scholarly journals Integration of geoscience frameworks into digital pathology analysis permits quantification of microarchitectural relationships in histological landscapes

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Timothy J. Kendall ◽  
Catherine M. Duff ◽  
Andrew M. Thomson ◽  
John P. Iredale
Keyword(s):  
Disease Model ◽  
Digital Pathology ◽  
Experimental Models ◽  
Neoplastic Disease ◽  
Multiple Organs ◽  
Trial Protocols ◽  
Histological Data ◽  
Histological Images ◽  
New Feature ◽  
Models Of Disease

Abstract Although gold-standard histological assessment is subjective it remains central to diagnosis and clinical trial protocols and is crucial for the evaluation of any preclinical disease model. Objectivity and reproducibility are enhanced by quantitative analysis of histological images but current methods require application-specific algorithm training and fail to extract understanding from the histological context of observable features. We reinterpret histopathological images as disease landscapes to describe a generalisable framework defining topographic relationships in tissue using geoscience approaches. The framework requires no user-dependent training to operate on all image datasets in a classifier-agnostic manner but is adaptable and scalable, able to quantify occult abnormalities, derive mechanistic insights, and define a new feature class for machine-learning diagnostic classification. We demonstrate application to inflammatory, fibrotic and neoplastic disease in multiple organs, including the detection and quantification of occult lobular enlargement in the liver secondary to hilar obstruction. We anticipate this approach will provide a robust class of histological data for trial stratification or endpoints, provide quantitative endorsement of experimental models of disease, and could be incorporated within advanced approaches to clinical diagnostic pathology.

scholarly journals Consideration of Gut Microbiome in Murine Models of Diseases

2021 ◽  
Vol 9 (5) ◽  
pp. 1062
Author(s):  
Chunye Zhang ◽  
Craig L. Franklin ◽  
Aaron C. Ericsson
Keyword(s):  
Animal Models ◽  
Mouse Models ◽  
Biomedical Research ◽  
Gut Microbiome ◽  
Close Association ◽  
Disease Model ◽  
Potential Contributor ◽  
Potential Factors ◽  
Models Of Disease ◽  
The Impact

The gut microbiome (GM), a complex community of bacteria, viruses, protozoa, and fungi located in the gut of humans and animals, plays significant roles in host health and disease. Animal models are widely used to investigate human diseases in biomedical research and the GM within animal models can change due to the impact of many factors, such as the vendor, husbandry, and environment. Notably, variations in GM can contribute to differences in disease model phenotypes, which can result in poor reproducibility in biomedical research. Variation in the gut microbiome can also impact the translatability of animal models. For example, standard lab mice have different pathogen exposure experiences when compared to wild or pet store mice. As humans have antigen experiences that are more similar to the latter, the use of lab mice with more simplified microbiomes may not yield optimally translatable data. Additionally, the literature describes many methods to manipulate the GM and differences between these methods can also result in differing interpretations of outcomes measures. In this review, we focus on the GM as a potential contributor to the poor reproducibility and translatability of mouse models of disease. First, we summarize the important role of GM in host disease and health through different gut–organ axes and the close association between GM and disease susceptibility through colonization resistance, immune response, and metabolic pathways. Then, we focus on the variation in the microbiome in mouse models of disease and address how this variation can potentially impact disease phenotypes and subsequently influence research reproducibility and translatability. We also discuss the variations between genetic substrains as potential factors that cause poor reproducibility via their effects on the microbiome. In addition, we discuss the utility of complex microbiomes in prospective studies and how manipulation of the GM through differing transfer methods can impact model phenotypes. Lastly, we emphasize the need to explore appropriate methods of GM characterization and manipulation.

Improved correlation of histological data with DCE MRI parameter maps by 3D reconstruction, reslicing and parameterization of the histological images

2005 ◽  
Vol 15 (6) ◽  
pp. 1079-1086 ◽  
Author(s):  
Fabian Kiessling ◽  
Martin Le-Huu ◽  
Tobias Kunert ◽  
Matthias Thorn ◽  
Silvia Vosseler ◽  
...  
Keyword(s):  
3D Reconstruction ◽  
Dce Mri ◽  
Histological Data ◽  
Histological Images

scholarly journals Dissecting the Molecular Features of Systemic Light Chain (AL) Amyloidosis: Contributions from Proteomics

Medicina ◽  
2021 ◽  
Vol 57 (9) ◽  
pp. 916
Author(s):  
Paola Rognoni ◽  
Giulia Mazzini ◽  
Serena Caminito ◽  
Giovanni Palladini ◽  
Francesca Lavatelli
Keyword(s):  
Light Chain ◽  
Amyloid Fibrils ◽  
Cell Clone ◽  
Experimental Models ◽  
Critical Discussion ◽  
Al Amyloidosis ◽  
Primary Role ◽  
Molecular Features ◽  
Amyloid Light Chain ◽  
Models Of Disease

Amyloidoses are characterized by aggregation of proteins into highly ordered amyloid fibrils, which deposit in the extracellular space of tissues, leading to organ dysfunction. In AL (amyloid light chain) amyloidosis, the most common form in Western countries, the amyloidogenic precursor is a misfolding-prone immunoglobulin light chain (LC), which, in the systemic form, is produced in excess by a plasma cell clone and transported to target organs though blood. Due to the primary role that proteins play in the pathogenesis of amyloidoses, mass spectrometry (MS)-based proteomic studies have gained an established position in the clinical management and research of these diseases. In AL amyloidosis, in particular, proteomics has provided important contributions for characterizing the precursor light chain, the composition of the amyloid deposits and the mechanisms of proteotoxicity in target organ cells and experimental models of disease. This review will provide an overview of the major achievements of proteomic studies in AL amyloidosis, with a presentation of the most recent acquisitions and a critical discussion of open issues and ongoing trends.

scholarly journals Tissue Type Recognition in Whole Slide Histological Images

2021 ◽  
Author(s):  
Alexander Khvostikov ◽  
Andrey Krylov ◽  
Ilya Mikhailov ◽  
Pavel Malkov ◽  
Natalya Danilova
Keyword(s):  
Digestive Tract ◽  
Lamina Propria ◽  
Gastric Adenocarcinoma ◽  
Digital Pathology ◽  
Tissue Type ◽  
Depth Of Invasion ◽  
Tubular Structures ◽  
Urgent Task ◽  
Histological Images ◽  
Whole Slide Images

Automatic layers recognition of the wall of the stomach and colon on whole slide images is an extremely urgent task in digital pathology as it can be used for automatic determining the depth of invasion of the digestive tract tumors. In this paper we propose a new CNN-based method of automatic tissue type recognition on whole slide histological images. We also describe an effective pipeline of training that uses 2 different training datasets. The proposed method of automatic tissue type recognition achieved 0.929 accuracy and 0.903 balanced accuracy on CRC-VAL-HE-7K dataset for 9-types classification and 0.98 accuracy and 0.926 balanced accuracy on the test subset of whole slide images from PATH-DT- MSU dataset for 5-types classification. The developed method makes it possible to classify the areas corresponding to the gastric own mucous glands in the lamina propria and also to distinguish the tubular structures of a highly differentiated gastric adenocarcinoma with normal glands.

Stimulated Raman histology for rapid and accurate intraoperative diagnosis of CNS tumors: prospective blinded study

2019 ◽  
pp. 1-7
Author(s):  
Daniel G. Eichberg ◽  
Ashish H. Shah ◽  
Long Di ◽  
Alexa M. Semonche ◽  
George Jimsheleishvili ◽  
...  
Keyword(s):  
Frozen Section ◽  
Cns Tumors ◽  
Rapid Acquisition ◽  
Novel Technology ◽  
Permanent Section ◽  
Diagnostic Time ◽  
Blinded Study ◽  
Histological Data ◽  
Histological Images ◽  
Stimulated Raman

OBJECTIVEIn some centers where brain tumor surgery is performed, the opportunity for expert intraoperative neuropathology consultation is lacking. Consequently, surgeons may not have access to the highest quality diagnostic histological data to inform surgical decision-making. Stimulated Raman histology (SRH) is a novel technology that allows for rapid acquisition of diagnostic histological images at the bedside.METHODSThe authors performed a prospective blinded cohort study of 82 consecutive patients undergoing resection of CNS tumors to compare diagnostic time and accuracy of SRH simulation to the gold standard, i.e., frozen and permanent section diagnosis. Diagnostic accuracy was determined by concordance of SRH-simulated intraoperative pathology consultation with a blinded board-certified neuropathologist, with official frozen section and permanent section results.RESULTSOverall, the mean time to diagnosis was 30.5 ± 13.2 minutes faster (p < 0.0001) for SRH simulation than for frozen section, with similar diagnostic correlation: 91.5% (κ = 0.834, p < 0.0001) between SRH simulation and permanent section, and 91.5% between frozen and permanent section (κ = 0.894, p < 0.0001).CONCLUSIONSSRH-simulated intraoperative pathology consultation was significantly faster and equally accurate as frozen section.

scholarly journals Improving Cancer Drug Discovery by Studying Cancer across the Tree of Life

2019 ◽  
Vol 37 (1) ◽  
pp. 11-17 ◽  
Author(s):  
Jason A Somarelli ◽  
Amy M Boddy ◽  
Heather L Gardner ◽  
Suzanne Bartholf DeWitt ◽  
Joanne Tuohy ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Tumor Development ◽  
Experimental Models ◽  
Cancer Drug ◽  
Success Rates ◽  
Comparative Oncology ◽  
Protective Mechanisms ◽  
Cancer Drug Discovery ◽  
Models Of Disease ◽  
The Cost

Abstract Despite a considerable expenditure of time and resources and significant advances in experimental models of disease, cancer research continues to suffer from extremely low success rates in translating preclinical discoveries into clinical practice. The continued failure of cancer drug development, particularly late in the course of human testing, not only impacts patient outcomes, but also drives up the cost for those therapies that do succeed. It is clear that a paradigm shift is necessary if improvements in this process are to occur. One promising direction for increasing translational success is comparative oncology—the study of cancer across species, often involving veterinary patients that develop naturally-occurring cancers. Comparative oncology leverages the power of cross-species analyses to understand the fundamental drivers of cancer protective mechanisms, as well as factors contributing to cancer initiation and progression. Clinical trials in veterinary patients with cancer provide an opportunity to evaluate novel therapeutics in a setting that recapitulates many of the key features of human cancers, including genomic aberrations that underly tumor development, response and resistance to treatment, and the presence of comorbidities that can affect outcomes. With a concerted effort from basic scientists, human physicians and veterinarians, comparative oncology has the potential to enhance the cost-effectiveness and efficiency of pipelines for cancer drug discovery and other cancer treatments.

scholarly journals Adipose tissue as a key player in obstructive sleep apnoea

2019 ◽  
Vol 28 (152) ◽  
pp. 190006 ◽  
Author(s):  
Silke Ryan ◽  
Claire Arnaud ◽  
Susan F. Fitzpatrick ◽  
Jonathan Gaucher ◽  
Renaud Tamisier ◽  
...  
Keyword(s):  
Adipose Tissue ◽  
Obstructive Sleep Apnoea ◽  
Sleep Apnoea ◽  
Experimental Models ◽  
Complex Interaction ◽  
Health Concern ◽  
Metabolic Dysfunction ◽  
Major Health ◽  
Multiple Organs ◽  
Obstructive Sleep

Obstructive sleep apnoea (OSA) is a major health concern worldwide and adversely affects multiple organs and systems. OSA is associated with obesity in >60% of cases and is independently linked with the development of numerous comorbidities including hypertension, arrhythmia, stroke, coronary heart disease and metabolic dysfunction. The complex interaction between these conditions has a significant impact on patient care and mortality. The pathophysiology of cardiometabolic complications in OSA is still incompletely understood; however, the particular form of intermittent hypoxia (IH) observed in OSA, with repetitive short cycles of desaturation and re-oxygenation, probably plays a pivotal role. There is fast growing evidence that IH mediates some of its detrimental effects through adipose tissue inflammation and dysfunction. This article aims to summarise the effects of IH on adipose tissue in experimental models in a comprehensive way. Data from well-designed controlled trials are also reported with the final goal of proposing new avenues for improving phenotyping and personalised care in OSA.

scholarly journals qPaLM: quantifying occult microarchitectural relationships in histopathological landscapes

2019 ◽  
Author(s):  
Timothy J Kendall ◽  
Catherine M Duff ◽  
Andrew M Thomson ◽  
John P Iredale
Keyword(s):  
Machine Learning ◽  
Landscape Metrics ◽  
Gold Standard ◽  
Tissue Imaging ◽  
Diagnostic Classification ◽  
Imaging Methods ◽  
Standard Assessment ◽  
Histological Images ◽  
New Feature ◽  
Image Datasets

AbstractOptimal tissue imaging methods should be easy to apply, not require use-specific algorithmic training, and should leverage feature relationships central to subjective gold-standard assessment. We reinterpret histological images as landscapes to describe quantitative pathological landscape metrics (qPaLM), a generalisable framework defining topographic relationships in tissue using geoscience approaches. qPaLM requires no user-dependent training to operate on all image datasets in a classifier-agnostic manner to quantify occult abnormalities, derive mechanistic insights, and define a new feature class for machine-learning diagnostic classification.

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