Assessing the impact of occlusal plane rotation on facial aesthetics in orthodontic treatment: a machine learning approach.

BACKGROUND: Adequate occlusal plane (OP) rotation through orthodontic therapy enables satisfying profile improvements for patients who are disturbed by their maxillomandibular imbalance but reluctant to surgery. The study aims to quantify profile improvements that OP rotation could produce in orthodontic treatment and whether the efficacy differs among skeletal types via machine learning. MATERIALS AND METHODS: Cephalometric radiographs of 903 patients were marked and analyzed by trained orthodontists with assistance of Uceph, a commercial software which use artificial intelligence to perform the cephalometrics analysis. Back-propagation artificial neural network (BP-ANN) models were then trained based on collected samples to fit the relationship among maxillomandibular structural indicators, SN-OP and P-A Face Height ratio (FHR), Facial Angle (FA). After corroborating the precision and reliability of the models by T-test and Bland-Altman analysis, simulation strategy and matrix computation were combined to predict the consequent changes of FHR, FA to OP rotation. Linear regression and statistical approaches were then applied for coefficient calculation and differences comparison. RESULTS: The regression scores calculating the similarity between predicted and true values reached 0.916 and 0.908 in FHR, FA models respectively, and almost all pairs were in 95% CI of Bland-Altman analysis, confirming the effectiveness of our models. Matrix simulation was used to ascertain the efficacy of OP control in aesthetic improvements. Intriguingly, though FHR change rate appeared to be constant across groups, in FA models, hypodivergent group displayed more sensitive changes to SN-OP than normodivergent, hypodivergent group, and Class III group significantly showed larger changes than Class I and II. CONCLUSIONS: Rotation of OP could yield differently to facial aesthetic improvements as more efficient in hypodivergent groups vertically and Class III groups sagittally.

Novel biomimetic peptide-loaded chitosan nanoparticles improve dentin bonding via promoting dentin remineralization and inhibiting endogenous matrix metalloproteinases.

OBJECTIVE: This study aims to synthesize novel chitosan nanoparticles loaded with an amelogenin-derived peptide QP5 (TMC-QP5/NPs), investigate their remineralization capability and inhibitory effects on endogenous matrix metalloproteinases (MMPs), and evaluate the dentin bonding properties of remineralized dentin regulated by TMC-QP5/NPs. METHODS: TMC-QP5/NPs were prepared by ionic crosslinking method and characterized by dynamic light scattering method, scanning electron microscopy, transmission electron microscope, atomic force microscope, Fourier transform infrared spectroscopy, and differential scanning calorimetry. The encapsulation and loading efficiency of TMC-QP5/NPs and the release of QP5 were examined. To evaluate the remineralization capability of TMC-QP5/NPs, the mechanical properties, and the changes in structure and composition of differently conditioned dentin were characterized. The MMPs inhibitory effects of TMC-QP5/NPs were explored by MMP Activity Assay and in-situ zymography. The dentin bonding performance was detected by interfacial microleakage and microshear bond strength (µSBS). RESULTS: TMC-QP5/NPs were successfully synthesized, with uniform size, good stability and biosafety. The encapsulation and loading efficiency of TMC-QP5/NPs was respectively 69.63 ± 2.22% and 13.21 ± 0.73%, with a sustained release of QP5. TMC-QP5/NPs could induce mineral deposits on demineralized collagen fibers and partial occlusion of dentin tubules, and recover the surface microhardness of dentin, showing better remineralization effects than QP5. Besides, TMC-QP5/NPs significantly inhibited the endogenous MMPs activity. The remineralized dentin induced by TMC-QP5/NPs exhibited less interfacial microleakage and higher µSBS, greatly improved dentin bonding. SIGNIFICANCE: This novel peptide-loaded chitosan nanoparticles improved resin-dentin bonding by promoting dentin remineralization and inactivating MMPs, suggesting a promising strategy for optimizing dentin adhesive restorations.

Revealing the representative facial traits of different sagittal skeletal types: decipher what artificial intelligence can see by Grad-CAM.

OBJECTIVES: Aesthetic improvement is a significant concern in dental therapy. While orthodontic treatment primarily targets hard tissue, the impact on soft tissue and the extent of these changes remains empirical. This study aims to unveil the intricate relationship between facial soft tissue and skeletal types using artificial intelligence (AI) analysis. METHODS: First, we collected a dataset of 1044 3-side-photographs and categorized them based on cephalometric measurements. After pre-processing and data augmentation, samples were fed to two independent models (Sfa, Res model) for training and testing. After validating that the Sfa model could accurately recognize the skeletal types based merely on photographs, Grad-CAM algorithm was utilized for model decipherment. Verification of the vital traits were carried out by facial adjustment simulation. RESULTS: The Sfa model demonstrated superior accuracy (0.9293) in identifying skeletal types based solely on soft tissue, compared to the Res model (0.8395) and even trained orthodontists (0.764), testifying our hypothesis that AI could be more capable of processing imperceptible cues compared to mankind. Intriguingly, Grad-CAM revealed that cheek volume, forehead, chin and nasolabial traits could be representative features of each type, exceeding the traditional knowledge which merely concerns mandible and chin. CONCLUSION: By constructing a deep learning model as a classifier and then decipher it with Grad-CAM, we revealed the subtle and unnoticed cues associating skeletal and soft tissue, as well as provided a novel approach that could aid practitioners in devising tailored treatment plans for enhanced esthetic outcomes. CLINICAL SIGNIFICANCE: The proposed AI methods offer valuable assistance to practitioners in identifying uncoordinated facial traits that may detract from a patient's attractiveness. By incorporating these insights into customized treatment plans, dental therapy can maximize esthetic benefits for individual patient.

Novel fenugreek gum-cellulose composite hydrogel with wound healing synergism: Facile preparation, characterization and wound healing activity evaluation.

Hydrogels can be used as bioactive dressings, which outperform traditional dressings and are widely used in wound hemostasis and healing. However, it is still a challenge to develop a hydrogel with good stability and strong mechanical properties for wound hemostasis and healing. Herein, we developed a novel composite polysaccharide hydrogel from fenugreek gum and cellulose. Fenugreek gum was combined with cellulose through hydrogen bonding to form a hydrogel to improve the mechanical properties of the composite hydrogel. The composite hydrogel had a porous structure, thermal stability, good water absorption and a sustained release effect. Furthermore, the composite hydrogel demonstrated good biocompatibility in vitro and in vivo. Notably, the superior performance of wound hemostasis and healing has been confirmed. Our results indicated that the composite hydrogel was a promising medical dressing and had the potential to promote wound healing.

Assembly simulation and synergistic chemotherapy of TPGS derivative functionalized polymersomes in hepatocellular carcinoma.

Aim: Multidrug resistance and severe side effects lead to poor therapeutic efficacy of monotherapy against hepatocellular carcinoma. Here, a targeting codelivery nanocomposite system is constructed to reverse tumor multidrug resistance and realize a combination therapy of doxorubicin and oxaliplatin. Methods & materials: We simulate the composited structure of TPGS-lactobionic acid conjugate (TLA) modified methyl poly(ethylene glycol)-polylactide (mPP) polymersomes using dissipative particle dynamics. Cellular uptake and drug retention, cytotoxicity, in vivo antitumor efficacy and toxicity were evaluated. Results: TLA/mPP composited polymersomes improved intracellular accumulation of coloaded drugs via active targeting ability and P-gp efflux inhibition, leading to strong cytotoxicity against HepG2/ADR cells and potent tumor inhibition (93.5%) for HepG2/ADR tumor. Conclusion: TLA/mPP composited polymersomes represent a promising multifunctional nanoplatform against hepatocellular carcinoma.