Dental implants have become increasingly important in daily dental offices. The degree of pain and discomfort experienced during a surgical procedure varies from one patient to another. Using advanced machine learning algorithms to predict pain, the dentist and the patient would make more informed decisions about the treatment. This study aims at Predicting postoperative discomfort using an AI-based multi-linear regression model. The functional parametric association between the eight parameters (age, sex, and operating technique) and the patient’s postoperative pain was established following implant surgery. The output was normalized information regarding both incidence and severity of immediate discomfort post-implant surgery. To enhance the generalization ability of the multiple linear regression (MLR) model and avoid overfitting, 825 cases were provided as the training set, while 207 cases were given for data authentication. In addition, 45 samples were used as controls to determine the model’s prediction accuracy. Evaluation of the given model reveals a Root Mean Squared Error of 0.108477788. This prototype predicted AI model postoperative pain following implant surgery with 89.6 % accuracy. Finally, this AI model exhibited clinical viability and utility in predicting postoperative pain after surgery.

Periodontal diseases are polymicrobial immune–inflammatory diseases that can severely destroy tooth-supporting structures. The critical bacteria responsible for this destruction include red complex bacteria such as Porphoromonas gingivalis, Tanerella forsythia and Treponema denticola. These organisms have developed adaptive immune mechanisms against bacteriophages/viruses, plasmids and transposons through clustered regularly interspaced short palindromic repeats (CRISPR) and their associated proteins (Cas). The CRISPR-Cas system contributes to adaptive immunity, and this acquired genetic immune system of bacteria may contribute to moderating the microbiome of chronic periodontitis. The current research examined the role of the CRISPR-Cas system of red complex bacteria in the dysbiosis of oral bacteriophages in periodontitis. Whole-genome sequences of red complex bacteria were obtained and investigated for CRISPR using the CRISPR identification tool. Repeated spacer sequences were analyzed for homologous sequences in the bacteriophage genome and viromes using BLAST algorithms. The results of the BLAST spacer analysis for T. denticola spacers had a 100% score (e value with a bacillus phage), and the results for T. forsthyia and P. gingivalis had a 56% score with a pectophage and cellulophage (e value: 0.21), respectively. The machine learning model of the identified red complex CRISPR sequences predicts with area an under the curve (AUC) accuracy of 100 percent, indicating phage inhibition. These results infer that red complex bacteria could significantly inhibit viruses and phages with CRISPR immune sequences. Therefore, the role of viruses and bacteriophages in modulating sub-gingival bacterial growth in periodontitis is limited or questionable.

Background and Objectives: Periodontitis is a chronic multifactorial inflammatory infectious disease marked by continuous degradation of teeth and surrounding parts. One of the most important periodontal pathogens is P. intermedia, and with its interpain A proteinase, it leads to an increase in lethal infection. Materials and Methods: The current study was designed to create a multi-epitope vaccine using an immunoinformatics method that targets the interpain A of P. intermedia. For the development of vaccines, P. intermedia peptides InpA were found appropriate. To create a multi-epitope vaccination design, interpain A, B, and T-cell epitopes were found and assessed depending on the essential variables. The vaccine construct was evaluated based on its stability, antigenicity, and allergenicity. Results: The vaccine construct reached a more significant population and was able to bind to both the binding epitopes of major histocompatibility complex (MHC)-I and MHC-II. Through the C3 receptor complex route, P. intermedia InpA promotes an immunological subunit. Utilizing InpA-C3 and vaccination epitopes as the receptor and ligand, the molecular docking and dynamics were performed using the ClusPro 2.0 server. Conclusion: The developed vaccine had shown good antigenicity, solubility, and stability. Molecular docking indicated the vaccine’s 3D structure interacts strongly with the complement C3. The current study describes the design for vaccine, and steady interaction with the C3 immunological receptor to induce a good memory and an adaptive immune response against Interpain A of P. intermedia.

Porphyromonas gingivalis is a key pathogen associated with periodontal disease linked to various systemic conditions. Accurate identification of P. gingivalis proteins is essential for understanding its pathogenicity and developing targeted interventions. Recent advances in whole-genome sequencing of P. gingivalis have enhanced the detection and classification of antimicrobial resistance (AMR) determinants, aiding in the early identification of resistance trends and improving patient care. In this study, we developed a deep learning approach using convolutional neural networks (CNNs) to classify P. gingivalis proteins based on their amino acid sequences. A dataset of 685 protein sequences, including 150 P. gingivalis proteins and 535 nonresistant variants, was compiled and split into training (60%), validation (20%), and test (20%) sets. The sequences were preprocessed by padding to 750 amino acids and one-hot encoded into a feature matrix. A CNN model, consisting of two convolutional layers, max pooling, dropout, and fully connected layers for binary classification, was designed and implemented in PyTorch with 6192,258 parameters. The model was trained using the Adam optimizer for 30 epochs with early stopping based on validation accuracy. The CNN model outperforms traditional methods like BLAST, HMM Profiles, and DeepSig in predicting and classifying AMR in P. gingivalis. The hypothetical ProtBERT model shows slightly better performance, with an accuracy of 97%. Key metrics like accuracy, precision, recall, F1 score, and the area under the curve were assessed. CNN and ProtBERT have high recall rates (0.93 and 0.95, respectively), indicating their effectiveness in predicting AMR classifications. Our CNN model outperforms SOTA methods in classifying P. gingivalis-resistant protein sequences, achieving 96.35% accuracy and an area under the curve of 0.98. Precise and rapid prediction of AMR based solely on protein sequences, potentially leading to earlier identification of resistance trends and improved antibiotic stewardship in periodontal treatment.