprolactone and barium titanate composite in biomedical applications.

The ceramic-polymer composite materials are widely known for their exceptional mechanical and biological properties. Polycaprolactone (PCL) is a biodegradable polymer material extensively used in various biomedical applications. At the same time, barium titanate (BT), a ceramic material, exhibits piezoelectric properties similar to bone, which is essential for osseointegration. Furthermore, a composite material that combines the benefits of PCL and BT results in an innovative composite material with enhanced properties for biomedical applications. Thus, this review is organised into three sections. Firstly, it aims to provide an overview of the current research on evaluating biological properties, including antibacterial activity, cytotoxicity and osseointegration, of PCL polymeric matrices in its pure form and reinforced structures with ceramics, polymers and natural extracts. The second section investigates the biological properties of BT, both in its pure form and in combination with other supporting materials. Finally, the third section provides a summary of the biological properties of the PCLBT composite material. Furthermore, the existing challenges of PCL, BT and their composites, along with future research directions, have been presented. Therefore, this review will provide a state-of-the-art understanding of the biological properties of PCL and BT composites as potential futuristic materials in biomedical applications.

3D printed denture base material: The effect of incorporating TiO2 nanoparticles and artificial ageing on the physical and mechanical properties.

OBJECTIVES: To evaluate the physical and mechanical properties of three-dimensional (3D) printed denture base resin incorporating TiO2 nanoparticles (NPs), subjected to a physical ageing process. METHODS: Acrylic denture base samples were prepared by a Stereolithography (SLA) 3D printing technique reinforced with different concentrations (0.10, 0.25, 0.50, and 0.75) of silanated TiO2 NPs. The resulting nanocomposite materials were characterized in terms of degree of conversion (DC), and sorption/solubility flexural strength, impact strength, Vickers hardness and Martens hardness and compared with unmodified resin and conventional heat-cured (HC) material. The nanocomposites were reassessed after subjecting them to ageing in artificial saliva. A fractured surface was studied under a scanning electron microscope (SEM). RESULTS: The addition of TiO2 NPs into 3D-printed resin significantly improved flexural strength/modulus, impact strength, Vickers hardness, and DC, while also slightly enhancing Martens hardness compared to the unmodified resin. Sorption values did not show any improvements, while solubility was reduced significantly. The addition of 0.10 wt% NPs provided the highest performance amongst the other concentrations, and 0.75 wt% NPs showed the lowest. Although ageing degraded the materials' performance to a certain extent, the trends remained the same. SEM images showed a homogenous distribution of the NPs at lower concentrations (0.10 and 0.25 wt%) but revealed agglomeration of the NPs with the higher concentrations (0.50 and 0.75 wt%). SIGNIFICANCE: The outcomes of this study suggested that the incorporation of TiO2 NPs (0.10 wt%) into 3D-printed denture base material showed superior performance compared to the unmodified 3D-printed resin even after ageing in artificial saliva. The nanocomposite has the potential to extend service life of denture bases in future clinical use.

Histological evaluation of the effects of bone morphogenetic protein 9 and angiopoietin 1 on bone healing.

Objectives: Bone healing remains a critical clinical orthopedic problem. Bone, which is a greatly vascularized tissue, depends on the tight temporal and spatial link between blood vessels and bone cells. Thus, angiogenesis is crucial for skeletal growth and bone fracture healing. The purpose of this study was to evaluate the efficacy of the local application of osteogenic and angiogenic factors such as bone morphogenetic protein 9 (BMP9) and angiopoietin 1 (Ang1), respectively, and their combination as an osteoinducer in the process of bone healing. Methods: Forty-eight male albino rats, weighing 300-400 g and aged 6-8 months, were utilized in this study. The animals underwent surgery on the medial side of the tibia bone. In the control group, an absorbable hemostatic sponge was locally applied to the bone defect, while experimental groups were separated into three groups. In Group I, 1 mg BMP9 was locally applied, Group II was treated with 1 mg Ang1, and Group III was treated with local application of a combination (0.5 mg BMP9 and 0.5 mg Ang1). All experimental groups were fixed with an absorbable hemostatic sponge. The rats were sacrificed on days 14 and 28 after surgery. Results: Local application of BMP9 alone, Ang1 alone, and their combination to a tibia defect caused osteoid tissue formation and significantly increased the number of bone cells. A gradual decrease in the number of trabecular bone, an increase in trabecular area, and no significant difference in the bone marrow area were noted. Conclusion: The combination of BMP9 and Ang1 has therapeutic potential in promoting the healing process of bone defects. Osteogenesis and angiogenesis are regulated by BMP9 and Ang1. These factors act together to accelerate bone regeneration more efficiently than either factor alone.

The effect of in-office bleaching agents on the Vickers hardness and surface topography of polished and unpolished CAD/CAM composite materials.

In-office bleaching, using hydrogen peroxide, is effective to remove dental enamel stains. However, bleaching agents can deteriorate surface properties of CAD-CAM materials. This in vitro study aimed to investigate the effect of in-office bleaching agents on Vickers hardness and surface topography of polished and unpolished dental CAD-CAM composite materials (Grandio blocs, Lava Ultimate, BRILLIANT Crios, Cerasmart), and a polymer-infiltrated ceramic network block (Vita Enamic). The specimens were randomly divided into two groups: unpolished or polished. The micro-hardness and surface topography of each group were measured before bleaching, after a 60 min bleaching period, and 24-h and one-month post-bleaching. In-office bleaching significantly influenced the Vickers hardness of both the polished and unpolished CAD/CAM composite blocks, with Vita Enamic exhibiting the least hardness stability among all groups. Furthermore, in-office bleaching significantly influenced the surface roughness of unpolished CAD/CAM composite blocks. There was a significant difference in hardness reduction between the polished and unpolished specimens for most of the investigated materials at different time points. The bleaching did not influence the surface roughness of the investigated polished group, except for Vita Enamic and Lava Ultimate. However, it did influence the surface roughness of the investigated materials in the unpolished group.

Influence of Storing Composite Filling Materials in a Low-pH Artificial Saliva on Their Mechanical Properties-An In Vitro Study.

Restorative composites are subjected to various influences in the oral cavity environment, such as high or low temperatures, the mechanical force generated during mastication, colonization of various microorganisms, and low pH, which may result from ingested food and the influence of microbial flora. This study aimed to investigate the effect of a recently developed commercial artificial saliva (pH = 4, highly acidic) on 17 commercially available restorative materials. After polymerization, the samples were stored in an artificial solution for 3 and 60 days and subjected to crushing resistance and flexural strength tests. The surface additions of the materials were examined in terms of the shapes and sizes of the fillers and elemental composition. When stored in an acidic environment, the resistance of the composite materials was reduced by 2-12%. Larger compressive and flexural strength resistance values were observed for composites that could be bonded to microfilled materials (invented before 2000). This may result from the filler structure taking an irregular form, which results in a faster hydrolysis of silane bonds. All composite materials meet the standard requirements when stored for a long period in an acidic environment. However, storage of the materials in an acid environment has a destructive impact on the materials' properties.

Assessing the Antifungal Activity of a Soft Denture Liner Loaded with Titanium Oxide Nanoparticles (TiO2 NPs).

AIM: Soft denture lining materials are susceptible to be colonized by different microorganisms, especially by Candida albicans (C. albicans), causing denture-induced stomatitis. This study was designed to evaluate the effectiveness of incorporating titanium dioxide nanoparticles (TiO2 NPs) into a soft denture liner towards reducing microbial activity. METHOD: A total of 40 PEMA-TiO2 nanocomposites samples were fabricated by adding 0.0 wt.% (control), 1.0 wt.%, 1.5 wt.%, and 2 wt.% TiO2 NPs to a heat cured soft denture lining material (polyethyl methacrylate, PEMA). The prepared samples were divided into four groups (n = 10) according to the content of TiO2 NPs. The uniformity of TiO2 NPS distribution within the denture liner matrix was assessed using a Scanning Electron Microscope (SEM). The viable count of C. albicans was evaluated to test the antifungal resistance of the developed composite. RESULTS: The SEM images showed fairly homogeneous dispersion, with patches of TiO2 NPs agglomeration within the PEMA matrix and an increasing concentration of NPs with higher NP content. The particle map and EDX analysis confirmed the evidence of the TiO2 NPs. The mean viable count results for the control (0.0 wt.%) and 1.0 wt.%, 1.5 wt.%, and 2 wt.% TiO2 groups were 139.80, 12.00, 6.20, and 1.00, respectively, with a significant difference from the control group (p < 0.05). The antifungal activity also increased with the increase in the concentration of TiO2 NPs. CONCLUSIONS: The addition of TiO2 NPs into a heat-cured soft denture liner provided antifungal activity as evidenced by the reduced colonization of C. albicans. The antimicrobial activity of the liner material increased with the increased concentration of TiO2 NPS.

Ensemble classification of integrated CT scan datasets in detecting COVID-19 using feature fusion from contourlet transform and CNN.

The COVID-19 disease caused by coronavirus is constantly changing due to the emergence of different variants and thousands of people are dying every day worldwide. Early detection of this new form of pulmonary disease can reduce the mortality rate. In this paper, an automated method based on machine learning (ML) and deep learning (DL) has been developed to detect COVID-19 using computed tomography (CT) scan images extracted from three publicly available datasets (A total of 11,407 images; 7397 COVID-19 images and 4010 normal images). An unsupervised clustering approach that is a modified region-based clustering technique for segmenting COVID-19 CT scan image has been proposed. Furthermore, contourlet transform and convolution neural network (CNN) have been employed to extract features individually from the segmented CT scan images and to fuse them in one feature vector. Binary differential evolution (BDE) approach has been employed as a feature optimization technique to obtain comprehensible features from the fused feature vector. Finally, a ML/DL-based ensemble classifier considering bagging technique has been employed to detect COVID-19 from the CT images. A fivefold and generalization cross-validation techniques have been used for the validation purpose. Classification experiments have also been conducted with several pre-trained models (AlexNet, ResNet50, GoogleNet, VGG16, VGG19) and found that the ensemble classifier technique with fused feature has provided state-of-the-art performance with an accuracy of 99.98%.