ABSTRACT
Causal inference in the field of infectious disease attempts to gain insight into the potential causal nature of an association between risk factors and diseases. Simulated causality inference experiments have shown preliminary promise in improving understanding of the transmission of infectious diseases but still lack sufficient quantitative causal inference studies based on real-world data. Here, we investigate the causal interactions between three different infectious diseases and related factors, using causal decomposition analysis, to characterize the nature of infectious disease transmission. We show that the complex interactions between infectious disease and human behavior have a quantifiable impact on transmission efficiency of infectious diseases. Our findings, by shedding light on the underlying transmission mechanism of infectious diseases, suggest that causal inference analysis is a promising approach to determine epidemiological interventions.
Subject(s)
Communicable Diseases , Humans , Causality , Communicable Diseases/epidemiology , Risk FactorsABSTRACT
Glioma is a type of tumor occurring in the central nervous system. In recent decades, specific gene mutations and molecular aberrations have been used to conduct the glioma classification and clinical decisions. Siglec10 is a member of the sialic acid-binding immunoglobulin superfamily. In this study, we investigated the expression and functions of siglec10 in gliomas. We analyzed the siglec10 expression in glioma patients with immunohistochemical (IHC) staining and evaluated the survival prognosis. The high siglec10 expression had a shorter survival prognosis than the low siglec10 expression in patients, especially in malignant gliomas. Bioinformatic datasets, including TCGA and CGGA, validated the IHC results and discovered the expression of siglec10 was higher in the malignant subtype than a benign subtype of gliomas. So, siglec10 is associated with the poor prognosis of gliomas. Furthermore, the related mechanisms of siglec10 in gliomas were investigated by functional enrichment analysis, including GSEA, GO, and KEGG analysis. Siglec10 was correlated with inflammatory mediators, inflammatory cells, and inflammatory pathways in gliomas. Siglec10 might take part in the immune response in the tumor microenvironment to induce glioma's progression and metastasis. This study showed siglec10 was a biomarker in glioma, and it might be the potential target of glioma immunotherapy in the future.
ABSTRACT
Background: No epidemiological study has determined the association between the anion gap (AG) and all-cause mortality in cerebral infarction patients after treatment with rtPA. This study is aimed at using AG levels as a prognostic factor for evaluating cerebral infarction patients after receiving rtPA treatment and to help the resident physicians accurately evaluate the therapeutic plan of rtPA. Methods: We extracted clinical data from the public database (MIMIC-IV database V1.0) and used the Kaplan-Meier curve to estimate the survival probabilities of cerebral infarction patients after rtPA treatment for the one-year, four-year, and whole period by log-rank test in 948 intensive care unit patients. Cox proportional hazard models were used to assess the association between AG and one-year, four-year, and whole period mortality in cerebral infarction patients after treatment with rtPA. Results: Kaplan-Meier survival curve indicated a higher AG value is significantly associated with an increased risk of one-year, four-year, and whole-period all-cause mortality in cerebral infarction patients after treatment with rtPA. Model I adjusted for ethnicity, age, gender, and skin tone. Model II adjusted for ethnicity, age, gender, skin tone, hypertension, diabetes, coronary atherosclerosis, congestive heart failure, peripheral vascular, hyperlipidemia, acute myocardial infarction (AMI), respiratory failure, and end-stage renal diseaseesrd (ESRD). On the basis of model II, model III adjusted for WBC, BUN, creatinine, platelet, MCH, MCHC, MCV, RBC, and RDW. In addition, there was better predictive ability between higher AG levels and mortality in certain subgroups, such as patients with platelet ≤ 247, RBC > 3.11. Conclusion: Serum AG is positively related to all-cause mortality in cerebral infarction patients after treatment with rtPA.
