Dynamic trajectory and cell communication of different cell clusters in malignant progression of glioblastoma / 北京大学学报(医学版)

Objective:To delve deeply into the dynamic trajectories of cell subpopulations and the communication network among immune cell subgroups during the malignant progression of glioblastoma(GBM),and to endeavor to unearth key risk biomarkers in the GBM malignancy progression,so as to provide a more profound understanding for the treatment and prognosis of this disease by integrating tran-scriptomic data and clinical information of the GBM patients.Methods:Utilizing single-cell sequencing data analysis,we constructed a cell subgroup atlas during the malignant progression of GBM.The Mono-cle2 tool was employed to build dynamic progression trajectories of the tumor cell subgroups in GBM.Through gene enrichment analysis,we explored the biological processes enriched in genes that significant-ly changed with the malignancy progression of GBM tumor cell subpopulations.CellChat was used to identify the communication network between the different immune cell subgroups.Survival analysis helped in identifying risk molecular markers that impacted the patient prognosis during the malignant pro-gression of GBM.This methodological approach offered a comprehensive and detailed examination of the cellular and molecular dynamics within GBM,providing a robust framework for understanding the disease's progression and potential therapeutic targets.Results:The analysis of single-cell sequencing data identified 6 different cell types,including lymphocytes,pericytes,oligodendrocytes,macrophages,glioma cells,and microglia.The 27 151 cells in the single-cell dataset included 3 881 cells from the pa-tients with low-grade glioma(LGG),10 166 cells from the patients with newly diagnosed GBM,and 13 104 cells from the patients with recurrent glioma(rGBM).The pseudo-time analysis of the glioma cell subgroups indicated significant cellular heterogeneity during malignant progression.The cell interaction analysis of immune cell subgroups revealed the communication network among the different immune sub-groups in GBM malignancy,identifying 22 biologically significant ligand-receptor pairs across 12 key bio-logical pathways.Survival analysis had identified 8 genes related to the prognosis of the GBM patients,among which SERPINE1,COL6A1,SPP1,LTF,C1S,AEBP1,and SAA1L were high-risk genes in the GBM patients,and ABCC8 was low-risk genes in the GBM patients.These findings not only provided new theoretical bases for the treatment of GBM,but also offered fresh insights for the prognosis assessment and treatment decision-making for the GBM patients.Conclusion:This research comprehensively and pro-foundly reveals the dynamic changes in glioma cell subpopulations and the communication patterns among the immune cell subgroups during the malignant progression of GBM.These findings are of significant im-portance for understanding the complex biological processes of GBM,providing crucial new insights for precision medicine and treatment decisions in GBM.Through these studies,we hope to provide more ef-fective treatment options and more accurate prognostic assessments for the patients with GBM.

Identification of diagnostic biomarkers for sarcopenia based on bioinformatics and machine learning / 中华临床营养杂志

Objective:To identify characteristic genes in sarcopenia patients through bioinformatics and machine learning, and to explore the clinical relevance of characteristic genes in the diagnosis of sarcopenia.Methods:The microarray data of GSE25941, GSE38718 and GSE9103 associated with sarcopenia were downloaded from the GEO database, followed by identification of differentially expressed genes (DEGs) associated with sarcopenia. Subsequently, functional analysis of the DEGs was performed using gene ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. The protein-protein interaction (PPI) network was constructed using STRING and Cytoscape, while biomarkers of sarcopenia were identified using LASSO regression and random forest analysis. The diagnostic performance of the characteristic gene was assessed by employing receiver operating characteristic (ROC) curve analysis. Furthermore, the expression levels of biomarkers for sarcopenia were validated using the external validation dataset of GSE28422. Finally, CIBERSORT was employed to analyze the infiltration of immune cells.Results:124 DEGs were identified between control and sarcopenia populations, which were primarily involved in growth factor receptor binding and cytokine activity. KEGG analysis revealed that the DEGs were predominantly associated with signaling pathways such as peroxisome proliferator-activated receptor signaling pathway, adipokine signaling pathway, Jak-STAT signaling pathway, and adenosine 5'-monophosphate (AMP)-activated protein kinase signaling pathway. Through machine learning techniques validated by ROC curve analysis and external datasets, three characteristic genes, namely DMRT2, FAM171A1, and ARHGAP36, were discovered. The infiltration analysis of immune cells revealed the potential involvement of mast cells, CD4 memory T cells, CD8 cells, γδT cells, and neutrophils in the pathophysiology of sarcopenia.Conclusion:DMRT2, FAM171A1 and ARHGAP36 can serve as diagnostic biomarkers of sarcopenia, and are closely related to the pathophysiological process of sarcopenia.

Nomogram for predicting the long-term outcomes of uterine artery embolization for adenomyosis.

OBJECTIVE: The present study aimed to develop a nomogram to predict long-term outcomes of uterine artery embolisation (UAE) for treating adenomyosis. MATERIALS AND METHODS: We reviewed data of 221 patients with adenomyosis who underwent UAE between May 2016 and January 2018. Predictive factors were identified using multivariate logistic regression analysis. A nomogram to predict the outcome of UAE was created for the training set. The performance of the predictive model was assessed by discrimination (quantified using the area under the curve, AUC) and calibration (evaluated by calibration curves and Hosmer-Lemeshow test) internally within the training set. Finally, an independent external validation was conducted using the validation set. RESULTS: In total, 201 patients were included. In the training set (n = 137), 96 (70.1%) exhibited a good response (GR), and 41 (39.9%) showed a poor response (PR). In the validation set (n = 64), 44 (68.7%) showed GR and 20 (31.3%) showed PR. The dysmenorrhoea score, T2 signal type, CA125, apparent diffusion coefficient, accompanying endometriosis, and accompanying fibroids were identified as associated factors and used in the nomogram. The AUC of the nomogram was 0.800 (95% confidence interval [CI] 0.724-0.877) and 0.798 (95% CI 0.686-0.909) in the training and validation sets, respectively. The calibration curves and Hosmer-Lemeshow test showed optimal agreement between predicted and actual probabilities (training set: P = 0.754; validation set: P = 0.453). CONCLUSIONS: We developed a nomogram that could predict the outcome of UAE in patients with adenomyosis. This model has the potential to select patients for UAE.

Research progress on radical concurrent chemoradiotherapy for locally advanced esophageal cancer / 中华放射肿瘤学杂志

The current standard treatment modality for unresectable locally advanced esophageal cancer is radical concurrent chemoradiotherapy. In this article, research progress on radiotherapy techniques, differences in radiotherapy dose and target volume, exploration in concurrent chemotherapy and immunotherapy was reviewed, aiming to provide reference and evidence for clinical treatment.

Study on Green Innovation in Pharmaceutical Enterprises Based on Grounded Theory / 中国药房

OBJECTIVE：To study the influential factors for green innovation in pharmaceutical enterprises based on grounded theory so as to provide reference for pharmaceutical enterprises to enhance their green innovation ability and the government to issue relevant green policies. METHODS ：The grounded theory research methods were used to select enterprise samples and follow-up research. Firstly ，56 staffs from 22 sample enterprises （all were pharmaceutical companies ）were interviewed in depth one-on-one and face-to-face on issues related to the influential factors for green innovation. Then ，open coding ，spindle coding and selective coding were carried out on the conversation record data ，and conduct theoretical saturation testing was conducted. Finally ， the influential factor model of green innovation in pharmaceutical enterprises was constructed ，and its main influential factors were analyzed. RESULTS & CONCLUSIONS ：The government demand ，social demand ，industry demand ，green innovation ability ， environmental awareness of leaders and overall vision of leaders have significant influence on the green innovation of pharmaceutical enterprises. Among them ，leadership decision （including leaders ’environmental awareness and leadership pattern ） is the internal decisive factor ，green innovation ability （including green innovation investment capacity ，green innovation research and development capacity ，green innovation transformation capacity and green innovation production capacity ）is the internal driving factor ，social system （including government needs ，social needs and industry needs ）is the external factor ；they are the main factors affecting the green innovation of pharmaceutical enterprises ，and they all have a positive effect on the green innovation of pharmaceutical companies.

ADC measurement in assessment of tumor grade and myometrial invasion of endometrial carcinoma / 实用放射学杂志

Objective To investigate the value of the apparent diffusion coefficient (ADC)measurement in assessment of tumor grade and myometrial invasion of endometrial carcinoma (EC).Methods 80 EC patients and 28 cervical cancer patients with normal endometrium were studied retrospectively.1.5T conventional MRI and DWI (b=0,1 000 s/mm2)were performed,and ADC values were calculated by two radiologists.Statistical analyses were performed by using SPSS 19.0 and Medcalc software.Results The mean ADC values (×10-3mm2/s)were 0.851±0.131,0.752±0.099,0.681±0.089 for G1,G2 and G3 EC,respectively.Significant statistical differences were achieved for the three groups (G1 vs G2:P=0.005;G2 vs G3:P=0.03;G1 vs G3:P< 0.000 1).For the prediction of G3,the area under the curve (AUC)of 0.851 and the cut-off value of ≤0.742×10-3mm2/s were identified,with the sensitivity, specificity and accuracy of 88.24%,76.19% and 85%,respectively.Conclusion There are significant statistical differences between histologic grades of EC.ADC measurement may have the potential to select G3 EC patients.

MRI characters of 52 cases of septate uterus / 中华全科医师杂志

Magnetic resonance imaging (MRI) findings of 52 patients with septate uterus were retrospectively analyzed.There were 19 cases of complete septate uterus and 32 cases of partial septate uterus confirmed by hysteroscopy or hysteroscopy combined with laparoscopy,and 1 case without surgery.According to MRI findings,19 cases were diagnosed as complete septate uterus,32 cases as partial septate uterus and 1 case as intrauterine adhesion.The results indicated that MRIcan be used in diagnosis of septate uterus.

Down-regulation of p110β expression increases chemosensitivity of colon cancer cell lines to oxaliplatin / 华中科技大学学报（医学）（英德文版）

This study examined the synergetic effect of class IA Phosphoinositide 3-kinases catalytic subunit p110β knockdown in conjunction with oxaliplatin treatment on colon cancer cells. Down-regulation of p110β by siRNA interference and oxaliplatin treatment were applied in colon cancer cell lines HT29, SW620 and HCT116. MTT assay was used to measure the inhibitory effect of p110β knockdown on the proliferation of colon cancer cell lines. SubG1 assay and Annexin-V FITC/PI double-labeling cytometry were applied to detect cell apoptosis. And cell cycle was evaluated by using PI staining and flow cytometry. The expression of caspase 3, cleaved PARP, p-Akt, T-Akt and p110β was determined by western blotting. The results suggested that down-regulation of p110β expression by siRNA obviously reduced cell number via accumulation in G(0)-G(1) phase of the cell cycle in the absence of notablely increased apoptosis in colon cancer cell lines HT29 and SW620 (S phase arrest in HCT116). Moreover, inhibition of p110β expression increased oxaliplatin-induced cell apoptosis and cell cycle arrest in HT29, HCT116 and SW620 cell lines. In addition, increases of cleaved caspase-3 and cleaved PARP induced by oxaliplatin treatment were determined by immunoblotting in p110β knockdown group compared with normal control group and wild-type group. It is concluded that down-regulated expression of p110β could inhibit colon cancer cells proliferation and result in increased chemosensitivity of colorectal cancer cells to oxaliplatin through augmentation of oxaliplatin-induced cell apoptosis and cell cycle arrest.