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Background: It is known that oral diseases such as periodontal (gum) disease are closely linked to various systemic diseases and disorders. Deep learning advances have the potential to make major contributions to healthcare, particularly in the domains that rely on medical imaging. Incorporating non-imaging information based on clinical and laboratory data may allow clinicians to make more comprehensive and accurate decisions. Methods: Here, we developed a multimodal deep learning method to predict systemic diseases and disorders from oral health conditions. A dual-loss autoencoder was used in the first phase to extract periodontal disease-related features from 1188 panoramic radiographs. Then, in the second phase, we fused the image features with the demographic data and clinical information taken from electronic health records (EHR) to predict systemic diseases. We used receiver operation characteristics (ROC) and accuracy to evaluate our model. The model was further validated by an unseen test dataset. Findings: According to our findings, the top three most accurately predicted chapters, in order, are the Chapters III, VI and IX. The results indicated that the proposed model could predict systemic diseases belonging to Chapters III, VI and IX, with AUC values of 0.92 (95% CI, 0.90–94), 0.87 (95% CI, 0.84–89) and 0.78 (95% CI, 0.75–81), respectively. To assess the robustness of the models, we performed the evaluation on the unseen test dataset for these chapters and the results showed an accuracy of 0.88, 0.82 and 0.72 for Chapters III, VI and IX, respectively. Interpretation: The present study shows that the combination of panoramic radiograph and clinical oral features could be considered to train a fusion deep learning model for predicting systemic diseases and disorders.

L-PRF is an autologous blood-derived biomaterial (ABDB) capable of releasing biologically active agents to promote healing. Little is known about its release profile of growth factors (GFs), cytokines, and MMPs. This study reported the in vitro and ex vivo release kinetics of GFs, cytokines, and MMPs from L-PRF at 6, 24, 72, and 168 h. The in vitro release rates of PDGF, TGF-β1, EGF, FGF-2, VEGF, and MMPs decreased over time with different rates, while those of IL-1β, IL-6, TNF-α, IL-8, and IL-10 were low at 6 h and then increased rapidly for up to 24 h and subsequently decreased. Of note, the release rates of the GFs followed first-order kinetics both in vitro and ex vivo. Higher rates of release were found ex vivo, suggesting that significant amounts of GFs were produced by the local cells within the wound. In addition, the half-life times of GFs locally produced in the wound, including PDGF-AA, PDGF-AB/BB, and VEGF, were significantly extended (p < 0.05). This work demonstrates that L-PRF can sustain the release of GFs and cytokines for up to 7 days, and it shows that the former can activate cells to produce additional mediators and amplify the communication network for optimizing the wound environment, thereby enhancing healing.

Periodontal ligament (PDL) cells play a pivotal role in periodontal and bone homeostasis and have promising potential for regenerative medicine and tissue engineering. There is compelling evidence that long non-coding RNAs (lncRNAs) are differentially expressed in PDL cells compared to other cell types and that these lncRNAs are involved in a variety of biological processes. This study systematically reviews the current evidence regarding the expression and regulatory functions of lncRNAs in PDL cells during various biological processes. A systematic search was conducted on PubMed, the Web of Science, Embase, and Google Scholar to include articles published up to 1 July 2021. Original research articles that investigated the expression or regulation of lncRNAs in PDL cells were selected and evaluated for a systematic review. Fifty studies were ultimately included, based on our eligibility criteria. Thirteen of these studies broadly explored the expression profiles of lncRNAs in PDL cells using microarray or RNA sequencing. Nineteen studies investigated the mechanisms by which lncRNAs regulate osteogenic differentiation in PDL cells. The remaining 18 studies investigated the mechanism by which lncRNAs regulate the responses of PDL cells to various stimuli, namely, lipopolysaccharide-induced inflammation, tumor necrosis factor alpha-induced inflammation, mechanical stress, oxidative stress, or hypoxia. We systematically reviewed studies on the expression and regulatory roles of lncRNAs in diverse biological processes in PDL cells, including osteogenic differentiation and cellular responses to inflammation, mechanical stress, and other stimuli. These results provide new insights that may guide the development of lncRNA-based therapeutics for periodontal and bone regeneration.

Oral mucosa as the front-line barrier in the mouth is constantly exposed to a complex microenvironment with multitudinous microbes. In this study, the interactions of mesoporous silica nanoparticles (MSNs) with primary human gingival epithelial cells were analyzed for up to 72 h, and their diffusion capacity in the reconstructed human gingival epithelia (RHGE) and porcine ear skin models was further assessed at 24 h. It was found that the synthesized fluorescent mesoporous silica nanoparticles (RITC-NPs) with low cytotoxicity could be uptaken, degraded, and/or excreted by the human gingival epithelial cells. Moreover, the RITC-NPs penetrated into the stratum corneum of RHGE in a time-dependent manner, while they were unable to get across the barrier of stratum corneum in the porcine ear skins. Consequently, the penetration and accumulation of RITC-NPs at the corneum layers of epithelia could form a “nanocoating-like barrier”. This preliminary proof-of-concept study suggests the feasibility of developing nanoparticle-based antimicrobial and anti-inflammatory agents through topical application for oral healthcare.

Scutellaria baicalensis (SB) is a traditional Chinese medicine for treating infectious and inflammatory diseases. Our recent study shows potent antibacterial effects of nanoparticle-encapsulated chlorhexidine (Nano-CHX). Herein, we explored the synergistic effects of the nanoparticle-encapsulated SB (Nano-SB) and Nano-CHX on oral bacterial biofilms. Loading efficiency of Nano-SB was determined by thermogravimetric analysis, and its releasing profile was assessed by high-performance liquid chromatographyusing baicalin (a flavonoid compound of SB) as the marker. The mucosal diffusion assay on Nano-SB was undertaken in a porcine model. The antibacterial effects of the mixed nanoparticles (Nano-MIX) of Nano-SB and Nano-CHX at 9:1 (w/w) ratio were analyzed in both planktonic and biofilm modes of representative oral bacteria. The Nano-MIX was effective on the mono-species biofilms of Streptococcus (S.) mutans, S. sobrinus, Fusobacterium (F.) nucleatum, and Aggregatibacter (A.) actinomycetemcomitans (MIC 50 μg/mL) at 24 h, and exhibited an enhanced effect against the multi-species biofilms such as S. mutans, F. nucleatum, A. actinomycetemcomitans, and Porphyromonas (P.) gingivalis (MIC 12.5 μg/mL) at 24 h that was supported by the findings of both scanning electron microscopy (SEM) and confocal scanning laser microscopy (CLSM). This study shows enhanced synergistic antibacterial effects of the Nano-MIX on common oral bacterial biofilms, which could be potentially developed as a novel antimicrobial agent for clinical oral/periodontal care.