Impact of Resolvin E1 on Experimental Periodontitis and Periodontal Biofilm

Objective: The goal of this project was to determine the impact of local inflammation on changes in the subgingival biofilm composition in ligature-induced periodontitis in rats using the specialized pro-resolving mediator (SPM), resolvin E1 (RvE1). Materials and Methods: The impact of RvE1 on the microbiota of ligature-induced periodontitis was assessed in two separate experiments; treatment of established periodontitis and prevention of ligature-induced periodontitis. In the treatment study, eighteen rats were separated into four groups comprising no ligature, ligature alone (no treatment), ligature with topical RvE1 treatment (ligature+RvE1) and, ligature with topical vehicle treatment (ligature + Vehicle). 3-0 silk ligatures were tied around maxillary second molars bilaterally for three weeks to induce disease. After three weeks, the treatment phase began with the application of RvE1 or vehicle (ethanol) every other day for an additional three weeks. Subgingival plaque samples were collected every four days throughout the experiment. The composition of the subgingival microbiota was initially screened by checkerboard DNA-DNA hybridization using probes on 40 subgingival species. Definitive, unbiased characterization of the subgingival microbiota was accomplished with next-generation sequencing using the Illumina MiSeq® platform. Six rats were sacrificed on Days 1, 21 and 42 and maxillae were dissected to collect samples for gingival RNA extraction, bone morphometric measurements, and histomorphometric analysis. Local tissue gene expression (Cxcl-1, Ptgs2, Nos2) was detected using qRT-PCR. Tissue specimens were prepared for histology and stained with H&E and tartrate resistant acid phosphatase (TRAP). In the prevention study, sixteen rats were separated into four groups (no ligature, ligature + RvE1 (0.1µg/µl), ligature + RvE1 (0.5 µg/µl), ligature + Vehicle). 5-0 silk ligatures were placed around maxillary second molars bilaterally to induce disease. At the time of ligature placement, animals received assigned treatment thrice weekly (M, W, F) for four weeks. Subgingival plaque samples were collected every four days (M and F). Four rats were sacrificed at baseline (Day 1) and the vehicle and two treatment groups (four each) were sacrificed at day 28 and samples processed as described above. The two-group comparisons were assessed by Student’s t-test. The multiple-group comparison was assessed by one-way ANOVA and post hoc tests. Results: In the first study (treatment), topical application of RvE1 significantly reversed the bone loss associated with periodontitis compared to the vehicle. RvE1 application significantly reduced the expression of Cxcl1 and osteoclast density compared to the vehicle application. In the prevention study, RvE1 treatment significantly prevented the bone loss during the disease progression. RvE1 application significantly reduced the expression of Ptgs2, Nos2 compared to the vehicle application. Osteoclast density and inflammatory cell infiltration in the RvE1 groups were significantly lower than these in the Vehicle group. The cell counts of bacterial species gradually increased and the subgingival microbiota shifted during the disease progression. In the treatment study, RvE1 treatment significantly reduced cell counts compared to the vehicle application at the end of treatment phase. The shift of subgingival microbiota was limited by the RvE1 treatment. In the prevention study, the taxonomic composition and diversity of subgingival microbiota was controlled by the RvE1 application. The change of subgingival microbiota appeared to be associated with the state of inflammation in the periodontal environment. Conclusion: Resolvin E1 treatment of existing ligature-induced periodontitis significantly regenerates lost alveolar bone and prevents alveolar bone loss. Resolvin E1 treatment limits microbial shifts and reduces total bacterial load by inhibiting inflammation of local environment in experimental periodontitis

A comprehensive artificial intelligence framework for dental diagnosis and charting

Background: The aim of this study was to develop artificial intelligence (AI) guided framework to recognize tooth numbers in panoramic and intraoral radiographs (periapical and bitewing) without prior domain knowledge and arrange the intraoral radiographs into a full mouth series (FMS) arrangement template. This model can be integrated with different diseases diagnosis models, such as periodontitis or caries, to facilitate clinical examinations and diagnoses. Methods: The framework utilized image segmentation models to generate the masks of bone area, tooth, and cementoenamel junction (CEJ) lines from intraoral radiographs. These masks were used to detect and extract teeth bounding boxes utilizing several image analysis methods. Then, individual teeth were matched with a patient’s panoramic images (if available) or tooth repositories for assigning tooth numbers using the multi-scale matching strategy. This framework was tested on 1240 intraoral radiographs different from the training and internal validation cohort to avoid data snooping. Besides, a web interface was designed to generate a report for different dental abnormalities with tooth numbers to evaluate this framework’s practicality in clinical settings. Results: The proposed method achieved the following precision and recall via panoramic view: 0.96 and 0.96 (via panoramic view) and 0.87 and 0.87 (via repository match) by handling tooth shape variation and outperforming other state-of-the-art methods. Additionally, the proposed framework could accurately arrange a set of intraoral radiographs into an FMS arrangement template based on positions and tooth numbers with an accuracy of 95% for periapical images and 90% for bitewing images. The accuracy of this framework was also 94% in the images with missing teeth and 89% with restorations. Conclusions: The proposed tooth numbering model is robust and self-contained and can also be integrated with other dental diagnosis modules, such as alveolar bone assessment and caries detection. This artificial intelligence-based tooth detection and tooth number assignment in dental radiographs will help dentists with enhanced communication, documentation, and treatment planning accurately. In addition, the proposed framework can correctly specify detailed diagnostic information associated with a single tooth without human intervention

Acute immune thrombocytopenic purpura in an adolescent with 2009 novel H1N1 influenza A virus infection

AbstractAlthough both leukopenia and thrombocytopenia are not uncommon hematological findings among patients with novel 2009 H1N1 influenza virus infection, immune thrombocytopenic purpura has rarely been shown to be associated with this novel influenza A infection. Here, we describe a previously healthy adolescent who presented with fever, influenza-like symptoms and acute onset of generalized petechiae and active oral mucosa bleeding on the third day of his illness. Severe leukopenia and thrombocytopenia were found. There was neither malignancy nor blast cells found by bone marrow aspiration. Real-time reverse transcriptase polymerase chain reaction was positive for novel 2009 H1N1 influenza infection. Novel influenza-associated atypical immune thrombocytopenic purpura was diagnosed. The patient recovered uneventfully after oseltamivir and methylprednisolone therapy

Use GPT-J Prompt Generation with RoBERTa for NER Models on Diagnosis Extraction of Periodontal Diagnosis from Electronic Dental Records

This study explored the usability of prompt generation on named entity recognition (NER) tasks and the performance in different settings of the prompt. The prompt generation by GPT-J models was utilized to directly test the gold standard as well as to generate the seed and further fed to the RoBERTa model with the spaCy package. In the direct test, a lower ratio of negative examples with higher numbers of examples in prompt achieved the best results with a F1 score of 0.72. The performance revealed consistency, 0.92-0.97 in the F1 score, in all settings after training with the RoBERTa model. The study highlighted the importance of seed quality rather than quantity in feeding NER models. This research reports on an efficient and accurate way to mine clinical notes for periodontal diagnoses, allowing researchers to easily and quickly build a NER model with the prompt generation approach.Comment: 2023 AMIA Annual Symposium, see https://amia.org/education-events/amia-2023-annual-symposiu

Use GPT-J Prompt Generation with RoBERTa for NER Models on Diagnosis Extraction of Periodontal Diagnosis from Electronic Dental Records

This study explored the usability of prompt generation on named entity recognition (NER) tasks and the performance in different settings of the prompt. The prompt generation by GPT-J models was utilized to directly test the gold standard as well as to generate the seed and further fed to the RoBERTa model with the spaCy package. In the direct test, a lower ratio of negative examples with higher numbers of examples in prompt achieved the best results with a F1 score of 0.72. The performance revealed consistency, 0.92-0.97 in the F1 score, in all settings after training with the RoBERTa model. The study highlighted the importance of seed quality rather than quantity in feeding NER models. This research reports on an efficient and accurate way to mine clinical notes for periodontal diagnoses, allowing researchers to easily and quickly build a NER model with the prompt generation approach

Extracting periodontitis diagnosis in clinical notes with RoBERTa and regular expression

This study aimed to utilize text processing and natural language processing (NLP) models to mine clinical notes for the diagnosis of periodontitis and to evaluate the performance of a named entity recognition (NER) model on different regular expression (RE) methods. Two complexity levels of RE methods were used to extract and generate the training data. The SpaCy package and RoBERTa transformer models were used to build the NER model and evaluate its performance with the manual-labeled gold standards. The comparison of the RE methods with the gold standard showed that as the complexity increased in the RE algorithms, the F1 score increased from 0.3-0.4 to around 0.9. The NER models demonstrated excellent predictions, with the simple RE method showing 0.84-0.92 in the evaluation metrics, and the advanced and combined RE method demonstrating 0.95-0.99 in the evaluation. This study provided an example of the benefit of combining NER methods and NLP models in extracting target information from free-text to structured data and fulfilling the need for missing diagnoses from unstructured notes.Comment: IEEE ICHI 2023, see https://ieeeichi.github.io/ICHI2023/program.htm

Development and Validation of a Rule-Based Algorithm to Identify Periodontal Diagnosis Using Structured Electronic Health Record Data

AIM: To develop and validate an automated electronic health record (EHR)-based algorithm to suggest a periodontal diagnosis based on the 2017 World Workshop on the Classification of Periodontal Diseases and Conditions. MATERIALS AND METHODS: Using material published from the 2017 World Workshop, a tool was iteratively developed to suggest a periodontal diagnosis based on clinical data within the EHR. Pertinent clinical data included clinical attachment level (CAL), gingival margin to cemento-enamel junction distance, probing depth, furcation involvement (if present) and mobility. Chart reviews were conducted to confirm the algorithm\u27s ability to accurately extract clinical data from the EHR, and then to test its ability to suggest an accurate diagnosis. Subsequently, refinements were made to address limitations of the data and specific clinical situations. Each refinement was evaluated through chart reviews by expert periodontists at the study sites. RESULTS: Three-hundred and twenty-three charts were manually reviewed, and a periodontal diagnosis (healthy, gingivitis or periodontitis including stage and grade) was made by expert periodontists for each case. After developing the initial version of the algorithm using the unmodified 2017 World Workshop criteria, accuracy was 71.8% for stage alone and 64.7% for stage and grade. Subsequently, 16 modifications to the algorithm were proposed and 14 were accepted. This refined version of the algorithm had 79.6% accuracy for stage alone and 68.8% for stage and grade together. CONCLUSIONS: Our findings suggest that a rule-based algorithm for suggesting a periodontal diagnosis using EHR data can be implemented with moderate accuracy in support of chairside clinical diagnostic decision making, especially for inexperienced clinicians. Grey-zone cases still exist, where clinical judgement will be required. Future applications of similar algorithms with improved performance will depend upon the quality (completeness/accuracy) of EHR data

Resolvin E1 Reverses Experimental Periodontitis and Dysbiosis

Periodontitis is a biofilm-induced inflammatory disease characterized by dysbiosis of the commensal periodontal microbiota. It is unclear how natural regulation of inflammation affects the periodontal biofilm. Promoters of active resolution of inflammation including Resolvin E1 (RvE1) effectively treat inflammatory periodontitis in animal models. The goals of this study were 1) to compare periodontal tissue gene expression in different clinical conditions, 2) to determine the impact of local inflammation on the composition of subgingival bacteria, and 3) to understand how inflammation impacts these changes. Two clinically-relevant experiments were performed in rats: prevention and treatment of ligature-induced periodontitis with RvE1 topical treatment. The gingival transcriptome was evaluated by RNA-seq sequencing of mRNA. The composition of the subgingival microbiota was characterized by 16S rDNA sequencing. Periodontitis was assessed by bone morphometric measurements and histomorphometry of block sections. H&E and, tartrate resistant acid phosphatase staining were used to characterize and quantify inflammatory changes. RvE1 treatment prevented bone loss in ligature induced periodontitis. Osteoclast density and inflammatory cell infiltration in the RvE1 groups were lower than those in the placebo group. RvE1 treatment reduced expression of inflammation-related genes returning the expression profile to one more similar to health. Treatment of established periodontitis with RvE1 reversed bone loss, reversed inflammatory gene expression and reduced osteoclast density. Assessment of the rat subgingival microbiota after RvE1 treatment revealed marked changes in both prevention and treatment experiments. The data suggest that modulation of local inflammation has a major role in shaping the composition of the subgingival microbiota