High normal alanine aminotransferase is an indicator for better response to antiviral therapy in chronic hepatitis B.

Background: Evidence shows people living with CHB even with a normal ALT (40U/L as threshold) suffer histological disease and there is still little research to evaluate the potential benefit of antiviral benefits in them. Methods: We retrospectively examined 1352 patients who underwent liver biopsy from 2017 to 2021 and then obtained their 1-year follow-up data to analyze. Results: ALT levels were categorized into high and low, with thresholds set at >29 for males and >15 for females through Youden's Index. The high normal ALT group showed significant histological disease at baseline (56.43% vs 43.82%, p< 0.001), and better HBV DNA clearance from treatment using PSM (p=0.005). Similar results were obtained using 2016 AASLD high normals (male >30, female >19). Further multivariate logistic analysis showed that high normal ALT (both criterias) was an independent predictor of treatment (OR 1.993, 95% CI 1.115-3.560, p=0.020; OR 2.000, 95% CI 1.055-3.793, p=0.034) Both of the models had higher AUC compared with current scoring system, and there was no obvious difference between the two models (AUC:0.8840 vs 0.8835). Conclusion: Male >30 or female >19 and Male >29 or female>15 are suggested to be better thresholds for normal ALT. Having a high normal ALT in CHB provides a potential benefit in antiviral therapy.

A novel genomic-clinicopathologic nomogram to improve prognosis prediction of hepatocellular carcinoma.

There is a lack of precise and clinical accessible model to predict the prognosis of hepatocellular carcinoma (HCC) in clinic practice currently. Here, an inclusive nomogram was developed by integrating genomic markers and clinicopathologic factors for predicting the outcome of patients with HCC. A total of 365 samples of HCC were obtained from the Cancer Genome Atlas (TCGA) database. The LASSO analysis was carried out to identify HCC-related mRNAs, and the multivariate Cox regression analysis was used to construct a genomic-clinicopathologic nomogram. As results, 9 mRNAs were finally identified as prognostic indicators, including RGCC, CDH15, XRN2, RAB3IL1, THEM4, PIF1, MANBA, FKTN and GABARAPL1, and used to establish a 9-mRNA classifier. Additionally, an inclusive nomogram was built up by combining the 9-mRNA classifier (P < 0.001) and clinicopathologic factors including age (P = 0.006) and metastasis (P < 0.001) to predict the mortality of HCC patients. Time-dependent receiver operating characteristic, index of concordance and calibration analyses indicated favorable accuracy of the model. Decision curve analysis suggested that appropriate intervention according to the established nomogram will bring net benefit when threshold probability was above 25%. The genomic-clinicopathologic model could be a reliable tool for predicting the mortality, helping determining the individualized treatment and probably improving HCC survival.

Nomogram predicting cancer-specific mortality in patients with esophageal adenocarcinoma: a competing risk analysis.

BACKGROUND: Many factors are reported to be related to the prognosis of patients with esophageal adenocarcinoma (EAC), but few reliable and straightforward tools for clinicians to estimate individual mortalities have been developed. This study aimed to evaluate the probability of cancer-specific death for patients with EAC and to build nomograms for predicting long-term cancer-specific mortality and overall mortality for EAC patients. METHODS: Between 2004 and 2013, a total of 20,623 patients were identified from the surveillance, epidemiology, and end results (SEER) database and randomly divided into training (N=14,436) and validation (N=6,187) cohorts. The cumulative incidence functions (CIFs) of EAC-specific death and other causes were evaluated at the 1st, 3rd, and 5th year after diagnosis. We integrated the significant prognostic factors to construct nomograms and subjected them to internal and external validation. RESULTS: The CIFs of EAC-specific survival at 1, 3, and 5 years after diagnosis were 60.9%, 37.1%, and 31.3%, respectively. Predictors for cancer-specific mortality for EAC comprised tumor grade, tumor extension, the involvement of lymph nodes, distant metastasis, surgery of primary site, insurance recode, and marital status. For overall mortality, it also included the predictor of age at diagnosis. The nomograms were well-calibrated and had good discriminative ability with concordance indexes (c-indexes) of 0.733, 0.728, and 0.728 for 1-, 3- and 5-year prognosis prediction of EAC-specific mortality respectively, and 0.726, 0.720, 0.719 for 1-, 3-, and 5-year prognosis prediction of overall mortality respectively. CONCLUSIONS: We proposed and validated the effective and convenient nomograms to predict cancer-specific mortality and the overall mortality for patients with EAC, which only require the basic information available in clinical practice.

Curcumin protects neural cells against ischemic injury in N2a cells and mouse brain with ischemic stroke.

Background and Purpose: Curcumin, a natural antioxidant isolated from Curcuma longa, has been reported to exert neuroprotective effect in animal models of ischemic stroke. However, the underlying mechanism is still not fully understood. The purpose of this study was to investigate the effect of curcumin treatment on neuronal apoptosis in the periinfarct cortex after cerebral ischemia/reperfusion (I/R) injury and in mouse N2a cells after oxygen-glucose deprivation/reoxygenation (OGD/R) injury and its underlying mechanism. Methods: The cerebral I/R injury was established by 1-hr middle cerebral artery occlusion (MCAO) and reperfusion in mice. Infarct volume was determined by TTC staining, and neurological score was evaluated by mNSS. Cell morphology in the ischemic boundary zone were detected by HE staining. The number and apoptotic rate of neurons in ischemic boundary zone were assayed by immunohistochemistry and TUNEL, respectively. Mouse neuroblastoma N2a cells were subjected to OGD/R. Cell viability was assessed with CCK-8. The mitochondrial membrane potential was measured using JC-1 staining. The expression of Bax, Bcl-2, and caspase-3 was detected using Western blotting. Besides, cellular distribution of Bax was determined by immunofluorescence assays. Results: Curcumin treatment reduced infarct volume, improved neurological function, alleviated the morphological damage of neurons, and increased neuronal survival rate after I/R injury in vivo. Moreover, curcumin treatment improved cell viability, reduced cell apoptosis, increased Bcl-2 protein levels while decreased Bax and caspase-3 expressions in mouse N2a cells after OGD/R injury. Besides, curcumin treatment inhibited Bax activation and maintained mitochondrial membrane integrity. Conclusion: Curcumin promotes neuron survival in vivo and in vitro to exert neuroprotective effects against ischemia injury. Moreover, our results for the first time demonstrated curcumin inhibited ischemia-induced mitochondrial apoptosis via restricting Bax activation, which may be one of the possible mechanisms underlying the neuroprotective effects of curcumin.

A microRNA expression profile for vascular invasion can predict overall survival in hepatocellular carcinoma.

BACKGROUND: The presence of vascular invasion (VI) in pathology specimens is a well-known unfavorable prognostic factor of hepatocellular carcinoma (HCC) recurrence and overall survival (OS). We investigated the vascular invasion related microRNA (miRNA) expression profiles and potential of prognostic value in HCC. METHODS: MiRNA and mRNA expression data for HCC were accessed from The Cancer Genome Atlas (TCGA). LASSO logistic regression models were used to develop a miRNA-based classifier for predicting VI. The predictive capability was accessed by area under receiver operating characteristics (AUC). Concordance index (C-index) and time-dependent receiver operating characteristic (td-ROC) were used to determine its prognostic value. We validated the predictive and prognostic accuracy of this classifier in an external independent cohort of 127 patients. Functionally relevant targets of miRNAs were determined using miRNA target prediction, experimental validation and correlation of miRNA and mRNA expression data. RESULTS: A 16-miRNA-based classifier was developed which identified VI accurately, with AUC of 0.731 and 0.727 in TCGA set and validation cohort, respectively. C-index and td-ROC showed that the classifier was able to stratify patients into risk groups strongly associated with OS. When stratified by tumor characteristics, the classifier was still a clinically and statistically significant prognostic model. The predictive and prognostic accuracy of the classifier was confirmed in validation cohort. Vascular invasion related miRNA/target pairs were identified by integrating expression patterns of predicted targets, which were validated in cell lines. CONCLUSIONS: A multi-miRNA-based classifier developed based on the presence of VI, which could effectively predict OS in HCC.