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Background/Aims: Aberrant skin barrier and intercorneocyte adhesion are potential contributors to the pathomechanism of sensitive skin (SS). Here we aimed to develop a novel and easy-to-apply method to analyze corneodesmosomes and to interrogate potential differences between corneocytes of subjects with SS and non-SS (NSS). Methods: Corneocytes of the volar forearm and upper outer quadrant of the left buttock of SS (n = 10) and NSS (n = 8) subjects were extracted as a function of depth using adhesive tape and stained with anti-desmoglein 1 (DSG1) antibody. The total area of corneocytes and the number and average size of cells per tape was estimated using image processing. Results: The total area of extracted corneocytes and the quantity of DSG1 decreased with depth. The level of decrease, total area of corneocytes, and average area of individual cells differed between anatomical locations. In SS, a larger total area of extracted corneocytes and a larger average cell size per tape was found at all inspected depths. Conclusion: The developed novel and easy-to-apply approach allows investigation of corneodesmosome components. We confirm a role of altered corneocytes in the pathomechanism of SS. The disclosed protocol can further be optimized in studies of skin conditions with strongly affected corneodesmosomes.

Contains fulltext : 171534.pdf (Publisher’s version ) (Open Access) Variations in the color and intensity of hematoxylin and eosin (H&E) stained histological slides can potentially hamper the effectiveness of quantitative image analysis. This paper presents a fully automated algorithm for standardization of whole-slide histopathological images to reduce the effect of these variations. The proposed algorithm, called whole-slide image color standardizer (WSICS), utilizes color and spatial information to classify the image pixels into different stain components. The chromatic and density distributions for each of the stain components in the hue-saturation-density color model are aligned to match the corresponding distributions from a template whole-slide image (WSI). The performance of the WSICS algorithm was evaluated on two datasets. The first originated from 125 H&E stained WSIs of lymph nodes, sampled from 3 patients, and stained in 5 different laboratories on different days of the week. The second comprised 30 H&E stained WSIs of rat liver sections. The result of qualitative and quantitative evaluations using the first dataset demonstrate that the WSICS algorithm outperforms competing methods in terms of achieving color constancy. The WSICS algorithm consistently yields the smallest standard deviation and coefficient of variation of the normalized median intensity measure. Using the second dataset, we evaluated the impact of our algorithm on the performance of an already published necrosis quantification system. The performance of this system was significantly improved by utilizing the WSICS algorithm. The results of the empirical evaluations collectively demonstrate the potential contribution of the proposed standardization algorithm to improved diagnostic accuracy and consistency in computer-aided diagnosis for histopathology data.

Contains fulltext : 167707.pdf (Publisher’s version ) (Open Access) Pathologists face a substantial increase in workload and complexity of histopathologic cancer diagnosis due to the advent of personalized medicine. Therefore, diagnostic protocols have to focus equally on efficiency and accuracy. In this paper we introduce 'deep learning' as a technique to improve the objectivity and efficiency of histopathologic slide analysis. Through two examples, prostate cancer identification in biopsy specimens and breast cancer metastasis detection in sentinel lymph nodes, we show the potential of this new methodology to reduce the workload for pathologists, while at the same time increasing objectivity of diagnoses. We found that all slides containing prostate cancer and micro- and macro-metastases of breast cancer could be identified automatically while 30-40% of the slides containing benign and normal tissue could be excluded without the use of any additional immunohistochemical markers or human intervention. We conclude that 'deep learning' holds great promise to improve the efficacy of prostate cancer diagnosis and breast cancer staging.

Bone material is composed of an organic matrix of collagen fibers and apatite nanoparticles. Previously, vibrational spectroscopy techniques such as infrared (IR) and Raman spectroscopy have proved to be particularly useful for characterizing the two constituent organic and inorganic phases of bone. In this work, we tested the potential use of high intensity synchrotron-based far-IR radiation (50–500 cm−1) to gain new insights into structure and chemical composition of bovine fibrolamellar bone. The results from our study can be summarized in the following four points: (I) compared to far-IR spectra obtained from synthetic hydroxyapatite powder, those from fibrolamellar bone showed similar peak positions, but very different peak widths, (II) during stepwise demineralization of the bone samples, there was no significant change neither to far-IR peak width nor position, demonstrating that mineral dissolution occurred in a uniform manner, (III) application of external loading on fully demineralized bone had no significant effect on the obtained spectra, while dehydration of samples resulted in clear differences. (IV) using linear dichroism, we showed that the anisotropic structure of fibrolamellar bone is also reflected in anisotropic far-IR absorbance properties of both the organic and inorganic phases. Far-IR spectroscopy thus provides a novel way to functionally characterize bone structure and chemistry, and with further technological improvements, has the potential to become a useful clinical diagnostic tool to better assess quality of collagen-based tissues.

Collagen is a versatile structural molecule in nature and is used as a building block in many highly organized tissues, such as bone, skin, and cornea. The functionality and performance of these tissues are controlled by their hierarchical organization ranging from the molecular up to macroscopic length scales. In the present study, polarized Raman microspectroscopic and imaging analyses were used to elucidate collagen fibril orientation at various levels of structure in native rat tail tendon under mechanical load. In situ humidity-controlled uniaxial tensile tests have been performed concurrently with Raman confocal microscopy to evaluate strain-induced chemical and structural changes of collagen in tendon. The methodology is based on the sensitivity of specific Raman scattering bands (associated with distinct molecular vibrations, such as the amide I) to the orientation and the polarization direction of the incident laser light. Our results, based on the changing intensity of Raman lines as a function of orientation and polarization, support a model where the crimp and gap regions of collagen hierarchical structure are straightened at the tissue and molecular level, respectively. However, the lack of measurable changes in Raman peak positions throughout the whole range of strains investigated indicates that no significant changes of the collagen backbone occurs with tensing and suggests that deformation is rather redistributed through other levels of the hierarchical structure.