G protein-coupled receptor 30 ( GPR30),a sort of novel functional estrogen transmembrane receptor,extensively participate in the pathological and physiological regulation by mediating the rapid nongenomic effects of the estrogen,which plays an important role in the occurrence and development of the hormone-related malignant neoplasms.

Objective:To investigate the expression of a new cytokine,thymic stromal lymphopoietin (TSLP) and its receptor TSLPR in the villi of human first trimester gestation.Methods:Villi were collected from women who had undergone an artificial abortion at 7-11 weeks of normal gestation.The trophoblast cells (Tros) were isolated and cultured.The total RNA was extracted using TRIzol reagent,from both villi and the Percoll-gradient-isolated Tros,then the DNA fragments of hTSLP and hTSLPR were amplificated by RT-PCR.Villous tissues were detected for TSLP by immunohistochemistry (IHC) and Western blot.Immunocytochemistry (ICC) was carried out on cultured trophoblast cells for TSLP/TSLPR expression.Levels of TSLP in the supernatants were detected by ELISA.Results:Normal villi and the cultured Tros transcript were found to express TSLP/TSLPR mRNA and secreted TSLP protein.In addition,TSLP receptor was also expressed on trophoblasts.Conclusion:Both TSLP and its receptor are expressed on villi and trophoblast cells,which suggests that TSLP plays an important role in maternal-fetal immuno-tolerance in human early pregnancy.

Objective To study the relationship between tyrosine kinase receptor B (TrkB)expression and anoikis-suppression and invasion in OVCAR3 ovarian cancer ceils. Methods The expression of TrkB mRNA in OVCAR3 ovarian cancer cells under two culture conditions :adhesive cells and ceil-spheroids were evaluated by RT-PCR and real-time PCR.The relationship between TrkB expression and anoikis-suppression of OVCAR3 ovarian cancer ceils was examined by RNA interference (RNAi) technic,anchorage independent culture and fluorescence-activated ceil sorting analysis.The difference in invasion and metastatic ability of OVCAR3 cells under two culture conditions and with or without TrkB silenced by small interfering RNA (siRNA) was investigated by matrigel invasion assay and in vivo studies.Results The expression of TrkB mRNA was highest in OVCAR3 ovarian cancer ceils,0.0240 ~ 0.0017,compared with the other three cell lines,0.0030±0.0006,0.0027±0.0009 and 0.0087±0.0003 respectively,andthe expression in OVCAR3 multicellular spheroids was significantly higher than that in ceils under monolayer adhesive culture,0.0437±0.0021 versus 0.0240±0.0017 (P＜0.01) . TrkB mediated anoikissuppression in OVCAR3 ovarian cancer ceils.OVCAR3 multiceilular spheroids had a higher invasion ability than OVCAR3 cells under monolayer adhesive culture,and the penetrating cells of the two groups were 71.8± 0.8 and 47.7±0.8 respectively (P＜0.01 ).The metastatic ability of OVCAR3 cells was attenuated when TrkB was silenced,and the volume of the tumors developed by OVCAR3 adhesive cells and OVCAR3 adhesive cells with TrkB silenced were (16.3±4.7) mm3 and(6.0±1.4) mm3 respectively (P＜0.01).Conclusion As an anoikis-suppressur,TrkB may increase the invasion and metastasis of OVCAR3 ovarian cancer cells.

Objective:To construct a postoperative exercise management program for elderly patients with lung cancer based on social cognitive theory, and to provide guidance for improving the postoperative exercise ability of elderly patients with lung cancer.Methods:Systematically searched UpToDate, PubMed, Web of Science, Cochrane Library, Medlive, Wanfang, CNKI and other databases for relevant literature on postoperative rehabilitation exercise for elderly patients with lung cancer, with the search time from the establishment of the database to February 13, 2023. Guided by social cognitive theory, a preliminary draft of the postoperative exercise management program for elderly patients with lung cancer was prepared based on the literature research, and the final draft was formed after revising the program content through expert meeting method.Results:The questionnaire recovery rate of expert meeting method was 12/12; the expert authority coefficient was 0.892; the importance coefficient of variation of each item was 0-0.150; the Kendall coordination coefficient was 0.262 ( P<0.001); the final exercise management program included 5 first-level items, 18 second-level items and 42 third-level items. Conclusions:The process of constructing the postoperative exercise management program for elderly patients with lung cancer has scientificity and reliability, and the content has rationality and comprehensiveness, which can provide guidance for improving the postoperative exercise rehabilitation of elderly patients with lung cancer.

Objective To seperate fetal nucleated red blood cells (fNRBCs) from the whole maternal peripheral blood effectively by designing a circular channel microfluidic chip. Methods A microfluidic chip is designed by utilizing the margination in blood flow and the specific adhesion characteristics of immuno-agent anti-CD147. With the whole umbilical cord blood, the effects of different shear forces on the enrichment of fNRBCs was studied by immunofluorescence counting. Results Increasing shear rate in microfluidic chip could improve the number of captured fNRBCs compared with the static adhesion. With the increase of shear rate of blood flow, the number of the captured cells increased at first, and then decreased. Conclusions The use of microfluid chip can effectively seperate fNRBCs from the whole blood. The results provide an experimental reference for the non-invasive prenatal diagnosis research and the exploration on the mechanism of fetal cell migration.

OBJECTIVE: To analyze the risk factors of in-hospital death in patients with sepsis in the intensive care unit (ICU) based on machine learning, and to construct a predictive model, and to explore the predictive value of the predictive model. METHODS: The clinical data of patients with sepsis who were hospitalized in the ICU of the Affiliated Hospital of Jining Medical University from April 2015 to April 2021 were retrospectively analyzed,including demographic information, vital signs, complications, laboratory examination indicators, diagnosis, treatment, etc. Patients were divided into death group and survival group according to whether in-hospital death occurred. The cases in the dataset (70%) were randomly selected as the training set for building the model, and the remaining 30% of the cases were used as the validation set. Based on seven machine learning models including logistic regression (LR), K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost) and artificial neural network (ANN), a prediction model for in-hospital mortality of sepsis patients was constructed. The receiver operator characteristic curve (ROC curve), calibration curve and decision curve analysis (DCA) were used to evaluate the predictive performance of the seven models from the aspects of identification, calibration and clinical application, respectively. In addition, the predictive model based on machine learning was compared with the sequential organ failure assessment (SOFA) and acute physiology and chronic health evaluation II (APACHE II) models. RESULTS: A total of 741 patients with sepsis were included, of which 390 were discharged after improvement, 351 died in hospital, and the in-hospital mortality was 47.4%. There were significant differences in gender, age, APACHE II score, SOFA score, Glasgow coma score (GCS), heart rate, oxygen index (PaO₂/FiO₂), mechanical ventilation ratio, mechanical ventilation time, proportion of norepinephrine (NE) used, maximum NE, lactic acid (Lac), activated partial thromboplastin time (APTT), albumin (ALB), serum creatinine (SCr), blood urea nitrogen (BUN), blood uric acid (BUA), pH value, base excess (BE), and K⁺ between the death group and the survival group. ROC curve analysis showed that the area under the curve (AUC) of RF, XGBoost, LR, ANN, DT, SVM, KNN models, SOFA score, and APACHE II score for predicting in-hospital mortality of sepsis patients were 0.871, 0.846, 0.751, 0.747, 0.677, 0.657, 0.555, 0.749 and 0.760, respectively. Among all the models, the RF model had the highest precision (0.750), accuracy (0.785), recall (0.773), and F1 score (0.761), and best discrimination. The calibration curve showed that the RF model performed best among the seven machine learning models. DCA curve showed that the RF model exhibited greater net benefit as well as threshold probability compared to other models, indicating that the RF model was the best model with good clinical utility. CONCLUSIONS The machine learning model can be used as a reliable tool for predicting in-hospital mortality in sepsis patients. RF models has the best predictive performance, which is helpful for clinicians to identify high-risk patients and implement early intervention to reduce mortality.
Humans ; Hospital Mortality ; Retrospective Studies ; ROC Curve ; Prognosis ; Sepsis/diagnosis* ; Intensive Care Units