Selenium as a biologically active element lends much support to health maintenance and disease prevention. It is now presenting pleiotropic effects on therapy and drug delivery. In this study, a profiling on the physiological functions, therapeutic significances, clinical/preclinical performances, and biomedical and drug delivery applications of selenium in different modalities was carried out. Major interests focused on selenium-based nanomedicines in confronting various diseases pertaining to selenium or not, especially in antitumor and antidiabetes. Furthermore, the article exclusively discusses selenium nanoparticles featured by ameliorative functions with emphasis on their applications in medical practice and drug delivery. The state-of-the-art in medical discovery as well as research and development on selenium and nano-selenium is discussed in this review.

The proteomic analysis from samples of patients with preeclampsia (PE) displayed a low level of ferritin light chains (FTL), but we do not know what the significance of reduced FTL in PE pathophysiology is. To address this question, we first demonstrated that FTL was expressed in first- and third-trimester cytotrophoblasts, including extravillous trophoblasts (EVTs), of the human placenta. Furthermore, a pregnant rat model of FTL knockdown was successfully established by intravenously injecting adenoviruses expressing shRNA targeting FTL. In pregnant rats with downregulated FTL, we observed PE-like phenotypes and impaired spiral arterial remodelling, implying a causal relationship between FTL downregulation and PE. Blocking ferroptosis with ferrostatin-1 (Fer-1) significantly rescued the above PE-like phenotypes in pregnant rats with FTL knockdown. Furthermore, using trophoblast cell line and chorionic villous explant culture assays, we showed that FTL downregulation induced cell death, especially ferroptosis, resulting in defective uterine spiral artery remodelling. Eventually, this conclusion from the animal model was verified in PE patients’ placental tissues. Taken together, this study revealed for the first time that FTL reduction during pregnancy triggered ferroptosis and then caused defective uterine spiral artery remodelling, thereby leading to PE.

Objective: The aim of this study was to use machine learning methods to analyze all available clinical and laboratory data obtained during prenatal screening in early pregnancy to develop predictive models in preeclampsia (PE).Material and Methods: Data were collected by retrospective medical records review. This study used 5 machine learning algorithms to predict the PE: deep neural network (DNN), logistic regression (LR), support vector machine (SVM), decision tree (DT), and random forest (RF). Our model incorporated 18 variables including maternal characteristics, medical history, prenatal laboratory results, and ultrasound results. The area under the receiver operating curve (AUROC), calibration and discrimination were evaluated by cross-validation.Results: Compared with other prediction algorithms, the RF model showed the highest accuracy rate. The AUROC of RF model was 0.86 (95% CI 0.80–0.92), the accuracy was 0.74 (95% CI 0.74–0.75), the precision was 0.82 (95% CI 0.79–0.84), the recall rate was 0.42 (95% CI 0.41–0.44), and Brier score was 0.17 (95% CI 0.17–0.17).Conclusion: The machine learning method in our study automatically identified a set of important predictive features, and produced high predictive performance on the risk of PE from the early pregnancy information.

Inflammation is generally thought to be involved in the occurrence and development of preeclampsia (PE), but its specific effect on PE remains unclear. In the present study, the expression levels of 92 inflammation-related proteins were measured in the late pregnancy maternal plasma from patients with PE (n = 15) and normal pregnant controls (n = 15) using the Olink inflammation panel based on the highly sensitive and specific proximity extension assay technology. A total of 28 inflammation-related markers differed between the PE and control groups. Among them, fibroblast growth factor 21 (FGF-21) and cysteine-cysteine motif chemokine ligand 20 (CCL20) had the largest fold changes. We further validated the levels of CCL20 in the late (43 with PE and 44 controls) and early (37 with PE and 37 controls) pregnancy maternal plasma using enzyme-linked immunosorbent assay (ELISA). To the best of our knowledge, for the first time, CCL20 was found to be upregulated in the late and early pregnancy plasma of patients with PE and had an area under the curve (AUC) of 0.753 and 0.668, respectively. In conclusion, patients with PE had increased levels of most inflammatory markers, and CCL20 might be a novel potential predictive and diagnostic biomarker for PE.