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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. [ABSTRACT FROM AUTHOR]

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 ${0.964}\pm {0.054}$ , significantly outperforming the original MeshSegNet. In the second stage, PointNet-Reg achieved a mean absolute error (MAE) of ${0}.{597}\pm {0}.{761} \, mm$ 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

We demonstrate that a lattice-Boltzmann lattice-spring method can be used to simulate a dynamic behavior of a suspension of a large number of flexible fibers in finite Reynolds number flows. In the method, lattice-Boltzmann equation is adopted to simulate fluid velocity and vorticity while lattice-spring model with three-body forces can be employed to model the bending deformation of solid bodies. In order to realize the non-slip boundary condition, a forcing term is simply calculated by using the Newtonian second law and imposed with an immersed boundary scheme. The method is validated by comparing the present results with experiments and existing theories and methods. Subsequently, the method is applied to simulate a dynamic process of flexible fibers settling on a static or moving screen/wire net while a fiber mat is simultaneously built over the screen and resists fluid flowing. The number of fibers, fiber density and flexibility, and ratio of the relative velocity of the screen/wire to fluid can be systematically varied at different levels. Their influences on drainage rate are computed and evaluated. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

Abstract 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. Highlights • We first investigate the effects and mechanisms of CDDO-Me in osteoclastogenesis. • CDDO-Me, SFN and tBHQ attenuate RANKL-induced osteoclastogenesis in RAW cell. • CDDO-Me, SFN and tBHQ suppress RANKL-induced intracellular ROS through activating NRF2-mediated antioxidant response. • CDDO-Me elicits its effects on antioxidant activation and osteoclastogenesis inhibition at much lower concentration than other compounds. [ABSTRACT FROM AUTHOR]

A hydroxide co-precipitation method is used to synthesize transition metal hydroxide (Ni 1/3 Co 1/3 Mn 1/3 (OH) 2 ), which is the precursor for layer-structured LiNi 1/3 Co 1/3 Mn 1/3 O 2 . The optimum pH range for the preparation of Ni 1/3 Co 1/3 Mn 1/3 (OH) 2 using a continuous stirred-tank reactor is calculated by taking into account the underlying chemical equilibria. The entire growth process of the Ni 1/3 Co 1/3 Mn 1/3 (OH) 2 particles is investigated by monitoring the structure, morphology, particle size distribution, and tap density as a function of the reaction time. The results confirm that the co-precipitation reaction in the presence of ammonia started with the formation of crystal nuclei and (001) plane dominated nanosheets. The reaction ended with spherical and dense hydroxide precursors. The crystal growth mechanism was interpreted during the co-precipitation process, which involved the quick nucleation of primary particles followed by its slow aggregation and crystallization. The electrochemical properties of the final cathode materials with different morphologies are also studied. The results show that the electrochemical performances of the final LiNi 1/3 Co 1/3 Mn 1/3 O 2 are strongly affected by the hierarchical structure of Ni 1/3 Co 1/3 Mn 1/3 (OH) 2 . [ABSTRACT FROM AUTHOR]