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Collagen Morphometry Exploration in ...

Huang, Xin,

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Collagen Morphometry Exploration in Health and Disease /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Collagen Morphometry Exploration in Health and Disease // Xin Huang.
作者:	Huang, Xin,
面頁冊數:	1 electronic resource (152 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-10, Section: B.
Contained By:	Dissertations Abstracts International86-10B.
標題:	Biology. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31767829
ISBN:	9798310390294

Collagen Morphometry Exploration in Health and Disease /
Huang, Xin,

Collagen Morphometry Exploration in Health and Disease /Xin Huang. - 1 electronic resource (152 pages)

Source: Dissertations Abstracts International, Volume: 86-10, Section: B.

Collagen morphometry refers to the quantitative analyses of collagen fibril morphology to understand its role in health and disease. This thesis explores the critical function of collagen morphometrics in tissue architecture and cancer progression. Initially, we perform quantitative analysis on collagen morphometrics in skin aging to assess whether collagen structure and mechanics variations can serve as biomarkers of biological age. The findings suggest that relative dermal collagen structure and mechanics variations can effectively differentiate skin samples across age groups.To bridge collagen morphological analysis with computational pathology, we develop convolutional neural network (CNN) models to predict the malignant transformation of oral epithelial dysplasia (OED) into oral squamous cell carcinoma. A novel approach integrating histopathological and clinical data was employed for more accurate risk assessment. The CNN model, trained on whole-slide images of OED, effectively distinguished benign dysplastic lesions from those progressing to carcinoma and generated an attention-based heatmap to enhance model interpretability. We further utilized collagen hybridizing peptide (CHP) fluorescence signals to assess correlations between collagen morphometrics and OED progression. Despite achieving good training accuracy, validation results highlighted potential overfitting, emphasizing the need for more extensive, diverse datasets for better generalizability. Nevertheless, the study demonstrates the feasibility of using CNN models to predict malignant transformation in OED by integrating clinical outcomes and morphometrical analyses.To better interpret the CHP staining result, we investigated the mechanism of CHP interactions with collagen fibrils in vitro. We developed high-density collagen scaffolds that emulate native tissue collagen density and found that CHP accelerates collagen fibril growth and promotes fibril alignment. Additionally, we used CNN models to identify collagen's D-banding patterns, reducing reliance on manual quantification of nanoscale collagen morphometrics.To better interpret the CHP staining result, we investigated the mechanism of CHP interactions with collagen fibrils in vitro. We developed high-density collagen scaffolds that emulate native tissue collagen density and found that CHP accelerates collagen fibril growth and promotes fibril alignment. Additionally, we used CNN models to identify collagen's D-banding patterns, reducing reliance on manual quantification of nanoscale collagen morphometrics.

English

ISBN: 9798310390294Subjects--Topical Terms:

522710
Biology.
Subjects--Index Terms:

Collagen morphometry

Collagen Morphometry Exploration in Health and Disease /
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520 $a Collagen morphometry refers to the quantitative analyses of collagen fibril morphology to understand its role in health and disease. This thesis explores the critical function of collagen morphometrics in tissue architecture and cancer progression. Initially, we perform quantitative analysis on collagen morphometrics in skin aging to assess whether collagen structure and mechanics variations can serve as biomarkers of biological age. The findings suggest that relative dermal collagen structure and mechanics variations can effectively differentiate skin samples across age groups.To bridge collagen morphological analysis with computational pathology, we develop convolutional neural network (CNN) models to predict the malignant transformation of oral epithelial dysplasia (OED) into oral squamous cell carcinoma. A novel approach integrating histopathological and clinical data was employed for more accurate risk assessment. The CNN model, trained on whole-slide images of OED, effectively distinguished benign dysplastic lesions from those progressing to carcinoma and generated an attention-based heatmap to enhance model interpretability. We further utilized collagen hybridizing peptide (CHP) fluorescence signals to assess correlations between collagen morphometrics and OED progression. Despite achieving good training accuracy, validation results highlighted potential overfitting, emphasizing the need for more extensive, diverse datasets for better generalizability. Nevertheless, the study demonstrates the feasibility of using CNN models to predict malignant transformation in OED by integrating clinical outcomes and morphometrical analyses.To better interpret the CHP staining result, we investigated the mechanism of CHP interactions with collagen fibrils in vitro. We developed high-density collagen scaffolds that emulate native tissue collagen density and found that CHP accelerates collagen fibril growth and promotes fibril alignment. Additionally, we used CNN models to identify collagen's D-banding patterns, reducing reliance on manual quantification of nanoscale collagen morphometrics.To better interpret the CHP staining result, we investigated the mechanism of CHP interactions with collagen fibrils in vitro. We developed high-density collagen scaffolds that emulate native tissue collagen density and found that CHP accelerates collagen fibril growth and promotes fibril alignment. Additionally, we used CNN models to identify collagen's D-banding patterns, reducing reliance on manual quantification of nanoscale collagen morphometrics.
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