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Maxillary protraction with or without expansion is a widely known orthopedic treatment modality in growing skeletal Class III patients. However, limited data are available regarding the outcomes of long-term changes in the maxilla. Aim of this meta-analysis was to assess the effectiveness of the long-term maxillary anteroposterior changes following a facemask therapy with or without rapid maxillary expansion in growing skeletal Class III patients. A comprehensive literature search was conducted using the databases of PubMed, Science Direct, Web of Science, and Embase. Randomized controlled trials and cohort studies, published up to Sep. 2020, with maxillary protraction and/or expansion as keywords were included in this meta-analysis. Risk of bias within and across studies were assessed using the Cochrane tools (RoB2.0 and ROBINS-I) and GRADE approach. Overall and subgroup comparisons with the random-effect model were performed in this meta-analysis. Meta-regression models were designed to determine potential heterogeneity. There was a statistically significant increase (Mean difference, 2.29°; 95% confidence interval, 1.86-2.73; and p < 0.001 after facemask (FM) protraction. Mean difference, 1.73°; 95% confidence interval, 1.36-2.11; and p < 0.001 after rapid maxillary expansion(RME) and facemask protraction) in the Sella-Nasion-A point (SNA) angle in the treatment groups as compared with the control groups, when measured during the less than 3-year follow-up period. However, no statistically significant changes (Mean difference, 0.28°; 95% confidence interval, -0.57-1.13; and p = 0.52 after facemask protraction. Mean difference, 0.34°; 95% confidence interval, -0.64-1.33; and p = 0.50 after rapid maxillary expansion and facemask protraction) were observed in the SNA angle in the groups, when measured after 3 years of follow-up. Meta-regression analysis also showed that with increased follow-up duration, the effectiveness of maxillary protraction decreased. This meta-analysis revealed that maxillary protraction therapy could be effective for a short-term in correcting maxillary hypoplasia and the treatment result was not affected by mean age and sex. However, with increased follow-up duration, the sagittal maxillary changes gradually decreased. Limitations on this review were only the SNA angle was used and clinical heterogeneity was not discussed. The quality of evidence was moderate. Further long-term observational studies are necessary for a comprehensive evaluation of the effects on maxillary skeletal changes.

Abstract Objective A two-way relationship between periodontitis and diabetes has been proposed. However, bidirectional epidemiological observation is limited and inconsistent. Objective Using the National Health Insurance Research Database of Taiwan (covering over 99% of the entire population), we aimed to estimate the development of diabetes in periodontitis patients or that of periodontitis in patients with type 2 diabetes mellitus (T2DM), respectively. Methods A total of 11 011 patients with severe periodontitis were recruited from 2000 to 2015. After matching by age, sex, and index date, 11 011 patients with mild periodontitis and 11 011 non-periodontitis controls were registered. Additionally, 157 798 patients with T2DM and 157 798 non-T2DM controls were enrolled, in whom the development of periodontitis was traced. Cox proportional hazards model was performed. Results Periodontitis patients tended to have a statistically high risk for T2DM. The adjusted hazard ratio was 1.94 (95% CI, 1.49-2.63, P < .01) and 1.72 (95% CI, 1.24-2.52, P < .01) for severe and mild periodontitis groups, respectively. The patients with severe periodontitis had a higher risk of having T2DM relative to those with mild periodontitis (1.17 [95% CI, 1.04-1.26, P < .001]). Conversely, the risk of periodontitis increased significantly in patients with T2DM (1.99 [95% CI, 1.42-2.48, P < .01]). However, high risk was observed for the outcome of severe periodontitis (2.08 [95% CI, 1.50-2.66, P < .001]), but not for mild periodontitis (0.97 [95% CI, 0.38-1.57, P = .462]). Conclusion We suggest that the bidirectional association is between T2DM and severe but not mild periodontitis.

Beamforming for rotating sound source localization (denoted by rotating beamforming) is widely used in engineering nowadays. Acoustic shielding, such as shafts and covers, is typically employed in the practical measurement of rotating source localization. Acoustic shielding blocks the signal propagation between rotating sources and the array, and thus affects the performance of rotating beamforming. However, the detailed effects of acoustic shielding on rotating beamforming are still unclear. In this study, the blocking rate that represents the degree of signal propagation path blockage is defined first. The effects of acoustic shielding on rotating beamforming are then investigated in simulations. Finally, these effects are verified by experiments. According to the results of the simulation and experiments, rotating beamforming can still localize rotating sources, as long as the signal propagation path is not completely blocked. However acoustic shielding decreases sound intensity accuracy, especially when the blocking rate is larger than 50%. When acoustic shielding is employed, the ability of rotating beamforming to compensate for Doppler effects weakens.

Allergic rhinitis (AR) is associated with various developmental issues that affecting dentition. We aimed to determine whether AR is associated with an increased risk of traumatic dental injuries (TDIs) in Taiwanese individuals. We used the Taiwan National Health Insurance Research Database (NHIRD) to conduct a nested case-control study. We compared an AR cohort with a matched cohort of patients without AR. New TDI cases were determined during our study period. To compare TDI risk between our study cohorts, we used Cox proportional regression analysis, and hazard ratios (HR) with 95% confidence intervals (CI) were calculated to quantify the association between AR exposure and TDI risk. In total, 76749 patients with AR (31715 male; 45034 female) were identified. In the AR and the non-AR cohorts, 312 patients in total had TDI. Patients with AR had a significantly higher risk of TDI than those without AR (aHR = 1.92; 95% CI = 1.459-2.525; P < 0.001). The risk of TDI was markedly higher in the AR cohort, except in the 3-12-year-old group, and with a CCI ≥ 1. AR patients had a future risk of TDI, indicating a potentially linked disease pathophysiology. The association between AR and TDI is greater among general patients. Clinicians and caregivers should be aware of potential TDI co-morbidity in patients with AR.

The identification of mixed monopole and dipole sound sources under highly randomized acoustic environments is of interest in many industrial applications. The DAMAS–MS method is one of the few methods that has been explicitly developed to address this problem. However, it suffers from a critical constraint in that it consistently exhibits limited accuracy in identifying monopole sources, which leads to their underestimation in the final results. To overcome this constraint, this paper proposed a novel identification framework that integrates vision transformer (ViT) with beamforming techniques. The framework leverages preliminary beamforming results to construct input features by extracting the real and imaginary components of the cross-spectral matrix at target frequencies and incorporating spatial position encodings derived from estimated source locations. To ensure adaptability to varying source densities, multiple ViT sub-models are trained on representative scenarios. This strategy enables effective generalization across the target range and supports multi-label identification of monopole and dipole sources with varied configurations. Furthermore, anechoic chamber experiments with synthesized monopole and dipole emitters validate the method’s stability under single-frequency excitation. Compared to the DAMAS–MS method, the proposed method achieves improved identification accuracy for monopole sources, while maintaining comparable performance in dipole source identification, underscoring its potential for practical applications.

Metastasis is the leading cause of death in lung adenocarcinomas. Identifying potential prognostic biomarkers and exploiting regulatory mechanisms could improve the diagnosis and treatment of lung cancer patients. We previously found that cluster of differentiation 109 (CD109) was upregulated in lung tumor tissues, and CD109 overexpression was correlated with the invasive and metastatic capacities of lung adenocarcinoma cells. However, the contribution of CD109 to lung tumorigenesis remains to be elucidated. In the present study, we identified that CD109 was upregulated in metastatic lung adenocarcinoma cells, and elevation of CD109 was correlated with epithelial-to-mesenchymal transition (EMT) traits in patients with lung adenocarcinoma. Functionally, CD109 expression was crucial for EMT gene expressions, tumor invasiveness, and cancer stemness properties. Moreover, elevation of CD109 was accompanied by upregulation of the yes-associated protein (YAP) signature in metastatic lung cancer cells and lung cancer patients, and activation of YAP was demonstrated to participate in CD109-elicited EMT gene expressions and tumor invasiveness. Our study reveals the molecular mechanism underlying CD109 in lung tumor aggressiveness, and CD109 could be a potential diagnostic and therapeutic target for lung cancer patients.

Knowing the rate at which particle radiation releases energy in a material, the “stopping power,” is key to designing nuclear reactors, medical treatments, semiconductor and quantum materials, and many other technologies. While the nuclear contribution to stopping power, i.e., elastic scattering between atoms, is well understood in the literature, the route for gathering data on the electronic contribution has for decades remained costly and reliant on many simplifying assumptions, including that materials are isotropic. We establish a method that combines time-dependent density functional theory (TDDFT) and machine learning to reduce the time to assess new materials to hours on a supercomputer and provide valuable data on how atomic details influence electronic stopping. Our approach uses TDDFT to compute the electronic stopping from first principles in several directions and then machine learning to interpolate to other directions at a cost of 10 million times fewer core-hours. We demonstrate the combined approach in a study of proton irradiation in aluminum and employ it to predict how the depth of maximum energy deposition, the “Bragg Peak,” varies depending on the incident angle—a quantity otherwise inaccessible to modelers and far outside the scales of quantum mechanical simulations. The lack of any experimental information requirement makes our method applicable to most materials, and its speed makes it a prime candidate for enabling quantum-to-continuum models of radiation damage. The prospect of reusing valuable TDDFT data for training the model makes our approach appealing for applications in the age of materials data science.

Semiconductors offer a promising platform for physical implementation of qubits, but their broad adoption is presently hindered by limited scalability and/or very low operating temperatures. Learning from the nitrogen-vacancy centers in diamond, our goal is to find equivalent optically active point defect centers in crystalline silicon, which could be advantageous for their scalability and integration with classical devices. Transition metal (TM) impurities in silicon are common paramagnetic deep defects, but a comprehensive theoretical study of the whole 3d series that considers generalized Koopmans’ condition is missing. We apply the HSE06(+U) method to examine their potential as optically active spin qubits and identify seven TM impurities that have optically allowed triplet–triplet transitions within the silicon band gap. These results provide the first step toward silicon-based qubits with higher operating temperatures for quantum sensing. Additionally, these point defects could lead to spin-photon interfaces in silicon-based qubits and devices for mid-infrared free-space communications.

This study aims to develop a generalizable method for designing a patient-specific reconstructive scaffold implant for a large distal lateral femur defect using finite element (FE) analysis and topology optimization. A 3D solid-core implant for the distal femur defect was designed to withhold the femur load. Data from FE analysis of the solid implant were use for topology optimization to obtain a ‘bone scaffold implant’ with light-weight internal cavity and surface lattice features to allow for filling with bone material. The bone scaffold implant weighed 69.6% less than the original solid-core implant. The results of FE simulation show that the bone repaired with the bone scaffold implant had lower total displacement (12%), bone plate von Mises stress (34%), bone maximum first principal stress (33%), and bone maximum first principal strain (32%) than did bone repaired with bone cement. The trend in experimental strain with increasing load on the composite femur was greater with bone cement than with the bone scaffold implant. This study presents a generalizable method for designing a patient-specific reconstructive scaffold implant for the distal lateral femur defect that has sufficient strength and space for filling with allograft bone.