Background: Mesenchymal stem cells (MSCs) derived from different tissues have variant biological characteristics, which may induce different performances in the treatment of diseases. At present, it is difficult to know which type of MSC is most suitable for acute liver failure (ALF), and there is no parallel study to compare MSCs from different tissues of the same donor. Methods: In this study, we derived MSCs from three different perinatal tissues of the same donor: cord lining (CL), cord–placenta junction (CPJ) and fetal placenta (FP), respectively, for compared gene expression profiles by transcriptome sequencing, and ability of proliferation and immune regulation in vitro. In addition, the therapeutic effects (e.g., survival rate, histological evaluation, biochemical analysis) of CL-MSCs, FP-MSCs and CPJ-MSCs on ALF mouse model were compared. Results: The transcriptome analysis showed that FP-MSCs have significantly high expression of chemokines compared to CPJ-MSCs and CL-MSCs, similar to the q-PCR result. Of note, we found that CPJ-MSCs and FP-MSCs could improve the survival rate of mice with ALF induced by carbon tetrachloride, but CL-MSCs had no difference with Sham group. Moreover, we also found that biomarkers of ALF (e.g., MDA, SOD and GSH-px) significantly improved post-CPJ-MSCs and FP-MSCs treatment, but not CL-MSCs and Sham group. However, CL-MSCs treatment leads to inflammatory reaction in the early stage (day 3) of ALF treatment but not found with other groups. Conclusions: It is important to select the MSCs derived from different tissues with variant performance for therapeutic purpose, and the CPJ-MSCs and FP-MSCs cells can significantly improve the syndrome of ALF which is highly recommended for a potential therapeutic options for ALF.

The prevalence and detection rates of adrenal incidentalomas have been on the rise globally, with more than 90% of these lesions pathologically classified as adrenocortical adenomas. Among these, approximately 30% of patients present with hormone-secreting adenomas, leading to the deterioration of their health, with some requiring surgical resection. The available methods for adrenal function evaluation are invasive and costly. Moreover, their accuracy is influenced by numerous factors. Therefore, it is imperative to develop non-invasive and simplified preoperative diagnostic approach. A retrospective study was performed on 169 patients from two tertiary medical centers. Subsequently, radiomics features were extracted after tumor margins were delineated layer-by-layer using a semi-automatic contouring approach. Feature selection was achieved in two cycles, with the first round utilizing a support vector machine (SVM) and the second round using a LASSO-based recursive feature elimination algorithm. Finally, logistic regression models were constructed using the clinico-radiological, radiomics, and a combination of both. After a comprehensive evaluation of the predictive indicators, the logistic regression classifier model based on the combined clinico-radiological and radiomic features had an AUC of (0.945, 0.927, 0.856) for aldosterone-producing adenoma (APA), (0.963, 0.889, 0.887) for cortisol-producing adenoma (CPA), and (0.940, 0.765, 0.816) for non-functioning adrenal adenoma (NAA) in the training set, validation set, and external test set, respectively. This model exhibited superior predictive performance in differentiating between the three adrenal adenoma subtypes. A logistic regression model was constructed using radiomics and clinico-radiological features derived from multi-phase enhanced CT images and conducted external validation. The combined model showed good overall performance, highlighting the feasibility of applying the model for preoperative differentiation and prediction of various types of ACA.