Objective:To explore the characteristics of systemic immune microenvironment during the progression of dextran sulfate sodium(DSS)-induced acute ulcerative colitis(UC)induced in mice by Mass cytometry(CyTOF).Methods:Male C57BL/6 mice were randomly divided into control group and model group.The control group was given normal drinking water for 15 d.The mouse in the model group were given 5%DSS in drinking water,which was changed to normal drinking water after 7 days.In the model group,peripheral blood was collected on days 4,9 and 15,respectively.CyTOF was used to detect the expressions of 33 immune cell markers and changes in cell subsets in peripheral blood of mice,and the characteristics of systemic immune microenvironment in mice with acute UC were analyzed.Results:The cluster analysis of 33 kinds of immune cell markers showed that CD45+cells in peripheral blood of mice with DSS induced acute UC were divided into 23 fine subgroups,among which the proportions of B cell subgroup,T cell subgroup and neutrophil subgroup showed significant changes.A further dimensional reduction cluster analysis of T cell subsets found significant differences in the composition and proportion of the 10 identified T cell subsets.Conclusion:The systemic immune micro-environment map of mice with acute UC induced by DSS has been successfully constructed,and heterogeneity has been found in the systemic immune microenvironment of mice with acute UC.The changes and activation degree of T cell subpopulations are closely re-lated to disease progression and inflammation level.The results of this study provide theoretical basis for assisting the diagnosis,moni-toring the risk,progression,treatment and prognosis of acute UC.

Objective To evaluate the recognition capability of AI-enabled Cellsee CS-BM1 automatic cell morphology analyzer for pe-ripheral blood smears and its roles in assisting manual classification,and explore the application value of AI system in the diagnosis network of tiered primary medical units.Methods The blood samples which triggered the re-examination rules were collected from six primary medical units,including the Laboratory Department of Shanghai Jiahui International Hospital,and so on,from March to No-vember 2023.The smears of peripheral blood were prepared and AI analyzer was used for pre-classification to evaluate its recognition performance in identifying the samples with abnormal WBC and RBC.The sensitivity,specificity,and accuracy of WBC classification by six junior and intermediate technicians,both with and without AI assistance,were analyzed.Additionally,the roles of the AI system in tiered diagnosis of primary medical units were also evaluated.Results The sensitivity,specificity,and accuracy of AI system in recognizing malignant primitive cells were 92.86％,95.16％,and 95.10％,respectively.The sensitivities of AI system in recognizing immature granulocytes,reactive lymphocytes,and nucleated RBCs were all greater than 90％.The sensitivity of AI system in identif-ying abnormal morphology of RBCs reached 99.59％,along with rapid quantitative analysis for various anomalous types of RBCs.In AI-assisted mode,the sensitivity of recognition for all cell types was improved to varying degrees by junior and intermediate technicians,and the sensitivity for recognizing malignant primitive cells,reactive lymphocytes,and immature granulocytes increased to 58.24％,53.39％,and 62.37％for junior technicians,and to 92.06％,83.24％,and 83.12％for intermediate technicians,respectively.The improvements for junior technicians were particularly significant,with increases of 12.46％,10.61％,and 3.71％for each cell type,respectively.Both groups achieved higher specificity and accuracy.Through AI pre-classification and manual review,a variety of pe-ripheral blood cell-related diseases were accurately diagnosed in the tiered healthcare practice of primary medical units,including 339 cases(11.13％)of red blood cell diseases,5 cases(0.16％)of platelet diseases,2 343 cases(76.90％)of infection-related disea-ses,and 28 cases(0.92％)of malignant hematological diseases.In addition,332 cases(10.90％)which lacked an obvious related cause or required further examinations were identified as well.Conclusion AI pre-classification has demonstrated strong cell recogni-tion capabilities and may assist technicians in improving the sensitivity,specificity,and accuracy of blood cell classification.AI could en-hance the disease-screening capabilities in the tiered diagnosis network of primary medical units,presenting a broad application prospect.

Colorectal cancer stands as a leading cause of cancer-related morbidity and mortality globally,with liver metastases being a significant determinant of patient prognosis.Conventional diagnostic methods,includ-ing imaging studies and biomarker testing,frequently exhibit inadequate sensitivity and specificity,underscoring the necessity for more advanced technologies.Recent advancements in genomics,transcriptomics,proteomics,me-tabolomics,and epigenomics have revolutionized our understanding of the biological mechanisms driving colorectal cancer.These methodologies enable comprehensive analyses of genetic mutations,gene expression profiles,protein modifications,and metabolic reprogramming,all of which are pivotal to the metastatic process.This article high-lights the advanced capabilities of artificial intelligence(AI)technologies in processing complex multi-omics data,thereby enhancing diagnostic accuracy and supporting personalized treatment strategies.It also addresses the challenges AI encounters in multi-omics analyses,such as ensuring data quality,improving model interpretability,and facilitating clinical translation.Additionally,it explores the potential integration of emerging technologies like single-cell sequencing and spatial omics into large-scale,multicenter studies to further enhance the clinical utility of these tools.

Objective To evaluate the recognition capability of AI-enabled Cellsee CS-BM1 automatic cell morphology analyzer for pe-ripheral blood smears and its roles in assisting manual classification,and explore the application value of AI system in the diagnosis network of tiered primary medical units.Methods The blood samples which triggered the re-examination rules were collected from six primary medical units,including the Laboratory Department of Shanghai Jiahui International Hospital,and so on,from March to No-vember 2023.The smears of peripheral blood were prepared and AI analyzer was used for pre-classification to evaluate its recognition performance in identifying the samples with abnormal WBC and RBC.The sensitivity,specificity,and accuracy of WBC classification by six junior and intermediate technicians,both with and without AI assistance,were analyzed.Additionally,the roles of the AI system in tiered diagnosis of primary medical units were also evaluated.Results The sensitivity,specificity,and accuracy of AI system in recognizing malignant primitive cells were 92.86％,95.16％,and 95.10％,respectively.The sensitivities of AI system in recognizing immature granulocytes,reactive lymphocytes,and nucleated RBCs were all greater than 90％.The sensitivity of AI system in identif-ying abnormal morphology of RBCs reached 99.59％,along with rapid quantitative analysis for various anomalous types of RBCs.In AI-assisted mode,the sensitivity of recognition for all cell types was improved to varying degrees by junior and intermediate technicians,and the sensitivity for recognizing malignant primitive cells,reactive lymphocytes,and immature granulocytes increased to 58.24％,53.39％,and 62.37％for junior technicians,and to 92.06％,83.24％,and 83.12％for intermediate technicians,respectively.The improvements for junior technicians were particularly significant,with increases of 12.46％,10.61％,and 3.71％for each cell type,respectively.Both groups achieved higher specificity and accuracy.Through AI pre-classification and manual review,a variety of pe-ripheral blood cell-related diseases were accurately diagnosed in the tiered healthcare practice of primary medical units,including 339 cases(11.13％)of red blood cell diseases,5 cases(0.16％)of platelet diseases,2 343 cases(76.90％)of infection-related disea-ses,and 28 cases(0.92％)of malignant hematological diseases.In addition,332 cases(10.90％)which lacked an obvious related cause or required further examinations were identified as well.Conclusion AI pre-classification has demonstrated strong cell recogni-tion capabilities and may assist technicians in improving the sensitivity,specificity,and accuracy of blood cell classification.AI could en-hance the disease-screening capabilities in the tiered diagnosis network of primary medical units,presenting a broad application prospect.

Colorectal cancer stands as a leading cause of cancer-related morbidity and mortality globally,with liver metastases being a significant determinant of patient prognosis.Conventional diagnostic methods,includ-ing imaging studies and biomarker testing,frequently exhibit inadequate sensitivity and specificity,underscoring the necessity for more advanced technologies.Recent advancements in genomics,transcriptomics,proteomics,me-tabolomics,and epigenomics have revolutionized our understanding of the biological mechanisms driving colorectal cancer.These methodologies enable comprehensive analyses of genetic mutations,gene expression profiles,protein modifications,and metabolic reprogramming,all of which are pivotal to the metastatic process.This article high-lights the advanced capabilities of artificial intelligence(AI)technologies in processing complex multi-omics data,thereby enhancing diagnostic accuracy and supporting personalized treatment strategies.It also addresses the challenges AI encounters in multi-omics analyses,such as ensuring data quality,improving model interpretability,and facilitating clinical translation.Additionally,it explores the potential integration of emerging technologies like single-cell sequencing and spatial omics into large-scale,multicenter studies to further enhance the clinical utility of these tools.

Objective:To explore the characteristics of systemic immune microenvironment during the progression of dextran sulfate sodium(DSS)-induced acute ulcerative colitis(UC)induced in mice by Mass cytometry(CyTOF).Methods:Male C57BL/6 mice were randomly divided into control group and model group.The control group was given normal drinking water for 15 d.The mouse in the model group were given 5%DSS in drinking water,which was changed to normal drinking water after 7 days.In the model group,peripheral blood was collected on days 4,9 and 15,respectively.CyTOF was used to detect the expressions of 33 immune cell markers and changes in cell subsets in peripheral blood of mice,and the characteristics of systemic immune microenvironment in mice with acute UC were analyzed.Results:The cluster analysis of 33 kinds of immune cell markers showed that CD45+cells in peripheral blood of mice with DSS induced acute UC were divided into 23 fine subgroups,among which the proportions of B cell subgroup,T cell subgroup and neutrophil subgroup showed significant changes.A further dimensional reduction cluster analysis of T cell subsets found significant differences in the composition and proportion of the 10 identified T cell subsets.Conclusion:The systemic immune micro-environment map of mice with acute UC induced by DSS has been successfully constructed,and heterogeneity has been found in the systemic immune microenvironment of mice with acute UC.The changes and activation degree of T cell subpopulations are closely re-lated to disease progression and inflammation level.The results of this study provide theoretical basis for assisting the diagnosis,moni-toring the risk,progression,treatment and prognosis of acute UC.

Purpose To investigate the relationship between imaging characteristics and shunt grading in acute cerebral infarction patients with right-to-left shunt.Materials and Methods The clinical and imaging data of 697 acute cerebral infarction patients who were hospitalized in the Second Affiliated Hospital of Nanchang University from January 2019 to December 2021 were collected,and the patients were divided into right-to-left shunt negative group(443 cases)and right-to-left shunt positive group(254 cases)according bubble test,compared the difference in demographic data and imaging characteristics between the two groups.In the shunt positive group,the imaging characteristics of the two groups of patients and their relationship with the shunt grade were analyzed.Results There were no statistical differences in gender,age,smoking,hyperlipidemia and history of coronary heart disease between the two groups(P>0.05),but there were statistical differences in the history of hypertension(OR=0.533,95%CI 0.380-0.748),diabetes(OR=0.649,95%CI 0.422-0.999)and cerebral infarction(OR=0.275,95%CI 0.136-0.555)(all P<0.05).The imaging characteristics of the two groups of patients showed that there were significant differences in the number of infarct foci(χ2=53.957,4.219;both P<0.05),but there was no statistical difference in the infarct location between the two groups(P>0.05).Among the patients with acute cerebral infarction combined with positive bubble test,the imaging manifestations of grading I and II patients were mainly unilateral anterior circulation or posterior circulation infarction,and deep and single infarction were more common.In patients with grading III and IV shunts,multiple infarcts with combined bilateral anterior and posterior circulations were more common.Conclusion The imaging manifestations of patients with acute cerebral infarction combined with right-to-left shunt have their own characteristics.Bubble test has high sensitivity in the diagnosis of right-to-left shunt.The comprehensive analysis can provide certain guidance for clinical practice.

Objective:To explore the feasibility of using quantitative parameters of double contrast-enhanced ultrasound (DCUS) to noninvasively predict lymphatic metastasis in gastric cancer before operation.Methods:From December 2021 to October 2023, 119 patients undergoing gastrectomy at the Second Hospital & Clinical Medical School, Lanzhou University, were enrolled retrospectively. Based on the pathological findings, they were divided into lymph node metastasis (N1, 94 cases) and non-lymph node metastasis (N0, 25 cases) groups. All patients underwent DCUS within 3 days before surgery. Previous to DCUS, lesions′ location and ultrasonic T-staging were recorded by conventional ultrasound. Quantitative parameters such as arrival time (AT), time to peak (TTP), baseline intensity (BI), peak intensity (PI) and wash-in slope (WIS) were obtained by the time-intensity curve (TIC) automatically, and then manually calculated enhanced intensity (ΔPI=PI–BI) and enhanced time (ΔTTP=TTP–AT). Binary Logistic regression analysis was used to screen independent risk factors for predicting lymph nodes metastasis in gastric cancer, and regression models were established.Results:Statistical tests revealed significant differences in ultrasonic T-staging ( P<0.001) and degree of differentiation ( P=0.015) between N1 and N0 group. Among DCUS quantitative parameters, statistical differences in PI, ΔPI and WIS were observed between the two groups (all P<0.05), while no significant differences were found in BI, AT, TTP and ΔTTP (all P>0.05). Logistic regression analysis showed that ultrasonic T-staging and WIS were independent risk factors for predicting lymphatic metastasis. The regression model built on the above two factors performed well in predicting lymph nodes metastasis, with an area under the curve of 0.905, accuracy of 93.3% (superior to the prediction model based on DCUS quantitative parameters alone, P<0.05), sensitivity of 95.7% and specificity of 84.0%. Conclusions:DCUS quantitative parameters may be helpful to evaluate lymphatic metastasis of gastric cancer prior to surgery, and the accuracy of prediction would be improved by combing with ultrasonic T-staging.

Cases of high-temperature requirement A serine peptidase 1(HTRA1)gene heterozygous mutation associated cerebral small vessel disease are relatively rare.Early and timely diagnosis and treatment can improve prognosis.The authors reported a 37 years old male patient admitted in Department of Neurology,the Second Affiliated Hospital of Nanchang University,whose initial symptom was transient right limb weakness,imaging suggested white matter lesions and the gene screening showed HTRA1(c.854 C＞T/p.Pro285Leu)heterozygous mutation.A family survey has been conducted and the characteristics of patients in this family are as follows:they present with ischemic cerebrovascular disease,coexist with cervical or lumbar disc herniation,male patients have hair loss,some patients have cognitive dysfunction,men tend to develop the disease at an earlier age than women,and the onset age is progressively earlier from generation to generation.Therefore,for young ischemic cerebrovascular disease patients with hair loss,cognitive dysfunction,cervical or lumbar disc herniation,and obvious white matter lesions on imaging,especially those without common risk factors for cerebrovascular disease,a family history should be inquired and genetic testing should be performed to screen for HTRA1 mutations.

Tumor-associated macrophages(TAMs)are the predominant cell group in the tumor microenvironment(TME)and are the most important regulatory cells of immune system suppression and tumor cell proliferation in TIME.Src homology-2 domain-containing protein tyrosine phosphatase 2(SHP-2)is a non-receptor protein tyrosine phosphatase that plays an important role in the transmission of signals from the cell surface to the nucleus.SHP-2 is a key intracellular regulatory factor mediating cell proliferation and differentiation and is involved in a variety of growth factor and cytokine signaling pathways linking the cell surface to the nucleus.Recent studies have shown that SHP-2 is a key enzyme in determining the function of TAMs,but because of its variable function,it plays different or even opposite roles in different solid TMEs.This paper reviews the function of SHP-2 in TAMs and related solid tumors to provide a comprehensive reference for tumor immunity and targeted therapy research.