Modeling fracture in multilayered teeth using the finite volume-based phase field method.

The present work, utilizing the finite volume-based phase field method (FV-based PFM), aims to investigate the initiation and propagation of cracks in the second molar of the left mandible under occlusal loading. By reconstructing cone beam computed tomography scans of the patient, the true morphology and internal mesostructure of the entire tooth are implemented into numerical simulations, including both 2D slice models and a realistic 3D model. Weibull functions are introduced to represent the tooth's heterogeneity, enabling the stochastic distribution characteristics of mechanical parameters. The results indicate that stronger heterogeneity leads to greater crack tortuosity, uneven damage distribution, and lower fracture stress. Additionally, different cusp angles (50° and 70°) and pre-existing fissure morphologies (i.e., U-shape, V-shape, IK-shape, I-shape, and IY-shape) also significantly affect the mechanical performance of the tooth. The study reveals that different cusp angles affect the location of crack initiation. Overall, this work demonstrates the utility of the FV-based PFM framework in capturing the complex fracture behavior of teeth, which can contribute to improved clinical treatment and prevention of tooth fractures. The insights gained from this study can inform the design of dental crown restorations and the optimization of cusp inclination and contact during clinical occlusal adjustments.

Developing a universal and reliable temporomandibular joint reference system for quantifying morphological and positional changes.

OBJECTIVES: This study aimed to provide a universal and reliable reference system quantifying temporomandibular joint (TMJ) morphological and positional changes. METHODS: Large field-of-view (FOV) cone-beam computed tomography (CBCT) images (20 TMJs) from 10 preorthognathic surgery patients and limited FOV CBCT images (40 TMJs) from 20 splint therapy-treated patients with temporomandibular disorders were collected. TMJ-specific reference system including a TMJ horizontal reference plane (TMJHP) and a local coordinate system (TMJCS) was constructed with landmarks on cranial base. Its application for TMJ measurements and its spatial relationship to common Frankfort horizontal plane (FHP) and maxillofacial coordinate system (MFCS) were evaluated. RESULTS: Five relevant landmarks were selected to optimally construct TMJ-specific reference system. General parallelism between TMJHP and FHP was demonstrated by minimal angular and constant distance deviation (1.714 ±â€…0.811º; 2.925 ±â€…0.817 mm). Additionally, tiny axial orientational deviations (0.181 ±â€…6.805º) suggested TMJCS rivaled MFCS. Moreover, small deviations in orientations and distances (1.232 ±â€…0.609º; 0.310 ±â€…0.202 mm) indicated considerable reliability for TMJCS construction, with intraclass correlation coefficients (ICCs) ranging from 0.999 to 1.000. Lastly, slight discrepancies in translations and rotations revealed high reliability for condylar positional and morphological measurements (ICC, 0.918-0.999). LIMITATIONS: TMJ-specific reference system was merely tested in two representative FOVs. CONCLUSIONS: This study provides a universal and reliable reference system for TMJ assessment that is applicable to both limited and large FOV CBCT. It would improve comparability among diverse studies and enable comprehensive evaluations of TMJ positional and morphological changes during TMJ-related treatment follow-up such as splint therapy and disease progression.

[Oral panorama reconstruction method based on pre-segmentation and Bezier function].

For patients with partial jaw defects, cysts and dental implants, doctors need to take panoramic X-ray films or manually draw dental arch lines to generate Panorama images in order to observe their complete dentition information during oral diagnosis. In order to solve the problems of additional burden for patients to take panoramic X-ray films and time-consuming issue for doctors to manually segment dental arch lines, this paper proposes an automatic panorama reconstruction method based on cone beam computerized tomography (CBCT). The V-network (VNet) is used to pre-segment the teeth and the background to generate the corresponding binary image, and then the Bezier curve is used to define the best dental arch curve to generate the oral panorama. In addition, this research also addressed the issues of mistakenly recognizing the teeth and jaws as dental arches, incomplete coverage of the dental arch area by the generated dental arch lines, and low robustness, providing intelligent methods for dental diagnosis and improve the work efficiency of doctors.

Visual Analytics of Multidimensional Oral Health Surveys: Data Mining Study.

BACKGROUND: Oral health surveys largely facilitate the prevention and treatment of oral diseases as well as the awareness of population health status. As oral health is always surveyed from a variety of perspectives, it is a difficult and complicated task to gain insights from multidimensional oral health surveys. OBJECTIVE: We aimed to develop a visualization framework for the visual analytics and deep mining of multidimensional oral health surveys. METHODS: First, diseases and groups were embedded into data portraits based on their multidimensional attributes. Subsequently, group classification and correlation pattern extraction were conducted to explore the correlation features among diseases, behaviors, symptoms, and cognitions. On the basis of the feature mining of diseases, groups, behaviors, and their attributes, a knowledge graph was constructed to reveal semantic information, integrate the graph query function, and describe the features of intrigue to users. RESULTS: A visualization framework was implemented for the exploration of multidimensional oral health surveys. A series of user-friendly interactions were integrated to propose a visual analysis system that can help users further achieve the regulations of oral health conditions. CONCLUSIONS: A visualization framework is provided in this paper with a set of meaningful user interactions integrated, enabling users to intuitively understand the oral health situation and conduct in-depth data exploration and analysis. Case studies based on real-world data sets demonstrate the effectiveness of our system in the exploration of oral diseases.

Hierarchical Self-Supervised Learning for 3D Tooth Segmentation in Intra-Oral Mesh Scans.

Accurately delineating individual teeth and the gingiva in the three-dimension (3D) intraoral scanned (IOS) mesh data plays a pivotal role in many digital dental applications, e.g., orthodontics. Recent research shows that deep learning based methods can achieve promising results for 3D tooth segmentation, however, most of them rely on high-quality labeled dataset which is usually of small scales as annotating IOS meshes requires intensive human efforts. In this paper, we propose a novel self-supervised learning framework, named STSNet, to boost the performance of 3D tooth segmentation leveraging on large-scale unlabeled IOS data. The framework follows two-stage training, i.e., pre-training and fine-tuning. In pre-training, three hierarchical-level, i.e., point-level, region-level, cross-level, contrastive losses are proposed for unsupervised representation learning on a set of predefined matched points from different augmented views. The pretrained segmentation backbone is further fine-tuned in a supervised manner with a small number of labeled IOS meshes. With the same amount of annotated samples, our method can achieve an mIoU of 89.88%, significantly outperforming the supervised counterparts. The performance gain becomes more remarkable when only a small amount of labeled samples are available. Furthermore, STSNet can achieve better performance with only 40% of the annotated samples as compared to the fully supervised baselines. To the best of our knowledge, we present the first attempt of unsupervised pre-training for 3D tooth segmentation, demonstrating its strong potential in reducing human efforts for annotation and verification.

The Oral Microbiome and Its Role in Systemic Autoimmune Diseases: A Systematic Review of Big Data Analysis.

Introduction: Despite decades of research, systemic autoimmune diseases (SADs) continue to be a major global health concern and the etiology of these diseases is still not clear. To date, with the development of high-throughput techniques, increasing evidence indicated a key role of oral microbiome in the pathogenesis of SADs, and the alterations of oral microbiome may contribute to the disease emergence or evolution. This review is to present the latest knowledge on the relationship between the oral microbiome and SADs, focusing on the multiomics data generated from a large set of samples. Methodology: By searching the PubMed and Embase databases, studies that investigated the oral microbiome of SADs, including systemic lupus erythematosus (SLE), rheumatoid arthritis (RA), and Sjögren's syndrome (SS), were systematically reviewed according to the PRISMA guidelines. Results: One thousand and thirty-eight studies were found, and 25 studies were included: three referred to SLE, 12 referred to RA, nine referred to SS, and one to both SLE and SS. The 16S rRNA sequencing was the most frequent technique used. HOMD was the most common database aligned to and QIIME was the most popular pipeline for downstream analysis. Alterations in bacterial composition and population have been found in the oral samples of patients with SAD compared with the healthy controls. Results regarding candidate pathogens were not always in accordance, but Selenomonas and Veillonella were found significantly increased in three SADs, and Streptococcus was significantly decreased in the SADs compared with controls. Conclusion: A large amount of sequencing data was collected from patients with SAD and controls in this systematic review. Oral microbial dysbiosis had been identified in these SADs, although the dysbiosis features were different among studies. There was a lack of standardized study methodology for each study from the inclusion criteria, sample type, sequencing platform, and referred database to downstream analysis pipeline and cutoff. Besides the genomics, transcriptomics, proteomics, and metabolomics technology should be used to investigate the oral microbiome of patients with SADs and also the at-risk individuals of disease development, which may provide us with a better understanding of the etiology of SADs and promote the development of the novel therapies.

Correlation between periodontitis and prostate-specific antigen levels in the elderly Chinese male population.

BACKGROUND: The comparison of prostate-specific antigen (PSA) levels among older individuals with different periodontal statuses has not been fully investigated. Here we aimed to explore the correlation between the staging and grading of periodontitis and PSA levels in an elderly Chinese male population, which may lead to a biopsy recommendation and prevent prostate cancer as early as possible. METHODS: The study included 996 individuals aged ≥ 55 years who participated in routine postretirement physical examinations. Periodontal data included probing depth and gingival recession on four sites/tooth and on two diagonal quadrants (1-3 or 2-4) while excluding third molars, and clinical attachment loss was calculated. Periodontal status was classified as none, mild-moderate or severe periodontitis according to the Centers for Disease Control and Prevention and the American Academy of Periodontology case definition. Blood samples, oral health status and sociodemographic characteristics were collected by using general and oral examinations and questionnaires. Linear and logistic regressions were used to estimate the correlation between periodontitis severity and PSA levels, respectively. RESULTS: A total of 479 men had mild-moderate periodontitis and 355 had severe periodontitis; 162 men were periodontally healthy individuals. After adjusting for potential confounders, PSA levels were significantly lower in the individuals without periodontitis than in those with mild-moderate (P = 0.04) or severe (P = 0.03) periodontitis. However, PSA levels did not significantly correlate with periodontitis severity (P = 0.06). Although the ORs of elevated PSA were not significant, individuals with PSA ≥ 4.0 ng/mL were more likely to have periodontitis. CONCLUSIONS: In a sample of an elderly Chinese male population, after adjusting for possible confounders, serum PSA levels in individuals with periodontitis were significantly higher than those in individuals without periodontitis, but serum PSA did not significantly correlate with periodontitis severity.

A two-stage deep learning architecture for radiographic staging of periodontal bone loss.

BACKGROUND: Radiographic periodontal bone loss is one of the most important basis for periodontitis staging, with problems such as limited accuracy, inconsistency, and low efficiency in imaging diagnosis. Deep learning network may be a solution to improve the accuracy and efficiency of periodontitis imaging staging diagnosis. This study aims to establish a comprehensive and accurate radiological staging model of periodontal alveolar bone loss based on panoramic images. METHODS: A total of 640 panoramic images were included, and 3 experienced periodontal physicians marked the key points needed to calculate the degree of periodontal alveolar bone loss and the specific location and shape of the alveolar bone loss. A two-stage deep learning architecture based on UNet and YOLO-v4 was proposed to localize the tooth and key points, so that the percentage of periodontal alveolar bone loss was accurately calculated and periodontitis was staged. The ability of the model to recognize these features was evaluated and compared with that of general dental practitioners. RESULTS: The overall classification accuracy of the model was 0.77, and the performance of the model varied for different tooth positions and categories; model classification was generally more accurate than that of general practitioners. CONCLUSIONS: It is feasible to establish deep learning model for assessment and staging radiographic periodontal alveolar bone loss using two-stage architecture based on UNet and YOLO-v4.

Automatic segmentation and detection of ectopic eruption of first permanent molars on panoramic radiographs based on nnU-Net.

AIM: The purpose of this research was to present an artificial intelligence (AI) model, which can automatically segment and detect ectopic eruption of first permanent molars (EMMs) in early mixed dentition on panoramic radiographs using the no-new-Net (nnU-Net) model. DESIGN: A total of 438 EMMs obtained from 285 panoramic radiographs were included in this study. An AI model based on nnU-Net was trained to segment and detect EMMs. The performance of the model was evaluated by the intersection over union (IoU), precision, F1-score, accuracy and FROC. Furthermore, the detecting performance of nnU-Net was compared with that of three dentists with different years of experience using the McNemar chi-squared test. The reliability of different dentists was evaluated by intraclass correlation coefficients (ICCs). RESULTS: The nnU-Net yielded an IoU of 0.834, a precision of 0.845, an F1-score of 0.902 and an accuracy of 0.990, whereas the dentists yielded a mean IoU of 0.530, a mean precision of 0.539, a mean F1-score of 0.699 and a mean accuracy of 0.811. The ICC of different dentists was 0.776. The statistical analysis of the McNemar chi-squared test showed that the nnU-Net results were statistically significant and superior to those of dentists (p < .05). CONCLUSION: This study validated an AI model based on nnU-Net for automatically segmenting and detecting EMMs more consistently and accurately on panoramic radiography.

Association of high-density lipoprotein cholesterol and periodontitis severity in Chinese elderly: a cross-sectional study.

OBJECTIVES: Periodontitis is a local inflammatory disease of high prevalence worldwide. Increasing evidence has shown its association with cardiovascular diseases. While high-density lipoprotein is an important protective factor in preventing cardiovascular diseases, this study aims to examine whether high-density lipoprotein cholesterol (HDL-C) level is associated with different status of periodontitis. MATERIALS AND METHODS: A total of 874 Chinese retirees (≥ 60 years of age) with different statuses of periodontitis were enrolled. Periodontal clinical data were collected to define periodontal disease severity (no, mild-moderate, severe). Peripheral blood was collected for serum lipid profile analysis. Linear and logistic regression analysis with adjustment for potential confounders (gender, age, BMI, alcohol intake, exercise frequency, smoking habits) were used to determine the association of periodontitis with HDL-C. RESULTS: After adjustments for confounders, linear regression analyses revealed a significant relationship between the decreased HDL-C and periodontitis severity (p < 0.05). Although the multivariable-adjusted ORs of decreased HDL-C were not statistically significant, logistic regression analyses showed Chinese elderly with severe periodontitis had higher odds of exhibiting clinically abnormal HDL-C levels than those without periodontitis. CONCLUSIONS: The elderly population with periodontitis showed HDL-C levels significantly lower than those without periodontitis. The severity of periodontitis was positively correlated with serum HDL-C levels. CLINICAL RELEVANCE: Periodontitis reduces HDL-C level in the elderly population, indicating that oral health should be paid attention to in the prevention and treatment of dyslipidemia.

Comparison of Positioning Accuracy Between 2 Different Implant Systems Using Mucosa-Supported Surgical Templates: A Retrospective Clinical Study.

Although guided implant surgery is widely practiced, clinical studies examining the differences in accuracy between implanting systems that use the same surgical guide are currently lacking. This study aimed to evaluate and compare the effects of different dental implanting systems on positioning accuracy using a uniform type of stereolithographic surgical guide to account for cumulative errors in guide production. One hundred BEGO Semados S implants (group A) and 91 NobelActive implants (group B) were inserted into patients using the same type of guide. The accuracy was assessed by matching the preoperative and postoperative cone-beam computerized tomography. The implant shoulder, tip, depth, and angular deviation were registered. Statistically significant differences between groups were determined using Student t test, bivariate correlation test, and generalized estimating equation. The angular deviation was 3.16 ± 1.74° in group A and 2.58 ± 1.41° in group B (P = .013); the depth deviation was 0.44 ± 0.23 mm in group A and 0.51 ± 0.22 mm in group B (P = .032). In terms of vertical accuracy, the Bego implant system is superior to the Nobel implant system using the same type of surgical guide, while the angle accuracy is opposite. Therefore, it is important to control the depth when using the template-guided surgery with the Nobel implant system. Similarly, angle control should be emphasized in the Bego implant system. Measurements of the deviations provide the basis for a clinical reference that will be useful in preoperative analysis for improving the safety and accuracy of guided implant surgical procedures.

Evaluation of a custom-designed human-robot collaboration control system for dental implant robot.

BACKGROUND: The purpose of this study is to develop a methodology to better control a human-robot collaboration for robotic dental implant placement. We have designed a human-robot collaborative implant system (HRCDIS) which is based on a zero-force hand-guiding concept and a operational task management workflow that can achieve highly accurate and stable osteotomy drilling based on a surgeon's decision and robotic arm movements during implant surgery. METHOD: The HRCDIS brings forth the robot arm positions, exact drilling location, direction and performs automatic drilling. The HRCDIS can also avoid complex programing in the robot. The purpose of the study is to evaluate the accuracy of drilling resulting from our developed operational task management method (OTMM). The OTMM can enable the robot to switch, pause, and resume drilling tasks. The force required for hand-guiding in a zero-force control mode of the robot was detected by a 6D force sensor. We compared our force data to those provided by the manufacturer's manual. The study was conducted on a phantom head with a 3D-printed jaw bone to verify the validity of our HRCDIS. We appraised the discrepancies between free-hand drillings and the HRCDIS controlled drillings at apical centre and head centre of the implant and implant angulation to the baseline data from a virtual surgical planning model. RESULTS: The average required force used by hand-guiding to operate the robot with HRCDIS was near 7 Newton which is much less than the manufacturer's specification (30 Newton). The results from our study showed that the average error at implant head was 1.04 ± 0.37 mm, 1.56 ± 0.52 mm at the implant apex, and deviation of implant angle was 3.74 ± 0.67°. CONCLUSIONS: The results from this study validate the merit of the human-robot collaboration control by the HRCDIS. Based on the improved navigation system using HRCDIS, a robotic implant placement can provide seamless drilling with ease, efficiency and accuracy.

Artificial Intelligence Model to Detect Real Contact Relationship between Mandibular Third Molars and Inferior Alveolar Nerve Based on Panoramic Radiographs.

This study aimed to develop a novel detection model for automatically assessing the real contact relationship between mandibular third molars (MM3s) and the inferior alveolar nerve (IAN) based on panoramic radiographs processed with deep learning networks, minimizing pseudo-contact interference and reducing the frequency of cone beam computed tomography (CBCT) use. A deep-learning network approach based on YOLOv4, named as MM3-IANnet, was applied to oral panoramic radiographs for the first time. The relationship between MM3s and the IAN in CBCT was considered the real contact relationship. Accuracy metrics were calculated to evaluate and compare the performance of the MM3-IANnet, dentists and a cooperative approach with dentists and the MM3-IANnet. Our results showed that in comparison with detection by dentists (AP = 76.45%) or the MM3-IANnet (AP = 83.02%), the cooperative dentist-MM3-IANnet approach yielded the highest average precision (AP = 88.06%). In conclusion, the MM3-IANnet detection model is an encouraging artificial intelligence approach that might assist dentists in detecting the real contact relationship between MM3s and IANs based on panoramic radiographs.

Deep Learning for Caries Detection and Classification.

OBJECTIVES: Deep learning methods have achieved impressive diagnostic performance in the field of radiology. The current study aimed to use deep learning methods to detect caries lesions, classify different radiographic extensions on panoramic films, and compare the classification results with those of expert dentists. METHODS: A total of 1160 dental panoramic films were evaluated by three expert dentists. All caries lesions in the films were marked with circles, whose combination was defined as the reference dataset. A training and validation dataset (1071) and a test dataset (89) were then established from the reference dataset. A convolutional neural network, called nnU-Net, was applied to detect caries lesions, and DenseNet121 was applied to classify the lesions according to their depths (dentin lesions in the outer, middle, or inner third D1/2/3 of dentin). The performance of the test dataset in the trained nnU-Net and DenseNet121 models was compared with the results of six expert dentists in terms of the intersection over union (IoU), Dice coefficient, accuracy, precision, recall, negative predictive value (NPV), and F1-score metrics. RESULTS: nnU-Net yielded caries lesion segmentation IoU and Dice coefficient values of 0.785 and 0.663, respectively, and the accuracy and recall rate of nnU-Net were 0.986 and 0.821, respectively. The results of the expert dentists and the neural network were shown to be no different in terms of accuracy, precision, recall, NPV, and F1-score. For caries depth classification, DenseNet121 showed an overall accuracy of 0.957 for D1 lesions, 0.832 for D2 lesions, and 0.863 for D3 lesions. The recall results of the D1/D2/D3 lesions were 0.765, 0.652, and 0.918, respectively. All metric values, including accuracy, precision, recall, NPV, and F1-score values, were proven to be no different from those of the experienced dentists. CONCLUSION: In detecting and classifying caries lesions on dental panoramic radiographs, the performance of deep learning methods was similar to that of expert dentists. The impact of applying these well-trained neural networks for disease diagnosis and treatment decision making should be explored.

Caries prevalence of the first permanent molars in 6-8 years old children.

Dental caries is one of the most common infectious diseases affecting 6-8-year-old children, especially their first permanent molars (FPMs). This study explored the prevalence of dental caries on FPMs by analyzing the oral health status of 1,423,720 children aged 6-8 years in Zhejiang Province, China. The data were extracted from the dental electronic records of the schoolchildren attending the Oral Health Promotion Project (OHPP), conducted during 2013-2017 in Zhejiang Province. Multiple logistic regression models were used to determine the factors affecting dental caries. Boys and girls accounted for 53.2% and 46.8% of the subjects, respectively. From 2013 to 2017, the prevalence of dental caries on FPMs increased: 2013: 20.4%; 2014: 25.3%; 2015: 24.5%; 2016: 27.0%; and 2017: 29.0%, despite the OHPP conducted. Based on multiple logistic regression model, girls had a significantly higher risk of FPM caries compared to boys (OR = 1.38, 95% CI: 1.37-1.39, p < 0.0001); compared with the caries rates in urban areas, the caries risk was significantly higher in rural areas (OR = 1.15, 95% CI: 1.14-1.16, p < 0.0001). In terms of geographic location in Zhejiang Province, the odds ratios of the caries risk of the east, south, west, and north were 1.35 (1.33-1.36), 1.3 (1.28-1.31), 0.81 (0.8-0.83), and 0.82 (0.81-0.84), respectively (p < 0.0001), by considering the central region as a reference. The caries prevalence of FPMs was high, with an increasing tendency and gender, social, cultural, and environmental factors affecting the caries prevalence.

Accuracy of dental implant surgery with robotic position feedback and registration algorithm: An in-vitro study.

BACKGROUND: The purpose of this study was to develop and validate a positioning method with hand-guiding and contact position feedback of robot based on a human-robot collaborative dental implant system (HRCDIS) for robotic guided dental implant surgery. METHODS: An HRCDIS was developed based on a light-weight cooperative robot arm, UR5. A three-dimensional (3D) virtual partially edentulous mandibular bone was reconstructed using the cone bone computed tomography images. After designing the preoperative virtual implant planning using the computer software, a fixation guide worn on teeth for linking and fixing positioning marker was fabricated by 3D printing. The fixation guide with the positioning marker and a resin model mimicking the oral tissues were assembled on a head phantom. The planned implant positions were derived by the coordinate information of the positioning marker. The drilling process using the HRCDIS was conducted after mimicking the experimental set-up and planning the drilling trajectory. Deviations between actual and planned implant positions were measured and analyzed. RESULTS: The head phantom experiments results showed that the error value of the central deviation at hex (refers to the center of the platform level of the implant) was 0.79 ± 0.17 mm, central deviation at the apex was 1.26 ± 0.27 mm, horizontal deviation at the hex was 0.61 ± 0.19 mm, horizontal deviation at the apex was 0.91 ± 0.55 mm, vertical deviation at the hex was 0.38 ± 0.17 mm, vertical deviation at the apex was 0.37 ± 0.20 mm, and angular deviation was 3.77 ± 1.57°. CONCLUSIONS: The results from this study preliminarily validate the feasibility of the accurate navigation method of the HRCDIS.

TRPM2 expression levels are associated with histological grading in patients with tongue squamous cell carcinoma.

Tongue squamous cell carcinoma (TSCC) accounts for a large proportion of cases of head and neck cancer. Transient receptor potential melastatin 2 (TRPM2) is a non­selective cation channel sensitive to oxidative stress. High TRPM2 expression has been reported in various types of cancer, including neuroblastoma, glioblastoma, non­small cell lung cancer and breast cancer. However, whether expression levels of TRPM2 are associated with aggressive clinical features in TSCC remains unclear. A total of 26 clinical sample tissues with TSCC were collected in the present study. The expression levels of the TRPM2 channel were determined by immunohistochemistry, western blot, and qPCR analysis. The content of malondialdehyde (MDA) and the activity of superoxide dismutase (SOD) were measured to reveal oxidative stress levels in TSCC tissues with different degrees of differentiation. The protein expression levels of caspase­8, Bcl­xL, Bax, caspase­9, cleaved caspase­9, caspase­3, cleaved caspase­3, poly [ADP­ribose] polymerase (PARP) and cleaved PARP were detected by western blot analysis. Analysis of the tissue specimens from 26 patients with TSCC showed that TRPM2 was not upregulated in all specimens. Notably, the expression levels of TRPM2 were associated with the histological grading of different tissues. The specimens with low TRPM2 expression were significantly associated with moderate or poor differentiation (P=0.003), and exhibited increased lipid peroxidation level and decreased SOD activity. Furthermore, the altered expression of pro­ and anti­apoptotic proteins indicated a significant upregulation of apoptosis in TSCC tissues with low TRPM2 expression. These results suggested that low TRPM2 expression in TSCC may inhibit the ability of cells to adapt to or resist the oxidative stress, resulting in increased susceptibility to apoptosis. Therefore, the oxidative stress­sensitive TRPM2 channel may serve as a potent biomarker, and the present study provides insights into the underlying mechanisms of tumor cell differentiation.

Early childhood caries and related risk factors: A cross-sectional study of children in Zhejiang Province, China.

OBJECTIVES: This work aims to exploring the risk factors related to ECC by investigating oral health status, related behaviours and habits of children and their parent's knowledge regarding oral hygiene. METHOD: A total of 2700 children aged 3-5 years and their parents from Zhejiang province were examined. Chi-square test or U test was used to analyse the difference of caries incidence among different groups. Logistic multiple factor stepwise regression model was used to analyse the risk factors influencing ECC prevalence. RESULTS: ECC prevalence rate in these children was 70.7%. The mean decayed missing filled teeth(dmft) scores were 4.33. Through analysis of logistic multiple factor stepwise regression model, risk factors related to the prevalence of ECC in children include: older age, region (coastal or mountainous area), higher frequency of bedtime dessert consumption and poor care of children's oral health by parents. In the questionnaire, 5 of 14 questions showed parents' poor oral knowledge or awareness. CONCLUSION: The prevalence rate of ECC in children aged 3-5 in this study was high and associated with older age, region (coastal or mountainous area), higher frequency of bedtime dessert consumption and poor care of children's oral health. Parents need to strengthen their understanding and action of dental caries.

Evaluation of the potential of pelletized biomass from different municipal solid wastes for use as solid fuel.

Four different municipal solid wastes (dog manure, horse manure, apple pomace waste and tea waste) and an industrial by-product (NovoGro) were used to produce solid fuel pellets. The mixtures followed a raw material to NovoGro ratio of 50:1. The pellets diameters varied between 4 and 5 mm, and the average length was 20 mm. The dog manure, horse manure, apple pomace waste and tea waste pellets were denoted as DN, HN, AN and TN, respectively. The combustion characteristics of the pelletized fuels were investigated, such as total moisture, ash content, calorific value and ash fusion point, etc. The physicochemical properties were analyzed by using a number of analytical techniques including X-ray fluorescence spectrometry (XRF), X-ray diffraction spectrometry (XRD), scanning electron microscopy (SEM) and Fourier transform infrared spectroscopy (FTIR). The results of the mechanical, thermal and morphological properties show that the raw materials were effectively combined with the NovoGro binder; furthermore, the DN, HN and TN pellets exhibited excellent mechanical and thermal properties, including high calorific values (>16.30 MJ/kg), high resistance to mechanical shock (>99%), high volatile matter contents, optimal softening temperatures and optimal ash contents. However, the high K, Ca, and Si contents of the AN can form low-melting-point eutectics, which can cause slagging. Moreover, the AN materials had large particle sizes, and high cellulose and hemicellulose contents led to high total moistures, low softening temperatures and low calorific values. The AN was not suitable for use as a fuel. The results suggested that NG is an effective binder for pelletization of biomass and showed the feasibility of using municipal solid wastes for energy production.

Digital design of scaffold for mandibular defect repair based on tissue engineering.

Mandibular defect occurs more frequently in recent years, and clinical repair operations via bone transplantation are difficult to be further improved due to some intrinsic flaws. Tissue engineering, which is a hot research field of biomedical engineering, provides a new direction for mandibular defect repair. As the basis and key part of tissue engineering, scaffolds have been widely and deeply studied in regards to the basic theory, as well as the principle of biomaterial, structure, design, and fabrication method. However, little research is targeted at tissue regeneration for clinic repair operations. Since mandibular bone has a special structure, rather than uniform and regular structure in existing studies, a methodology based on tissue engineering is proposed for mandibular defect repair in this paper. Key steps regarding scaffold digital design, such as external shape design and internal microstructure design directly based on triangular meshes are discussed in detail. By analyzing the theoretical model and the measured data from the test parts fabricated by rapid prototyping, the feasibility and effectiveness of the proposed methodology are properly verified. More works about mechanical and biological improvements need to be done to promote its clinical application in future.