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Abstract Background This study highlights the need for precise and efficient methods to measure palatal mucosal thickness in the maxillary anterior teeth, particularly for soft tissue augmentation in the aesthetic zone. The research evaluates three digital imaging techniques, suggesting that Cone Beam Computed Tomography (CBCT) combined with intraoral scanners (IOS) is a promising approach for reliable clinical assessment. Methods Ten volunteers with healthy periodontium were selected, and three methods were employed: CBCT-based indirect gingival imaging, modified soft tissue CBCT (ST-CBCT), and CBCT combined with IOS. Measurements of palatal mucosal thickness were taken at multiple points along the palatal gingival margin. Statistical analysis included Bland-Altman plots for method agreement and intraclass Correlation Coefficient (ICC) analysis for reliability. All measurements were standardized, repeated for consistency, and accurate to 0.01 mm to ensure reliability. Results The Bland-Altman plots showed that less than 5% of the points for palatal mucosal thickness differences measured by the gingival indirect radiographic method, modified ST-CBCT, and CBCT combined with IOS were located outside the 95% limits of agreement (LoA). The mean value of the differences was within 0.2 mm, indicating good clinical agreement among the three methods. The inter- and intra-study ICC values for palatal mucosal thickness measurements of the maxillary anterior teeth using the three CBCT methods were greater than 0.75 (P

Abstract Background Human periodontal ligament stem cells (hPDLSCs) are important candidate seed cells for periodontal tissue engineering, but the presence of lipopolysaccharide(LPS) in periodontal tissues inhibits the self-renewal and osteogenic differentiation of hPDLSCs. Our previous studies demonstrated that TAZ is a positive regulator of osteogenic differentiation of hPDLSCs, but whether TAZ can protect hPDLSCs from LPS is still unknown. The present study aimed to explore the regulatory effect of TAZ on the osteogenic differentiation of hPDLSCs in an LPS-induced inflammatory model, and to preliminarily reveal the molecular mechanisms related to the NF-κB signaling pathway. Methods LPS was added to the culture medium of hPDLSCs. The influence of LPS on hPDLSC proliferation was analyzed by CCK-8 assays. The effects of LPS on hPDLSC osteogenic differentiation were detected by Alizarin Red staining, ALP staining, Western Blot and qRT-PCR analysis of osteogenesis-related genes. The effects of LPS on the osteogenic differentiation of hPDLSCs with TAZ overexpressed or knocked down via lentivirus were analyzed. NF-κB signaling in hPDLSCs was analyzed by Western Blot and immunofluorescence. Results LPS inhibited the osteogenic differentiation of hPDLSCs, inhibited TAZ expression, and activated the NF-κB signaling pathway. Overexpressing TAZ in hPDLSCs partly reversed the negative effects of LPS on osteogenic differentiation and inhibited the activation of the NF-κB pathway by LPS. TAZ knockdown enhanced the inhibitory effects of LPS on osteogenesis. Conclusion Overexpressing TAZ could partly reverse the inhibitory effects of LPS on the osteogenic differentiation of hPDLSCs, possibly through inhibiting the NF-κB signaling pathway. TAZ is a potential target for improving hPDLSC-based periodontal tissue regeneration in inflammatory environments.

Abstract Objective This study assessed the impacts of in vitro culture times of cleavage embryos on clinical pregnancy outcomes. Methods This retrospective cohort study was performed at the Reproductive Medicine Department of Hainan Modern Women and Children’s Hospital in China between January 2018 and December 2022. Patients who first underwent frozen embryo transfer with in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) cycles on day 3 were included. According to the time of embryo culture after thawing, the embryos were divided into long-term culture group(18-20 h) and short-term culture group (2-4 h). The clinical pregnancy rate was regarded as he primary outcome. To minimize confounding factors and reduce selection bias, the propensity score matching was used to balance the effects of known confounding factors and to reduce selection bias. Stratified analyses and multiple logistic regression analyses were used to evaluate the risk factors affecting the clinical pregnancy outcomes after matching. Results General characteristics between two groups were comparable after matching. In the long-term culture group, 266/381 (69.81%) embryos had more than 10 blastomeres, and 75/381 (19.68%) reached the morula stage. After overnight culture, the implantation rate (27.97% vs. 14.28%, P = 0.018) and clinical pregnancy rate (38.46% vs. 22.5%, P = 0.05) were increased in the group with proliferating blastomeres. The long-term culture group trended to have a higher clinical pregnancy rate compared with the short-term culture group (35.74% vs. 29.79%). No statistical differences in clinical pregnancy outcomes between the two groups were observed after matching, including the rates of implantation (25.46% vs23.98%), miscarriages (25% vs. 22.85%), ongoing pregnancy rate (76.2% vs. 77.15%) and live birth rate (26.8% vs. 22.98%). Stratified analyses were performed according to the age of the patients. After matching, there were no significant differences in the clinical pregnancy, implantation and miscarriage rates between the two groups for patients > 35 or ≤ 35 years of age. Subgroup analyses were performed according to the quality of the transferred embryos. There were no significant differences in the clinical outcomes, between two groups after embryos transferred with the same quality. Multivariate Logistic regression analysis was used to evaluate the influencing factors of clinical pregnancy outcomes after matching. Culture time was not found to be an independent predictor for clinical pregnancy [OR 0.742, 95%CI 0.487 ~ 1.13; P = 0.165]. The age of oocyte retrieval [OR 0.906, 95%CI 0.865 ~ 0.949; P

Abstract Background Early identification of the occurrence of arrhythmia in patients with acute myocardial infarction plays an essential role in clinical decision-making. The present study attempted to use machine learning (ML) methods to build predictive models of arrhythmia after acute myocardial infarction (AMI). Methods A total of 2084 patients with acute myocardial infarction were enrolled in this study. (All data is available on Github: https://github.com/wangsuhuai/AMI-database1.git) . The primary outcome is whether tachyarrhythmia occurred during admission containing atrial arrhythmia, ventricular arrhythmia, and supraventricular tachycardia. All data is randomly divided into a training set (80%) and an internal testing set (20%). Apply three machine learning algorithms: decision tree, random forest (RF), and artificial neural network (ANN) to learn the training set to build a model, then use the testing set to evaluate the prediction performance, and compare it with the model built by the Global Registry of Acute Coronary Events (GRACE) risk variable set. Results Three ML models predict the occurrence of tachyarrhythmias after AMI. After variable selection, the artificial neural network (ANN) model has reached the highest accuracy rate, which is better than the model constructed using the Grace variable set. After applying SHapley Additive exPlanations (SHAP) to make the model interpretable, the most important features are abnormal wall motion, lesion location, bundle branch block, age, and heart rate. Among them, RBBB (odds ratio [OR]: 4.21; 95% confidence interval [CI]: 2.42–7.02), ≥ 2 ventricular walls motion abnormal (OR: 3.26; 95% CI: 2.01–4.36) and right coronary artery occlusion (OR: 3.00; 95% CI: 1.98–4.56) are significant factors related to arrhythmia after AMI. Conclusions We used advanced machine learning methods to build prediction models for tachyarrhythmia after AMI for the first time (especially the ANN model that has the best performance). The current study can supplement the current AMI risk score, provide a reliable evaluation method for the clinic, and broaden the new horizons of ML and clinical research. Trial registration Clinical Trial Registry No.: ChiCTR2100041960.