Objective:To investigate the clinical significance of the primary tumor size in patients with endometrial carcinoma (EC).Methods:A total of 385 patients with EC admitted to Peking University People's Hospital from January 2006 to December 2016 with complete follow up data were selected, whose tumor size data before biopsy were retrospectively studied.Results:(1) The mean diameter of the primary tumor was (3.6±1.8) cm (range: 1-15 cm). And 48 cases were 0-<2 cm, 78 cases were 2-<3 cm, 92 cases were 3-<4 cm, 73 cases were 4-<5 cm, 94 cases were ≥5 cm. The diameter of the tumor was associated with age <60 years old, premenopause, CA 125≥35 kU/L, non-parturition, poor differentiation, stage Ⅲ-Ⅳ, depth of myometrial infiltration ≥1/2, cervical interstitial involvement, adnexal metastasis and lymph node metastasis (all P<0.05), but not associated with body mass index, hypertension, diabetes mellitus, pathology, lymph-vascular space invasion (all P>0.05). (2) Among the 334 patients underwent lymphadenectomy, 45 (13.5%, 45/334) cases with lymph node metastasis were observed. Stratified analysis showed that lymph node metastasis and recurrence rate of patients with EC gradually increased with the increase of tumor size ( P<0.05). Adopting 2, 3, 4 and 5 cm as cut-off values of tumor size, there were significant differences in the rate of lymph node metastasis and recurrence among them observed ( P<0.05), except for lymph node metastasis rate and recurrence rate when the cut-off value was 2 cm ( P>0.05). (3) An receiver operating characteristic (ROC) curve analysis showed that a tumor diameter of 4.25 cm was the cut-off prognostic value to predict lymph node metastasis and recurrence of EC. Conclusions:Tumor diameter is significantly correlated with lymph node metastasis and recurrence in patients with EC. Tumor size should be considered in determining the scope of surgery and adjuvant therapy.

Objective:To analyze brain topological property data through machine learning methods and explore changes in brain network topological properties in patients with schizophrenia.Methods:From January 2022 to August 2023, functional magnetic resonance imaging data of 60 patients with schizophrenia and 56 healthy controls were collected , and the data were preprocessed to construct brain functional networks and extract global and nodal topological properties. All subjects were divided into a training group and a testing group.The data of training group were fitted based on support vector machine, and the predictive performance was evaluated through cross-validation.The model was optimized by recursive feature elimination algorithm, then the indicators that contributed the most to predictive performance were extrated.The classification performance of the testing group was calculated based on the trained model with optimal predictive performance.SPSS 20.0 software was used for data analysis, the independent t-test and χ2 test were used for comparing the differences between the two groups. Results:The support vector machine achieved an accuracy of 75.00% in predicting the test group of schizophrenia patients based on all indicators. After removing redundant features and combining with the recursive feature elimination algorithm, the accuracy of the SVM model in predicting the test group increased to 90.00%. The nodal global efficiency(Ne)of the left superior temporal gyrus, right dorsal agranular insula, bilateral dorsal granular insula, bilateral caudal cingulate gyrus, and left lateral orbitofrontal cortex in the model contributed the most to classification.Compared to the control group, patients with schizophrenia had abnormal Ne values in these brain regions.Conclusion:There are multiple brain regions with abnormal Ne values in patients with schizophrenia, indicating that the abnormalities in information integration and transmission functions may be related to the imbalance in the dynamic equilibrium of the patients' brain networks.

BACKGROUND:Parkinson's disease has the main pathological changes in the midbrain,especially in the dense substantia nigra,leading to impaired motor and non-motor function in patients.At present,research is limited by cellular heterogeneity,and its pathogenesis still needs to be further elucidated.In recent years,single-cell RNA sequencing(scRNA-seq)has gradually been applied in neurodegenerative diseases,which is of great significance for understanding intercellular heterogeneity,disease development mechanisms,and treatment strategies. OBJECTIVE:To review the research progress of scRNA-seq technology applied to Parkinson's disease in recent years,providing a theoretical basis for the application of scRNA-seq in the treatment and diagnosis of Parkinson's disease. METHODS:The first author used a computer system to search for relevant literature in the CNKI,WanFang,PubMed,and Web of Science databases,with the Chinese search terms"single-cell RNA sequencing,Parkinson's disease,cell heterogeneity,cell subtypes,dopaminergic neurons,glial cells"and English search terms"single-cell RNA seq,Parkinson disease,heterogenicity,subtypes,dopaminergic neurons,glial cells."71 articles were ultimately included for review and analysis. RESULTS AND CONCLUSION:(1)scRNA-seq is a high-throughput experimental technique that utilizes RNA sequencing at the single-cell level to quantify gene expression profiles in specific cell populations,revealing cellular mysteries at the molecular level.Compared with traditional sequencing techniques,scRNA-seq technology is used to reveal the diversity of cell types and changes in specific gene expression in complex tissues under various physiological and pathological conditions through automatic clustering analysis of cell transcriptome.(2)By using scRNA-seq,the development process of dopaminergic neurons and the unique functional characteristics of various cell subtypes are elucidated,in order to better understand potential therapeutic molecular targets.(3)The use of scRNA-seq analysis has improved our understanding of the response of Parkinson's disease glial cells,enabling us to comprehensively map and characterize different cell type populations,identify specific glial cell subpopulations related to neurodegeneration,and draw valuable single cell maps as reference data for future research.(4)The application of scRNA-seq to detect embryonic mice and stem cells will help improve the in vitro differentiation protocol and quality control of cell therapy,as well as evaluate the overall cell quality and developmental stage of dopaminergic neurons derived from stem cells.

Diffuse intrinsic pontine glioma (DIPG) is the main cause of brain tumor-related death among children. Until now, there is still a lack of effective therapy with prolonged overall survival for this disease. A typical strategy for preclinical cancer research is to find out the molecular differences between tumor tissue and para-tumor normal tissue, in order to identify potential therapeutic targets. Unfortunately, it is impossible to obtain normal tissue for DIPG because of the vital functions of the pons. Here we report the human fetal hindbrain-derived neural progenitor cells (pontine progenitor cells, PPCs) as normal control cells for DIPG. The PPCs not only harbored similar cell biological and molecular signatures as DIPG glioma stem cells, but also had the potential to be immortalized by the DIPG-specific mutation H3K27M in vitro. These findings provide researchers with a candidate normal control and a potential medicine carrier for preclinical research on DIPG.
Animals ; Brain Stem Neoplasms ; genetics ; metabolism ; pathology ; Cell Line, Tumor ; Cellular Senescence ; Female ; Glioma ; genetics ; metabolism ; pathology ; Histones ; genetics ; Humans ; Mice, Inbred NOD ; Mice, SCID ; Neoplasm Transplantation ; Neoplastic Stem Cells ; drug effects ; metabolism ; pathology ; Neural Stem Cells ; drug effects ; metabolism ; pathology ; Pons ; embryology ; metabolism ; pathology ; Primary Cell Culture