Objectives: The objective of this study is to develop a deep learning (DL) model for fast and accurate mandibular canal (MC) segmentation on cone beam computed tomography (CBCT). Methods: A total of 220 CBCT scans from dentate subjects needing oral surgery were used in this study. The segmentation ground truth is annotated and reviewed by two senior dentists. All patients were randomly splitted into a training dataset (n = 132), a validation dataset (n = 44) and a test dataset (n = 44). We proposed a two-stage 3D-UNet based segmentation framework for automated MC segmentation on CBCT. The Dice Similarity Coefficient (DSC) and 95% Hausdorff Distance (95% HD) were used as the evaluation metrics for the segmentation model. Results: The two-stage 3D-UNet model successfully segmented the MC on CBCT images. In the test dataset, the mean DSC was 0.875 ± 0.045 and the mean 95% HD was 0.442 ± 0.379. Conclusions: This automatic DL method might aid in the detection of MC and assist dental practitioners to set up treatment plans for oral surgery evolved MC.

Deep neural network-based programs can be applied to protein structure modeling by inputting amino acid sequences. Here, we aimed to evaluate the AlphaFold2-modeled myocilin wild-type and variant protein structures and compare to the experimentally determined protein structures. Molecular dynamic and ligand binding properties of the experimentally determined and AlphaFold2-modeled protein structures were also analyzed. AlphaFold2-modeled myocilin variant protein structures showed high similarities in overall structure to the experimentally determined mutant protein structures, but the orientations and geometries of amino acid side chains were slightly different. The olfactomedin-like domain of the modeled missense variant protein structures showed fewer folding changes than the nonsense variant when compared to the predicted wild-type protein structure. Differences were also observed in molecular dynamics and ligand binding sites between the AlphaFold2-modeled and experimentally determined structures as well as between the wild-type and variant structures. In summary, the folding of the AlphaFold2-modeled MYOC variant protein structures could be similar to that determined by the experiments but with differences in amino acid side chain orientations and geometries. Careful comparisons with experimentally determined structures are needed before the applications of the in silico modeled variant protein structures.

BACKGROUND Biological age (BA) and frailty represent two distinct health measures that offer valuable insights into the aging process. Existing machine learning (ML) predictors of BA and frailty, derived from blood-based biomarkers, lack the capability to compare and analyze these biomarkers comprehensively. Such comparisons may provide deeper insights into these two distinct aging pathways. Objective: This study aimed to develop a framework to compare and analyze biomarkers by combining BA and frailty ML predictors with eXplainable Artificial Intelligence (XAI) techniques. OBJECTIVE This study aimed to develop a framework to compare and analyze biomarkers by combining BA and frailty ML predictors with eXplainable Artificial Intelligence (XAI) techniques. METHODS We utilized data from middle-aged and older Chinese adults (≥45 years) in the 2011/2012 wave (n=9702) and the 2015/2016 wave (n=9455, as test set validation) of the China Health and Retirement Longitudinal Study (CHARLS). Sixteen blood-based biomarkers were used to predict BA and frailty. Four tree-based ML algorithms were employed in the training and validation, and performance metrics were compared to select the best models. Then, SHapley Additive exPlanations (SHAP) analysis was conducted on the selected models. RESULTS CatBoost performed the best in the BA predictor, and Gradient Boosting performed the best in the frailty predictor. Traditional ML feature importance identified cystatin C and glycated hemoglobin as the major contributors for their respective models. However, subsequent SHAP analysis demonstrated that only cystatin C was the primary contributor in both models, suggesting that it plays an important role in both pathways. CONCLUSIONS Our novel framework integrates BA and frailty predictors with XAI techniques to compare and analyze biomarkers. The present approach leverages routine blood biomarkers and can easily incorporate additional biomarkers, providing a scalable and comprehensive toolset that offers a quantitative understanding of interesting biomarkers and complex physiological traits. CLINICALTRIAL