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Objective: To assess the tooth movement trends during the three stages of maxillary dentition distalization with clear aligners (CA) and to compare the efficacy of different mini-screw anchorage systems., Materials and Methods: Three-dimensional (3D) finite element models of three anchorage systems (A, control group; B, buccal mini-screw anchorage group; C, palatal mini-screw anchorage group) were established. Three stages of simulating maxillary dentition distalization with CA included maxillary molar distalization (stage 1), maxillary premolar distalization (stage 2) and maxillary anterior teeth retraction (stage 3). Therefore, a total of nine models were constructed to analyse the 3D displacement of maxillary teeth during the distalization process., Results: The displacement pattern of maxillary dentition during distalization was similar across the three groups, but with varying magnitudes. During stage 1, groups B and C exhibited greater amounts of molar distalization compared to group A. Group C also demonstrated the least amount of labial movement of the maxillary central incisor compared to the other two groups. During stage 2, the mesial displacement of the maxillary first molar was less significant in groups B and C than in group A. In the final stage, group C exhibited a greater amount of maxillary anterior retraction compared to groups A and B., Conclusion: The palatal mini-screw anchorage system was effective in reducing anchorage loss and improving the efficacy of maxillary dentition distalization with CA., (© 2024 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Background: Orthodontic tooth movement (OTM) is a dynamic equilibrium of bone remodeling, involving the osteogenesis of new bone and the osteoclastogenesis of old bone, which is mediated by mechanical force. Periodontal ligament stem cells (PDLCSs) in the periodontal ligament (PDL) space can transmit mechanical signals and regulate osteoclastogenesis during OTM. KAT6A is a histone acetyltransferase that plays a part in the differentiation of stem cells. However, whether KAT6A is involved in the regulation of osteoclastogenesis by PDLSCs remains unclear., Results: In this study, we used the force-induced OTM model and observed that KAT6A was increased on the compression side of PDL during OTM, and also increased in PDLSCs under compression force in vitro. Repression of KAT6A by WM1119, a KAT6A inhibitor, markedly decreased the distance of OTM. Knockdown of KAT6A in PDLSCs decreased the RANKL/OPG ratio and osteoclastogenesis of THP-1. Mechanistically, KAT6A promoted osteoclastogenesis by binding and acetylating YAP, simultaneously regulating the YAP/TEAD axis and increasing the RANKL/OPG ratio in PDLSCs. TED-347, a YAP-TEAD4 interaction inhibitor, partly attenuated the elevation of the RANKL/OPG ratio induced by mechanical force., Conclusion: Our study showed that the PDLSCs modulated osteoclastogenesis and increased the RANKL/OPG ratio under mechanical force through the KAT6A/YAP/TEAD4 pathway. KAT6A might be a novel target to accelerate OTM., (© 2024. The Author(s).)

Wound healing is a complex challenge that demands urgent attention in the clinical realm. Efficient angiogenesis is a pivotal factor in promoting wound healing. microRNA-146a (miR-146a) inhibitor has angiogenic potential in the periodontal ligament. However, free microRNAs (miRNAs) are poorly delivered into cells due to their limited tissue specificity and low intracellular delivery efficiency. To address this hurdle, we developed a nanocarrier for targeted delivery of the miR-146a inhibitor into endothelial cells. It is composed of a polyethylenimine (PEI)-modified mesoporous silica nanoparticle (MSN) core and a pentapeptide (YIGSR) layer that recognizes endothelial cells. In vitro, we defined that the miR-146a inhibitor and adiponectin (ADP) can modulate angiogenesis and the remodeling of periodontal tissues by activating the ERK and Akt signaling pathways. Then, we confirm the specificity of YIGSR to endothelial cells, and importantly, the nanocarrier effectively delivers the miR-146a inhibitor into endothelial cells, promoting angiogenesis. In a C57 mouse skin wound model, the miR-146a inhibitor is successfully delivered into endothelial cells at the wound site using the nanocarrier, resulting in the formation of new blood vessels with strong CD31 expression. Additionally, no significant differences are found in the expression levels of inflammatory markers interleukin-6 and tumor necrosis factor-α. This outcome not only brings new strategies for angiogenesis but also exhibits broader implications for bone remodeling and wound healing. The breakthrough holds significance for future research and clinical interventions.

A novel model called error loss network (ELN) is proposed to build an error loss function for supervised learning. The ELN is similar in structure to a radial basis function (RBF) neural network, but its input is an error sample and output is a loss corresponding to that error sample. That means the nonlinear input-output mapper of the ELN creates an error loss function. The proposed ELN provides a unified model for a large class of error loss functions, which includes some information-theoretic learning (ITL) loss functions as special cases. The activation function, weight parameters, and network size of the ELN can be predetermined or learned from the error samples. On this basis, we propose a new machine learning paradigm where the learning process is divided into two stages: first, learning a loss function using an ELN; second, using the learned loss function to continue to perform the learning. Experimental results are presented to demonstrate the desirable performance of the new method.

Craniofacial bone defects induced by congenital malformations, trauma, or diseases frequently challenge the orthodontic or restorative treatment. Stem cell-based bone regenerative approaches emerged as a promising method to resolve bone defects. Microenvironment physical cues, such as the matrix elastic modulus or matrix topography, regulate stem cell differentiation via multiple genes. We constructed gelatin methacryloyl (GelMA), a well-known scaffold, to investigate the impact of elastic modulus on osteogenic differentiation in a three-dimensional environment. Confocal microscope was used to observe and assess the condensates fission and fusion. New bone formation was evaluated by micro-computed tomography at 6 weeks in calvarial defect rat. We found that the light curing increased elastic modulus of GelMA, and the pore size of GelMA decreased. The expression of osteogenic markers was inhibited in hBMSCs cultured in the low-elastic-modulus GelMA. In contrast, the expression of YAP, TAZ and TEAD was increased in the hBMSCs in the low-elastic-modulus GelMA. Furthermore, YAP assembled via liquid-liquid phase separation (LLPS) into condensates that were sensitive to 1'6-hexanediol. YAP recruit TAZ and TEAD4, but not RUNX2 into the condensates. In vivo, we also found that hBMSCs in high-elastic-modulus GelMA was more apt to form new bone. This study provides new insight into the mechanism of osteogenic differentiation. Reagents that can regulate the elastic modulus of substrate or LLPS may be applied to promote bone regeneration., (© 2023 Wiley Periodicals LLC.)

Minimum error entropy with fiducial points (MEEF) has received a lot of attention, due to its outstanding performance to curb the negative influence caused by non-Gaussian noises in the fields of machine learning and signal processing. However, the estimate of the information potential of MEEF involves a double summation operator based on all available error samples, which can result in large computational burden in many practical scenarios. In this paper, an efficient quantization method is therefore adopted to represent the primary set of error samples with a smaller subset, generating a quantized MEEF (QMEEF). Some basic properties of QMEEF are presented and proved from theoretical perspectives. In addition, we have applied this new criterion to train a class of linear-in-parameters models, including the commonly used linear regression model, random vector functional link network, and broad learning system as special cases. Experimental results on various datasets are reported to demonstrate the desirable performance of the proposed methods to perform regression tasks with contaminated data., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Self-distillation methods utilize Kullback-Leibler divergence (KL) loss to transfer the knowledge from the network itself, which can improve the model performance without increasing computational resources and complexity. However, when applied to salient object detection (SOD), it is difficult to effectively transfer knowledge using KL. In order to improve SOD model performance without increasing computational resources, a non-negative feedback self-distillation method is proposed. Firstly, a virtual teacher self-distillation method is proposed to enhance the model generalization, which achieves good results in pixel-wise classification task but has less improvement in SOD. Secondly, to understand the behavior of the self-distillation loss, the gradient directions of KL and Cross Entropy (CE) loss are analyzed. It is found that KL can create inconsistent gradients with the opposite direction to CE in SOD. Finally, a non-negative feedback loss is proposed for SOD, which uses different ways to calculate the distillation loss of the foreground and background respectively, to ensure that the teacher network transfers only positive knowledge to the student. The experiments on five datasets show that the proposed self-distillation methods can effectively improve the performance of SOD models, and the average F β is increased by about 2.7% compared with the baseline network., Competing Interests: The authors declare there are no competing interests., (©2023 Chen et al.)

Recently, polarization-based models for camouflaged object segmentation have attracted research attention. However, to construct this camouflaged object segmentation model, the main challenge is to effectively fuse polarization and light intensity features. Therefore, we propose a multi-modal camouflaged object segmentation method via gated fusion. First, the spatial positioning module is designed to perform channel calibration and global spatial attention alignment between polarization mode and light intensity mode from high-level feature representation to locate object positioning accurately. Then, the gated fusion module (GFM) is designed to selectively fuse the object information contained in the polarization and light intensity features. Among them, semantic information of location features is introduced in the GFM to guide each mode to aggregate dominant features. Finally, the features of each layer are aggregated to obtain an accurate segmentation result map. At the same time, considering the lack of public evaluation and training data on light intensity-polarization (I-P) camouflaged detection, we build the light I-P camouflaged detection dataset. Experimental results demonstrate that our proposed method outperforms other typical multi-modal segmentation methods in this dataset.

Osteoclastogenesis in alveolar bone induced by compression stress triggers orthodontic tooth movement. Compression stress also stimulates angiogenesis, which is essential for osteoclastogenesis. However, the effects of osteoclastogenesis induced by compression on angiogenesis are poorly understood. In vivo, we found the markers of angiogenesis increased during orthodontic bone remodeling. In vitro, osteoclast-derived exosomes increased proliferation, migration, and tube formation of human umbilical vein endothelial cells (HUVECs), as well as expression of vascular endothelial growth factor and CD31. The promotive effects of exosomes derived from compressed osteoclasts were greater than those derived from osteoclasts without compression. Next, we analyzed changes in the microRNA transcriptome after compression stress and focused on microRNA146a-5p (miR-146a), which was significantly decreased by compression. Transfection of an inhibitor of miR-146a stimulated angiogenesis of HUVECs while miR-146a mimics repressed angiogenesis. Adiponectin (ADP) was confirmed to be a target of miR-146a by dual luciferase reporter assay. In HUVECs treated with exosomes, we detected increased ADP which promoted angiogenesis. Knockdown of ADP in HUVECs reduced the promotive effects of exosomes. Our results demonstrate that the decreased miR-146a observed in osteoclasts after compression promotes angiogenesis by targeting ADP, suggesting a novel method to interfere with bone remodeling induced by compression stress., (© 2021. Science China Press and Springer-Verlag GmbH Germany, part of Springer Nature.)

Introduction: Patients with severe periodontitis typically present with pathologic tooth migration. To improve esthetics and masticatory function, orthodontic treatment is required. Research on periodontal orthodontic treatment has been sparse, particularly from the microbial perspective. Hence, we analyzed the microbial and clinical changes in patients with well-controlled periodontitis in the early stage of orthodontic treatment., Methods: Ten patients with well-controlled periodontitis were asked to collect saliva before and 1 and 3 months after appliance placement (T0, T1, and T2, respectively) and underwent clinical examinations before and 1, 3, and 6 months after appliance placement (T0, T1, T2, and T3, respectively). The microbial community of saliva was analyzed by 16S rRNA gene sequencing. Gingival index, the plaque index, and the probing pocket depth were clinically assessed., Results: The plaque index significantly increased from T0 to T1 and decreased at T2 and T3. The probing pocket depth and gingival index increased slightly at T2, but not significantly, in both the high-risk site and low-risk site. The alpha and beta diversity increased at T1. The microbial community structure was similar at T0 and T2. The relative abundance of core genera and periodontal pathogens was stable during the initial 3 months of orthodontic treatment., Conclusions: The orthodontic appliance promoted plaque accumulation and altered the microbial community of patients with well-controlled periodontitis during the first month of orthodontic treatment. The microbial community returned to the basal composition at 3 months after appliance placement, and the periodontal inflammation during the 6-months orthodontic treatment was under control., (Copyright © 2021 American Association of Orthodontists. Published by Elsevier Inc. All rights reserved.)

Circular RNAs(circRNAs) are a large family of RNAs shaping covalently closed ring-like molecules and have become a hotspot with thousands of newly published studies. Stem cells are undifferentiated cells and have great potential in medical treatment due to their self-renewal ability and differentiation capacity. Abundant researches have unveiled that circRNAs have unique expression profile during the differentiation of stem cells and could serve as promising biomarkers of these cells. There are key circRNAs relevant to the differentiation, proliferation, and apoptosis of stem cells with certain mechanisms such as sponging miRNAs, interacting with proteins, and interfering mRNA translation. Moreover, several circRNAs have joined in the interplay between stem cells and lymphocytes. Our review will shed lights on the emerging roles of circRNAs in regulating the fate of diverse stem cells.

How to design a powerful classifier with strong generalization capability is still an active topic in the brain computer interface (BCI) researches. In this paper, we propose a new classifier, which has the same structure of the recently proposed broad learning system (BLS), but the l 2 norm based optimization model in BLS is replaced by a mixed-nrom based one. To optimize the proposed model efficiently, the augmented Lagrange multiplier (ALM) method is utilized. The most attractive feature of the proposed classifier is that it has the potential to maintain good performance in various noise environments, by flexibly setting the value of mixed parameter. Thus, compared with the standard BLS, as well as many existing classifiers, the proposed one is expected to be a more reasonable choice for classifying electroencephalography (EEG) signals, which are usually polluted by various artifacts. The experiments on two publicly available data sets are presented to confirm the desirable performance of the new method.

Trichostatin A (TSA), a histone deacetylase inhibitor, is used as an anti-carcinogenic and radiosensitizing agent in various cancers. However, the role and mechanism underlying its radiosensitization of tongue squamous cell carcinoma (TSCC) remains unclear. Thus, in this study we aimed to confirm the promotion of miR‑375 expression by TSA, and to investigate the effects of TSA and miR‑375 in the radiosensitivity of TSCC cells. The results showed that TSA had significant radiosensitizing effects on TSCC cells and miR‑375 overexpression had effects similar to TSA in sensitizing these cells to radiotherapy. By contrast, miR‑375 knockdown attenuated apoptosis induced by radiation combined with TSA. Mechanistically, the histone acetylation status of the miR‑375 promoter region was increased by TSA, resulting in the upregulation of miR‑375, which led to a decline of PDK1 and phosphorylated AKT. Taken together, our data suggest that TSA increases the radiosensitization and apoptosis in TSCC cells at least partially via miR‑375, and TSA or miR‑375 in combination with radiotherapy may provide a valuable approach for the treatment of TSCC.

As United States Food and Drug Administration-approved implantable material, carbon fiber-reinforced polyetheretherketone (CFRPEEK) possesses an adjustable elastic modulus similar to cortical bone and is a prime candidate to replace surgical metallic implants. The bioinertness and inferior osteogenic properties of CFRPEEK, however, limit its clinical application as orthopedic/dental implants. In this study, CFRPEEK-nanohydroxyapatite ternary composites (PEEK/n-HA/CF) with variable surface roughness have been successfully fabricated. The effect of surface roughness on their in vitro cellular responses of osteoblast-like MG-63 cells (attachment, proliferation, apoptosis, and differentiation) and in vivo osseointegration is evaluated. The results show that the hydrophilicity and the amount of Ca ions on the surface are significantly improved as the surface roughness of composite increases. In cell culture tests, the results reveal that the cell proliferation rate and the extent of osteogenic differentiation of cells are a function of the size of surface roughness. The composite with moderate surface roughness significantly increases cell attachment/proliferation and promotes the production of alkaline phosphatase (ALP) activity and calcium nodule formation compared with the other groups. More importantly, the PEEK/n-HA/CF implant with appropriate surface roughness exhibits remarkably enhanced bioactivity and osseointegration in vivo in the animal experiment. These findings will provide critical guidance for the design of CFRPEEK-based implants with optimal roughness to regulate cellular behaviors, and to enhance biocompability and osseointegration. Meanwhile, the PEEK/n-HA/CF ternary composite with optimal surface roughness might hold great potential as bioactive biomaterial for bone grafting and tissue engineering applications.