Int J Med Sci
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Observational Study
Development and validation a simple model for identify malignant ascites.
The differential diagnosis of benign ascites and malignant ascites is incredibly challenging for clinicians. This research aimed to develop a user-friendly predictive model to discriminate malignant ascites from non-malignant ascites through easy-to-obtain clinical parameters. All patients with new-onset ascites fluid were recruited from January 2014 to December 2018. ⋯ With a cut-off level of 0.83, the sensitivity, specificity, accuracy, and area under the ROC of the model for identifying malignant ascites in the development dataset were 84.7%, 88.8%, 87.6%, and 0.874 (95% confidence interval [CI], 0.822-0.926), respectively, and 80.9%, 82.6%, 81.5%, and 0.863 (95% CI,0.817-0.913) in the validation dataset, respectively. The diagnostic model has a similar high diagnostic performance in both the development and validation datasets. The mathematical diagnostic model based on the five markers is a user-friendly method to differentiate malignant ascites from benign ascites with high efficiency.
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Large-scale loss-of-function screening database such as Cancer Dependency Map (Depmap) provide abundant resources. Investigation of these potential dependency genes from human cancer cell lines in the real-world patients cohort would evaluate their prognostic value thus facilitate their clinical application and guide drug development. ⋯ A dependency gene validated in cell lines didn't directly represent its role in corresponding patients with same histological type and their prognostic value might be determined by multiple factors including dependency driven types, genetic alteration rates and expression levels. GET4 and CRB3 were the independent prognostic factors for ccRCC patients. CRB3 seemed like a potential broad tumor suppressor gene while GET4 might be a ccRCC preferential dependency gene with a ligandable structure.
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Objective: The easy liver fibrosis test (eLIFT) is a novel predictor of liver fibrosis in chronic liver disease (CLD). This study aimed to evaluate the predictive value of the eLIFT for liver inflammation and fibrosis in CLD patients. Methods: We enrolled 1125 patients with CLD who underwent liver biopsy. ⋯ When discriminating G≥3 inflammation, the AUROC of the eLIFT was comparable to that of the APRI and GPR but superior to that of the FIB-4. There were no significant differences between the four indexes for predicting S≥2 and S4. Conclusion: The eLIFT is a potentially useful noninvasive predictor of liver inflammation and fibrosis in patients with CLD.
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Background: B-type natriuretic peptide (BNP) is a well-known predictor for prognosis in patients with cardiac and renal diseases. However, there is a lack of studies in patients with advanced hepatic disease, especially patients who underwent liver transplantation (LT). We evaluated whether BNP could predict the prognosis of patients who underwent LT. ⋯ The optimal cutoff values for cBNP at T2 and T4 were 137 and 187, respectively. Conclusions: The cBNP model showed the improved predictive ability for mortality within 1-month of LT. It could help clinicians stratify mortality risk and be a useful biomarker in patients undergoing LT.
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Background: Retinopathy of prematurity (ROP) is a retinal disease that causes blindness in premature infants. This study aimed to reveal the changes in amino acids and derivatives in the plasma of ROP patients compared with premature infants without ROP. Methods: Metabolomics targeting amino acids and their derivatives was conducted to assess their plasma levels in ROP patients (n=58) and premature infants without ROP (n=25), and KEGG pathway analysis was used to identify the involved pathways. ⋯ The involved pathways included biosynthesis of amino acids, arginine and proline metabolism, and arginine biosynthesis. Conclusion: The plasma levels of citrulline, creatinine, arginine, and aminoadipic acid were significantly changed in ROP patients. These metabolites could be considered potential biomarkers of ROP, and their related metabolic pathways might be involved in ROP pathogenesis.