HyperAttention and Linformer-Based ß-catenin Sequence Prediction For Bone Formation.

Introduction Beta (ß)-catenin, a pivotal protein in bone development and homeostasis, is implicated in various bone disorders. Peptide-based therapeutics offer a promising approach due to their specificity and potential for reduced side effects. Attention networks are widely used for peptide sequence prediction, specifically sequence-to-sequence models. Hence, the current study aims to develop a HyperAttention and informatics-based ß-catenin sequence prediction for bone formation. Methods ß-catenin protein sequences were downloaded and quality-checked using UniProt and FASTA sequences using DeepBio (Deep Bio Inc., Seoul, South Korea) for predictive analysis. Data was analyzed for duplicates, outliers, and missing values. The data was then split into training and testing sets, with 80% of the data used for training and 20% for testing, and peptide sequences were encoded and subjected to algorithms. Results The HyperAttention and Linformer models perform well in predictive sequence, with HyperAttention correctly predicting 87% of instances and Linformer predicting 89%. Both models have higher sensitivity and specificity, with Linformer showing better identification of 91% of negative instances and slightly better sensitivity. Conclusion The HyperAttention and Linformer models effectively predict peptide sequences with high specificity and sensitivity. Further optimization and development are needed for optimal application and balance between positive and negative instances.

Graph Neural Network-Based Drug Gene Interactions of Wnt/ß-Catenin Pathway in Bone Formation.

Introduction The Wnt/ß-catenin pathway is crucial for bone formation and remodeling, regulating osteoblast differentiation, bone remodeling, and skeletal homeostasis. Dysregulation of the Wnt/ß-catenin pathway is linked to bone-related diseases like osteoporosis, osteoarthritis, and osteosarcoma. The strategies to modulate this pathway include Wnt agonists, inhibitors, and small molecules. Graph neural networks (GNNs) have shown potential in understanding drug-gene interactions, providing accurate predictions, identifying novel drug-target pairs, and enabling personalized drug therapy. So we aim to predict GNN-based drug-gene interactions of Wnt/ß-catenin pathway in bone formation. Methodology The drug-gene interactions of Wnt signaling were annotated and preprocessed using Cytoscape, a powerful tool for building drug-gene interactions. Data was imported, nodes representing drugs and genes were created, and edges represented their interactions. GNNs were used to prepare data for nodes, genes, and drugs. GNNs are designed to operate on graph-structured data, capable of learning complex relationships between the nodes. The architecture consists of several steps: graph representation, message passing, node representation update, graph-level readout, and prediction or output. A data representation system is a GNN with an Adam optimizer, 100 epochs, a learning rate of 0.001, and entropy loss. Results The network has 108 nodes, 134 edges, and 2.444 neighbors, with a diameter of 4, radius of 2, and characteristic path length of 2.635. It lacks clustering, sparse connectivity, wide connection variation, and moderate centralization. The GNN model's drug-gene interactions demonstrate high precision, recall, F1 score, and accuracy, with a high sensitivity to true-positives and low false-negatives. Conclusion The study employs a GNN model to predict drug-gene interactions in the Wnt/ß-catenin pathway, demonstrating high precision and accuracy, but further research is needed.

Exploring the interplay between Porphyromonas gingivalis KGP gingipain, herpes virus MicroRNA-6, and Icp4 transcript in periodontitis: Computational and clinical insights.

BACKGROUND: Porphyromonas gingivalis, a major pathogen in periodontitis, produces KGP (Lys-gingipain), a cysteine protease that enhances bacterial virulence by promoting tissue invasion and immune evasion. Recent studies highlight microRNAs' role in viral latency, potentially affecting lytic replication through host mechanisms. Herpes virus (HSV) establishes latency via interactions between microRNA-6 (miRH-6) and the ICP4 transcription factor in neural ganglia. This suggests a potential link between periodontitis and HSV-induced latency. This study aims to identify and validate the insilico inhibitory interaction of P. gingivalis KGP with ICP4 transcripts and correlate the presence of viral latency-associated transcript micro-RNA-6 with periodontitis. METHODS: Computational docking analysis was performed to investigate the potential interaction between ICP4 and KGP gingipain. The binding energy and RMSD ligand values were calculated to determine the interaction's strength. Ten patients with recurrent clinical attachment loss despite conventional therapy were included in the clinical study. Subgingival tissue samples were collected post-phase I therapy, and HSV microRNA-6 presence was detected via polymerase chain reaction and confirmed through gel electrophoresis. RESULTS: Computational docking identified the ICP4-KGP gingipain complex with the lowest binding energy (-288.29 kJ mol^1) and an RMSD ligand of 1.5 Angstroms, indicating strong interaction potential. Gel electrophoresis confirmed miRH-6 presence in all samples. CONCLUSION: The identification of miRNA-6 in periodontitis patients and the strong interaction potential between P. gingivalis KGP gingipain and ICP4 transcripts indicate a possible link between bacterial virulence factors and viral latency dynamics in periodontal tissues. These results highlight the complex interplay between oral pathogens, viral microRNAs, and host immune responses in periodontitis.

Deep Learning Based Micro-RNA Analysis of Lipopolysaccharide Exposed Periodontal Ligament Stem Cells Exosomes Reveal Apoptotic and Inflammasome Derived Pathway Activation.

Background: The production of inflammatory factors in periodontium is increased by LPS, particularly from P. gingivalis, and the damage to periodontal tissues is exacerbated. Exosomes from periodontal ligament stem cells change regeneration and repair brought on by bacterial LPS. MiRNAs are carried by exosomes to recipient cells to affect epigenetic functions. Thus, this study aims to utilize deep learning algorithms to uncover novel micro-RNA biomarkers in bacterial LPS-exposed PDLSC stem cells to understand the activation pathway. Methods: Using NCBI GEO DATA SET GSE163489, the most differentially expressed micro RNAs were found to differ between healthy and LPS-induced PDLSC cells. Deep learning analysis, employing a Random Forest, Artificial Neural Network c, a Support Vector Machine (SVM), and a Linear Regression model implemented within the orange data mining toolkit, identified novel microRNA biomarkers. The orange data mining toolkit was utilized for deep learning analysis of microRNA expression data, providing a user-friendly environment for machine learning tasks like classification, regression, and clustering. Results: Random Forest emerged as the superior model, achieving the highest R 2 score (.985) and the lowest RMSE (0.189) compared to Neural Networks (R 2 = .952, RMSE = 0.332), Linear Regression (R 2 = .949, RMSE = 0.343), and SVM (R 2 = .931, RMSE = 0.398). This suggests its superior ability to capture the underlying patterns in the microRNA expression data. Given its robust performance, Random Forest holds promise for identifying novel biomarkers, developing more accurate diagnostic tools, and potentially guiding the stratification of patients for targeted therapeutic interventions in periodontal disease. Conclusion: The current study utilizes deep learning analysis of microRNA expression data to identify novel biomarkers associated with inflammasome activation and anti-apoptotic pathways. These findings hold promise for guiding the development of novel therapeutic strategies for periodontal disease. However, future studies are warranted to validate these biomarkers using independent datasets and experimental methods.

Variational Autoencoders for Generative Drug-Gene Interactions in Periodontal Bone Resorption.

Introduction Periodontal bone resorption is a significant dental problem causing tooth loss and impaired oral function. It is influenced by factors such as bacterial plaque, genetic predisposition, smoking, systemic diseases, medications, hormonal changes, and poor oral hygiene. This condition disrupts bone remodeling, favoring resorptive processes. Variational autoencoders (VAEs) can learn the distribution of drug-gene interactions from existing data, identify potential drug targets, and predict therapeutic effects. This study investigates the generation of drug-gene interactions in periodontal bone resorption using VAEs. Methods A bone resorptive drugs dataset was retrieved from Probes and Drugs and analyzed using Cytoscape (https://cytoscape.org/) and CytoHubba (https://apps.cytoscape.org/apps/cytohubba), powerful tools for studying drug-gene interactions in bone resorption. The dataset was then prepared for matrix representation, with normalized input data. It was subsequently divided into training, validation, and testing sets. We then built an encoder-decoder network, defined a loss function, optimized parameters, and fine-tuned hyperparameters. Using VAEs, we generated new drug-gene interactions, assessed model performance, and visualized the latent space with reconstructed drug-gene interactions for further insights. Results The analysis revealed the top hub genes in drug-gene interactions, including Matrix Metalloproteinase (MMP) 14, MMP 9, HIF1A, STAT1, MAPT, CAS9, MMP2, CASP3, MMP1, and MAK1. The VAE's reconstruction accuracy was measured using mean squared error (MSE), with an average squared difference of 0.077. Additionally, the KL divergence value was 2.349, and the average reconstruction log-likelihood was -246. Conclusion The generative variational encoder model for drug-gene interactions in bone resorption demonstrates high accuracy and reliability in representing complex drug-gene relationships within this context.

Gradient Boosting Prediction of Overlapping Genes From Weighted Co-expression and Differential Gene Expression Analysis of Wnt Pathway: An Artificial Intelligence-Based Bioinformatics Study.

Introduction The Wnt (wingless-related integration site) signalling pathway is crucial for bone formation and remodelling, regulating the commitment of mesenchymal stem cells (MSCs) to the osteoblastic lineage. It triggers the transcriptional activation of Wnt target genes and promotes osteoblast proliferation and survival. Weighted co-expression network analysis (WGCNA) and differential gene expression analysis help researchers understand gene roles. Gradient boosting, a machine learning technique, enhances understanding of genetic and molecular mechanisms contributing to overlap genes, improving gene regulation and functional genomics. The aim is to predict overlapping genes in the Wnt signalling pathway. Methods Differential gene expression analysis was performed using the National Center for Biotechnology Information (NCBI) geo dataset-GSE251951, focusing on the effect of Wnt signaling on treatment. The WGCNA module was analyzed using the iDEP tool to identify interconnected gene clusters. Hub genes were identified by calculating module eigengenes, correlated with external traits, and ranked based on module membership values. The study utilized gradient boosting, an ensemble learning method, to predict models, evaluate their performance using metrics like accuracy, precision, recall, and F1 score, and adjust predictions based on gradient and learning rate. Results The dendrogram uses the "Dynamic TreeCut" algorithm to analyze gene clusters, aiding researchers in understanding gene modules and biological processes, identifying co-expressed genes, and discovering new pathways. The confusion matrix displays 88 actual and predicted cases. The gradient boosting model achieves 78.9% accuracy in predicting Wnt pathway overlapping genes, with a respectable area under the curve (AUC) and classification accuracy values. It accurately predicts 73.9% of samples, with a high precision ratio and low recall. Conclusion Future research should enhance differential expression analysis and WGCNA to identify key Wnt pathway genes, improve sensitivity, specificity, hyperparameter tuning, and validation experiments, and use larger datasets.

Comparing Graph Sample and Aggregation (SAGE) and Graph Attention Networks in the Prediction of Drug-Gene Associations of Extended-Spectrum Beta-Lactamases in Periodontal Infections and Resistance.

INTRODUCTION: Gram-negative bacteria exhibit more antibiotic resistance than gram-positive bacteria due to their cell wall structure and composition differences. Porins, or protein channels in these bacteria, can allow small, hydrophilic antibiotics to diffuse, affecting their susceptibility. Mutations in porin protein genes can also impair antibiotic entry. Predicting drug-gene associations of extended-spectrum beta-lactamases (ESBLs) is crucial as they confer resistance to beta-lactam antibiotics, challenging the treatment of infections. This aids clinicians in selecting suitable treatments, optimizing drug usage, enhancing patient outcomes, and controlling antibiotic resistance in healthcare settings. Graph-based neural networks can predict drug-gene associations in periodontal infections and resistance. The aim of the study was to predict drug-gene associations of ESBLs in periodontal infections and resistance. METHODS: The study focuses on analyzing drug-gene associations using probes and drugs. The data was converted into graph language, assigning nodes and edges for drugs and genes. Graph neural networks (GNNs) and similar algorithms were implemented using Google Colab and Python. Cytoscape and CytoHubba are open-source software platforms used for network analysis and visualization. GNNs were used for tasks like node classification, link prediction, and graph-level prediction. Three graph-based models were used: graph convolutional network (GCN), Graph SAGE, and graph attention network (GAT). Each model was trained for 200 epochs using the Adam optimizer with a learning rate of 0.01 and a weight decay of 5e-4. RESULTS: The drug-gene association network has 57 nodes, 79 edges, and a 2.730 characteristic path length. Its structure, organization, and connectivity are analyzed using the GCN and Graph SAGE, which show high accuracy, precision, recall, and an F1-score of 0.94. GAT's performance metrics are lower, with an accuracy of 0.68, precision of 0.47, recall of 0.68, and F1-score of 0.56, suggesting that it may not be as effective in capturing drug-gene relationships. CONCLUSION: Compared to ESBLs, both GCN and Graph SAGE demonstrate excellent performance with accuracy, precision, recall, and an F1-score of 0.94. These results indicate that GCN and Graph SAGE are highly effective in predicting drug-gene associations related to ESBLs. GCN and Graph SAGE outperform GAT in predicting drug-gene associations for ESBLs. Improvements include data augmentation, regularization, and cross-validation. Ethical considerations, fairness, and open-source implementations are crucial for future research in precision periodontal treatment.

Deep Learning Prediction of Inflammatory Inducing Protein Coding mRNA in P. gingivalis Released Outer Membrane Vesicles.

Aim: The Insilco study uses deep learning algorithms to predict the protein-coding pg m RNA sequences. Material and methods: The NCBI GEO DATA SET GSE218606's GEO R tool discovered P.G's outer membrane vesicles' most differentially expressed mRNA. Genemania analyzed differentially expressed gene networks. Transcriptomics data were collected and labeled on P. gingivalis protein-coding mRNA sequence and pseudogene, lincRNA, and bidirectional promoter lincRNA. Orange, a machine learning tool, analyzed and predicted data after preprocessing. Naïve Bayes, neural networks, and gradient descent partition data into training and testing sets, yielding accurate results. Cross-validation, model accuracy, and ROC curve were evaluated after model validation. Results: Three models, Neural Networks, Naive Bayes, and Gradient Boosting, were evaluated using metrics like Area Under the Curve (AUC), Classification Accuracy (CA), F1 Score, Precision, Recall, and Specificity. Gradient Boosting achieved a balanced performance (AUC: 0.72, CA: 0.41, F1: 0.32) compared to Neural Networks (AUC: 0.721, CA: 0.391, F1: 0.314) and Naive Bayes (AUC: 0.701, CA: 0.172, F1: 0.114). While statistical tests revealed no significant differences between the models, Gradient Boosting exhibited a more balanced precision-recall relationship. Conclusion: In silico analysis using machine learning techniques successfully predicted protein-coding mRNA sequences within Porphyromonas gingivalis OMVs. Gradient Boosting outperformed other models (Neural Networks, Naive Bayes) by achieving a balanced performance across metrics like AUC, classification accuracy, and precision-recall, suggests its potential as a reliable tool for protein-coding mRNA prediction in P. gingivalis OMVs.

Advanced machine learning for estimating vascular occlusion percentage in patients with ischemic heart disease and periodontitis.

Objective: The study aimed to assess the efficacy of advanced machine learning algorithms in estimating the percentage of vascular occlusion in ischemic heart disease (IHD) cases with periodontitis. Methods: This study involved 300 IHD patients aged 45 to 65 with stage III periodontitis undergoing coronary angiograms. Dental and periodontal examinations assessed various factors. Coronary angiograms categorized patients into three groups based on artery stenosis. Clinical data were processed, outliers were identified, and machine learning algorithms were applied for analysis using the orange tool, including confusion matrices and receiver operating characteristic (ROC) curves for assessment. Results: The results showed that Random Forest, Naïve Bayes, and Neural Networks were 97 %, 84 %, and 92 % accurate, respectively. Random Forest did exceptionally well in identifying the severity of conditions, with 95.70 % accuracy for mild cases, 84.80 % for moderate cases, and a perfect 100.00 % for severe cases. Conclusions: The current study, using Periodontal Inflammatory Surface Area (PISA) scores, revealed that the Random Forest model accurately predicted the percentage of vascular occlusion.

AI-based 3D-QSAR model of FDA-approved repurposed drugs for inhibiting sclerostin.

BACKGROUND: Wnt activation promotes bone formation and prevents bone loss. The Wnt pathway antagonist sclerostin and additional anti-sclerostin antibodies were discovered as a result of the development of the monoclonal antibody romosozumab. These monoclonal antibodies greatly increase the risk of cardiac arrest. Three-dimensional quantitative structure-activity relationships (3D-QSAR) predicts biological activities of ligands based on their three-dimensional features by employing powerful chemometric investigations such as artificial neural networks (ANNs) and partial least squares (PLS). OBJECTIVE: In this study, ligand-receptor interactions were investigated using 3D-QSAR Comparative molecular field analysis (CoMFA). Estimates of steric and electrostatic characteristics in CoMFA are made using Lennard-Jones and Coulomb potentials. METHODS: To identify the conditions necessary for the activity of these molecules, fifty Food and Drug Administration (FDA)-approved medications were chosen for 3D-QSAR investigations and done by CoMFA. For QSAR analysis, there are numerous tools available. This study employed Open 3D-QSAR for analysis due to its simplicity of use and capacity to produce trustworthy results. Four tools were used for the analysis on this platform: Py-MolEdit, Py-ConfSearch, and Py-CoMFA. RESULTS: Maps that were generated were used to determine the screen's r2 (Coefficient of Multiple Determinations) value and q2 (correlation coefficient). These numbers must be fewer than 1, suggesting a good, trustworthy model. Cross-validated (q2) 0.532 and conventional (r2) correlation values of 0.969 made the CoMFA model statistically significant. The model showed that hydroxamic acid inhibitors are significantly more sensitive to the steric field than the electrostatic field (70%) (30%). This hypothesis states that steric (43.1%), electrostatic (26.4%), and hydrophobic (20.3%) qualities were important in the design of sclerostin inhibitors. CONCLUSION: With 3D-QSAR and CoMFA, statistically meaningful models were constructed to predict ligand inhibitory effects. The test set demonstrated the model's robustness. This research may aid in the development of more effective sclerostin inhibitors that are synthesised using FDA-approved medications.

Machine learning in the detection of dental cyst, tumor, and abscess lesions.

BACKGROUND AND OBJECTIVE: Dental panoramic radiographs are utilized in computer-aided image analysis, which detects abnormal tissue masses by analyzing the produced image capacity to recognize patterns of intensity fluctuations. This is done to reduce the need for invasive biopsies for arriving to a diagnosis. The aim of the current study was to examine and compare the accuracy of several texture analysis techniques, such as Grey Level Run Length Matrix (GLRLM), Grey Level Co-occurrence Matrix (GLCM), and wavelet analysis in recognizing dental cyst, tumor, and abscess lesions. MATERIALS & METHODS: The current retrospective study retrieved a total of 172 dental panoramic radiographs with lesion including dental cysts, tumors, or abscess. Radiographs that failed to meet technical criteria for diagnostic quality (such as significant overlap of teeth, a diffuse image, or distortion) were excluded from the sample. The methodology adopted in the study comprised of five stages. At first, the radiographs are improved, and the area of interest was segmented manually. A variety of feature extraction techniques, such GLCM, GLRLM, and the wavelet analysis were used to gather information from the area of interest. Later, the lesions were classified as a cyst, tumor, abscess, or using a support vector machine (SVM) classifier. Eventually, the data was transferred into a Microsoft Excel spreadsheet and statistical package for social sciences (SPSS) (version 21) was used to conduct the statistical analysis. Initially descriptive statistics were computed. For inferential analysis, statistical significance was determined by a p value < 0.05. The sensitivity, specificity, and accuracy were used to find the significant difference between assessed and actual diagnosis. RESULTS: The findings demonstrate that 98% accuracy was achieved using GLCM, 91% accuracy using Wavelet analysis & 95% accuracy using GLRLM in distinguishing between dental cyst, tumor, and abscess lesions. The area under curve (AUC) number indicates that GLCM achieves a high degree of accuracy. The results achieved excellent accuracy (98%) using GLCM. CONCLUSION: The GLCM features can be used for further research. After improving the performance and training, it can support routine histological diagnosis and can assist the clinicians in arriving at accurate and spontaneous treatment plans.

CRISPR-Cas-Based Adaptive Immunity Mediates Phage Resistance in Periodontal Red Complex Pathogens.

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.

Indigenous Oral and Gut Phages Defeat the Deadly NDM-1 Superbug.

Aim: Antibiotics treat various diseases by targeting microorganisms by killing them or reducing their multiplication rate. New Delhi Metallo-beta-lactamase-1 (NDM-1) is produced by bacteria possessing the resistance gene blaNDM-1, the enzyme that makes bacteria resistant to beta-lactams. Bacteriophages, especially Lactococcus, have shown their ability to break down lactams. Hence, the current study computationally evaluated the binding potential of Lactococcus bacteriophages with NDM using Molecular docking and dynamics. Methods: Modelling of NDM I-TASSER for Main tail protein gp19 OS=Lactococcus phage LL-H or Lactobacillus delbrueckii subsp. lactis after downloading from UNIPROT ID- Q38344. Cluspro tool helps in Understanding cellular function and organization with protein-protein interactions. MD simulations(19) typically compute atom movements over time. Simulations were used to predict the ligand binding status in the physiological environment. Results: The best binding affinity score was found -1040.6 Kcal/mol compared to other docking scores. MD simulations show in RMSD values for target remains within 1.0 Angstrom, which is acceptable. The ligand-protein fit to receptor protein RMSD values of 2.752 fluctuates within 1.5 Angstrom after equilibration. Conclusions: Lactococcus bacteriophages showed a strong affinity to the NDM. Hence, this hypothesis, supported by evidence from a computational approach, will solve this life-threatening superbug problem.

Designing Novel Multi-Epitope Vaccine Construct against Prevotella intermedia-Interpain A: An Immunoinformatics Approach.

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.

Clinical efficacy of subepithelial connective tissue graft and A-PRF in the treatment of gingival recession.

OBJECTIVE: The current study was designed to clinically compare and evaluate subepithelial connective tissue graft (SCTG) and advanced platelet-rich fibrin (A-PRF) membrane-based root coverage in the treatment of gingival recession type 1 (RT1). METHOD AND MATERIALS: The current study involved 17 patients with bilateral gingival recession (RT1). Thirty-four sites were randomly allocated to test (A-PRF) and control (SCTG) sites and all the procedures were performed by a single operator. A single blinded observer evaluated the test and control sites at baseline, 3 months, and 6 months. The clinical parameters such as recession depth, recession width, width of keratinized gingiva, clinical attachment level, and percentage of root coverage were recorded. P < .05 was considered statistically significant. RESULTS: The mean recession depth at baseline for control and test groups was 3.06 ± 0.56 mm and 2.35 ± 0.49 mm, respectively (P < .001). At the end of the study period, the mean recession depth was 0.53 ± 0.62 mm in the control group and 1.12 ± 0.49 mm in the test group (P < .05). No complications were associated with both the groups. The mean percentage of root coverage was 84.31 ± 17.89% in the control group and 51.96 ± 15.45% in the test group (P < .001). CONCLUSION: In conclusion, the study results suggest that both SCTG and A-PRF can be used in treating gingival recessions. However, SCTG is a better material in achieving root coverage and increasing keratinized tissue width. (Quintessence Int 2023;54:134-141; doi: 10.3290/j.qi.b3512389).