Surgical plates have been extensively used in head and neck reconstruction and conventional plates are mass-produced with universal configurations. To overcome disadvantages of conventional surgical plates, we have been exploring patient-specific surgical plates using the three-dimensional (3D) printing technology. We hypothesized that the application of 3D-printed patient-specific surgical plates in head and neck reconstruction is feasible, safe and precise. We are conducting a prospective clinical trial to assess the feasibility, safety and accuracy of applying 3D-printed patient-specific surgical plates in head and neck reconstruction. The primary endpoint was the intraoperative success rate. Secondary endpoints included the incidence and severity of postoperative adverse events within six months postoperatively. The accuracy of surgical outcomes was also explored by comparing the planned and final positions of the maxilla, mandible and grafted bone segments. From December 2016 to October 2017, ten patients were enrolled and underwent head and neck reconstruction using 3D-printed patient-specific surgical plates. The patient-specific surgical plates adapted to bone surface precisely and no plate-bending was performed. The intraoperative success rate was 100%. The average follow-up period was 6.5 months. No major adverse events were observed. The mean absolute distance deviation of integral mandible or maxilla was 1.40 ± 0.63 mm, which showed a high accuracy of reconstruction. The 3D printing of patient-specific surgical plates could be effective in head and neck reconstruction. Surgical procedures were simplified. The precise jaw reconstruction was achieved with high accuracy. Long-term results with a larger sample size are warranted to support a final conclusion. The study protocol has been registered in ClinicalTrials.gov with a No. of NCT03057223.

Programmed cell death protein-1 (PD-1) on T-cells combined with programmed cell death ligand-1 (PD-L1) critically accounts for tumor immune evasion. Anti-PD-1 (aPD-1) blocks the binding of PD-1 to PD-L1, thus allowing T-cell activation for tumor cell eradication. Currently, the major challenges for cancer immunotherapy are how to improve the response rate and overcome drug resistance. Dermal administration turns out to be a promising route for immunotherapy since skin is a highly active immune organ containing a large population of resident antigen-presenting cells. Microneedle arrays can pierce the immune-cell-rich epidermis, leading to a robust T-cell response in the microenvironment of tumor cells. Herein, we successfully developed a microneedle patch loaded with pH-responsive tumor-targeted lipid nanoparticles (NPs), which allows local delivery of aPD-1 and cisplatin (CDDP) precisely to cancer tissues for cancer therapy. For in vivo studies, aPD-1/CDDP@NPs delivered through microneedles effectively boosted the immune response, thereby a remarkable effect on tumor regression was realized. Synergistic anticancer mechanisms were therefore activated through robust microneedle-induced T-cell response, blockage of PD-1 in T-cells by aPD-1, and an increase in direct cytotoxicity of CDDP in tumor cells. Strikingly, transdermal delivery using MNs increased the response rate in the animal model unresponsive to aPD-1 systemic therapy. This exhibited promise in the treatment of immunotherapy-unresponsive cancers. Taken together, microneedle-mediated local delivery of nano-encapsulated chemotherapeutic and immunotherapeutic agents at tumor skin sites provides a novel treatment strategy and insights into cancer therapy.

Machine-intelligence platforms for the prediction of the probability of malignant transformation of oral potentially malignant disorders are required as adjunctive decision-making platforms in contemporary clinical practice. This study utilized time-to-event learning models to predict malignant transformation in oral leukoplakia and oral lichenoid lesions. A total of 1098 patients with oral white lesions from two institutions were included in this study. In all, 26 features available from electronic health records were used to train four learning algorithms—Cox-Time, DeepHit, DeepSurv, random survival forest (RSF)—and one standard statistical method—Cox proportional hazards model. Discriminatory performance, calibration of survival estimates, and model stability were assessed using a concordance index (c-index), integrated Brier score (IBS), and standard deviation of the averaged c-index and IBS following training cross-validation. This study found that DeepSurv (c-index: 0.95, IBS: 0.04) and RSF (c-index: 0.91, IBS: 0.03) were the two outperforming models based on discrimination and calibration following internal validation. However, DeepSurv was more stable than RSF upon cross-validation. External validation confirmed the utility of DeepSurv for discrimination (c-index—0.82 vs. 0.73) and RSF for individual survival estimates (0.18 vs. 0.03). We deployed the DeepSurv model to encourage incipient application in clinical practice. Overall, time-to-event models are successful in predicting the malignant transformation of oral leukoplakia and oral lichenoid lesions.

Salivary biomarkers can improve the efficacy, efficiency, and timeliness of oral and maxillofacial disease diagnosis and monitoring. Oral and maxillofacial conditions in which salivary biomarkers have been utilized for disease‐related outcomes include periodontal diseases, dental caries, oral cancer, temporomandibular joint dysfunction, and salivary gland diseases. However, given the equivocal accuracy of salivary biomarkers during validation, incorporating contemporary analytical techniques for biomarker selection and operationalization from the abundant multi‐omics data available may help improve biomarker performance. Artificial intelligence represents one such advanced approach that may optimize the potential of salivary biomarkers to diagnose and manage oral and maxillofacial diseases. Therefore, this review summarized the role and current application of techniques based on artificial intelligence for salivary biomarker discovery and validation in oral and maxillofacial diseases.