Global public health faces substantial challenges from malignant tumors and infectious diseases. Vaccination provides an approach for treating and preventing these diseases. Oral vaccinations are particularly advantageous in disease treatment and prevention due to their non-invasive nature, high patient compliance, convenience, cost-effectiveness, and capacity to stimulate comprehensive and adaptive immune responses. However, the overwhelming majority of oral vaccines remain in experimental development, struggling with clinical and commercial translation due to their suboptimal efficacy. Thus, enhancing scientists' understanding of the interaction between vaccines and gastrointestinal immune system, creating antigen delivery systems suitable for the gut mucosal environment, developing more potent antigenic epitopes, and using personalized combination therapies are critical for advancing the next generation of oral vaccines. This article explores the fundamental principles and applications of current oral anti-tumor and anti-infective vaccines and discusses considerations necessary for designing future oral vaccines.

Objective:To investigate the value of nomogram model based on CT features in differentiating ectopic pancreas (EP) from gastrointestinal stromal tumors (GIST) with a long diameter less than 3 cm.Methods:This study was a case-control study. The clinical and imaging data of 43 patients with EP and 90 patients with GIST confirmed by pathology in the Affiliated Hospital of Guizhou Medical University from August 2013 to March 2024 were retrospectively analyzed. Preoperative CT images were analyzed to obtain qualitative features (number of lesions, location, morphology, growth pattern, borders, cystic degeneration, calcification, ulceration, catheter sign, central umbilication) and quantitative features (lesion long diameter, short diameter, long/short diameter, lesion and normal pancreas arterial-phase and venous-phase CT values, and enhancement ratio). Statistical analyses, including independent sample t-tests, Mann-Whitney U tests, χ2 tests, and Fisher exact tests, were performed to compare CT characteristics between the two groups. Binary logistic regression analysis was used to obtain independent predictors to identify the two groups, to establish a joint model, and to draw a nomogram. The discriminative performance of the independent predictors and the combined model was assessed using receiver operating characteristic (ROC) curves, while calibration curves were used to evaluate model fit. Results:The differences in age, location, morphology, border, catheter sign, central umbilication, short diameter, long/short diameter, arteriovenous phase enhancement CT value and arteriovenous phase enhancement ratio were statistically significant between the EP group and the GIST group (all P<0.05). The logistic analysis showed that the differences in age ( OR=0.920, 95% CI 0.885-0.956, P<0.001), border ( OR=5.994, 95% CI 2.111-17.022, P=0.001), long/short diameter ( OR=7.820, 95% CI 1.841-33.224, P=0.005), and venous phase enhancement ratio ( OR=8.847, 95% CI 1.103-70.972, P=0.040) were the independent predictors for distinguishing EP from GIST, and the area under the ROC curve (AUC) were 0.782 (95% CI 0.698-0.866), 0.684 (95% CI 0.600-0.767), 0.705 (95% CI 0.607-0.803), and 0.693 (95% CI 0.605-0.781), respectively. Combined age, border, long diameter/short diameter and venous phase enhancement ratio were plotted in a nomogram with an AUC of 0.881 (95% CI 0.817-0.945), sensitivity and specificity of 74.4% and 93.3%, respectively. The calibration curve demonstrated a strong agreement between predicted and actual probabilities (Hosmer-Lemeschow test, P=0.267). Conclusions:CT imaging reveals significant differences between EP and small GISTs (<3 cm). EP is more likely when patients are younger and lesions exhibit indistinct borders, a higher long-to-short diameter ratio, and greater venous-phase enhancement. The nomogram derived from CT features provides a valuable tool for differentiating EP from GIST.

Objective To utilize single-cell and peripheral blood transcriptomic data from 3D brain organoids,combined with machine learning,to analyze the role of mitochondrial autophagy genes in schizophrenia(SCZ).Methods By integrating two machine learning algorithms,we identified differentially expressed mitochondrial autophagy-related genes between schizophrenia patients and healthy controls using peripheral blood RNA sequencing data.The relationship between mitophagy gene,immune cells and inflammatory factors was further explored.Comprehensive single-cell analysis was used to explore the signaling pathways and specific transcription factors based on mitophagy genes.Results Using machine learning,seven key mitophagy genes expressed in schizophrenia patients were identified.Based on Mitoscore analysis,at the single-cell level,neurons with high mitochondrial autophagy activity(Mitohigh_Neuron)formed new interactions with endothelial cells via the SPP1 signaling pathway.Conclusion This study identified two subtypes of mitophagy and seven key mitophagy genes in schizophrenia,providing new insights into the pathogenesis of the disease.

OBJECTIVES: To investigate the effects of methylenetetrahydrofolate reductase (MTHFR) rs1801133 and γ-glutamyl hydrolase (GGH) rs11545078 gene polymorphisms on plasma concentrations and toxicity following high-dose methotrexate (MTX) therapy in children with acute lymphoblastic leukemia (ALL). METHODS: Children with ALL treated at the Xuzhou Children's Hospital of Xuzhou Medical University from January 2021 to April 2024 were selected for this study. Genotypes of MTHFR rs1801133 and GGH rs11545078 were determined using multiplex polymerase chain reaction. MTX plasma concentrations were measured by enzyme-multiplied immunoassay technique, and toxicity was graded according to the Common Terminology Criteria for Adverse Events version 5.0. The relationships between MTHFR rs1801133 and GGH rs11545078 genotypes and both MTX plasma concentrations and associated toxicities were analyzed. RESULTS: In the low-risk ALL group, the MTHFR rs1801133 genotype was associated with increased MTX plasma concentrations at 72 hours (P<0.05). In the intermediate- to high-risk group, the MTHFR rs1801133 genotype was associated with increased MTX plasma concentrations at 48 hours (P<0.05), and the GGH rs11545078 genotype was associated with increased MTX plasma concentrations at 48 hours (P<0.05). In the intermediate- to high-risk group, the MTHFR rs1801133 genotype was associated with the occurrence of reduced hemoglobin (P<0.05), and the GGH rs11545078 genotype was associated with the occurrence of thrombocytopenia (P<0.05). CONCLUSIONS Detection of MTHFR rs1801133 and GGH rs11545078 genotypes can be used to predict increased MTX plasma concentrations and the occurrence of toxic reactions in high-dose MTX treatment of ALL, enabling timely interventions to enhance safety.
Humans ; Methotrexate/toxicity* ; Methylenetetrahydrofolate Reductase (NADPH2)/genetics* ; Precursor Cell Lymphoblastic Leukemia-Lymphoma/blood* ; Male ; Female ; Child ; Child, Preschool ; gamma-Glutamyl Hydrolase/genetics* ; Antimetabolites, Antineoplastic/adverse effects* ; Infant ; Polymorphism, Genetic ; Adolescent ; Genotype ; Polymorphism, Single Nucleotide

Human papilloma virus(HPV),a cancer-causing DNA virus,is a most common sexually transmitted virus and one of the major public health problems worldwide currently.Although HPV infection is relatively common in men,routine HPV detection is still difficult to be applied in clinical practice due to the lack of standard HPV detection methods and the complexity of its detection.Recent studies have explored the relationship between HPV and genitourinary tumors,revealed different results because of geographic differences,histological subtypes and detection methods,and stressed the importance of clarifying the role of HPV in the development and progression of genitourinary tumors.This review focuses on the complicated relationship of HPV with male genitourinary tumors,reveals its main carcinogenic mechanisms,and presents a new insight into the impact of HPV on the genitourinary system.

Aim To investigate the mechanism of Guanxinning injection regulating cardiac immune mi-croenvironment to improve myocardial infarction in mice.Methods In this study,MI model was estab-lished by permanent ligation of left anterior descending coronary artery in mice.The mice were divided into five groups:sham operation group,model group,Guanxinning injection low dose group,Guanxinning in-jection high dose group and positive drug captopril group.Hearts were weighed,heart tissues were collect-ed,and Masson staining was used for pathological anal-ysis of heart tissues;immunofluorescence staining was used to detect apoptosis and CCL21 expression in the infarct border zone;flow cytometry was used to detect the proportion of immune cells in myocardial ischemia tissues and lymph nodes;PCR was used to detect CCL21 expression in heart and in vitro human lymphat-ic endothelial cells(HLEC).Results Compared with the model group,the low and high dose groups of Guanxinning injection significantly improved cardiac hypertrophy.Apoptosis in the border zone of myocardi-al infarction was reduced in the low and high dose groups of Guanxinning injection and captopril group.Compared with the model group,the proportion of leu-kocytes in the infarct border zone was dreduced and the proportion of CD4+T cells,Treg cells,and CD8+T cells in the mediastinal lymph nodes and infarct border zone of the heart was regulated in the low and high dose groups of Guanxinning injection;CCL21 secretion by the heart and lymphatic vessels increased.Conclu-sions Guanxinning injection can significantly improve cardiac hypertrophy and fibrosis in MI mice,reduce ap-optosis in the infarct border zone,and play a role in an-ti-myocardial ischemia injury by promoting CCL21 ex-pression in lymphatic vessels to regulate the proportion of mediastinal lymph nodes and cardiac T cells after myocardial infarction.

Aim To investigate the mechanism of Guanxinning injection regulating cardiac immune mi-croenvironment to improve myocardial infarction in mice.Methods In this study,MI model was estab-lished by permanent ligation of left anterior descending coronary artery in mice.The mice were divided into five groups:sham operation group,model group,Guanxinning injection low dose group,Guanxinning in-jection high dose group and positive drug captopril group.Hearts were weighed,heart tissues were collect-ed,and Masson staining was used for pathological anal-ysis of heart tissues;immunofluorescence staining was used to detect apoptosis and CCL21 expression in the infarct border zone;flow cytometry was used to detect the proportion of immune cells in myocardial ischemia tissues and lymph nodes;PCR was used to detect CCL21 expression in heart and in vitro human lymphat-ic endothelial cells(HLEC).Results Compared with the model group,the low and high dose groups of Guanxinning injection significantly improved cardiac hypertrophy.Apoptosis in the border zone of myocardi-al infarction was reduced in the low and high dose groups of Guanxinning injection and captopril group.Compared with the model group,the proportion of leu-kocytes in the infarct border zone was dreduced and the proportion of CD4+T cells,Treg cells,and CD8+T cells in the mediastinal lymph nodes and infarct border zone of the heart was regulated in the low and high dose groups of Guanxinning injection;CCL21 secretion by the heart and lymphatic vessels increased.Conclu-sions Guanxinning injection can significantly improve cardiac hypertrophy and fibrosis in MI mice,reduce ap-optosis in the infarct border zone,and play a role in an-ti-myocardial ischemia injury by promoting CCL21 ex-pression in lymphatic vessels to regulate the proportion of mediastinal lymph nodes and cardiac T cells after myocardial infarction.

Objective To propose a novel identification algorithm based on ensemble learning for assessing the severity of acute respiratory distress syndrome(ARDS)to achieve continuous monitoring of the disease severity.Methods Firstly,leve-raging the open-source MIMIC-Ⅳ database,a variety of non-invasive physiological parameters of patients were extracted and subjected to preliminary preprocessing.A multivariate feature selection algorithm was employed to rank these parameters and calculate feature importance scores through weighted computation.Secondly,based on the feature importance scores,a subset search algorithm was utilized to identify the subset of features that could yield optimal performance across four machine learning algorithms:neural networks,logistic regression,AdaBoost and XGBoost.Finally,a soft voting ensemble method was designed using a generalized linear regression model to integrate the results of each single machine learning algorithm,and a multivariate ensemble learning algorithm was proposed by combining the optimal feature subsets.The algorithm proposed when used to identify the severity of ADRS was evaluated with MIMIC-Ⅳ database,and compared with the traditional algorithms.Results The sensitivity,specificity,accuracy and AUC of the algorithm were 87.15％,89.23％,88.34％and 0.923 4,respectively,all of which outperformed those of the traditional algorithms.Conclusion The ARDS severity identification algorithm based on ensemble learning is capable of achieving continuous and real-time monitoring of the severity of ARDS,thereby offering robust support for the early identification and warning of ARDS in patients.[Chinese Medical Equipment Journal,2025,46(2):1-9]

Objective To propose a novel identification algorithm based on ensemble learning for assessing the severity of acute respiratory distress syndrome(ARDS)to achieve continuous monitoring of the disease severity.Methods Firstly,leve-raging the open-source MIMIC-Ⅳ database,a variety of non-invasive physiological parameters of patients were extracted and subjected to preliminary preprocessing.A multivariate feature selection algorithm was employed to rank these parameters and calculate feature importance scores through weighted computation.Secondly,based on the feature importance scores,a subset search algorithm was utilized to identify the subset of features that could yield optimal performance across four machine learning algorithms:neural networks,logistic regression,AdaBoost and XGBoost.Finally,a soft voting ensemble method was designed using a generalized linear regression model to integrate the results of each single machine learning algorithm,and a multivariate ensemble learning algorithm was proposed by combining the optimal feature subsets.The algorithm proposed when used to identify the severity of ADRS was evaluated with MIMIC-Ⅳ database,and compared with the traditional algorithms.Results The sensitivity,specificity,accuracy and AUC of the algorithm were 87.15％,89.23％,88.34％and 0.923 4,respectively,all of which outperformed those of the traditional algorithms.Conclusion The ARDS severity identification algorithm based on ensemble learning is capable of achieving continuous and real-time monitoring of the severity of ARDS,thereby offering robust support for the early identification and warning of ARDS in patients.[Chinese Medical Equipment Journal,2025,46(2):1-9]

Objective:To investigate the value of nomogram model based on CT features in differentiating ectopic pancreas (EP) from gastrointestinal stromal tumors (GIST) with a long diameter less than 3 cm.Methods:This study was a case-control study. The clinical and imaging data of 43 patients with EP and 90 patients with GIST confirmed by pathology in the Affiliated Hospital of Guizhou Medical University from August 2013 to March 2024 were retrospectively analyzed. Preoperative CT images were analyzed to obtain qualitative features (number of lesions, location, morphology, growth pattern, borders, cystic degeneration, calcification, ulceration, catheter sign, central umbilication) and quantitative features (lesion long diameter, short diameter, long/short diameter, lesion and normal pancreas arterial-phase and venous-phase CT values, and enhancement ratio). Statistical analyses, including independent sample t-tests, Mann-Whitney U tests, χ2 tests, and Fisher exact tests, were performed to compare CT characteristics between the two groups. Binary logistic regression analysis was used to obtain independent predictors to identify the two groups, to establish a joint model, and to draw a nomogram. The discriminative performance of the independent predictors and the combined model was assessed using receiver operating characteristic (ROC) curves, while calibration curves were used to evaluate model fit. Results:The differences in age, location, morphology, border, catheter sign, central umbilication, short diameter, long/short diameter, arteriovenous phase enhancement CT value and arteriovenous phase enhancement ratio were statistically significant between the EP group and the GIST group (all P<0.05). The logistic analysis showed that the differences in age ( OR=0.920, 95% CI 0.885-0.956, P<0.001), border ( OR=5.994, 95% CI 2.111-17.022, P=0.001), long/short diameter ( OR=7.820, 95% CI 1.841-33.224, P=0.005), and venous phase enhancement ratio ( OR=8.847, 95% CI 1.103-70.972, P=0.040) were the independent predictors for distinguishing EP from GIST, and the area under the ROC curve (AUC) were 0.782 (95% CI 0.698-0.866), 0.684 (95% CI 0.600-0.767), 0.705 (95% CI 0.607-0.803), and 0.693 (95% CI 0.605-0.781), respectively. Combined age, border, long diameter/short diameter and venous phase enhancement ratio were plotted in a nomogram with an AUC of 0.881 (95% CI 0.817-0.945), sensitivity and specificity of 74.4% and 93.3%, respectively. The calibration curve demonstrated a strong agreement between predicted and actual probabilities (Hosmer-Lemeschow test, P=0.267). Conclusions:CT imaging reveals significant differences between EP and small GISTs (<3 cm). EP is more likely when patients are younger and lesions exhibit indistinct borders, a higher long-to-short diameter ratio, and greater venous-phase enhancement. The nomogram derived from CT features provides a valuable tool for differentiating EP from GIST.