Quantification of liver fibrosis—a comparative study

Liver disease has been targeted as the fifth most common cause of death worldwide and tends to steadily rise. In the last three decades, several publications focused on the quantification of liver fibrosis by means of the estimation of the collagen proportional area (CPA) in liver biopsies obtained from digital image analysis (DIA). In this paper, early and recent studies on this topic have been reviewed according to these research aims: the datasets used for the analysis, the employed image processing techniques, the obtained results, and the derived conclusions. The purpose is to identify the major strengths and “gray-areas” in the landscape of this topic

Immunohistochemistry image analysis : protein, nuclei and gland

This thesis focus on the analysis of digitized microscopic image, especially on IHC stained colour images. The corresponding contributions focused on the automatic detection of stain colour and glands, the segmentation and quantification of cell nuclei, the analysis of liver cirrhosis and the development of a semi-automatic toolbox. Colour is the most important feature in the analysis of immunostained images. We developed a statistical colour detection model for stain colour detection based on the histograms of collected colour pixels. This is acting on the approach "what you see is what you get" which outperforms the other methods on the detection of several kinds of stain colour. Verifying the presence of nuclei and quantifying positive nuclei is the foundation of cancer grading. We developed a novel seeded nuclei segmentation method which greatly improves the segmentation accuracy and reduces both over-segmentation and under-segmentation. This method has been demonstrated to be robust and accurate in both segmentation and quantification against manual labelling and counting in the evaluation process. The analysis of gland architecture, which reflects the cancer stage, has evolved into an important aspect of cancer detection. A novel morphology-based approach has been developed to segment gland structures in H-DAB stained images. This method locates the gland by focusing on its morphology and intensity characteristics, which covers variations in stain colours in different IHC images. The evaluation results have demonstrated the improvements of accuracy and efficiency. For the successive development of three methods, we put them in a semi-automatic toolbox for the aid of IHC image analysis. It can detect different kinds of stain colour and the basic components in an IHC image. The user created models and parameters can be saved and transferred to different users for the reproduction of detection results in different laboratories. To demonstrate the flexibility of our developed stained colour detection technique, the tool has been extended to the analysis of liver cirrhosis. It is a novel method based on our statistical colour detection model which greatly improves the analysis accuracy and reduces the time cost

Immunohistochemistry image analysis : protein, nuclei and gland

This thesis focus on the analysis of digitized microscopic image, especially on IHC stained colour images. The corresponding contributions focused on the automatic detection of stain colour and glands, the segmentation and quantification of cell nuclei, the analysis of liver cirrhosis and the development of a semi-automatic toolbox. Colour is the most important feature in the analysis of immunostained images. We developed a statistical colour detection model for stain colour detection based on the histograms of collected colour pixels. This is acting on the approach "what you see is what you get" which outperforms the other methods on the detection of several kinds of stain colour. Verifying the presence of nuclei and quantifying positive nuclei is the foundation of cancer grading. We developed a novel seeded nuclei segmentation method which greatly improves the segmentation accuracy and reduces both over-segmentation and under-segmentation. This method has been demonstrated to be robust and accurate in both segmentation and quantification against manual labelling and counting in the evaluation process. The analysis of gland architecture, which reflects the cancer stage, has evolved into an important aspect of cancer detection. A novel morphology-based approach has been developed to segment gland structures in H-DAB stained images. This method locates the gland by focusing on its morphology and intensity characteristics, which covers variations in stain colours in different IHC images. The evaluation results have demonstrated the improvements of accuracy and efficiency. For the successive development of three methods, we put them in a semi-automatic toolbox for the aid of IHC image analysis. It can detect different kinds of stain colour and the basic components in an IHC image. The user created models and parameters can be saved and transferred to different users for the reproduction of detection results in different laboratories. To demonstrate the flexibility of our developed stained colour detection technique, the tool has been extended to the analysis of liver cirrhosis. It is a novel method based on our statistical colour detection model which greatly improves the analysis accuracy and reduces the time cost

Genomics and metabonomics in severe alcoholic hepatitis

Severe alcoholic hepatitis is a florid presentation of alcohol-related liver disease and is associated with very high short-term mortality, in excess of 20% within 28 days. Severe alcoholic hepatitis occurs in a minority of patients who develop alcohol-related liver disease. A combination of genetic and environmental factors is likely to predispose to severe alcoholic hepatitis. To date the clinical phenotype has not been extensively examined in candidate gene studies and has been the subject of a single, small genome-wide association study. A genome-wide association study of severe alcoholic hepatitis identified two loci potentially associated with the risk of developing severe alcoholic hepatitis: i) A strong association with PNPLA3, a well-recognised risk locus for alcohol-related liver disease, and ii) a novel but weaker association with SLC38A4, an amino acid transporter. The primary genetic variant at each locus was evaluated to determine whether there was an influence on disease phenotype or outcome. The primary variant in PNPLA3, rs738409, is a missense variant. Analyses indicated a deleterious effect of homozygosity on medium-term survival in addition to more severe disease on baseline histology and a slower recovery in liver function over the short-term period; consistent with established literature in alcohol-related cirrhosis. In contrast the primary variant in SLC38A4, rs11183620, is intronic with no clear evidence for an effect on gene expression or function. Analyses did not indicate an influence on histology, clinical phenotypes or outcomes. In light of the locus’ novelty further work was undertaken to determine any potential contribution to disease pathogenesis. SLC38A4 was down-regulated in whole liver tissue in severe alcoholic hepatitis. Experiments with cell lines in culture suggested the pro-inflammatory cytokine IL-1 as a potential driver. SLC38A4 knockdown resulted in upregulation of some cellular responses associated with nutrient deprivation. There was no influence of the variant on serum amino acid profiles. The functional significance of SLC38A4 down-regulation remains the subject of ongoing work.Open Acces

Non-alcoholic fatty liver disease in type-2 diabetes mellitus: population analysis, metabolic profile and referral management pathway

Introduction: Non-alcoholic fatty liver disease is strongly associated with type-2 diabetes mellitus, with diabetic patients being at higher risk for adverse outcomes. The aim of this thesis was to explore in detail the clinical and metabolic phenotype of diabetics screened for NAFLD in primary care and to develop a referral management pathway for this population. Moreover, this thesis investigated the impact of alterations of the gut-liver axis on the severity of liver disease in such cohort. Methods: In this cross-sectional study, consecutive diabetic patients from primary care were screened for liver disease and NAFLD. Nuclear magnetic resonance and liquid chromatography-mass spectrometry were used to explore the metabolic profile of the patients against severity of liver disease. Stool meta-taxagenomics allowed for the analysis of the composition of the microbiome, while gut permeability was investigated using an in-vitro model and an ex-vivo measurement of faecal protease activity. Inflammatory cytokines profile was also analysed in serum as well as in faecal samples. Results: Clinically significant NAFLD was highly prevalent in the diabetic population in primary care. According to the results of this study, applying FIB-4 with a cut-off of 1.3 in this population would miss up to 38% of the patients with significant liver disease. The BIMAST score, which was derived based on simple clinical parameters, was validated both internally and externally, outperformed conventional screening methods and optimised risk-stratification in primary care. Among the metabolites, only lysine deficiency was associated with increased hepatic collagen content. Moreover, specific changes in gut microbiome were associated with more severe liver disease, while intestinal permeability tended to increase with liver disease severity. A combination of host and microbiota-related factors were associated with a leakier gut in this population. Conclusions: Current risk-stratification for NAFLD among diabetics in primary care can be improved. Exploring the gut-liver axis may offer diagnostic as well as therapeutical insights in this population.Open Acces

Epigenetic biomarkers in the progression of Barrett’s Oesophagus to oesophageal adenocarcinoma

Introduction Barrett’s Oesophagus (BO) represents a benign condition with no life limiting consequences. 0.33% of BO patients progress to oesophageal adenocarcinoma (OADC) which is a potentially catastrophic illness with high associated morbidity and mortality. If patients at high risk of progression to cancer could be identified, they could potentially be treated at an earlier disease stage. Aims To assess existing epigenetic biomarkers predicting progression from BO to OADC and validate the novel methylation biomarker OR3A4. Explore the functional relevance of OR3A4 and assess the immunological landscape of BO which progresses to OADC. Methods Genome wide methylation analysis and validation pyrosequencing of OR3A4 in BO tissue samples was performed. An OR3A4 over-expressing vector was transfected into BO and OADC cell lines and cell functional assays, total RNA sequencing and real time PCR was performed. Multispectral immunohistochemistry was performed to investigate the immunological landscape of BO. Results OR3A4 is hypomethylated in patients that progress from BO to OADC and OR3A4 over-expression has a functional effect in BO in vitro models and may contribute to immunological changes in BO tissues. Conclusion Hypomethylation of OR3A4 may provide a mechanism to explain the progression of BO to OADC and predict progression of disease

Исследование пространственной структуры коллагена с применением методов многофотонной микроскопии и машинного обучения

Настоящий обзор посвящен анализу подходов и имеющихся результатов в области выявления информативных характеристик в экспериментальных данных инфракрасной многофотонной мультимодальной микроскопии коллагеновых структур в биоткани и разработке на этой основе алгоритмов автоматической классификации дезорганизации пространственной структуры коллагена при различных заболеваниях с использованием машинного обучения – разновидности методов искусственного интеллекта