Kumarzimmerman4023

Currently, insufficient clinical data are available to address whether low-level viremia (LLV) observed during antiviral treatment will adversely affect the clinical outcome or whether treatment strategies should be altered if LLV occurs. This study compared the clinical outcomes of patients with a maintained virological response (MVR) and patients who experienced LLV and their treatment strategies.

A retrospective cohort of 674 patients with chronic hepatitis B virus (HBV) infection who received antiviral treatment for more than 12 months was analyzed for the development of end-stage liver disease and treatment strategies during the follow-up period. End-stage liver disease included decompensated liver cirrhosis and hepatocellular carcinoma (HCC).

During a median 42-month follow-up, end-stage liver disease developed more frequently in patients who experienced LLV than in those who experienced MVR (7.73% and 15.85% vs. 0.77% and 5.52% at 5 and 10 years, respectively;

=0.000). The trend was consistent after propensity score matching. In the high-risk group of four HCC risk models, LLV patients had a higher risk of HCC development (

<0.05). By Cox proportional hazard model analysis, LLV was an independent risk factor for end-stage liver disease and HCC (hazard ratio [HR]=6.280, confidence interval [CI]=2.081-18.951,

=0.001; HR=5.108, CI=1.392-18.737, respectively;

=0.014). Patients achieved a lower rate of end-stage liver disease by adjusting treatment compared to continuing the original treatment once LLV occurred (

<0.05).

LLV is an independent risk factor for end-stage liver disease and HCC, and treatment adjustments can be considered.

LLV is an independent risk factor for end-stage liver disease and HCC, and treatment adjustments can be considered.

It is critical but challenging to predict the prognosis of hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF). This study systematically summarized and evaluated the quality and performance of available clinical prediction models (CPMs).

A keyword search of articles on HBV-ACLF CPMs published in PubMed from January 1995 to April 2020 was performed. Both the quality and performance of the CPMs were assessed.

Fifty-two CPMs were identified, of which 31 were HBV-ACLF specific. The modeling data were mostly derived from retrospective (83.87%) and single-center (96.77%) cohorts, with sample sizes ranging from 46 to 1,202. Three-month mortality was the most common endpoint. The Asian Pacific Association for the Study of the Liver consensus (51.92%) and Chinese Medical Association liver failure guidelines (40.38%) were commonly used for HBV-ACLF diagnosis. Serum bilirubin (67.74%), the international normalized ratio (54.84%), and hepatic encephalopathy (51.61%) were the most frequent variables used in models. Model discrimination was commonly evaluated (88.46%), but model calibration was seldom performed. The model for end-stage liver disease score was the most widely used (84.62%); however, varying performance was reported among the studies.

Substantial limitations lie in the quality of HBV-ACLF-specific CPMs. Disease severity of study populations may impact model performance. The clinical utility of CPMs in predicting short-term prognosis of HBV-ACLF remains to be undefined.

Substantial limitations lie in the quality of HBV-ACLF-specific CPMs. Disease severity of study populations may impact model performance. The clinical utility of CPMs in predicting short-term prognosis of HBV-ACLF remains to be undefined.

Timely and effective assessment scoring systems for predicting the mortality of patients with hepatitis E virus-related acute liver failure (HEV-ALF) are urgently needed. The present study aimed to establish an effective nomogram for predicting the mortality of HEV-ALF patients.

The nomogram was based on a cross-sectional set of 404 HEV-ALF patients who were identified and enrolled from a cohort of 650 patients with liver failure. this website To compare the performance with that of the model for end-stage liver disease (MELD) scoring and CLIF-Consortium-acute-on-chronic liver failure score (CLIF-C-ACLFs) models, we assessed the predictive accuracy of the nomogram using the concordance index (C-index), and its discriminative ability using time-dependent receiver operating characteristics (td-ROC) analysis, respectively.

Multivariate logistic regression analysis of the development set carried out to predict mortality revealed that γ-glutamyl transpeptidase, albumin, total bilirubin, urea nitrogen, creatinine, international normalized ratio, and neutrophil-to-lymphocyte ratio were independent factors, all of which were incorporated into the new nomogram to predict the mortality of HEV-ALF patients. The area under the curve of this nomogram for mortality prediction was 0.671 (95% confidence interval 0.602-0.740), which was higher than that of the MELD and CLIF-C-ACLFs models. Moreover, the td-ROC and decision curves analysis showed that both discriminative ability and threshold probabilities of the nomogram were superior to those of the MELD and CLIF-C-ACLFs models. A similar trend was observed in the validation set.

The novel nomogram is an accurate and efficient mortality prediction method for HEV-ALF patients.

The novel nomogram is an accurate and efficient mortality prediction method for HEV-ALF patients.

This study aimed to determine the performance of the non-invasive score using noncontrast-enhanced MRI (CHESS-DIS score) for detecting portal hypertension in cirrhosis.

In this international multicenter, diagnostic study (ClinicalTrials.gov, NCT03766880), patients with cirrhosis who had hepatic venous pressure gradient (HVPG) measurement and noncontrast-enhanced MRI were prospectively recruited from four university hospitals in China (

=4) and Turkey (

=1) between December 2018 and April 2019. A cohort of patients was retrospectively recruited from a university hospital in Italy between March 2015 and November 2017. After segmentation of the liver on fat-suppressed T1-weighted MRI maps, CHESS-DIS score was calculated automatically by an in-house developed code based on the quantification of liver surface nodularity.

A total of 149 patients were included, of which 124 were from four Chinese hospitals (training cohort) and 25 were from two international hospitals (validation cohort). A positive correlation between CHESS-DIS score and HVPG was found with the correlation coefficients of 0.36 (

<0.0001) and 0.55 (

<0.01) for the training and validation cohorts, respectively. The area under the receiver operating characteristic curve of CHESS-DIS score in detection of clinically significant portal hypertension (CSPH) was 0.81 and 0.9 in the training and validation cohorts, respectively. The intraclass correlation coefficients for assessing the inter- and intra-observer agreement were 0.846 and 0.841, respectively.

A non-invasive score using noncontrast-enhanced MRI was developed and proved to be significantly correlated with invasive HVPG. Besides, this score could be used to detect CSPH in patients with cirrhosis.

A non-invasive score using noncontrast-enhanced MRI was developed and proved to be significantly correlated with invasive HVPG. Besides, this score could be used to detect CSPH in patients with cirrhosis.

Intrahepatic cholangiocarcinoma (ICC) is a malignant tumor derived from intrahepatic bile duct epithelial cells. Accumulating studies report that microRNAs are widely involved in tumor migration and metastasis by regulation of target genes. miR-7-5p has been confirmed to inhibit tumor metastasis and to be related to prognosis for several malignant tumors. Our study investigated the underlying functions of miR-7-5p in ICC.

The expression of miR-7-5p in ICC tissues but also in ICC cell lines was analyzed by real-time PCR. By analyzing the relationship between the clinicopathological parameters of 60 ICC patients and the expression level of miR-7-5p, the effect of miR-7-5p on the prognosis was clarified. After transfected with miR-7-5p mimics or miR-7-5p inhibitor, cell counting kit-8 assay was applied to evaluate the cells proliferation, flow cytometry was applied to analyze the cells apoptosis, wound healing assay and transwell chamber assay were applied to analyze the cell invasion and migration. A lucifesuggest that miR-7-5p plays a pivotal role in ICC invasion by regulating MyD88. Ampliative insight into the key factors of ICC invasion may result in the development of new treatment options for ICC.

The immune system plays vital roles in hepatocellular carcinoma (HCC) initiation and progression. The present study aimed to construct an immune-gene related prognostic signature (IRPS) for predicting the prognosis of HCC patients.

Gene expression data were retrieved from The Cancer Genome Atlas database. The IRPS was established via least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis. The prognostic values of the IRPS were further validated using the International Cancer Genome Consortium (ICGC) dataset.

A total of 62 genes were identified as candidate immune-related prognostic genes. According to the results of Lasso and multivariate Cox regression analysis, we established an IRPS and confirmed its stability and reliability in the ICGC dataset. The IRPS was significantly associated with advanced clinicopathological characteristics. Both Cox regression analyses revealed that the IRPS could be independent risk factors influencing prognosis of HCC patients. The relationships between the IRPS and infiltration of immune cells demonstrated that the IRPS was associated with immune cell infiltration. Furthermore, a nomogram was constructed to estimate the survival probability of HCC patients.

The IRPS was effective for predicting prognosis of HCC patients, which might serve as novel prognostic and therapeutic biomarkers for HCC.

The IRPS was effective for predicting prognosis of HCC patients, which might serve as novel prognostic and therapeutic biomarkers for HCC.We argue that artificial networks are explainable and offer a novel theory of interpretability. Two sets of conceptual questions are prominent in theoretical engagements with artificial neural networks, especially in the context of medical artificial intelligence (1) Are networks explainable, and if so, what does it mean to explain the output of a network? And (2) what does it mean for a network to be interpretable? We argue that accounts of "explanation" tailored specifically to neural networks have ineffectively reinvented the wheel. In response to (1), we show how four familiar accounts of explanation apply to neural networks as they would to any scientific phenomenon. We diagnose the confusion about explaining neural networks within the machine learning literature as an equivocation on "explainability," "understandability" and "interpretability." To remedy this, we distinguish between these notions, and answer (2) by offering a theory and typology of interpretation in machine learning. Interpretation is something one does to an explanation with the aim of producing another, more understandable, explanation. As with explanation, there are various concepts and methods involved in interpretation Total or Partial, Global or Local, and Approximative or Isomorphic. Our account of "interpretability" is consistent with uses in the machine learning literature, in keeping with the philosophy of explanation and understanding, and pays special attention to medical artificial intelligence systems.