METHODS: Thirty-three cell death-associated genes were selected from a literature review. The "DESeq2" R package was used to identify differentially expressed cell death-associated genes between normal prostate tissue (GTEx) and prostate cancer tissue (TCGA) samples. Biological functional enrichment analysis of differentially expressed cell death genes was performed using R statistical software packages, such as "clusterProfiler," "org.Hs.eg.db," "enrichplot," "ggplot2," and "GOplot." Univariate Cox and LASSO Cox regression analyses were conducted to identify prognostic genes associated with the immune microenvironment using the "survival" package. Finally, a predictive model was established based on Gleason score, T stage, and cell death-associated genes.odel was established based on Gleason score, T stage, and cell death-associated genes.
RESULTS: Seventeen differentially expressed genes related to pyroptosis were screened out. Based on these differentially expressed genes, biological function enrichment analysis showed that they were related to pyroptosis of prostate cells. Based on univariate Cox and (LASSO) Cox regression analysis, four pyroptosis-related genes (CASP3, PLCG1, GSDMB, GPX4) were determined to be related to the prognosis of prostate cancer, and the immune correlation analysis of the four pyroptosis-related genes was performed. The expression of CASP3, PLCG1 and GSDMB was positively correlated with the proportion of immune cells, and the expression of GPX4 was negatively correlated with the proportion of immune cells. A predictive nomogram was established by combining Gleason score, T and pyroptosis genes. The nomogram was accompanied by a calibration curve and used to predict 1 -, 2 -, and 5-year survival in PAAD patients.
CONCLUSION: Cell death-associated genes (CASP3, PLCG1, GSDMB, GPX4) play crucial roles in modulating the immune microenvironment and can be used to predict the prognosis of prostate cancer.
METHODS: 99 adult patients at four training and research hospitals who had undergone an abdominal contrast computed tomography scan in the ED with the final diagnosis of splenic abscess from January 2004 to November 2017 were recruited. Evaluation for sarcopenia was performed via calculating the psoas cross-sectional area at the level of the third lumbar vertebra and normalising for height, before checking it against pre-defined values. Univariate analyses were used to evaluate the differences between survivors and non-survivors. Sensitivity, specificity, and predictive values of the presence of sarcopenia in predicting in-hospital mortality were calculated. Kaplan-Meier methods, log-rank test, and Cox proportional hazards model were also performed to examine survival between groups with sarcopenia versus non-sarcopenia.
RESULTS: Splenic abscess patients with sarcopenia were 7.56 times more at risk of in-hospital mortality than those without sarcopenia (multivariate-adjusted HR: 7.56; 95% CI: 1.55-36.93). Presence of sarcopenia was found to have 84.62% sensitivity and 96.49% negative predictive value in predicting mortality.
CONCLUSION: Sarcopenia is associated with poor prognoses of in-hospital mortality in patients with splenic abscess presenting to the ED. We recommend its use in the ED to rapidly risk stratify and predict outcome to guide treatment strategies.
MATERIALS AND METHODS: We analyzed 46 histologically proven glioma (WHO grades II-IV) patients using standard 3T magnetic resonance imaging brain tumor protocol and IOP sequence. Lipid fraction was derived from the IOP sequence signal-loss ratio. The lipid fraction of solid nonenhancing region of glioma was analyzed, using a three-group analysis approach based on volume under surface of receiver-operating characteristics to stratify the prognostic factors into three groups of low, medium, and high lipid fraction. The survival outcome was evaluated, using Kaplan-Meier survival analysis and Cox regression model.
RESULTS: Significant differences were seen between the three groups (low, medium, and high lipid fraction groups) stratified by the optimal cut-off point for overall survival (OS) (p ≤ 0.01) and time to progression (p ≤ 0.01) for solid nonenhancing region. The group with high lipid fraction had five times higher risk of poor survival and earlier time to progression compared to the low lipid fraction group. The OS plot stratified by lipid fraction also had a strong correlation with OS plot stratified by WHO grade (R = 0.61, p < 0.01), implying association to underlying histopathological changes.
CONCLUSION: The lipid fraction of solid nonenhancing region showed potential for prognostication of glioma. This method will be a useful adjunct in imaging protocol for treatment stratification and as a prognostic tool in glioma patients.
METHODS: Demographic, histopathologic and clinical outcomes of 93 PABC patients obtained from our database were compared to 1424 non-PABC patients.
RESULTS: PABC patients presented at a younger age. They had higher tumor and nodal stages, higher tumor grade, were more likely to be hormone receptor negative and had a higher incidence of multicentric and multifocal tumors. Histological examination after definitive surgery showed no significant difference in tumor size and number of positive lymph nodes suggesting similar neoadjuvant treatment effects. Despite this, PABC patients had worse outcomes with poorer overall survival and disease-free survival, OS (P
METHODS: The Web of Science, SCOPUS, and PUBMED databases were searched to find eligible studies. The standardized mean difference (SMD) and 95% confidence interval (CI) were used to evaluate the differences in NLR, MLR, and PLR levels between SAP and non-SAP patients. The meta-analysis was conducted using the software "Review Manager" (RevMan, version 5.4.1, September 2020). The random-effect model was used for the pooling analysis if there was substantial heterogeneity. Otherwise, the fixed-effect model was adopted.
RESULTS: Twelve studies comprising 6302 stroke patients were included. The pooled analyses revealed that patients with SAP had significantly higher levels of NLR, MLR, and PLR than the non-SAP group. The SMD, 95% CI, p-value, and I2 for them were respectively reported as (0.88, 0.70-1.07, .00001, 77%); (0.94, 0.43-1.46, .0003, 93%); and (0.61, 0.47-0.75, .001, 0%). Subgroup analysis of NLR studies showed no significant differences in the effect size index between the severity of the stroke, the sample size, and the period between the stroke onset and the blood sampling.
CONCLUSION: This systematic review and meta-analysis suggest that an elevated NLR, MLR, and PLR were associated with SAP, indicating that they could be promising blood-based biomarkers for predicting SAP. Large-scale prospective studies from various ethnicities are recommended to validate this association before they can be applied in clinical practice.