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.

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 prepare a variety of quality control(QC)materials for SARS-CoV-2 variants as an addition to the conven-tional SARS-CoV-2 nucleic acid QC products for the laboratory detection of mutant strains by optimizing the preparation and purification process of MS2 phage virus-like particle(VLP)technique,and evaluate their performances.Methods The typical mutation sequence fragments or full length S genes were designed and synthesized according to the genomic information of SARS-CoV-2 variants.Then,they were inserted into the downstream of maturase gene,coat protein and the pac-site of MS2 phage to construct a series of recombi-nant expression vectors.After induced by the prokaryotic expression system,the VLP products were purified through the polyethylenei-mine precipitation,ultrafiltration,nuclease digestion,and gel filtration chromatography.The obtained VLP were validated by the nucle-ic acid electrophoresis,protein electrophoresis,protein concentration determination,and fluorescence PCR,and their performances such as nucleic acid residue and stability were also evaluated.Results A total of 10 kinds of VLP containing the targeted sequences of the gene to be tested were prepared.The length of the foreign sequence wrapped in them ranged from 297 bp to 3 822 bp,which could be combined into a variety of QC materials for the mutation detection of different SARS-CoV-2 variants.The prepared VLP QC materials could not be effectively amplified without nucleic acid extraction or reverse transcription steps during the routine nucleic acid detection.The simulated QC samples remained stable after repeated freeze-thaw cycles.They could be stored stably for 2 months at 25 ℃ and 4 weeks at 37 ℃.Conclusion The established preparation and combined purification process of VLP QC materials can encapsulate vari-ous exogenous nucleic acid sequences with different lengths into the viral coat protein to form VLP,with high production efficiency.The VLP QC products prepared by the above process have stable performance and almost no residual exogenous nucleic acid,which can ef-fectively meet clinical requirements and ensure the quality of laboratory testing.

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.

The monkeypox pandemic has caused two public health emergencies of international concern from 2022 to 2025,causing great impact on the global health system. Early diagnosis and treatment of the monkeypox virus depend on quick, sensitive, and accurate detection methods. The clustered regular interspaced short palindromic repeats (CRISPR)/CRISPR-associated (Cas) system, characterized by its unique target gene sequence recognition, and cis-and trans-cleavage activity, has been regarded as the next-generation nucleic acid detection tool in the field of pathogen diagnosis. CRISPR/Cas has been used for detecting monkeypox virus in combination with different amplification and result readout approaches. Although it still faces many challenges, the CRISPR/Cas system is expected to achieve diversified development and provide strong technical support for rapid, accurate, and affordable detection approaches for infectious diseases.

The CRISPR/Cas system has emerged as a promising platform for multiplex nucleic acid detection in recent years. Compared with traditional methods such as qPCR and NGS, CRISPR-based assays offer distinct advantages, including simplicity, high sensitivity, and low cost, making them particularly suitable for pathogen screening and early cancer diagnosis. To address the core challenges of CRISPR-based multiplex detection, three types of strategies have been developed: one-pot, spatial separation, and logic gate.These strategies enable simultaneous recognition of multiple targets and effective signal amplification. Applications span viral detection, antimicrobial resistance profiling, and tumor biomarker analysis. Despite persistent challenges such as signal cross interference and system complexity, the technology is evolving rapidly and holds strong potential for clinical translation.

Objective:To assess the accuracy of preoperative enhanced CT in evaluating the degree of lymph node metastasis in gallbladder cancer.Methods:A retrospective analysis was performed on the enhanced CT imaging data of 124 gallbladder cancer patients who underwent surgical treatment at Henan Provincial People's Hospital from Jan 2017 to Dec 2018. Imaging staging was used to classify lymph node metastasis. Pathological and imaging data of 70 patients with confirmed postoperative lymph node pathology were compared to evaluate the accuracy of imaging methods in detecting lymph node involvement.Results:Lymph node metastasis in the 124 surgical patients was categorized into three groups using imaging evaluation methods. The overall accuracy of determining lymph node positivity and negativity was 63%, with a sensitivity of 64% and specificity of 62%. The accuracy of detecting lymph node metastasis in gallbladder cancer was higher when lymph node fusion and internal necrosis were observed. The overall survival rate differed significantly among gallbladder cancer patients at different lymph node imaging stages ( P<0.05). Conclusion:CT imaging evaluation has diagnostic value for lymph node metastasis in gallbladder cancer and has a certain predictive effect on the prognosis of patients.