Non-alcoholic liver disease (NAFLD) defines liver abnormalities ranging from simple steatosis to nonalcoholic steatohepatitis with or without cirrhosis development, occurring in the absence of significant alcohol consumption, use of teratogenic medication, or hereditary disorders. The association between NAFLD and metabolic syndrome is well documented and widely recognized. Obesity, type 2 diabetes mellitus (T2DM), and dyslipidemia are the most common metabolic risk factors associated with NAFLD. Among the components of metabolic syndrome, current evidence strongly indicates obesity and diabetes as hepatocellular carcinoma (HCC) risk factors. There is also growing evidence that suggests an increased risk of HCC in NAFLD patients, even surpassing other etiologies in some high-income countries. Epidemiologic data demonstrate a parallel rise in prevalence of obesity, diabetes, NAFLD, and HCC. As obesity and its related diseases have steadily afflicted larger populations, HCC incidence is expected to increase in the future. Pathophysiologic mechanisms that underlie NAFLD development and subsequent progression to nonalcoholic steatohepatitis and cirrhosis (insulin resistance and hyperinsulinemia, oxidative stress, hepatic stellate cell activation, cytokine/adipocytokine signaling pathways, and genetic and environmental factors) appear to play a significant role in the development of NAFLD-related HCC. However, a comprehensive view of molecular mechanisms linking obesity, T2DM, and NAFLD-related HCC, as well as the exact sequence of molecular events, is still not understood in its entirety. Good-quality data are still necessary, and efforts should continue towards better understanding the underlying carcinogenic mechanisms of NAFLD-related HCC. In this paper, we aimed to centralize the most important links supporting these relationships, focusing on obesity, T2DM, and NAFLD-related HCC, as well as point out the major gaps in knowledge regarding the underlying molecular mechanisms behind them.
"Psychosocial stress" is an increasingly common concept in the challenging and highly-demanding modern society of today. Organic response to stress implicates two major components of the stress system, namely the hypothalamic-pituitary-adrenal axis and the sympathetic nervous system. Stress is anamnestically reported by patients during the course of disease, usually accompanied by a decline in their overall health status. As the mechanisms involving glucocorticoids and catecholamines have been deciphered, and their actions on immune cell function deeper understood, it has become clear that stress has an impact on hepatic inflammatory response. An increasing number of articles have approached the link between psychosocial stress and the negative evolution of hepatic diseases. This article reviews a number of studies on both human populations and animal models performed in recent years, all linking stress, mainly of psychosocial nature, and the evolution of three important liver-related pathological entities: viral hepatitis, cirrhosis and hepatocellular carcinoma.
Neural network analysis of contrast-enhanced ultrasonography - obtained TICs seems a promising field of development for future techniques, providing fast and reliable diagnostic aid for the clinician.
Objective: Our main aim was to investigate the serum lipid levels in a series of patients with liver cirrhosis of viral origin. Subjects and Methods: The study comprised 90 patients, 60 with viral liver cirrhosis, equally divided between hepatitis virus C (HCV) and B (HBV) etiologies, and 30 control patients with no known liver pathology. Patients were investigated during a 5-year period in the 1st Medical Clinic of the Emergency County Hospital of Craiova, Romania. The following series of serum lipid parameters were recorded: lipemia, total cholesterol and cholesteryl ester, high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, very-low-density lipoprotein (VLDL) cholesterol and triglyceride (TG) values. Statistical analysis of these parameters was performed using the ANOVA test followed by Tukey multiple comparison teststo compare replicate means; p < 0.05 was considered statistically significant. Results: We observed significantly lower values for serum lipids (543.5 and 549.37 mg/dl in the HBV and HCV cirrhosis subgroups, compared with 649.9 mg/dl in controls), total cholesterol (143.6 and 147.9 vs. 198.0 mg/dl, respectively), cholesteryl esters (83.6 and 80, compared to 147.9 mg/dl, respectively), LDL cholesterol (91.6 and 88.5 vs. 132.4 mg/dl) in both cirrhosis groups when compared with controls (p < 0.001), as well as HDL cholesterol (32.1 and 36.9 vs. 47.3 mg/dl, p < 0.05). However, TG and VLDL cholesterol values of controls and cirrhosis groups were similar (p > 0.05). We did not register any differences between the two cirrhosis groups (p > 0.05). Conclusion: Our data showed that both HCV and HBV cirrhosis severely impaired liver lipid metabolism. Late stages of the disease resulted in a pseudonormalization of VLDL cholesterol and TG values.
Background and Aims. Hepatocellular carcinoma (HCC) remains a leading cause of death by cancer worldwide. Computerized diagnosis systems relying on novel imaging markers gained significant importance in recent years. Our aim was to integrate a novel morphometric measurement—the fractal dimension (FD)—into an artificial neural network (ANN) designed to diagnose HCC. Material and Methods. The study included 21 HCC and 28 liver metastases (LM) patients scheduled for surgery. We performed hematoxylin staining for cell nuclei and CD31/34 immunostaining for vascular elements. We captured digital images and used an in-house application to segment elements of interest; FDs were calculated and fed to an ANN which classified them as malignant or benign, further identifying HCC and LM cases. Results. User intervention corrected segmentation errors and fractal dimensions were calculated. ANNs correctly classified 947/1050 HCC images (90.2%), 1021/1050 normal tissue images (97.23%), 1215/1400 LM (86.78%), and 1372/1400 normal tissues (98%). We obtained excellent interobserver agreement between human operators and the system. Conclusion. We successfully implemented FD as a morphometric marker in a decision system, an ensemble of ANNs designed to differentiate histological images of normal parenchyma from malignancy and classify HCCs and LMs.
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