Controversies and Opportunities in the Use of Inflammatory Markers for Diagnosis or Risk Prediction in Fatty Liver Disease

In the Western society, non-alcoholic fatty liver disease (NAFLD), characterized by the excessive accumulation of fat in the liver, represents the most common cause of chronic liver disease. If left untreated, approximately 15%-20% of patients with NAFLD will progress to non-alcoholic steatohepatitis (NASH), in which lobular inflammation, hepatocyte ballooning and fibrogenesis further contribute to a distorted liver architecture and function. NASH initiation has significant effects on liver-related mortality, as even the presence of early stage fibrosis increases the chances of adverse patient outcome. Therefore, adequate diagnostic tools for NASH are needed, to ensure that relevant therapeutic actions can be taken as soon as necessary. To date, the diagnostic gold standard remains the invasive liver biopsy, which is associated with several drawbacks such as high financial costs, procedural risks, and inter/intra-observer variability in histology analysis. As liver inflammation is a major hallmark of disease progression, inflammation-related circulating markers may represent an interesting source of non-invasive biomarkers for NAFLD/NASH. Examples for such markers include cytokines, chemokines or shed receptors from immune cells, circulating exosomes related to inflammation, and changing proportions of peripheral blood mononuclear cell (PBMC) subtypes. This review aims at documenting and critically discussing the utility of such novel inflammatory markers for NAFLD/NASH-diagnosis, patient stratification and risk prediction

Serum CXCL5 Detects Early Hepatocellular Carcinoma and Indicates Tumor Progression

Chemokines or chemotactic cytokines play a pivotal role in the immune pathogenesis of liver cirrhosis and hepatocellular carcinoma (HCC). Nevertheless, comprehensive cytokine profiling data across different etiologies of liver diseases are lacking. Chemokines might serve as diagnostic and prognostic biomarkers. In our study, we analyzed serum concentrations of 12 inflammation-related chemokines in a cohort of patients (n = 222) with cirrhosis of different etiologies and/or HCC. We compared 97 patients with cirrhosis and treatment-naive HCC to the chemokine profile of 125 patients with cirrhosis but confirmed absence of HCC. Nine out of twelve chemokines were significantly elevated in sera of cirrhotic patients with HCC compared to HCC-free cirrhosis controls (CCL2, CCL11, CCL17, CCL20, CXCL1, CXCL5, CXCL9, CXCL10, CXCL11). Among those, CXCL5, CXCL9, CXCL10, and CXCL11 were significantly elevated in patients with early HCC according to the Barcelona Clinic Liver Cancer (BCLC) stages 0/A compared to cirrhotic controls without HCC. In patients with HCC, CXCL5 serum levels were associated with tumor progression, and levels of CCL20 and CXCL8 with macrovascular invasion. Importantly, our study identified CXCL5, CXCL9, and CXCL10 as universal HCC markers, independent from underlying etiology of cirrhosis. In conclusion, regardless of the underlying liver disease, patients with cirrhosis share an HCC-specific chemokine profile. CXCL5 may serve as a diagnostic biomarker in cirrhotic patients for early HCC detection as well as for tumor progression

Integrative miRNA and Gene Expression Profiling Analysis of Human Quiescent Hepatic Stellate Cells.

Unveiling the regulatory pathways maintaining hepatic stellate cells (HSC) in a quiescent (q) phenotype is essential to develop new therapeutic strategies to treat fibrogenic diseases. To uncover the miRNA-mRNA regulatory interactions in qHSCs, HSCs were FACS-sorted from healthy livers and activated HSCs (aHSCs) were generated in vitro. MiRNA Taqman array analysis showed HSCs expressed a low number of miRNAs (n = 259), from which 47 were down-regulated and 212 up-regulated upon activation. Computational integration of miRNA and gene expression profiles revealed that 66% of qHSC-associated miRNAs correlated with more than 6 altered target mRNAs (17,28 ± 10,7 targets/miRNA) whereas aHSC-associated miRNAs had an average of 1,49 targeted genes. Interestingly, interaction networks generated by miRNA-targeted genes in qHSCs were associated with key HSC activation processes. Next, selected miRNAs were validated in healthy and cirrhotic human livers and miR-192 was chosen for functional analysis. Down-regulation of miR-192 in HSCs was found to be an early event during fibrosis progression in mouse models of liver injury. Moreover, mimic assays for miR-192 in HSCs revealed its role in HSC activation, proliferation and migration. Together, these results uncover the importance of miRNAs in the maintenance of the qHSC phenotype and form the basis for understanding the regulatory networks in HSCs

A hybrid condition-based maintenance policy for continuously monitored components with two degradation thresholds

Condition-based maintenance (CBM) makes use of the actual condition of the component to decide when to maintain and/or replace the component, thereby maximising the lifetime of the machine, while minimising the number of service interventions. In this paper we combine CBM on one (monitored) component, with periodic preventive maintenance (PM) and corrective maintenance (CM) on the other components of the same machine/system. We implement two thresholds on the degradation level to decide when to service the monitored component: when the degradation level of the monitored component surpasses a first ‘opportunistic’ threshold, the monitored component will be serviced together with other components, for instance with a (planned) PM intervention, or upon breakdown of another component, requiring CM. In case none of these opportunities have taken place, and the degradation level surpasses a second ‘intervention’ threshold, an additional maintenance intervention is planned for the monitored component in order to prevent a failure. Both thresholds are optimised to minimise the total expected maintenance costs of the monitored component, or to minimise the downtime of the machine due to maintenance on the monitored component. We perform an extensive numerical experiment to demonstrate the potential gains of this hybrid policy with two thresholds compared to using a traditional PM policy, and we identify its key drivers of performance. We also benchmark our results when only one threshold is implemented. Our model is validated and applied at an OEM in the compressed air and generator industry

A hybrid condition-based maintenance policy for continuously monitored components with two degradation thresholds

Condition-based maintenance (CBM) is a maintenance strategy that makes use of the actual condition (degradation level) of the component to decide when the maintenance and/or replacement of the component is executed, thereby maximising the lifetime of the machine, while minimising the number of maintenance interventions. In this paper, we combine CBM on one (monitored) component, with periodic preventive maintenance (PM) and corrective maintenance (CM) on the other components of the same machine/system. We implement two thresholds on the degradation level to decide when to service the monitored component: when the degradation level of the monitored component surpasses a first ’opportunistic’ threshold, the monitored component will be serviced together with the other components, for instance with a (planned) PM intervention, or upon breakdown of another component, requiring CM. In case none of these opportunities have taken place, and the degradation level surpasses a second ’intervention’ threshold, an additional maintenance intervention is planned for the monitored component in order to prevent a failure. Both thresholds are optimized to minimise the total expected maintenance costs of the monitored component, or to minimise the downtime of the machine due to maintenance on the monitored component. We perform an extensive numerical experiment to demonstrate the potential gains of this hybrid policy compared to using a traditional periodic PM policy, and we identify its key drivers of performance. We also benchmark our results when only one threshold is implemented. Our model is validated and applied at an OEM in the compressed air and generator industry.nrpages: 24status: publishe

Numerical study of inventory management under various maintenance policies

Capital assets, such as manufacturing equipment, require maintenance to remain functioning. Maintenance can be performed when a component breaks down and needs replacement (i.e., corrective maintenance), or the maintenance and part replacement can be performed preventively. Preventive maintenance can be planned on a periodic basis (periodic maintenance), or it can be triggered by a certain monitored condition (condition-based maintenance). Preventive maintenance policies are gaining traction in the business world, but for many companies it is unclear what their impact is on the resulting inventory requirements for the spare parts that are used for the maintenance interventions. We study the impact of the maintenance policy on the inventory requirements and the corresponding costs for a setting that is realistic at an OEM in the compressed air industry. Preventive policies increase the total demand for spare parts compared to corrective maintenance, since the former do not exploit the entire useful life of the components. This leads to higher inventory requirements. At the same time, the preventive policies inhibit advance demand information, as the interventions, and correspondingly the spare parts demands, are planned in advance. Using a simulation study, we show that by using this advance demand information in managing the spare part inventory, the increase in inventory requirements of preventive maintenance policies can to a large extent be offset; for condition-based maintenance, we find that inventories can even be lower compared to corrective maintenance, provided that the advance demand information is used correctly when managing inventories. Our analysis sheds light on the behaviour of the inventory related costs under various maintenance policies.status: publishe

Inventory management under various maintenance policies

Capital assets, such as manufacturing equipment, require maintenance to remain functioning. Maintenance can be performed when a component breaks down and needs replacement (i.e., corrective maintenance), or the maintenance and part replacement can be performed preventively. Preventive maintenance can be planned on a periodic basis (periodic maintenance), or it can be triggered by a certain monitored condition (condition-based maintenance). Preventive maintenance policies are gaining traction in the business world, but for many companies it is unclear what their impact is on the resulting inventory requirements for the spare parts that are used for the maintenance interventions. For a setting that is realistic at an OEM in the compressed air industry with whom we collaborate, we study the impact of the maintenance policy on the inventory requirements and the corresponding costs. Preventive policies increase the total demand for spare parts compared to corrective maintenance, since the former do not exploit the entire useful life of the components. This leads to higher inventory requirements. At the same time, the preventive policies inhibit advance demand information, as the interventions, and correspondingly the spare parts demands, are planned in advance. Using a simulation study, we show that by using this advance demand information in managing the spare part inventory, the increase in inventory requirements of preventive maintenance policies can to a large extent be offset; for condition-based maintenance, we find that inventories can even be lower compared to corrective maintenance, provided that the advance demand information is used correctly when managing inventories. For the OEM with whom we work, our analysis sheds light on the behaviour of the inventory related costs under various maintenance policies.nrpages: 22status: publishe

The miRFIB-Score: A Serological miRNA-Based Scoring Algorithm for the Diagnosis of Significant Liver Fibrosis

Background: The current diagnosis of early-stage liver fibrosis often relies on a serological or imaging-based evaluation of the stage of fibrosis, sometimes followed by an invasive liver biopsy procedure. Novel non-invasive experimental diagnostic tools are often based on markers of hepatocyte damage, or changes in liver stiffness and architecture, which are late-stage characteristics of fibrosis progression, making them unsuitable for the diagnosis of early-stage liver fibrosis. miRNAs control hepatic stellate cell (HSC) activation and are proposed as relevant diagnostic markers. Methods: We investigated the possibility of circulating miRNAs, which we found to be dysregulated upon HSC activation, to mark the presence of significant liver fibrosis (F ≥ 2) in patients with chronic alcohol abuse, chronic viral infection (HBV/HCV), and non-alcoholic fatty liver disease (NAFLD). Results: miRNA-profiling identified miRNA-451a, miRNA-142-5p, Let-7f-5p, and miRNA-378a-3p to be significantly dysregulated upon in vitro HSC activation, and to be highly enriched in their extracellular vesicles, suggesting their potential use as biomarkers. Analysis of the plasma of patients with significant liver fibrosis (F ≥ 2) and no or mild fibrosis (F = 0–1), using miRNA-122-5p and miRNA-29a-3p as positive control, found miRNA-451a, miRNA-142-5p, and Let-7f-5p, but not miRNA-378a-3p, able to distinguish between the two patient populations. Using logistic regression analysis, combining all five dysregulated circulating miRNAs, we created the miRFIB-score with a predictive value superior to the clinical scores Fibrosis-4 (Fib-4), aspartate aminotransferase/alanine aminotransferase (AST/ALT) ratio, and AST to platelet ratio index (APRI). The combination of the miRFIB-score with circulating PDGFRβ-levels further increased the predictive capacity for the diagnosis of significant liver fibrosis. Conclusions: The miRFIB- and miRFIBp-scores are accurate tools for the diagnosis of significant liver fibrosis in a heterogeneous patient population