Lipid profile in the aqueous humor of patients with myopia.

We examined the lipid profiles in the aqueous humor (AH) of myopic patients to identify differences and investigate the relationships among dissertating lipids. Additionally, we assessed spherical equivalents and axial lengths to explore the pathogenesis of myopia. Ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) was employed to qualitatively and quantitatively analyze the lipid composition of samples from myopic patients with axial lengths <26 mm (Group A) and >28 mm (Group B). Differences in lipid profiles between the two groups were determined using univariate and multivariate analyses. Receiver operator characteristic (ROC) curves were used to identify discriminating lipids. Spearman correlation analysis explored the associations between lipid concentrations and biometric parameters. Three hundred and nine lipids across 21 lipid classes have been identified in this study. Five lipids showed significant differences between Group B and Group A (VIP > 1, P < 0.05): BMP (20:3/22:3), PG (22:1/24:0), PS (14:1/22:4), TG (44:2)_FA18:2, and TG (55:3)_FA18:1. The area under the curve (AUC) for these lipids was >0.75. Notably, the concentrations of BMP (20:3/22:3), PS (14:1/22:4), and TG (55:3)_FA18:1 were correlated with spherical equivalents, while BMP (20:3/22:3) and PS (14:1/22:4) correlated with axial lengths. Our study identified five differential lipids in myopic patients, with three showing significant correlations with the degree of myopia. These findings enhance our understanding of myopia pathogenesis through lipidomic alterations, emphasizing changes in cell membrane composition and function, energy metabolism and storage, and pathways involving inflammation, peroxisome proliferator-activated receptors (PPAR), and metabolic processes related to phosphatidylserine, phosphatidylglycerol, triglycerides, polyunsaturated fatty acids, and cholesterol.

Correlation between peri-implant bone mineral density and primary implant stability based on artificial intelligence classification.

Currently, the classification of bone mineral density (BMD) in many research studies remains rather broad, often neglecting localized changes in BMD. This study aims to explore the correlation between peri-implant BMD and primary implant stability using a new artificial intelligence (AI)-based BMD grading system. 49 patients who received dental implant treatment at the Affiliated Hospital of Stomatology of Fujian Medical University were included. Recorded the implant stability quotient (ISQ) after implantation and the insertion torque value (ITV). A new AI-based BMD grading system was used to obtain the distribution of BMD in implant site, and the bone mineral density coefficients (BMDC) of the coronal, middle, apical, and total of the 1 mm site outside the implant were calculated by model overlap and image overlap technology. Our objective was to investigate the relationship between primary implant stability and BMDC values obtained from the new AI-based BMD grading system. There was a significant positive correlation between BMDC and ISQ value in the coronal, middle, and total of the implant (P < 0.05). However, there was no significant correlation between BMDC and ISQ values in the apical (P > 0.05). Furthermore, BMDC was notably higher at implant sites with greater ITV (P < 0.05). BMDC calculated from the new AI-based BMD grading system could more accurately present the BMD distribution in the intended implant site, thereby providing a dependable benchmark for predicting primary implant stability.

Construction of a new automatic grading system for jaw bone mineral density level based on deep learning using cone beam computed tomography.

To develop and verify an automatic classification method using artificial intelligence deep learning to determine the bone mineral density level of the implant site in oral implant surgery from radiographic data obtained from cone beam computed tomography (CBCT) images. Seventy patients with mandibular dentition defects were scanned using CBCT. These Digital Imaging and Communications in Medicine data were cut into 605 training sets, and then the data were processed with data standardization, and the Hounsfiled Unit (HU) value level was determined as follows: Type 1, 1000-2000; type 2, 700-1000; type 3, 400-700; type 4, 100-400; and type 5, - 200-100. Four trained dental implant physicians manually identified and classified the area of the jaw bone density level in the image using the software LabelMe. Then, with the assistance of the HU value generated by LabelMe, a physician with 20 years of clinical experience confirmed the labeling level. Finally, the HU mean values of various categories marked by dental implant physicians were compared to the mean values detected by the artificial intelligence model to assess the accuracy of artificial intelligence classification. After the model was trained on 605 training sets, the statistical results of the HU mean values of various categories in the dataset detected by the model were almost the same as the HU grading interval on the data annotation. This new classification provides a more detailed solution to guide surgeons to adjust the drilling rate and tool selection during preoperative decision-making and intraoperative hole preparation for oral implant surgery.