Nanoparticles‐based drug delivery strategies have been widely researched for cancer therapy. However, most of them are expected to accumulate in tumor sites via the enhanced permeability and retention (EPR) effect, which is insufficient to deliver the loaded drug into tumors. Cell membrane–camouflaged nanoparticles have obtained much attention for their excellent stability and long blood circulation and reduced the macrophage cells uptake in drug delivery. Herein, bone marrow–derived mesenchymal stem cell membrane vesicle (SCV)–coated paclitaxel (PTX)–loaded poly (lactide‐co‐glycolide) (PLGA) nanoparticles (SCV/PLGA/PTX) were fabricated as the efficient orthotopic breast cancer–targeted drug delivery system. The SCV/PLGA/PTX showed excellent stability, more controlled PTX release, and more effective antitumor effect in vitro. After administration in vivo, SCV/PLGA/PTX exhibited the long‐term retention and enhanced accumulation at tumor sites due to the immune escape and mesenchymal stem cell–mimicking cancer‐targeting capacity. As expected, the SCV/PLGA/PTX could significantly suppress the primary tumor growth by increased apoptosis and necrosis areas within tumor tissues and attenuated the toxic side effects of PTX in 4T1 orthotopic breast cancer model. The study indicated the mesenchymal stem cell membrane coating strategy was highly efficient for targeted drug delivery, which provided a new insight for precise and effective breast cancer treatment.

Semaphorin 5A, a member of semaphorin family, was originally identified as axonal guidance factor functioning during neuronal development. Previously, we showed that the expression of semaphorin 5A might contribute to the metastasis of gastric cancer. However, its functional roles and mechanism(s) in invasion and metastasis of gastric cancer remain unclear. By using human gastric caner cell lines Parental SGC7901, SGC7901-siScrambled and SGC7901-siSema 5A, we found that semaphorin 5A significantly promoted the invasive and metastatic abilities of gastric cancer cell in vitro. Semaphorin 5A increased the expression of MMP9 by activating phosphorylated ErK1/2 in gastric cancer cell. Furthermore, MEK inhibitor PD98059 and MMP9 antibody (Ab) significantly inhibited in vitro invasive and metastatic abilities induced by semaphorin 5A. Taken together, the present work revealed a novel function of semaphorin 5A that the existence of semaphorin 5A could promote invasion and metastasis of gastric cancer by regulating MMP9 expression, at least partially, via the MEK/ERKs signal transduction pathway. Semaphorin 5A and its regulated molecules could be the potential targets for cancer therapy.

Objective. Preterm birth (PTB) was one of the leading causes of neonatal death. Predicting PTB in the first trimester and second trimester will help improve pregnancy outcomes. The aim of this study is to propose a prediction model based on machine learning algorithms for PTB. Method. Data for this study were reviewed from 2008 to 2018, and all the participants included were selected from a hospital in China. Six algorisms, including Naive Bayesian (NBM), support vector machine (SVM), random forest tree (RF), artificial neural networks (ANN), K-means, and logistic regression, were used to predict PTB. The receiver operating characteristic curve (ROC), accuracy, sensitivity, and specificity were used to assess the performance of the model. Results. A total of 9550 pregnant women were included in the study, of which 4775 women had PTB. A total of 4775 people were randomly selected as controls. Based on 27 weeks of gestation, the area under the curve (AUC) and the accuracy of the RF model were the highest compared with other algorithms (accuracy: 0.816; AUC = 0.885, 95% confidence interval (CI): 0.873–0.897). Meanwhile, there was positive association between the accuracy and AUC of the RF model and gestational age. Age, magnesium, fundal height, serum inorganic phosphorus, mean platelet volume, waist size, total cholesterol, triglycerides, globulins, and total bilirubin were the main influence factors of PTB. Conclusion. The results indicated that the prediction model based on the RF algorithm had a potential value to predict preterm birth in the early stage of pregnancy. The important analysis of the RF model suggested that intervention for main factors of PTB in the early stages of pregnancy would reduce the risk of PTB.

Background Molecular size determination of circulating free fetal DNA in maternal plasma is an important detection method for noninvasive prenatal testing (NIPT). The fetal DNA molecule is the primary factor determining the overall performance of NIPT and its clinical interpretation. The proportion of cell-free fetal DNA molecules is expressed as the fetal DNA fraction in the plasma of pregnant women. Methods We proposed an effective method to deduce fetal chromosomal aneuploidy based on the proportion of a certain range of DNA fragment lengths from maternal plasma. We gradually narrowed the range of the upper and lower boundary via a traversing algorithm. Results We explored the optimal range of the upper and lower boundary by using size-based DNA fragment length. Using this range, the accuracy of the sensitivity and specificity could be improved by up to 100% for detecting the three most common autosomal aneuploidies, namely trisomy 13, trisomy 18, trisomy 21 in the sample set. Conclusions Numerical experiments demonstrate that our method is effective and efficient. The program is available upon request.