Universal in vivo Textural Model for Human Skin based on Optical Coherence Tomograms

Currently, diagnosis of skin diseases is based primarily on visual pattern recognition skills and expertise of the physician observing the lesion. Even though dermatologists are trained to recognize patterns of morphology, it is still a subjective visual assessment. Tools for automated pattern recognition can provide objective information to support clinical decision-making. Noninvasive skin imaging techniques provide complementary information to the clinician. In recent years, optical coherence tomography has become a powerful skin imaging technique. According to specific functional needs, skin architecture varies across different parts of the body, as do the textural characteristics in OCT images. There is, therefore, a critical need to systematically analyze OCT images from different body sites, to identify their significant qualitative and quantitative differences. Sixty-three optical and textural features extracted from OCT images of healthy and diseased skin are analyzed and in conjunction with decision-theoretic approaches used to create computational models of the diseases. We demonstrate that these models provide objective information to the clinician to assist in the diagnosis of abnormalities of cutaneous microstructure, and hence, aid in the determination of treatment. Specifically, we demonstrate the performance of this methodology on differentiating basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) from healthy tissue

Interpretable deep learning for guided structure-property explorations in photovoltaics

The performance of an organic photovoltaic device is intricately connected to its active layer morphology. This connection between the active layer and device performance is very expensive to evaluate, either experimentally or computationally. Hence, designing morphologies to achieve higher performances is non-trivial and often intractable. To solve this, we first introduce a deep convolutional neural network (CNN) architecture that can serve as a fast and robust surrogate for the complex structure-property map. Several tests were performed to gain trust in this trained model. Then, we utilize this fast framework to perform robust microstructural design to enhance device performance.Comment: Workshop on Machine Learning for Molecules and Materials (MLMM), Neural Information Processing Systems (NeurIPS) 2018, Montreal, Canad

Cn-AMP2 from green coconut water is an anionic anticancer peptide

Globally, death due to cancers is likely to rise to over 20 million by 2030,which has created an urgent need for novel approaches to anticancer therapies such as the development of host defence peptides. Cn-AMP2 (TESYFVFSVGM), an anionic host defence peptide from green coconut water of the plant Cocos nucifera, showed anti-proliferative activity against the 1321N1 and =U87MG human glioma cell lines with IC50 values of 1.25 and 1.85mM, respectively. The membrane interactive formof the peptide was found to be an extended conformation, which primarily included β-type structures (levels>45%) and random coil architecture (levels>45%). On the basis of these and other data, it is suggested that the short anionic N-terminal sequence(TES) of Cn-AMP2 interacts with positively charged moieties in the cancer cell membrane. Concomitantly, the long hydrophobic C-terminal sequence (YFVFSVGM) of the peptide penetrates the membrane core region, thereby driving the translocation of Cn-AMP2 across the cancer cell membrane to attack intracellular targets and induce anti-proliferative mechanisms. This work is the first to demonstrate that anionic host defence peptides have activity against human glioblastoma, which potentially provides an untapped source of lead compounds for development as novel agents in the treatment of these and other cancers. Copyright © 2014 European Peptide Society and John Wiley & Sons, Ltd

Maintenance of head and neck tumor on-chip: gateway to personalized treatment?

Aim: Head and neck squamous cell carcinomas (HNSCC) are solid tumors with low overall survival (40–60%). In a move toward personalized medicine, maintenance of tumor biopsies in microfluidic tissue culture devices is being developed. Methodology/ results: HNSCC (n = 15) was dissected (5–10 mg) and either analyzed immediately or cultured in a microfluidic device (37°C) for 48 h. No difference was observed in morphology between pre- and postculture specimens. Dissociated samples were analyzed using trypan blue exclusion (viability), propidium iodide flow cytometry (death) and MTS assay (proliferation) with no significant difference observed highlighting tissue maintenance. Computational fluid dynamics showed laminar flow within the system. Conclusion: The microfluidic culture system successfully maintained HNSCC for 48 h, the culture system will allow testing of different treatment modalities with response monitoring. Lay abstract: Head and neck cancers often have a poor treatment outcome. In order to study the response of the tissue, a miniaturized culture system has been developed to keep a small piece of tumor alive. In the current study, we show that small pieces of cancer tissue can be maintained in the system, using tissue structure and viability of single cells as a guide. In future work, patient equivalent treatments can be applied to these microculture systems to investigate individual patient tumor responses, which could help to guide treatment selection

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

Accurate detection of dysmorphic nuclei using dynamic programming and supervised classification

A vast array of pathologies is typified by the presence of nuclei with an abnormal morphology. Dysmorphic nuclear phenotypes feature dramatic size changes or foldings, but also entail much subtler deviations such as nuclear protrusions called blebs. Due to their unpredictable size, shape and intensity, dysmorphic nuclei are often not accurately detected in standard image analysis routines. To enable accurate detection of dysmorphic nuclei in confocal and widefield fluorescence microscopy images, we have developed an automated segmentation algorithm, called Blebbed Nuclei Detector (BleND), which relies on two-pass thresholding for initial nuclear contour detection, and an optimal path finding algorithm, based on dynamic programming, for refining these contours. Using a robust error metric, we show that our method matches manual segmentation in terms of precision and outperforms state-of-the-art nuclear segmentation methods. Its high performance allowed for building and integrating a robust classifier that recognizes dysmorphic nuclei with an accuracy above 95%. The combined segmentation-classification routine is bound to facilitate nucleus-based diagnostics and enable real-time recognition of dysmorphic nuclei in intelligent microscopy workflows

Achieving the Way for Automated Segmentation of Nuclei in Cancer Tissue Images through Morphology-Based Approach: a Quantitative Evaluation

In this paper we address the problem of nuclear segmentation in cancer tissue images, that is critical for specific protein activity quantification and for cancer diagnosis and therapy. We present a fully automated morphology-based technique able to perform accurate nuclear segmentations in images with heterogeneous staining and multiple tissue layers and we compare it with an alternate semi-automated method based on a well established segmentation approach, namely active contours. We discuss active contours’ limitations in the segmentation of immunohistochemical images and we demonstrate and motivate through extensive experiments the better accuracy of our fully automated approach compared to various active contours implementations