Artificial Intelligence for Predicting the Aesthetic Component of the Index of Orthodontic Treatment Need.

The aesthetic component (AC) of the Index of Orthodontic Treatment Need (IOTN) is internationally recognized as a reliable and valid method for assessing aesthetic treatment need. The objective of this study is to use artificial intelligence (AI) to automate the AC assessment. A total of 1009 pre-treatment frontal intraoral photos with overjet values were collected. Each photo was graded by an experienced calibration clinician. The AI was trained using the intraoral images, overjet, and two other approaches. For Scheme 1, the training data were AC 1-10. For Scheme 2, the training data were either the two groups AC 1-5 and AC 6-10 or the three groups AC 1-4, AC 5-7, and AC 8-10. Sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were measured for all approaches. The performance was tested without overjet values as input. The intra-rater reliability for the grader, using kappa, was 0.84 (95% CI 0.76-0.93). Scheme 1 had 77% sensitivity, 88% specificity, 82% accuracy, 89% PPV, and 75% NPV in predicting the binary groups. All other schemes offered poor tradeoffs. Findings after omitting overjet and dataset supplementation results were mixed, depending upon perspective. We have developed deep learning-based algorithms that can predict treatment need based on IOTN-AC reference standards; this provides an adjunct to clinical assessment of dental aesthetics.

Effectiveness of Machine Learning in Predicting Orthodontic Tooth Extractions: A Multi-Institutional Study.

The study aimed to evaluate the effectiveness of machine learning in predicting whether orthodontic patients would require extraction or non-extraction treatment using data from two university datasets. A total of 1135 patients, with 297 from University 1 and 838 from University 2, were included during consecutive enrollment periods. The study identified 20 inputs including 9 clinical features and 11 cephalometric measurements based on previous research. Random forest (RF) models were used to make predictions for both institutions. The performance of each model was assessed using sensitivity (SEN), specificity (SPE), accuracy (ACC), and feature ranking. The model trained on the combined data from two universities demonstrated the highest performance, achieving 50% sensitivity, 97% specificity, and 85% accuracy. When cross-predicting, where the University 1 (U1) model was applied to the University 2 (U2) data and vice versa, there was a slight decrease in performance metrics (ranging from 0% to 20%). Maxillary and mandibular crowding were identified as the most significant features influencing extraction decisions in both institutions. This study is among the first to utilize datasets from two United States institutions, marking progress toward developing an artificial intelligence model to support orthodontists in clinical practice.

Artificial Intelligence for 3D Reconstruction from 2D Panoramic X-rays to Assess Maxillary Impacted Canines.

The objective of this study was to explore the feasibility of current 3D reconstruction in assessing the position of maxillary impacted canines from 2D panoramic X-rays. A dataset was created using pre-treatment CBCT data from a total of 123 patients, comprising 74 patients with impacted canines and 49 patients without impacted canines. From all 74 subjects, we generated a dataset containing paired 2D panoramic X-rays and pseudo-3D images. This pseudo-3D image contained information about the location of the impacted canine in the buccal/lingual, mesial/distal, and apical/coronal positions. These data were utilized to train a deep-learning reconstruction algorithm, a generative AI. The location of the crown of the maxillary impacted canine was determined based on the output of the algorithm. The reconstruction was evaluated using the structure similarity index measure (SSIM) as a metric to indicate the quality of the reconstruction. The prediction of the impacted canine's location was assessed in both the mesiodistal and buccolingual directions. The reconstruction algorithm predicts the position of the impacted canine in the buccal, middle, or lingual position with 41% accuracy, while the mesial and distal positions are predicted with 55% accuracy. The mean SSIM for the output is 0.71, with a range of 0.63 to 0.84. Our study represents the first application of AI reconstruction output for multidisciplinary care involving orthodontists, periodontists, and maxillofacial surgeons in diagnosing and treating maxillary impacted canines. Further development of deep-learning algorithms is necessary to enhance the robustness of dental reconstruction applications.

Comparison of maxillary anterior tooth movement between Invisalign and fixed appliances.

INTRODUCTION: This research project aimed to compare the number of maxillary incisors and canine movement between Invisalign and fixed orthodontic appliances using artificial intelligence and identify any limitations of Invisalign. METHODS: Sixty patients (Invisalign, n = 30; braces, n = 30) were randomly selected from the Ohio State University Graduate Orthodontic Clinic archive. Peer Assessment Rating (PAR) analysis was used to indicate the severity of the patients in both groups. To analyze the incisors and canine movement, specific landmarks were identified on incisors and canines using an artificial intelligence framework, two-stage mesh deep learning. Total average tooth movement in the maxilla and individual (incisors and canine) tooth movement in 6 directions (buccolingual, mesiodistal, vertical, tipping, torque, rotation) were then analyzed at a significance level of α = 0.05. RESULTS: Based on the posttreatment Peer Assessment Rating scores, the quality of finished patients in both groups was similar. In maxillary incisors and canines, there was a significant difference in movement between Invisalign and conventional appliances for all 6 movement directions (P <0.05). The greatest differences were with rotation and tipping of the maxillary canine, along with incisor and canine torque. The smallest statistical differences observed for incisors and canines were crown translational tooth movement in the mesiodistal and buccolingual directions. CONCLUSIONS: When comparing fixed orthodontic appliances to Invisalign, patients treated with fixed appliances were found to have significantly more maxillary tooth movement in all directions, especially with rotation and tipping of the maxillary canine.

Optineurin regulates NRF2-mediated antioxidant response in a mouse model of Paget's disease of bone.

Degenerative diseases affecting the nervous and skeletal systems affect the health of millions of elderly people. Optineurin (OPTN) has been associated with numerous neurodegenerative diseases and Paget's disease of bone (PDB), a degenerative bone disease initiated by hyperactive osteoclastogenesis. In this study, we found age-related increase in OPTN and nuclear factor E2-related factor 2 (NRF2) in vivo. At the molecular level, OPTN could directly interact with both NRF2 and its negative regulator Kelch-like ECH-associated protein 1 (KEAP1) for up-regulating antioxidant response. At the cellular level, deletion of OPTN resulted in increased intracellular reactive oxygen species and increased osteoclastogenic potential. At the tissue level, deletion of OPTN resulted in substantially increased oxidative stress derived from leukocytes that further stimulate osteoclastogenesis. Last, curcumin attenuated hyperactive osteoclastogenesis induced by OPTN deficiency in aged mice. Collectively, our findings reveal an OPTN-NRF2 axis maintaining bone homeostasis and suggest that antioxidants have therapeutic potential for PDB.

Assessment of malalignment factors related to the Invisalign treatment time aided by automated imaging processes.

OBJECTIVES: To identify predictors regarding the type and severity of malocclusion that affect total Invisalign treatment duration based on an intraoral digital scan. MATERIALS AND METHODS: The subjects of this retrospective clinical cohort were 116 patients treated with Invisalign. A deep learning method was used for automated tooth segmentation and landmark identification of the initial and final digital models. The changes in the six degrees of freedom (DOF), representing types of malalignment, were measured. Linear regression was performed to find the contributing factors associated with treatment time. In addition, the Peer Assessment Rating (PAR) score and a composite score combining 6 DOF were correlated separately to the treatment time. RESULTS: The number of trays differed between sexes (P = .0015). The absolute maximum torque was marginally associated with the total number of trays (P = .0518), while the rest of the orthodontic tooth movement showed no correlation. The composite score showed a higher correlation with the total number of trays (P = .0045) than did individual tooth movement. Pretreatment upper and lower anterior segment PAR scores were positively associated with the treatment time (P < .001). CONCLUSIONS: There is not enough evidence to conclude that certain types of tooth movement affect the total aligner treatment time. A composite score seems to be a better predictor for total treatment time than do individual malalignment factors in aligner treatment. Upper and lower anterior malalignment factors have a significant effect on the treatment duration.

Accuracy of three-dimensional printed models derived from cone-beam computed tomography.

OBJECTIVES: To determine the accuracy of three-dimensional (3D) printed models fabricated from cone-beam computed tomography (CBCT) scans of human mandibular dry skulls in comparison with models derived from intraoral scanner (IOS) data. MATERIALS AND METHODS: Six human mandibular dry skulls were scanned by IOS and CBCT. Digital models (DMs) constructed from the IOS and CBCT data were fabricated physically using a 3D printer. The width and thickness of individual teeth and intercanine and molar widths were measured using a digital caliper. The accuracy of the DMs was compared between IOS and CBCT. Paired t-tests were used for intergroup comparisons. RESULTS: All intraclass correlation coefficient values for the three measurements (mesial-distal, buccal-lingual, width) exceeded 0.9. For the mandibular teeth, there were significant discrepancies in model accuracy between the IOS (average discrepancies of 0.18 ± 0.08 mm and 0.16 ± 0.12 mm for width and thickness, respectively) and CBCT (0.28 ± 0.07 mm for width, 0.37 ± 0.2 mm for thickness; P < .01). Intercanine (P = .38) and molar widths (P = .41) showed no significant difference between groups. CONCLUSIONS: There was a statistically significant difference in the accuracy of DMs obtained from CBCT and IOS; however, this did not seem to result in any important clinical difference. CBCT could be routinely used as an orthodontic diagnostic tool and for appliance construction.

Two-Stage Mesh Deep Learning for Automated Tooth Segmentation and Landmark Localization on 3D Intraoral Scans.

Accurately segmenting teeth and identifying the corresponding anatomical landmarks on dental mesh models are essential in computer-aided orthodontic treatment. Manually performing these two tasks is time-consuming, tedious, and, more importantly, highly dependent on orthodontists' experiences due to the abnormality and large-scale variance of patients' teeth. Some machine learning-based methods have been designed and applied in the orthodontic field to automatically segment dental meshes (e.g., intraoral scans). In contrast, the number of studies on tooth landmark localization is still limited. This paper proposes a two-stage framework based on mesh deep learning (called TS-MDL) for joint tooth labeling and landmark identification on raw intraoral scans. Our TS-MDL first adopts an end-to-end iMeshSegNet method (i.e., a variant of the existing MeshSegNet with both improved accuracy and efficiency) to label each tooth on the downsampled scan. Guided by the segmentation outputs, our TS-MDL further selects each tooth's region of interest (ROI) on the original mesh to construct a light-weight variant of the pioneering PointNet (i.e., PointNet-Reg) for regressing the corresponding landmark heatmaps. Our TS-MDL was evaluated on a real-clinical dataset, showing promising segmentation and localization performance. Specifically, iMeshSegNet in the first stage of TS-MDL reached an averaged Dice similarity coefficient (DSC) at 0.964±0.054 , significantly outperforming the original MeshSegNet. In the second stage, PointNet-Reg achieved a mean absolute error (MAE) of 0.597±0.761 mm in distances between the prediction and ground truth for 66 landmarks, which is superior compared with other networks for landmark detection. All these results suggest the potential usage of our TS-MDL in orthodontics.

Biomimetic polydopamine-laced hydroxyapatite collagen material orients osteoclast behavior to an anti-resorptive pattern without compromising osteoclasts' coupling to osteoblasts.

Polydopamine-assisted modification for bone substitute materials has recently shown great application potential in bone tissue engineering due to its excellent biocompatibility and adhesive properties. A scaffold material's impact on osteoclasts is equally as important as its impact on osteoblasts when considering tissue engineering for bone defect repair, as healthy bone regeneration requires an orchestrated coupling between osteoclasts and osteoblasts. How polydopamine-functionalized bone substitute materials modulate the activity of osteoblast lineage cells has been extensively investigated, but much less is known about their impact on osteoclasts. Moreover, most of the polydopamine-functionalized materials would need to additionally load a biomolecule to exert the modulation on osteoclast activity. Herein, we demonstrated that our biomimetic polydopamine-laced hydroxyapatite collagen (PDHC) scaffold material, which does not need to load additional bioactive agent, is sufficiently able to modulate osteoclast activity in vitro. First, PDHC showed an anti-resorptive potential, characterized by decreased osteoclast differentiation and resorption capacity and changes in osteoclasts' transcriptome profile. Next, cAMP response element-binding protein (CREB) activity was found to mediate PDHC's anti-osteoclastogenic effect. Finally, although PDHC altered clastokines expression pattern of osteoclasts, as revealed by transcriptomic and secretomic analysis, osteoclasts' coupling to osteoblasts was not compromised by PDHC. Collectively, this study demonstrated the PDHC material orients osteoclast behavior to an anti-resorptive pattern without compromising osteoclasts' coupling to osteoblasts. Such a feature is favorable for the net increase of bone mass, which endows the PDHC material with great application potential in preclinical/clinical bone defect repair.

Machine learning from clinical data sets of a contemporary decision for orthodontic tooth extraction.

OBJECTIVE: To examine the robustness of the published machine learning models in the prediction of extraction vs non-extraction for a diverse US sample population seen by multiple providers. SETTING AND SAMPLE POPULATION: Diverse group of 838 patients (208 extraction, 630 non-extraction) were consecutively enrolled. MATERIALS AND METHODS: Two sets of input features (117 and 22) including clinical and cephalometric variables were identified based on previous studies. Random forest (RF) and multilayer perception (MLP) models were trained using these feature sets on the sample population and evaluated using measures including accuracy (ACC) and balanced accuracy (BA). A technique to identify incongruent data was used to explore underlying characteristics of the data set and split all samples into 2 groups (G1 and G2) for further model training. RESULTS: Performance of the models (75%-79% ACC and 72%-76% BA) on the total sample population was lower than in previous research. Models were retrained and evaluated using G1 and G2 separately, and individual group MLP models yielded improved accuracy for G1 (96% ACC and 94% BA) and G2 (88% ACC and 85% BA). RF feature ranking showed differences between top features for G1 (maxillary crowding, mandibular crowding and L1-NB) and G2 (age, mandibular crowding and lower lip to E-plane). CONCLUSIONS: An incongruent data pattern exists in a consecutively enrolled patient population. Future work with incongruent data segregation and advanced artificial intelligence algorithms is needed to improve the generalization ability to make it ready to support clinical decision-making.

Deficiency of optineurin enhances osteoclast differentiation by attenuating the NRF2-mediated antioxidant response.

Abnormally increased resorption contributes to bone degenerative diseases such as Paget's disease of bone (PDB) through unclear mechanisms. Recently, the optineurin (OPTN) gene has been implicated in PDB, and global OPTN knockout mice (Optn-/-) were shown to exhibit increased formation of osteoclasts (osteoclastogenesis). Growing evidence, including our own, has demonstrated that intracellular reactive oxygen species (ROS) stimulated by receptor activator of nuclear factor kappa-B ligand (RANKL) can act as signaling molecules to promote osteoclastogenesis. Here, we report that OPTN interacts with nuclear factor erythroid-derived factor 2-related factor 2 (NRF2), the master regulator of the antioxidant response, defining a pathway through which RANKL-induced ROS could be regulated for osteoclastogenesis. In this study, monocytes from Optn-/- and wild-type (Optn+/+) mice were utilized to differentiate into osteoclasts, and both qRT-PCR and tartrate-resistant acid phosphatase (TRAP) staining showed that the Optn-/- monocytes exhibited enhanced osteoclastogenesis compared to the Optn+/+ cells. CellROX® staining, qRT-PCR, and Western blotting indicated that OPTN deficiency reduced the basal expression of Nrf2, inhibited the expression of NRF2-responsive antioxidants, and increased basal and RANKL-induced intracellular ROS levels, leading to enhanced osteoclastogenesis. Coimmunoprecipitation (co-IP) showed direct interaction, and immunofluorescence staining showed perinuclear colocalization of the OPTN-NRF2 granular structures during differentiation. Finally, curcumin and the other NRF2 activators attenuated the hyperactive osteoclastogenesis induced by OPTN deficiency. Collectively, our findings reveal a novel OPTN-mediated mechanism for regulating the NRF2-mediated antioxidant response in osteoclasts and extend the therapeutic potential of OPTN in the aging process resulting from ROS-triggered oxidative stress, which is associated with PDB and many other degenerative diseases.

3D morphometric quantification of maxillae and defects for patients with unilateral cleft palate via deep learning-based CBCT image auto-segmentation.

OBJECTIVE: This study aimed to quantify the 3D asymmetry of the maxilla in patients with unilateral cleft lip and palate (UCP) and investigate the defect factors responsible for the variability of the maxilla on the cleft side using a deep-learning-based CBCT image segmentation protocol. SETTING AND SAMPLE POPULATION: Cone beam computed tomography (CBCT) images of 60 patients with UCP were acquired. The samples in this study consisted of 39 males and 21 females, with a mean age of 11.52 years (SD = 3.27 years; range of 8-18 years). MATERIALS AND METHODS: The deep-learning-based protocol was used to segment the maxilla and defect initially, followed by manual refinement. Paired t-tests were performed to characterize the maxillary asymmetry. A multiple linear regression was carried out to investigate the relationship between the defect parameters and those of the cleft side of the maxilla. RESULTS: The cleft side of the maxilla demonstrated a significant decrease in maxillary volume and length as well as alveolar length, anterior width, posterior width, anterior height and posterior height. A significant increase in maxillary anterior width was demonstrated on the cleft side of the maxilla. There was a close relationship between the defect parameters and those of the cleft side of the maxilla. CONCLUSIONS: Based on the 3D volumetric segmentations, significant hypoplasia of the maxilla on the cleft side existed in the pyriform aperture and alveolar crest area near the defect. The defect structures appeared to contribute to the variability of the maxilla on the cleft side.

Enhanced home palliative care could reduce emergency department visits due to non-organic dyspnea among cancer patients: a retrospective cohort study.

BACKGROUND: Dyspnea is a common trigger of emergency department visits among terminally ill and cancer patients. Frequent emergency department (ED) visits at the end of life are an indicator of poor-quality care. We examined emergency department visit rates due to dyspnea symptoms among palliative patients under enhanced home palliative care. METHODS: Our home palliative care team is responsible for patient management by palliative care specialists, residents, home care nurses, social workers, and chaplains. We enhanced home palliative care visits from 5 days a week to 7 days a week, corresponding to one to two extra visits per week based on patient needs, to develop team-based medical services and formulate standard operating procedures for dyspnea care. RESULTS: Our team cared for a total of 762 patients who exhibited 512 ED visits, 178 of which were due to dyspnea (mean ± SD age, 70.4 ± 13.0 years; 49.4% male). Dyspnea (27.8%) was the most common reason recorded for ED visits, followed by pain (19.0%), GI symptoms (15.7%), and fever (15.3%). The analysis of Group A versus Group B revealed that the proportion of nonfamily workers (42.9% vs. 19.4%) and family members (57.1% vs. 80.6%) acting as caregivers differed significantly (P < 0.05). Compared to the ED visits of the Group A, the risk was decreased by 30.7% in the Group B (P < 0.05). CONCLUSIONS: This study proves that enhanced home palliative care with two additional days per week and formulated standard operating procedures for dyspnea could significantly reduce the rate of ED visits due to non-organic dyspnea during the last 6 months of life.

Effects of a single bout of short-duration high-intensity and long-duration low-intensity exercise on insulin resistance and adiponectin/leptin ratio.

OBJECTIVES: Moderate-intensity exercise improves insulin sensitivity, which may depend on the intensity, duration, and frequency of exercise. We examined the effects of a single bout of short-duration high-intensity exercise (HIE) and long-duration lowintensity exercise (LIE) on insulin sensitivity and the adiponectin/leptin ratio in individuals with different body mass indices (BMIs) who do not exercise regularly. METHODS: We enrolled 42 healthy volunteers aged 20-64 years and divided them into two groups based on BMI: BMI <24 kg/m2 and BMI ≥27 kg/m2. They were randomly assigned to either the short-duration (20 min) HIE (70%-80% heart rate reserve, HRR) or long-duration (60 min) LIE training groups (30%-40% HRR). Glucose, insulin, adiponectin, and leptin levels were assessed before training and at 0, 30, 60, and 120 min after training. RESULTS: We finally analyzed 27 normal weight and 9 obese individuals. No significant differences were observed in the baseline information of both BMI groups. Homeostatic model assessment for insulin resistance significantly improved for both exercise patterns in the normal weight group and for the HIE pattern in the obese group (P < 0.01), whereas the adiponectin/leptin ratio increased significantly only among normal weight participants with the LIE intervention. CONCLUSION: Both exercise patterns in BMI <24 kg/m2 and BMI ≥27 kg/m2 benefit on insulin resistance. Therefore, people can choose the way they can fit to improve insulin resistance both short-duration high-intensity exercise and long-duration low-intensity exercise.

Deep Multi-Scale Mesh Feature Learning for Automated Labeling of Raw Dental Surfaces From 3D Intraoral Scanners.

Precisely labeling teeth on digitalized 3D dental surface models is the precondition for tooth position rearrangements in orthodontic treatment planning. However, it is a challenging task primarily due to the abnormal and varying appearance of patients' teeth. The emerging utilization of intraoral scanners (IOSs) in clinics further increases the difficulty in automated tooth labeling, as the raw surfaces acquired by IOS are typically low-quality at gingival and deep intraoral regions. In recent years, some pioneering end-to-end methods (e.g., PointNet) have been proposed in the communities of computer vision and graphics to consume directly raw surface for 3D shape segmentation. Although these methods are potentially applicable to our task, most of them fail to capture fine-grained local geometric context that is critical to the identification of small teeth with varying shapes and appearances. In this paper, we propose an end-to-end deep-learning method, called MeshSegNet, for automated tooth labeling on raw dental surfaces. Using multiple raw surface attributes as inputs, MeshSegNet integrates a series of graph-constrained learning modules along its forward path to hierarchically extract multi-scale local contextual features. Then, a dense fusion strategy is applied to combine local-to-global geometric features for the learning of higher-level features for mesh cell annotation. The predictions produced by our MeshSegNet are further post-processed by a graph-cut refinement step for final segmentation. We evaluated MeshSegNet using a real-patient dataset consisting of raw maxillary surfaces acquired by 3D IOS. Experimental results, performed 5-fold cross-validation, demonstrate that MeshSegNet significantly outperforms state-of-the-art deep learning methods for 3D shape segmentation.

Body mass index and albumin levels are prognostic factors for long-term survival in elders with limited performance status.

Elderly long-term care facility residents typically have musculoskeletal conditions that may lead to long-term disability and increased mortality. Our main objective was to explore the relationship between body mass index (BMI), albumin levels, and mortality in elderly individuals with limited performance status. Among 182 participants (mean age, 78.8 years; 57% women), 11%, 64%, and 25% had serum albumin levels of <2.8, 2.8-3.5, and >3.5 g/dL, respectively. After multivariate adjustments, diastolic blood pressure >90 mmHg was associated with all-cause mortality [hazard ratio (HR) = 2.08, 95% confidence interval (CI) = 1.13-3.82; P = 0.018]. In addition, BMI <18.5 kg/m2 and albumin level <2.8 g/dL associated with higher mortality than BMI = 18.5-24 kg/m2 and albumin level > 3.5 g/dL (HR = 1.80, 95% CI = 1.11-2.94 and HR = 2.54, 95% CI 1.22-5.30, respectively; P = 0.018 and 0.013, respectively). Highest mortality was noted in participants with albumin levels <2.8 g/dL and BMIs <18.5 kg/m2 (HR = 6.12, 95% CI = 1.85-20.21, P = 0.003). Combined hypoalbuminemia (albumin level < 2.8 g/dL) and low BMI (<18.5 kg/m2) may be a useful prognostic indicator of high mortality risk in elderly individuals with limited performance status.

Machine learning in orthodontics: Introducing a 3D auto-segmentation and auto-landmark finder of CBCT images to assess maxillary constriction in unilateral impacted canine patients.

OBJECTIVES: To (1) introduce a novel machine learning method and (2) assess maxillary structure variation in unilateral canine impaction for advancing clinically viable information. MATERIALS AND METHODS: A machine learning algorithm utilizing Learning-based multi-source IntegratioN frameworK for Segmentation (LINKS) was used with cone-beam computed tomography (CBCT) images to quantify volumetric skeletal maxilla discrepancies of 30 study group (SG) patients with unilaterally impacted maxillary canines and 30 healthy control group (CG) subjects. Fully automatic segmentation was implemented for maxilla isolation, and maxillary volumetric and linear measurements were performed. Analysis of variance was used for statistical evaluation. RESULTS: Maxillary structure was successfully auto-segmented, with an average dice ratio of 0.80 for three-dimensional image segmentations and a minimal mean difference of two voxels on the midsagittal plane for digitized landmarks between the manually identified and the machine learning-based (LINKS) methods. No significant difference in bone volume was found between impaction ([2.37 ± 0.34] [Formula: see text] 104 mm3) and nonimpaction ([2.36 ± 0.35] [Formula: see text] 104 mm3) sides of SG. The SG maxillae had significantly smaller volumes, widths, heights, and depths (P < .05) than CG. CONCLUSIONS: The data suggest that palatal expansion could be beneficial for those with unilateral canine impaction, as underdevelopment of the maxilla often accompanies that condition in the early teen years. Fast and efficient CBCT image segmentation will allow large clinical data sets to be analyzed effectively.

CDDO-Me, Sulforaphane and tBHQ attenuate the RANKL-induced osteoclast differentiation via activating the NRF2-mediated antioxidant response.

Metabolic bone diseases are global public health concerns and are primarily caused by uncontrolled osteoclast (OC) formation and activation. During OC differentiation, intracellular reactive oxygen species (ROS) stimulated by receptor activator of nuclear factor kappa-B ligand (RANKL) can serve as the signaling molecules to promote osteoclastic genes expression. Nuclear factor erythroid-2 related factor 2 (NRF2), a master mediator of cellular antioxidant response, also plays a critical role in OC differentiation through the regulation of redox homeostasis. In this study, we investigated the effects of three NRF2 inducers on osteoclastogenesis, including Bardoxolone methyl (CDDO-Me), Sulforaphane (SFN), and tert-butylhydroquinone (tBHQ). By treating RAW cells with three compounds, we found that NRF2 was activated and its downstream antioxidant genes were upregulated, and the RANKL-induced intracellular ROS production and osteoclastogenesis were impaired. Additionally, the expression of nuclear factor of activated T cells c1 (NFATC1), C-FOS and tumor necrosis factor alpha (TNFα) were inhibited after acute exposures (6 h) to the three compounds. Furthermore, suppressed the expression of osteoclast differentiation-associated genes, tartrate-resistant acid phosphatase (TRAP), cathepsin K (CTSK), matrix metalloproteinase-9 (MMP-9) and dendritic cell-specific transmembrane protein (DC-STAMP) were observed after prolonged exposures (5 days) to the compounds. Taken together, these results suggest that CDDO-Me, SFN and tBHQ attenuate RANKL-induced osteoclastogenesis via activation of NRF2-mediated antioxidant response. Among these compounds, relatively low concentrations of CDDO-Me showed stronger active and inhibitory effects on antioxidant response and osteoclastogenesis, respectively.

Synergistic effect of sorafenib with ionizing radiation on human oral cancer cells.

BACKGROUND/AIM: Although anticancer effects of sorafenib on renal, liver and colon carcinomas are well-known, its combination effect with ionizing radiation on oral squamous cell carcinoma (OSCC) is unclear. Herein human SAS cells, an OSCC cell line, were used in order to elucidate this combination effect. MATERIALS AND METHODS: Both SAS and SAS/nuclear factor kappa-B-luciferase (SAS/NF-κB-luc2) cell lines were used in the study. Cell viability, NF-κB activation, and protein expression of NF-κB downstream effectors were determined by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assay, NF-κB-luc2 reporter gene system, NF-κB/DNA binding activity and western blotting. RESULTS: Sorafenib significantly increased radiation-induced cytotoxicity and apoptosis via both mitochondrial-dependent and independent pathways. In addition, NF-κB activity and downstream effector protein expression induced by radiation was suppressed by sorafenib in SAS/NF-κB-luc2 cells. CONCLUSION: Combination of sorafenib with radiation for the treatment of human OSCC shows a synergistic effect via suppression of radiation-induced NF-κB activity and its regulated downstream effector proteins.

Simulation of swimming of a flexible filament using the generalized lattice-spring lattice-Boltzmann method.

A generalized lattice-spring lattice-Boltzmann model (GLLM) is introduced by adding a three-body force in the traditional lattice-spring model. This method is able to deal with bending deformation of flexible biological bodies in fluids. The interactions between elastic solids and fluid are treated with the immersed boundary-lattice Boltzmann method. GLLM is validated by comparing the present results with the existing theoretical and simulation results. As an application of GLLM, swimming of flagellum in fluid is simulated and propulsive force as a function of driven frequency and fluid structures at various Reynolds numbers 0.15-5.1 are presented in this paper.