Root canal treatment (RCT) is an essential dental procedure aimed at preserving natural teeth by eliminating infected pulp and preventing reinfection. Predicting RCT success remains a clinical challenge due to anatomical complexities and diagnostic limitations of traditional 2D imaging. Cone-beam computed tomography (CBCT) offers three-dimensional visualization, potentially enhancing diagnostic accuracy and prognostic evaluation. This systematic review and meta-analysis aimed to evaluate the role of CBCT in predicting the success of RCT by identifying anatomical and pathological factors that influence treatment outcomes. This review is the first to separately synthesize diagnostic accuracy and prognostic evidence for CBCT in predicting non-surgical RCT outcomes using subgroup analyses of voxel size and follow-up duration. Following PRISMA guidelines, electronic databases including PubMed, Scopus, Web of Science, and Embase were searched for relevant studies published between 2013 and 2024. Studies were categorized as (1) diagnostic accuracy studies or (2) prognostic studies assessing whether CBCT-identified variables predicted RCT success/failure. Data extraction and quality assessment were performed using appropriate critical appraisal tools. Meta-analysis was conducted to determine pooled effect sizes for periapical lesion detection, root fracture identification, and canal configuration assessment. Twelve studies (6 diagnostic accuracy; 6 prognostic outcome) involving various designs and sample sizes were included. Meta-analysis of diagnostic accuracy studies showed pooled sensitivity/specificity of 0.88/0.84 for periapical lesions (I² = 47%), 0.81/0.86 for vertical root fractures (I² = 52%), and 0.91/0.88 for canal morphology (I² = 28%). Prognostic meta-analysis demonstrated significant associations between CBCT-detected pathology and risk of post-treatment failure: periapical lesions (OR = 2.63; 95% CI 1.78–3.58; p < 0.001; I² = 39%), root fractures (OR = 3.48; 95% CI 2.05–5.12; p < 0.001; I² = 44%), and complex canal anatomy (OR = 1.94; 95% CI 1.20–2.82; p = 0.004; I² = 28%). Subgroup analyses showed stronger prediction with voxel size < 0.2 mm and follow-ups ≥ 12 months. While CBCT demonstrates improved diagnostic capability and offers valuable prognostic insight in complex cases, its findings cannot establish causation and do not guarantee treatment success. CBCT should therefore be used selectively, considering its cost, radiation exposure, and the need for operator expertise.

Introduction: Large language models (LLMs) are increasingly used by patients seeking endodontic information, yet their clinical reliability and safety in patient-centred communication remain uncertain. Methods: This study evaluated the clinical reliability and safety of three contemporary LLMs (ChatGPT GPT-4o, Claude Sonnet 4.5, and Gemini 3 Flash) using 50 patient-centred endodontic questions (35 frequently asked questions and 15 scenario-based prompts). Each question was submitted six times per model in independent sessions. Responses were anonymised and independently assessed by four examiners using a structured Clinical Reliability and Safety Framework. Due to poor inter-examiner agreement, analyses were conducted using examiner stratification. Reproducibility was assessed using word count variability, embedding-based semantic similarity, and lexical distance metrics. Results: Statistically significant differences in clinical reliability were observed across all examiners. ChatGPT consistently received the lowest scores, whereas Gemini most frequently achieved the highest ratings. Model differentiation was clearer for structured frequently asked questions and selected clinical domains than for scenario-based prompts. All models demonstrated stable response lengths across repeated runs. Gemini showed the highest semantic consistency despite greater surface-level rewording. Discussion: Contemporary LLMs demonstrate clinically meaningful variability beyond factual accuracy, particularly in safety framing and clinical actionability. Reliability is influenced by question structure and clinical context. Multidimensional, examiner-aware evaluation frameworks are necessary to meaningfully assess safety and support responsible integration of LLMs into endodontic patient communication.

Recapitulating the host-pathogen interface at the epithelial or mucosal barrier in vitro remains a challenging prospect for infection biologists. While in-house grown 2D epithelial monolayers lack true representation of the in vivo situation, commercially available tissue models are often overlooked due to their cost and practicality. However, with careful planning, such models provide reproducible platforms for a vast array of different applications. Here, we report the use of epithelial models that can be utilized for a wide variety of experimental purposes to investigate host-pathogen interactions in various ecological niches, such as the oral cavity, skin, and vaginal mucosa. From simple planktonic cells to complex biofilm co-culture, epithelial models are used to assess microbial adherence and invasion, and to evaluate the host response at a transcriptional and/or protein level, with scope for more detailed profiling using different omics approaches. Furthermore, these biological systems can be used as more accurate test beds for evaluating conventional and novel antimicrobial activity in a complex host-pathogen microenvironment in vitro. The protocols described herein document how models are handled upon arrival and prepared in the laboratory for co-culture stimulation with biofilm communities. The methods detail how experimental outputs are achieved from the model systems, including the processing of tissue, the co-culture setup, and data generation. These experiments include host gene expression through single- and multiplex qPCR analyses and inflammatory protein detection using ELISAs. In conclusion, epithelial models provide useful in vitro systems for preclinical investigatory studies into simple or complex host-pathogen interactions.

Recapitulating the host-pathogen interface at the epithelial or mucosal barrier in vitro remains a challenging prospect for infection biologists. While in-house grown 2D epithelial monolayers lack true representation of the in vivo situation, commercially available tissue models are often overlooked due to their cost and practicality. However, with careful planning, such models provide reproducible platforms for a vast array of different applications. Here, we report the use of epithelial models that can be utilized for a wide variety of experimental purposes to investigate host-pathogen interactions in various ecological niches, such as the oral cavity, skin, and vaginal mucosa. From simple planktonic cells to complex biofilm co-culture, epithelial models are used to assess microbial adherence and invasion, and to evaluate the host response at a transcriptional and/or protein level, with scope for more detailed profiling using different omics approaches. Furthermore, these biological systems can be used as more accurate test beds for evaluating conventional and novel antimicrobial activity in a complex host-pathogen microenvironment in vitro. The protocols described herein document how models are handled upon arrival and prepared in the laboratory for co-culture stimulation with biofilm communities. The methods detail how experimental outputs are achieved from the model systems, including the processing of tissue, the co-culture setup, and data generation. These experiments include host gene expression through single- and multiplex qPCR analyses and inflammatory protein detection using ELISAs. In conclusion, epithelial models provide useful in vitro systems for preclinical investigatory studies into simple or complex host-pathogen interactions.

Objectives: The identification of novel antimicrobial agents for use in root canal treatment may provide opportunities to improve treatment outcomes. This study aimed to assess the antimicrobial efficacy of different molecular weights of chitosan (CS), and how modification with CS may impact on the antimicrobial, physico-mechanical and biological properties of Biodentine™, a calcium-silicate-based material used in endodontics. Methods: C. albicans biofilms were treated with either 3% sodium hypochlorite (NaOCl) or a 0.05% or 0.1% CS solution for 5 min. The growth medium was replenished, and cells were re-incubated for additional 72 h. Regrowth of biofilms was assessed using a colorimetric XTT assay. Additionally, multispecies biofilms were established and the regrowth of biofilms on Biodentine discs were quantified following the addition of 0.5 wt% and 1 wt% of CS powder using qPCR. The physico-mechanical and biological properties of the new composite of Biodentine and CS were also evaluated. Results: Viability readings revealed significant initial biofilm inhibitory effects of CS solutions, followed by significant regrowth after 72 h. Upon the addition of CS to Biodentine, significant reductions in multispecies biofilm regrowth were determined. Notably, the antibiofilm activity of CS was found to be increased as the molecular weight decreased. The addition of powdered CS of low molecular weight showed a reduction in the mechanical properties of Biodentine, whereas no detrimental effects on the other material properties were noted. Conclusions: Chitosan may not be useful as an alternative irrigant to NaOCl. Addition of CS to Biodentine represents a potential means of augmenting the antimicrobial activity of Biodentine against persistent microorganisms following endodontic therapy. Despite the reductions in mechanical properties of the material, the new composite still represents a viable material option when material strength and hardness are not critical.