Introduction:Automated orthodontic diagnosis requires integration of multimodal geometric evidence with clinically grounded treatment reasoning. Existing multimodal models often lack the spatial precision required for orthodontic phenotyping, whereas specialized deep-learning tools usually remain disconnected from treatment planning. Materials and methods:We developed OrthoAgent, a training-free hierarchical multi-agent framework for multimodal orthodontic diagnosis and treatment planning. Dedicated perception agents extract structured findings from 2D images, cone-beam computed tomography, and intraoral scans. These findings are integrated into a patient-specific diagnostic baseline, while a retrieval-augmented generation module grounds treatment reasoning in a curated dental knowledge corpus without modifying perception-derived patient facts. OrthoAgent was retrospectively evaluated on 69 real-world orthodontic cases using objective diagnostic tasks, blinded expert review, clinician-rated reusability, a time-tracked human–AI collaboration study, and paired ablation comparisons. Results and discussion:OrthoAgent achieved a mean accuracy of 90.5% across 12 classification tasks and low error across 17 continuous measurements. Generated reports received an overall expert score of 3.43/5, with the highest score for safety (4.19/5). Clinician-rated reusability corresponded to an estimated 46.8% reduction in editing burden, and direct workflow timing showed a 41.74% reduction in end-to-end completion time. In 20 paired comparisons, retrieval-grounded reports were numerically preferred over non-retrieval reports in 14 cases.Conclusion:OrthoAgent provides a proof-of-concept framework for structured, evidence-informed orthodontic decision support under clinician oversight. Larger prospective, multi-center studies are needed to establish robustness and generalizability.

Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices. They hold promise for treating neurological disorders, restoring motor function, and enhancing human-computer interaction. Although non-invasive BCIs dominate research due to their accessibility and safety, advances in invasive and minimally-invasive technologies have improved signal fidelity and expanded applications. Despite these advancements, ethical concerns such as data privacy, informed consent, mental autonomy, and potential misuse must be addressed to ensure responsible implementation. Regulatory frameworks have been struggling to keep pace with rapid technological progress, raising concerns about safety, standardization, and long-term viability. Practical barriers such as high costs, limited accessibility, and the need for specialized training further complicate deployment. Additionally, the long-term neural impact of invasive BCIs and their seamless integration into daily life remain areas of active investigation. This review provides a comprehensive overview of the current BCI landscape. It examines foundational biosignals and technological approaches, highlights groundbreaking applications in healthcare (including neuroprosthetics, rehabilitation, and cognitive enhancement), and offers a critical analysis of the ethical, regulatory, and technical challenges. Ultimately, the review reflects the immense transformative potential and the multifaceted complexities of BCIs, emphasizing the imperative for responsible innovation and robust interdisciplinary collaboration to navigate their future development.

Prosthodontics is rapidly entering the digital era, with computer-aided design and manufacturing (CAD/CAM) soon becoming the mainstream method for fixed restoration design. Recent advances in image recognition, data analytics, and decision systems are accelerating the use of artificial intelligence (AI), shifting workflows from simple automation to adaptive, learning-based design. Nevertheless, clear clinical guidance remains limited. This expert consensus sets out core principles, technology categories, and priority use cases for AI in fixed restorations, and proposes Standardized Operating Procedures that span from preoperative planning, digital impression processing, tooth preparation evaluation, to functional design and personalized esthetic design. It also defines quality control checkpoints and ethical safeguards that highlight the central role of the clinician in reviewing and validating AI outputs. Recommendations are provided for data governance, including security, privacy, and auditability. Finally, the document outlines near-term development needs such as interoperable data standards, transparent model reporting, and clinically oriented validation metrics. The goal is to support standardized, safe, and effective clinical adoption of AI in fixed dental restorations.

Introduction: To develop and validate DAR-AI, a clinically applicable artificial intelligence system for reference-free dentoalveolar aesthetic reconstruction of alveolar bone defects. Materials and methods: DAR-AI was constructed using a Point Cloud Completion Network integrated with a score-based denoising module to automate and standardize reconstruction according to an anatomical morphology-oriented strategy. The system was validated using data from 170 patients across five centers, with an additional 8 cases used for clinical human-machine comparison. Performance was assessed by geometric accuracy, robustness between internal and external testing sets, blinded human-machine comparison, operation time, and feasibility of integration into clinical workflows. Results and discussion: On the external testing set, DAR-AI achieved a Chamfer Distance of 1.191 ± 0.273 mm and a mesh reconstruction RMS of 0.465 ± 0.090 mm, with no statistically significant difference compared with the internal set. In the blinded human-machine comparison, the AI workflow maintained high expert acceptance (most scores > 4.0/5.0) and significantly reduced operation time. The generated 3D models were successfully incorporated into pre-operative communication, 3D-printed bone graft fabrication, and titanium mesh fabrication. Conclusion: DAR-AI provides a rapid, accurate, and generalizable solution for dentoalveolar reconstruction by overcoming the constraints of missing anatomical references. This generative AI model demonstrates clinically acceptable planning performance, with high expert acceptance in most assessment dimensions, and holds significant potential for broader applications in complex dentoalveolar aesthetic reconstruction tasks.

Introduction:Oral potentially malignant disorders (OPMDs) carry a substantial risk of malignant transformation into oral squamous cell carcinoma (OSCC). Dysregulated nucleotide metabolism supports uncontrolled proliferation during malignant transformation. This study aimed to explore the effect and mechanism of PRMT5 on the malignant progression of OPMDs via modulating pyrimidine metabolism, and to identify potential therapeutic targets for intervention in OPMDs. Materials and methods:Single-cell transcriptomic analysis, transcriptome sequencing (RNA-seq), and non-targeted metabolomic analyses were conducted to clarify the correlation between PRMT5 expression and pyrimidine metabolism in dysplastic cells. Real-time PCR, Western blot, chromatin immunoprecipitation (ChIP), luciferase reporter assays, and targeted metabolomic analyses were used to explore the molecular regulatory mechanism. The effect of PRMT5/FOXM1 on malignant phenotypes was examined through in vitro and in vivo assays. PRMT5 and FOXM1 protein expression levels in clinical specimens were analyzed by immunohistochemistry. Results and discussion:Single-cell transcriptomic analysis revealed that pyrimidine metabolism was aberrantly activated in dysplastic and malignant epithelial cells, which was closely associated with elevated PRMT5 expression. Notably, PRMT5⁺ dysplastic cells were more prone to transform into malignant cells. Functional assays revealed that PRMT5 inhibition significantly reduced the proliferation, migration, and invasion capabilities of dysplastic oral keratinocytes (DOK). RNA-seq and metabolomic analyses demonstrated that PRMT5 blockade repressed pyrimidine metabolism. Mechanistically, PRMT5 epigenetically activated FOXM1 transcription through the dual modifications of H3R2me2s and H3K4me3, thereby forming a regulatory axis with FOXM1 to upregulate key pyrimidine metabolic enzymes (including TK1, RRM2, TYMS, and CAD). Exogenous addition of dNTPs partially reversed the inhibitory effects induced by PRMT5 inhibition. In vivo and clinical specimens further validated the oncogenic role of the PRMT5-FOXM1-pyrimidine metabolism axis. Conclusion:PRMT5 plays a critical regulatory role in the malignant transformation of OPMDs by activating FOXM1 to rewire pyrimidine metabolism, highlighting PRMT5 as a promising therapeutic target for preventing the progression of OPMDs and treating OSCC.

Introduction:Sutures are essential for preventing surgical site infections (SSIs) and promoting wound healing, yet current drug-based antibacterial strategies often fail to sustain long-term efficacy and may induce drug resistance. This study developed a drug-free dual-function electroactive (DE) suture with piezoelectric properties, and evaluate its antibacterial and wound-healing performance in vitro and in vivo. Materials and methods:DE sutures were fabricated and tested under low-intensity pulsed ultrasound (LIPUS). Antibacterial activity was assessed via ROS generation and bacterial viability assays. In vivo evaluation was performed using rat full thickness incision model with histological and immunohistochemical analyses of inflammation, vascularization, and tissue regeneration. Results and discussion:LIPUS-activated DE sutures generated ROS that disrupted bacterial membranes, significantly reducing bacterial viability.In vivo, DE sutures attenuated inflammation, promoted angiogenesis, and enhanced tissue regeneration. Multi-strand designs showed improved mechanical and pro-healing performance. Conclusion:This piezoelectric-enabled, LIPUS-triggered DE suture provides a drug-free strategy for simultaneous infection control and tissue repair, offering a promising approach in regenerative tissue engineering.

Introduction:Diabetic wounds are characterized by infection, oxidative stress, persistent inflammation, and impaired angiogenesis. This study developed a pathology-adaptive sprayable hydrogel for diabetic wound repair. Materials and methods:A GelMA/HAMA hydrogel incorporating copper-based metal–organic framework nanoparticles and metformin was constructed through thiol–ene crosslinking and dynamic covalent interactions. Its physicochemical properties, responsive release, biological functions, and therapeutic efficacy were evaluated in vitro and in diabetic mouse skin and oral mucosal wound models. Results and discussion:The hydrogel exhibited rapid gelation, sprayability, self-healing, wet-tissue adhesion, and microenvironment-responsive Cu²⁺/metformin release. It demonstrated antibacterial and cytocompatible properties while promoting cell migration and angiogenesis. Cu²⁺ was associated with HIF-1α/VEGF-related angiogenic signaling, whereas metformin activated AMPK, inhibited NF-κB signaling, and promoted reparative macrophage polarization. In vivo, the hydrogel accelerated wound closure, collagen deposition, re-epithelialization, and vascularization while reducing inflammation. Conclusion:The hydrogel integrates responsive therapeutic release with antibacterial, angiogenic, and immunomodulatory effects, providing a promising approach for diabetic skin and oral mucosal wound repair.

Introduction: Radiation sialadenitis is a debilitating complication of head and neck cancer radiotherapy for which no effective therapy exists. Conventional MSC-based strategies face translational barriers, and crude MSC-conditioned medium (MSC-CM) provides only limited efficacy, underscoring the need for cell-free alternatives. Materials and methods: Rat salivary gland MSCs were characterized and cultured on porcine decellularized matrix hydrogel to generate optimized concentrated conditioned medium (CC-CM). CC-CM was tested in irradiated SGMSCs and in a rat submandibular gland irradiation model; senescence, apoptosis, and transcriptomic changes were assessed. Apoptosis dependence was examined using the pan-caspase inhibitor Z-VAD-FMK, and senescent cell clearance was evaluated by flow cytometry, live-cell imaging, and co-staining for senescence and apoptosis markers. Results and discussion: CC-CM demonstrated markedly superior therapeutic effects against radiation sialadenitis compared with conventional MSC-CM. Mechanistically, irradiation induces an early apoptosis-resistant state in SGMSCs that precedes the senescence phenotype, and the accumulated senescent cells are refractory to apoptosis. CC-CM eliminates these pathogenic senescent cells, exhibiting senolytic activity. Dynamic live cell imaging revealed that CC-CM restores the impaired apoptotic process in irradiated SGMSCs. CC-CM increased the number of dead cells in a caspase-dependent manner, and co-staining for senescence markers and cleaved caspase-3 confirmed that CC-CM directs senescent cells into apoptosis. This senolytic activity was further validated in vivo in irradiated submandibular gland tissues. Pharmacological blockade of apoptosis attenuated the therapeutic effects of CC-CM, establishing the functional necessity of apoptosis restoration. Conclusion: Tissue-specific decellularized matrix priming endows the MSC secretome with the capacity to eliminate radiation-induced senescent cells through restoration of the apoptotic program. This mechanism-based, cell-free platform represents a promising strategy for treating radiation-induced salivary gland injury.