A hydrogel with improved interfacial compatibility was formed to promote osteogenesis by establishing electrostatic bonds.

Background: This Consensus Workshop dealt with diagnostic methodologies in the context of surveillance, screening, assessment of stage and grade, prognosis, monitoring and prediction of periodontal status. Several elements provided the impetus for the workshop, including the limited quality of available research on diagnostic tests, the rapid development of new technologies, the implementation of the 2018 classification and the declarations of the World Health Organisation on diagnosis and oral health. Aim: To update and evaluate the evidence on diagnostic methods, considering recent advances in knowledge and the implementation of the 2018 classification. Methods: The European Workshop Committee of the European Federation of Periodontology guided the development of a consensus report after commissioning eight systematic reviews within three working groups. The reviews were discussed during the in‐person consensus meeting involving 70 participants from 21 different countries. Results: Working Group 1 discussed innovations in traditional diagnostic approaches, justified manual probing as the reference standard and assessed the value of image‐based methods. Working Group 2 analysed diagnostic tests based on microbial and host biomarkers and genetic diagnostic tests. Working Group 3 covered emerging technologies to be used within dental and non‐dental clinical settings, focusing principally on the impact of questionnaire‐based assessments and artificial intelligence systems (AIS) in interpreting different data modalities. Conclusion: Although manual periodontal probing is firmly established as the reference standard, additional approaches based on imaging, biomarkers, host genetics, questionnaires and the development of emerging applied data science methods (e.g., AIS) are increasingly integrated in periodontal diagnostics.

Background: While large language models (LLMs) such as ChatGPT and Google Bard have shown significant promise in various fields, their broader impact on enhancing patient health care access and quality, particularly in specialized domains such as oral health, requires comprehensive evaluation. Objective: This study aims to assess the effectiveness of Google Bard, ChatGPT-3.5, and ChatGPT-4 in offering recommendations for common oral health issues, benchmarked against responses from human dental experts. Methods: This comparative analysis used 40 questions derived from patient surveys on prevalent oral diseases, which were executed in a simulated clinical environment. Responses, obtained from both human experts and LLMs, were subject to a blinded evaluation process by experienced dentists and lay users, focusing on readability, appropriateness, harmlessness, comprehensiveness, intent capture, and helpfulness. Additionally, the stability of artificial intelligence responses was also assessed by submitting each question 3 times under consistent conditions. Results: Google Bard excelled in readability but lagged in appropriateness when compared to human experts (mean 8.51, SD 0.37 vs mean 9.60, SD 0.33; P=.03). ChatGPT-3.5 and ChatGPT-4, however, performed comparably with human experts in terms of appropriateness (mean 8.96, SD 0.35 and mean 9.34, SD 0.47, respectively), with ChatGPT-4 demonstrating the highest stability and reliability. Furthermore, all 3 LLMs received superior harmlessness scores comparable to human experts, with lay users finding minimal differences in helpfulness and intent capture between the artificial intelligence models and human responses. Conclusions: LLMs, particularly ChatGPT-4, show potential in oral health care, providing patient-centric information for enhancing patient education and clinical care. The observed performance variations underscore the need for ongoing refinement and ethical considerations in health care settings. Future research focuses on developing strategies for the safe integration of LLMs in health care settings.

Aim: Treatment of periodontitis, a chronic inflammatory disease driven by biofilm dysbiosis, remains challenging due to patients' poor performance and adherence to the necessary oral hygiene procedures. Novel, artificial intelligence‐enabled multimodal‐sensing toothbrushes (AI‐MST) can guide patients' oral hygiene practices in real‐time and transmit valuable data to clinicians, thus enabling effective remote monitoring and guidance. The aim of this trial was to assess the effect of such a system as an adjunct to clinical practice guideline‐conform treatment.Materials and Methods: This was a single‐centre, double‐blind, standard‐of‐care controlled, randomized, parallel‐group, superiority trial. Male and female adults with generalized Stage II/III periodontitis were recruited at the Shanghai Ninth People's Hospital, China. Subjects received a standard‐of‐care oral hygiene regimen or a technology‐enabled, theory‐based digital intervention consisting of an AI‐MST and targeted doctor's guidance by remote micromessaging. Additionally, both groups received guideline‐conform periodontal treatment. The primary outcome was the resolution of inflamed periodontal pockets (≥4 mm with bleeding on probing) at 6 months. The intention‐to‐treat (ITT) analysis included all subjects who received the allocated treatment and at least one follow‐up.Results: One hundred patients were randomized and treated (50 tests/controls) between 1 February and 30 November 2022. Forty‐eight tests (19 females) and 47 controls (16 females) were analysed in the ITT population. At 6 months, the proportion of inflamed periodontal pockets decreased from 80.7% (95% confidence interval [CI] 76.5–84.8) to 52.3% (47.7–57.0) in the control group, and from 81.4% (77.1–85.6) to 44.4% (39.9–48.9) in the test group. The inter‐group difference was 7.9% (1.6–14.6, p < .05). Test subjects achieved better levels of oral hygiene (p < .001). No significant adverse events were observed.Conclusions: The tested digital health intervention significantly improved the outcome of periodontal therapy by enhancing the adherence and performance of self‐performed oral hygiene. The model breaks the traditional model of oral health care and has the potential to improve efficiency and reduce costs (NCT05137392).

Local immunoinflammatory events instruct skeletal stem cells (SSCs) to repair/regenerate bone after injury, but mechanisms are incompletely understood. We hypothesized that specialized Tregs are necessary for bone repair and interact directly with SSCs through organ-specific messages. Both in human patients with bone fracture and a mouse model of bone injury, we identified a bone injury–responding Treg subpopulation with bone-repair capacity marked by CCR8. Local production of CCL1 induced a massive migration of CCR8 ⁺ Tregs from periphery to the injury site. Depending on secretion of progranulin (PGRN), a protein encoded by the granulin ( Grn ) gene, CCR8 ⁺ Tregs supported the accumulation and osteogenic differentiation of SSCs and thereby bone repair. Mechanistically, we revealed that CCL1 enhanced expression levels of basic leucine zipper ATF-like transcription factor (BATF) in CCR8 ⁺ Tregs, which bound to the Grn promoter and increased Grn translational output and then PGRN secretion. Together, our work provides a new perspective in osteoimmunology and highlights possible ways of manipulating Treg signaling to enhance bone repair and regeneration.