BACKGROUND:Radiotherapy significantly improves survival rates in patients with various malignant tumors.However,with prolonged post-treatment survival,many patients face the risk of radiation-related cardiac toxicity.This is especially true after chest radiotherapy,where the risk of radiation-induced heart disease significantly increases,becoming one of the most severe complications affecting prognosis survival.OBJECTIVE:To identify diagnostic markers of endothelial cellular senescence in radiation-induced heart disease through systematic transcriptomic analysis.METHODS:Firstly,genes associated with cellular senescence were screened from the CellAge database and intersected with the transcriptomic training dataset of a mouse model of radiation-induced heart disease to identify differentially expressed senescence-related genes.Secondly,weighted gene co-expression network analysis and machine learning were used to identify key hub genes that play critical roles in radiation-induced heart disease.The expression of these genes was validated using a dataset of radiation-induced endothelial injury.Additionally,the quanTlseq method was employed to assess the immune infiltration status related to radiation-induced heart disease.The expression levels of key genes and their association with survival in esophageal squamous cell carcinoma patients receiving chest radiotherapy were explored through the analysis of The Cancer Genome Atlas database.RESULTS AND CONCLUSION:(1)Systematic transcriptomic analysis identified CCND1 as the core gene of endothelial cellular senescence in radiation-induced heart disease,and this finding was validated in the mouse model of radiation-induced heart disease.(2)The diagnostic model constructed from these data indicated that CCND1 had high specificity and sensitivity for diagnosing radiation-induced heart disease.(3)Immune infiltration analysis revealed significant immune response dysregulation in the mouse model of radiation-induced heart disease,and CCND1 was closely related to various immune cells.(4)Kaplan-Meier survival analysis showed that CCND1 was associated with poorer disease-specific survival in esophageal squamous cell carcinoma patients receiving chest radiotherapy.This study systematically uncovers,for the first time,the pivotal role of CCND1 in endothelial cell senescence associated with radiation-induced heart disease.CCND1,a gene integral to cell cycle regulation,can induce cellular senescence when abnormally expressed.Furthermore,the findings highlight its potential as an early diagnostic marker.

BACKGROUND:Radiotherapy significantly improves survival rates in patients with various malignant tumors.However,with prolonged post-treatment survival,many patients face the risk of radiation-related cardiac toxicity.This is especially true after chest radiotherapy,where the risk of radiation-induced heart disease significantly increases,becoming one of the most severe complications affecting prognosis survival.OBJECTIVE:To identify diagnostic markers of endothelial cellular senescence in radiation-induced heart disease through systematic transcriptomic analysis.METHODS:Firstly,genes associated with cellular senescence were screened from the CellAge database and intersected with the transcriptomic training dataset of a mouse model of radiation-induced heart disease to identify differentially expressed senescence-related genes.Secondly,weighted gene co-expression network analysis and machine learning were used to identify key hub genes that play critical roles in radiation-induced heart disease.The expression of these genes was validated using a dataset of radiation-induced endothelial injury.Additionally,the quanTlseq method was employed to assess the immune infiltration status related to radiation-induced heart disease.The expression levels of key genes and their association with survival in esophageal squamous cell carcinoma patients receiving chest radiotherapy were explored through the analysis of The Cancer Genome Atlas database.RESULTS AND CONCLUSION:(1)Systematic transcriptomic analysis identified CCND1 as the core gene of endothelial cellular senescence in radiation-induced heart disease,and this finding was validated in the mouse model of radiation-induced heart disease.(2)The diagnostic model constructed from these data indicated that CCND1 had high specificity and sensitivity for diagnosing radiation-induced heart disease.(3)Immune infiltration analysis revealed significant immune response dysregulation in the mouse model of radiation-induced heart disease,and CCND1 was closely related to various immune cells.(4)Kaplan-Meier survival analysis showed that CCND1 was associated with poorer disease-specific survival in esophageal squamous cell carcinoma patients receiving chest radiotherapy.This study systematically uncovers,for the first time,the pivotal role of CCND1 in endothelial cell senescence associated with radiation-induced heart disease.CCND1,a gene integral to cell cycle regulation,can induce cellular senescence when abnormally expressed.Furthermore,the findings highlight its potential as an early diagnostic marker.

BACKGROUND:Fibromyalgia syndrome,as a common rheumatic disease,is related to central sensitization and immune abnormalities.However,the specific mechanism has not been elucidated,and there is a lack of specific diagnostic markers.Exploring the possible pathogenesis of this disease has important clinical significance. OBJECTIVE:To screen the potential diagnostic marker genes of fibromyalgia syndrome and analyze the possible immune infiltration characteristics based on bioinformatics methods,such as weighted gene co-expression network analysis(WGCNA),and machine learning. METHODS:Gene expression profiles in peripheral serum of fibromyalgia syndrome patients and healthy controls were obtained from the gene expression omnibus(GEO)database.The differentially co-expressed genes were screened in the expression profile by differential analysis and WGCNA analysis.Least absolute shrinkage and selection operator(LASSO)and support vector machine-recursive feature elimination(SVM-RFE)machine learning algorithm were further used to identify hub biomarkers,and draw receiver operating characteristic curve(ROC)to evaluate the accuracy of diagnosing fibromyalgia syndrome.Finally,single sample gene set enrichment analysis(ssGSEA)and gene set enrichment analysis(GSEA)were used to evaluate the immune cell infiltration and pathway enrichment in patients with fibromyalgia syndrome. RESULTS AND CONCLUSION:Eight down-regulated differentially expressed genes(DEGs)were obtained after differential analysis of the GSE67311 dataset according to the conditions of log2|(FC)|>0 and P<0.05.After WGCNA analysis,497 genes were included in the module(MEdarkviolet)with the highest positive correlation(r=0.22,P=0.04),and 19 genes were included in the module(MEsalmon2)with the highest negative correlation(r=-0.41,P=6×10-5).After intersecting DEGs and the module genes of WGCNA,seven genes were obtained.Four genes were screened out by LASSO regression algorithm and five genes were screened out by SVM-RFE machine learning algorithm.After the intersection of the two,three core genes were identified,which were germinal center associated signaling and motility like,integrin beta-8,and carboxypeptidase A3.The areas under the ROC curve of the three core genes were 0.744,0.739,and 0.734,respectively,indicating that they have good diagnostic value and can be used as biomarkers for fibromyalgia syndrome.The results of immune infiltration analysis showed that memory B cells,CD56 bright NK cells,and mast cells were significantly down-regulated in patients with fibromyalgia syndrome compared with the control group(P<0.05),and were significantly positively correlated with the above three biomarkers(P<0.05).The enrichment analysis suggested that there were nine fibromyalgia syndrome enrichment pathways,mainly related to olfactory transduction pathway,neuroactive ligand-receptor interaction,and infection pathway.The above results showed that the occurrence and development of fibromyalgia syndrome are related to the involvement of multiple genes,abnormal immune regulation,and multiple pathways imbalance.However,the interactions between these genes and immune cells,as well as their relationships with various pathways need to be further investigated.

The incidence of digestive system diseases is high. So digestive system pathology is widely concerned. In the past 10 years, Chinese pathologists insist on hard work and have made significant progress. This article reviews the research and practice progress in digestive system tumors and non-tumors in the past 10 years, including the establishment of diagnostic criteria for gastrointestinal tract, hepatobiliary and pancreatic diseases, the establishment and classification of new diseases with special morphology, the classification and standard update of gastrointestinal and pancreatic neuroendocrine tumors, the progress of molecular pathology, and non-tumor lesions of the digestive system. The future development trend of digestive system pathology is also prospected.

The incidence of digestive system diseases is high. So digestive system pathology is widely concerned. In the past 10 years, Chinese pathologists insist on hard work and have made significant progress. This article reviews the research and practice progress in digestive system tumors and non-tumors in the past 10 years, including the establishment of diagnostic criteria for gastrointestinal tract, hepatobiliary and pancreatic diseases, the establishment and classification of new diseases with special morphology, the classification and standard update of gastrointestinal and pancreatic neuroendocrine tumors, the progress of molecular pathology, and non-tumor lesions of the digestive system. The future development trend of digestive system pathology is also prospected.

Objective:To propose a method of cervical cytology screening based on deep convolutional neural network and compare it with the diagnosis of cytologists.Method:The deep segmentation network was used to extract 618 333 regions of interest (ROI) from 5, 516 cytological pathological images. Combined with the experience of physicians, the deep classification network with the ability to analyze ROI was trained. The classification results were used to construct features, and the decision model was used to complete the classification of cytopathological images.Results:The sensitivity and specificity were 89.72%, 58.48%, 33.95% and 95.94% respectively. Among the smears derived from four different preparation methods, this algorithm had the best effect on natural fallout with a sensitivity of 91.10%, specificity of 69.32%, positive predictive rate of 41.41%, and negative predictive rate of 97.03%.Conclusion:Deep convolutional neural network image recognition technology can be applied to cervical cytology screening.