Graft Survival in Liver Transplantation: An Artificial Neuronal Network Assisted Analysis of the Importance of Comorbidities.

OBJECTIVES: Liver transplant represents a widespread therapeutic option for patients with end-stage liver failure. Up to now, most of the scores describing the probability of liver graft survival have shown poor predictive performance. With this in mind, the present study seeks to analyze the predictive value of recipient comorbidities on liver graft survival within the first year. MATERIALS AND METHODS: The study included prospectively collected data from patients who received a liver transplant at our center from 2010 to 2021. A predictive model was then developed through an Artificial Neural Network that included the parameters associated with graft loss as identified by the Spanish Liver Transplant Registry report and comorbidities with prevalence >2% present in our study cohort. RESULTS: Most patients in our study were men (75.5%); mean age was 54.8 ± 9.6 years. The main cause of transplant was cirrhosis (86.7%), and 67.4% of patients had some associated comorbidities. Graft loss due to retransplant or death with dysfunction occurred in 14% of cases. Of all the variables analyzed, we found 3 comorbidities associated with graft loss (as shown by informative value and normalized informative value, respectively): antiplatelet and/or anticoagulants treatments (0.124 and 78.4%), previous immunosuppression (0.110 and 69.6%), and portal thrombosis (0.105 and 66.3%). Remarkably, our model showed a C statistic of 0.745 (95% CI, 0.692-0.798; asymptotic P < .001), which was higher than others found in previous studies. CONCLUSIONS: Our model identified key parameters that may influence graft loss, including specific recipient comorbidities. The use of artificial intelligence methods could reveal connections that may be overlooked by conventional statistics.

The role of extracellular matrix on liver stem cell fate: A dynamic relationship in health and disease.

The liver stem cell niche is a specialized and dynamic microenvironment with biomechanical and biochemical characteristics that regulate stem cell behavior. This is feasible due to the coordination of a complex network of secreted factors, small molecules, neural, blood inputs and extracellular matrix (ECM) components involved in the regulation of stem cell fate (self-renewal, survival, and differentiation into more mature phenotypes like hepatocytes and cholangiocytes). In this review, we describe and summarize all the major components that play essential roles in the liver stem cell niche, in particular, growth factor signaling and the biomechanical properties of the ECM.

Naturally-Derived Biomaterials for Tissue Engineering Applications.

Naturally-derived biomaterials have been used for decades in multiple regenerative medicine applications. From the simplest cell microcarriers made of collagen or alginate, to highly complex decellularized whole-organ scaffolds, these biomaterials represent a class of substances that is usually first in choice at the time of electing a functional and useful biomaterial. Hence, in this chapter we describe the several naturally-derived biomaterials used in tissue engineering applications and their classification, based on composition. We will also describe some of the present uses of the generated tissues like drug discovery, developmental biology, bioprinting and transplantation.

Multiple approaches to assess fourteen non-invasive serum indexes for the diagnosis of liver fibrosis in chronic hepatitis C patients.

BACKGROUND: The aim of this study was to compare fourteen non-invasive indexes/scores: AAR, APRI, Fibroindex, MODEL3, Forns index, FIB4, GUCI, FI, FCI, Pohl score, AP index, CDS, HGM-1 and HGM-2, in order to diagnose the hepatic fibrosis stage in a survey of patients with chronic hepatitis C. METHODS: 84 patients with chronic hepatitis C were studied. Liver fibrosis was staged according to the Scheuer scoring system. The diagnostic accuracy of these indexes/scores was evaluated by AUROC, contingency tables and logistic regression analysis. RESULTS: The best AUROCs (>0.9) to discriminate cirrhosis (F=4), were observed for CDS, FI, AAR, MODEL3, FIB4, HGM-2 and FCI. To discriminate at least advance fibrosis (F≥3), the best AUROCs (>0.89) were for CDS, FI, FIB4, HGM2-2, MODEL3 and FCI. To discriminate at least significant fibrosis (F≥2), the best AUROCs (>0.8) were for FIB4, GUCI, APRI, FI, Forns index, HGM-2 and FCI. Contingency tables and logistic regression analysis supported the results obtained by AUROC. CONCLUSIONS: This study compares the diagnostic performance of fourteen indexes for the diagnosis of liver fibrosis stage in the same group of CHC patients. These results allow the selection of the best indexes for further studies in larger populations, in order to build diagnostic algorithms as an alternative to liver biopsy for fibrosis staging in patients with chronic HCV infection. These algorithms would allow to take therapeutical decisions and the continuous follow-up of hepatic fibrosis in these patients.