7 research outputs found
Assessment of a Comparative Bayesian-Enhanced Population-Based Decision Model for COVID-19 Critical Care Prediction in the Dominican Republic Social Security Affiliates
Introduction: The novel coronavirus disease 2019 (COVID-19) has been a major health concern worldwide. This study aims to develop a Bayesian model to predict critical outcomes in patients with COVID-19. Methods: Sensitivity and specificity were obtained from previous meta-analysis studies. The complex vulnerability index (IVC-COV2 index for its abbreviation in Spanish) was used to set the pretest probability. Likelihood ratios were integrated into a Fagan nomogram for posttest probabilities, and IVC-COV2 + National Early Warning Score (NEWS) values and CURB-65 scores were generated. Absolute and relative diagnostic gains (RDGs) were calculated based on pretest and posttest differences. Results: The IVC-COV2 index was derived from a population of 1,055,746 individuals and was based on mortality in high-risk (71.97%), intermediate-risk (26.11%), and low-risk (1.91%) groups. The integration of models in which IVC-COV2 intermediate + NEWS â„ 5 and CURB-65 \u3e 2 led to a number needed to (NNT) diagnose that was slightly improved in the CURB-65 model (2 vs. 3). A comparison of diagnostic gains revealed that neither the positive likelihood ratio (P = 0.62) nor the negative likelihood ratio (P = 0.95) differed significantly between the IVC-COV2 NEWS model and the CURB-65 model. Conclusion: According to the proposed mathematical model, the combination of the IVC-COV2 intermediate score and NEWS or CURB-65 score yields superior results and a greater predictive value for the severity of illness. To the best of our knowledge, this is the first population-based/mathematical model developed for use in COVID-19 critical care decision-making
Epigenetic mechanisms in hepatic stellate cell activation during liver fibrosis and carcinogenesis
Liver fibrosis is an essential component of chronic liver disease (CLD) and hepatocarcinogenesis.
The fibrotic stroma is a consequence of sustained liver damage combined with exacerbated extracellular
matrix (ECM) accumulation. In this context, activation of hepatic stellate cells (HSCs) plays a key role in
both initiation and perpetuation of fibrogenesis. These cells suffer profound remodeling of gene expression
in this process. This review is focused on the epigenetic alterations participating in the transdifferentiation
of HSCs from the quiescent to activated state. Recent advances in the field of DNA methylation and
post-translational modifications (PTM) of histones (acetylation and methylation) patterns are discussed here,
together with altered expression and activity of epigenetic remodelers. We also consider recent advances
in translational approaches, including the use of epigenetic marks as biomarkers and the promising
antifibrotic properties of epigenetic drugs that are currently being used in patients
Epigenetics in liver fibrosis: could HDACs be a therapeutic target?
Chronic liver diseases (CLD) represent a worldwide health problem. While CLDs may
have diverse etiologies, a common pathogenic denominator is the presence of liver fibrosis. Cirrhosis,
the end-stage of CLD, is characterized by extensive fibrosis and is markedly associated with the
development of hepatocellular carcinoma. The most important event in hepatic fibrogenesis is
the activation of hepatic stellate cells (HSC) following liver injury. Activated HSCs acquire a
myofibroblast-like phenotype becoming proliferative, fibrogenic, and contractile cells. While transient
activation of HSCs is part of the physiological mechanisms of tissue repair, protracted activation
of a wound healing reaction leads to organ fibrosis. The phenotypic changes of activated HSCs
involve epigenetic mechanisms mediated by non-coding RNAs (ncRNA) as well as by changes in
DNA methylation and histone modifications. During CLD these epigenetic mechanisms become
deregulated, with alterations in the expression and activity of epigenetic modulators. Here we
provide an overview of the epigenetic alterations involved in fibrogenic HSCs transdifferentiation
with particular focus on histones acetylation changes. We also discuss recent studies supporting the
promising therapeutic potential of histone deacetylase inhibitors in liver fibrosis
Dual Pharmacological Targeting of HDACs and PDE5 Inhibits Liver Disease Progression in a Mouse Model of Biliary Inflammation and Fibrosis
Liver fibrosis, a common hallmark of chronic liver disease (CLD), is characterized by
the accumulation of extracellular matrix secreted by activated hepatic fibroblasts and stellate cells (HSC). Fibrogenesis involves multiple cellular and molecular processes and is intimately linked
to chronic hepatic inflammation. Importantly, it has been shown to promote the loss of liver
function and liver carcinogenesis. No effective therapies for liver fibrosis are currently available.
We examined the anti-fibrogenic potential of a new drug (CM414) that simultaneously inhibits
histone deacetylases (HDACs), more precisely HDAC1, 2, and 3 (Class I) and HDAC6 (Class II) and
stimulates the cyclic guanosine monophosphate (cGMP)-protein kinase G (PKG) pathway activity
through phosphodiesterase 5 (PDE5) inhibition, two mechanisms independently involved in liver
fibrosis. To this end, we treated Mdr2-KO mice, a clinically relevant model of liver inflammation
and fibrosis, with our dual HDAC/PDE5 inhibitor CM414. We observed a decrease in the expression
of fibrogenic markers and collagen deposition, together with a marked reduction in inflammation.
No signs of hepatic or systemic toxicity were recorded. Mechanistic studies in cultured human
HSC and cholangiocytes (LX2 and H69 cell lines, respectively) demonstrated that CM414 inhibited
pro-fibrogenic and inflammatory responses, including those triggered by transforming growth factor
ÎČ (TGFÎČ). Our study supports the notion that simultaneous targeting of pro-inflammatory and
fibrogenic mechanisms controlled by HDACs and PDE5 with a single molecule, such as CM414,
can be a new disease-modifying strategy
Activation of the unfolded protein response (UPR) is associated with cholangiocellular injury, fibrosis and carcinogenesis in an experimental model of fibropolycystic liver disease
Polycystic liver disease (PLD) is a group of rare disorders that result from structural changes in the biliary tree development in the liver. In the present work, we studied alterations in molecular mechanisms and signaling pathways that might be responsible for these pathologies. We found that activation of the unfolded protein response, a process that occurs in response to an accumulation of unfolded or misfolded proteins in the lumen of the endoplasmic reticulum, as well as the scarring of the liver tissue, contribute to the pathogenesis of PLD and the development of cancer. As a preclinical animal model we have used mutant mice of a specific signaling pathway, the c-Jun N-terminal kinase 1/2 (Jnk1/2). These mice resemble a perfect model for the study of PLD and early cancer development
Pilot multi-omic analysis of human bile from benign and malignant biliary strictures: A machine-learning approach
Cholangiocarcinoma (CCA) and pancreatic adenocarcinoma (PDAC) may lead to the
development of extrahepatic obstructive cholestasis. However, biliary stenoses can also be caused
by benign conditions, and the identification of their etiology still remains a clinical challenge.
We performed metabolomic and proteomic analyses of bile from patients with benign (n = 36)
and malignant conditions, CCA (n = 36) or PDAC (n = 57), undergoing endoscopic retrograde
cholangiopancreatography with the aim of characterizing bile composition in biliopancreatic disease
and identifying biomarkers for the differential diagnosis of biliary strictures. Comprehensive analyses
of lipids, bile acids and small molecules were carried out using mass spectrometry (MS) and nuclear
magnetic resonance spectroscopy (1H-NMR) in all patients. MS analysis of bile proteome was
performed in five patients per group. We implemented artificial intelligence tools for the selection
of biomarkers and algorithms with predictive capacity. Our machine-learning pipeline included
the generation of synthetic data with properties of real data, the selection of potential biomarkers
(metabolites or proteins) and their analysis with neural networks (NN). Selected biomarkers were
then validated with real data. We identified panels of lipids (n = 10) and proteins (n = 5) that when
analyzed with NN algorithms discriminated between patients with and without cancer with an
unprecedented accurac