Histone demethylase KDM3B mediates matrix stiffness-induced osteogenic differentiation of adipose-derived stem cells.

Biomechanical signals in the extracellular niche are considered promising for programming the lineage specification of stem cells. Recent studies have reported that biomechanics, such as the microstructure of nanomaterials, can induce adipose-derived stem cells (ASCs) to differentiate into osteoblasts, mediating gene regulation at the epigenetic level. Therefore, in this study, transcriptome expression levels of histone demethylases in ASCs were screened after treatment with different matrix stiffnesses, and histone lysine demethylase 3B (KDM3B) was found to promote osteogenic differentiation of ASCs in response to matrix stiffness, indicating a positive modulatory effect on this biological process. ASCs exhibited widespread and polygonal shapes with a distinct bundle-like expression of vinculin parallel to the axial cytoskeleton along the cell margins on the stiff matrix rather than round shapes with a smeared and shorter expression on the soft matrix. Comparatively rigid polydimethylsiloxane material directed ASCs into an osteogenic phenotype in inductive culture media via the upregulation of osteocalcin, alkaline phosphatase, and runt-related transcription factor 2. Treatment with KDM3B-siRNA decreased the expression of osteogenic differentiation markers and impaired mitochondrial dynamics and mitochondrial membrane potential. These results illustrate the critical role of KDM3B in the biomechanics-induced osteogenic commitment of ASCs and provide new avenues for the further application of stem cells as potential therapeutics for bone regeneration.

Flexible Ag2Se Thermoelectric Films Enable the Multifunctional Thermal Perception in Electronic Skins.

Skin is critical for shaping our interactions with the environment. The electronic skin (E-skin) has emerged as a promising interface for medical devices to replicate the functions of damaged skin. However, exploration of thermal perception, which is crucial for physiological sensing, has been limited. In this work, a multifunctional E-skin based on flexible thermoelectric Ag2Se films is proposed, which utilizes the Seebeck effect to replicate the sensory functions of natural skin. The E-skin can enable capabilities including temperature perception, tactile perception, contactless perception, and material recognition by analyzing the thermal conduction behaviors of various materials. To further validate the capabilities of constructed E-skins, a wearable device with multiple sensory channels was fabricated and tested for gesture recognition. This work highlights the potential for using flexible thermoelectric materials in advanced biomedical applications including health monitoring and smart prosthetics.

Promoted Na Solubility and Modified Band Structure for Achieving Exceptional Average ZT by Extra Mn Doping in PbTe.

Na doping strategy provides an effective avenue to upgrade the thermoelectric performance of PbTe-based materials by optimizing electrical properties. However, the limited solubility of Na inherently restricts the efficiency of doping, resulting in a relatively low average ZT, which poses challenges for the development and application of subsequent devices. Herein, to address this issue, the introduced spontaneous Pb vacancies and additional Mn doping synergistically promote Na solubility with a further modified valence band structure. Furthermore, the induced massive point defects and multiscale microstructure greatly strengthen the scattering of phonons over a wide frequency range, leading to a remarkable ultralow lattice thermal conductivity of ∼0.42 W m-1 K-1. As a result, benefiting from the significantly enhanced Seebeck coefficient and superior thermal transports, a high peak ZT of ∼2.1 at 773 K and an excellent average ZT of ∼1.4 between 303 and 823 K are simultaneously achieved in Pb0.93Na0.04Mn0.02Te. This work proposes a simple and constructive method to obtain high-performance PbTe-based materials and is promising for the development of thermoelectric power generation devices.

Streptococcus mutans sigX-inducing peptide inhibits the virulence of Candida albicans and oral candidiasis through the Ras1-cAMP-Efg1 pathway.

Oral candidiasis is the most common fungal infectious disease in the human oral cavity, and Candida albicans is the major pathogenic agent. Increasing drug resistance and the lack of new types of antifungals greatly increase the challenges for treating fungal infections. Targeting hyphal transition provides a promising strategy to inhibit the virulence of C. albicans and overcome drug resistance. This study aimed to investigate the effects and mechanisms of sigX-inducing peptide (XIP), a quorum-sensing signal peptide secreted by Streptococcus mutans, on C. albicans hyphal development and biofilm formation in vitro and oropharyngeal candidiasis in vivo. XIP significantly inhibited C. albicans yeast-to-hypha transition and biofilm formation in a dose-dependent manner from 0.01 to 0.1 µM. XIP significantly downregulated expression of genes from the Ras1-cAMP-Efg1 pathway (RAS1, CYR1, TPK2, EFG1 and UME6), a key pathway to regulate C. albicans hyphal development. Importantly, XIP reduced the levels of key molecules cAMP and ATP from this pathway, while the addition of exogenous cAMP and overexpression of RAS1 restored the hyphal development inhibited by XIP. XIP also lost its hyphal inhibitory effects on ras1Δ/Δ and efg1Δ/Δ strains. These results further confirmed that XIP inhibited hyphal development through downregulation of the Ras1-cAMP-Efg1 pathway. A murine oropharyngeal candidiasis model was employed to evaluate the therapeutic effects of XIP on oral candidiasis. XIP effectively reduced the infected epithelial area, fungal burden, hyphal invasion and inflammatory infiltrates. These results revealed the antifungal effects of XIP, and highlighted that XIP can be a potential antifungal peptide against C. albicans infection.

Evaluation of Velopharyngeal Closure Ratio by a New Semi-Automatic Detection Method Based on Nasopharyngoscopy With Image Processing Method.

ABSTRACT: The goal of this study is to develop and validate a novel semi-automatic detection method (SADM) under nasopharyngoscopy based on the image processing technique, which can assist the evaluation of the velar closure ratio (VCR). After the development of the SADM, 72 patients were enrolled. The reliability of SADM was evaluated by repeated measurements. Velar closure ratio are given by conventional nasopharyngoscopy method and SADM were compared. Velar closure ratios given by SADM were further translated into a trichotomous classification for velopharyngeal function diagnosis, that is, velopharyngeal closure (VPC), marginal VPC (MVPC), and velopharyngeal incomplete. The 2 VCR-thresholds used for the translation were explored and validated. As results shown, SADM was proved to be reliable with an intraclass correlation coefficient of 0.996 (95% confidence interval: 0.993-0.997, P < 0.001). Intraclass correlation coefficient between conventional nasopharyngoscopy method and SADM was 0.954 (95% confidence interval: 0.927- 0.971, P < 0.001). Velar closure ratio-thresholds were set at 0.82 and 0.92 according to the ROC curve. Diagnostic sensitivity and specificity for velopharyngeal incomplete were 1.00 and 1.00. MVPC had 0.58 sensitivity and 0.96 specificities while VPC had 0.92 and 0.79, respectively. No statistically significant difference was found between the diagnosis of SADM and speech pathologists (P > 0.1). In conclusion, this study successfully developed an accurate and reliable semi-automatic method to evaluate VCR, which could help improve the efficacy of VCR evaluation and velopharyngeal function diagnosis.

[Advances in the application of machine learning in maxillofacial cysts and tumors].

The application of artificial intelligence in medicine has gradually received attention along with its development. Many studies have shown that machine learning has a wide range of applications in stomatology, especially in the clinical diagnosis and treatment of maxillofacial cysts and tumors. This article reviews the application of machine learning in maxillofacial cyst and tumor to provide a new method for the diagnosis of oral and maxillofacial diseases.

CLPNet: Cleft Lip and Palate Surgery Support With Deep Learning.

Cleft lip and palate is the most common congenital malformation in oral and maxillofacial region. As a kind of facial plastic surgery, the most important factor for the success of cleft lip and palate repair surgery is the design of surgical markers and incisions. However, general hospitals especially in rural areas lack dependable medical resources, which makes the effect of the surgery hard to guarantee. To solve this problem, we propose a novel robotic surgery assistant technology based on deep learning to help reduce the technical threshold and improve the overall effect of cleft lip and palate repair surgery. For the first time, a robust dataset of cleft lip and palate cases is established, which can be used to train the model to locate surgical markers and incisions. Secondly, we build a strong baseline on this dataset by using state-of-the-art Hourglass architecture and residual learning, with two neoteric block designs, one of which enables stronger capability of generalization, while the other greatly reduces the complexity of the model, thus making efficient application possible. Finally, by comparing with other facial feature extraction methods, our models achieve the best results on multiple metrics, showing their strong superiority and adaptability on this task.