METHODS: The expression of PXMP4 mRNA in HCC tissues and corresponding adjacent tissues was detected by Q-PCR, and the expression of PXMP4 protein was detected by Western blot and immunohistochemistry. The correlation of PXMP4 protein expression with clinicopathological features and prognosis of HCC was analyzed.
RESULTS: The expression levels of PXMP4 mRNA and protein in HCC tissues were significantly higher than those in adjacent tissues (P < 0.05), and its high expression was significantly correlated with tumor differentiation, lymph node metastasis, depth of invasion and TNM stage (P < 0.05). Patients with high expression of PXMP4 had a poor prognosis (P < 0.05).
CONCLUSION: The high expression of PXMP4 may promote the occurrence and development of HCC, and inhibition of PXMP4 may be one of the potential molecular targets for targeted therapy of HCC.
MATERIALS AND METHODS: The study included Hirschsprung patients aged ≥3 and <18 years who underwent Yancey- Soave surgery at our hospital. The functional outcomes were evaluated using the Krickenbeck classification to determine voluntary bowel movement (VBM), constipation and soiling.
RESULTS: Most (82.6%) patients showed VBM, 26.1% had constipation and 4.3% suffered from soiling. Among 23 patients who received Yancey-Soave surgery, 8 (34.8%) had eosinophilia and 5 (21.7%) had lymphocytosis. However, no significant differences were observed between eosinophilia and non-eosinophilia groups for VBM (p=1.0), constipation (p= 0.621) or soiling (p=0.738). Similarly, no significant differences were found between lymphocytosis and nonlymphocytosis groups for VBM (p=1.0), constipation (p=0.545) or soiling (p=0.973). Moreover, no other prognostic factors affected the functional outcomes after Yancey- Soave surgery (p>0.05).
CONCLUSION: Our study shows that eosinophilia and lymphocytosis might not affect the functional outcome of patients with HSCR following Yancey-Soave surgery. In addition, sex, aganglionosis type, age at definitive surgery and nutritional status might not influence the functional outcome after definitive surgery. Further, a more extensive study is essential to clarify our findings.
METHODS: A cohort of 4,240 Sepsis-3 patients was analyzed, with 783 experiencing 30-day mortality and 3,457 surviving. Fifteen biomarkers were selected using feature ranking methods, including Extreme Gradient Boosting (XGBoost), Random Forest, and Extra Tree, and the Logistic Regression (LR) model was used to assess their individual predictability with a fivefold cross-validation approach for the validation of the prediction. The dataset was balanced using the SMOTE-TOMEK LINK technique, and a stacking-based meta-classifier was used for 30-day mortality prediction. The SHapley Additive explanations analysis was performed to explain the model's prediction.
RESULTS: Using the LR classifier, the model achieved an area under the curve or AUC score of 0.99. A nomogram provided clinical insights into the biomarkers' significance. The stacked meta-learner, LR classifier exhibited the best performance with 95.52% accuracy, 95.79% precision, 95.52% recall, 93.65% specificity, and a 95.60% F1-score.
CONCLUSIONS: In conjunction with the nomogram, the proposed stacking classifier model effectively predicted 30-day mortality in Sepsis patients. This approach holds promise for early intervention and improved outcomes in treating Sepsis cases.
Methods: This worldwide multicentre observational study included 153 surgical departments across 56 countries over a 4-month study period between February 1, 2018, and May 31, 2018.
Results: A total of 3137 patients were included, with 1815 (57.9%) men and 1322 (42.1%) women, with a median age of 47 years (interquartile range [IQR] 28-66). The overall in-hospital mortality rate was 8.9%, with a median length of stay of 6 days (IQR 4-10). Using multivariable logistic regression, independent variables associated with in-hospital mortality were identified: age > 80 years, malignancy, severe cardiovascular disease, severe chronic kidney disease, respiratory rate ≥ 22 breaths/min, systolic blood pressure < 100 mmHg, AVPU responsiveness scale (voice and unresponsive), blood oxygen saturation level (SpO2) < 90% in air, platelet count < 50,000 cells/mm3, and lactate > 4 mmol/l. These variables were used to create the PIPAS Severity Score, a bedside early warning score for patients with acute peritonitis. The overall mortality was 2.9% for patients who had scores of 0-1, 22.7% for those who had scores of 2-3, 46.8% for those who had scores of 4-5, and 86.7% for those who have scores of 7-8.
Conclusions: The simple PIPAS Severity Score can be used on a global level and can help clinicians to identify patients at high risk for treatment failure and mortality.