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Background: Signal peptide-CUB-EGF domain-containing protein 3 (SCUBE3), a secreted multifunctional glycoprotein whose transcript expression is restricted to the tooth germ epithelium during the development of embryonic mouse teeth, has been demonstrated to play a crucial role in the regulation of tooth development. Based on this, we hypothesized that epithelium-derived SCUBE3 contributes to bio-function in dental mesenchymal cells (Mes) via epithelium–mesenchyme interactions. Methods: Immunohistochemical staining and a co-culture system were used to reveal the temporospatial expression of the SCUBE3 protein during mouse tooth germ development. In addition, human dental pulp stem cells (hDPSCs) were used as a Mes model to study the proliferation, migration, odontoblastic differentiation capacity, and mechanism of rhSCUBE3. Novel pulp–dentin-like organoid models were constructed to further confirm the odontoblast induction function of SCUBE3. Finally, semi-orthotopic animal experiments were performed to explore the clinical application of rhSCUBE3. Data were analysed using one-way analysis of variance and t-tests. Results: The epithelium-derived SCUBE3 translocated to the mesenchyme via a paracrine pathway during mouse embryonic development, and the differentiating odontoblasts in postnatal tooth germ subsequently secreted the SCUBE3 protein via an autocrine mechanism. In hDPSCs, exogenous SCUBE3 promoted cell proliferation and migration via TGF-β signalling and accelerated odontoblastic differentiation via BMP2 signalling. In the semi-orthotopic animal experiments, we found that SCUBE3 pre-treatment-induced polarized odontoblast-like cells attached to the dental walls and had better angiogenesis performance. Conclusion: SCUBE3 protein expression is transferred from the epithelium to mesenchyme during embryonic development. The function of epithelium-derived SCUBE3 in Mes, including proliferation, migration, and polarized odontoblastic differentiation, and their mechanisms are elaborated for the first time. These findings shed light on exogenous SCUBE3 application in clinic dental pulp regeneration. [ABSTRACT FROM AUTHOR]

Background: Human dental pulp stem cells (hDPSCs) have received widespread attention in the fields of tissue engineering and regenerative medicine. Although amphiregulin (AREG) has been shown to play a vital function in the biological processes of various cell types, its effects on DPSCs remain largely unknown. The aim of this study was to explore the specific role of AREG as a biologically active factor in the regeneration of dental pulp tissue. Methods: The growth of hDPSCs, together with their proliferation and apoptosis, in response to AREG was examined by CCK-8 assay and flow cytometry. We explored the effects of AREG on osteo/odontogenic differentiation in vitro and investigated the regeneration and mineralization of hDPSCs in response to AREG in vivo. The effects of AREG gain- and loss-of-function on DPSC differentiation were investigated following transfection using overexpression plasmids and shRNA, respectively. The involvement of the mitogen-activated protein kinase (MAPK) or phosphatidylinositol 3-kinase (PI3K)/Akt pathways in the mineralization process and the expression of odontoblastic marker proteins after AREG induction were investigated by using Alizarin Red S staining and Western blotting, respectively. Results: AREG (0.01–0.1 µg/mL) treatment of hDPSCs from 1 to 7 days increased hDPSCs growth and affected apoptosis minimally compared with negative controls. AREG exposure significantly promoted hDPSC differentiation, shown by increased mineralized nodule formation and the expression of odontoblastic marker protein expression. In vivo micro-CT imaging and quantitative analysis showed significantly greater formation of highly mineralized tissue in the 0.1 μg/mL AREG exposure group in DPSC/NF-gelatin-scaffold composites. AREG also promoted extracellular matrix production, with collagen fiber, mineralized matrix, and calcium salt deposition on the composites, as shown by H&E, Masson, and Von Kossa staining. Furthermore, AREG overexpression boosted hDPSC differentiation while AREG silencing inhibited it. During the differentiation of hDPSCs, AREG treatment led to phosphorylation of extracellular signal-regulated kinase (ERK), c-Jun N-terminal kinase (JNK), and PI3K/Akt. Notably, a specific inhibitor of ERK, JNK, and PI3K/Akt signaling markedly reduced AREG-induced differentiation, as well as levels of phosphorylated ERK and JNK in hDPSCs. Conclusions: The data indicated that AREG promoted odontoblastic differentiation and facilitated regeneration and mineralization processes in hDPSCs. [ABSTRACT FROM AUTHOR]

Background: Acute kidney injury (AKI) is the most common and serious complication of sepsis, accompanied by high mortality and disease burden. The early prediction of AKI is critical for timely intervention and ultimately improves prognosis. This study aims to establish and validate predictive models based on novel machine learning (ML) algorithms for AKI in critically ill patients with sepsis.Methods: Data of patients with sepsis were extracted from the Medical Information Mart for Intensive Care III (MIMIC- III) database. Feature selection was performed using a Boruta algorithm. ML algorithms such as logistic regression (LR), k-nearest neighbors (KNN), support vector machine (SVM), decision tree, random forest, Extreme Gradient Boosting (XGBoost), and artificial neural network (ANN) were applied for model construction by utilizing tenfold cross-validation. The performances of these models were assessed in terms of discrimination, calibration, and clinical application. Moreover, the discrimination of ML-based models was compared with those of Sequential Organ Failure Assessment (SOFA) and the customized Simplified Acute Physiology Score (SAPS) II model.Results: A total of 3176 critically ill patients with sepsis were included for analysis, of which 2397 cases (75.5%) developed AKI during hospitalization. A total of 36 variables were selected for model construction. The models of LR, KNN, SVM, decision tree, random forest, ANN, XGBoost, SOFA and SAPS II score were established and obtained area under the receiver operating characteristic curves of 0.7365, 0.6637, 0.7353, 0.7492, 0.7787, 0.7547, 0.821, 0.6457 and 0.7015, respectively. The XGBoost model had the best predictive performance in terms of discrimination, calibration, and clinical application among all models.Conclusion: The ML models can be reliable tools for predicting AKI in septic patients. The XGBoost model has the best predictive performance, which can be used to assist clinicians in identifying high-risk patients and implementing early interventions to reduce mortality. [ABSTRACT FROM AUTHOR]