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in category pathology

525 results found. For more information, click each entry to expand.

1: ACE2 and TMPRSS2 are expressed on the human ocular surface, suggesting susceptibility to SARS-CoV-2 infection
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Posted to bioRxiv 09 May 2020

ACE2 and TMPRSS2 are expressed on the human ocular surface, suggesting susceptibility to SARS-CoV-2 infection
14,320 downloads pathology

Lingli Zhou, Zhenhua Xu, Gianni M. Castiglione, Uri S Soiberman, Charles G. Eberhart, Elia J Duh

Purpose: Conjunctival signs and symptoms are observed in a subset of patients with COVID-19, and SARS-CoV-2 has been detected in tears, raising concerns regarding the eye both as a portal of entry and carrier of the virus. The purpose of this study was to determine whether ocular surface cells possess the key factors required for cellular susceptibility to SARS-CoV-2 entry/infection. Methods: We analyzed human post-mortem eyes as well as surgical specimens for the expression of ACE2 (the receptor for SARS-CoV-2) and TMPRSS2, a cell surface-associated protease that facilitates viral entry following binding of the viral spike protein to ACE2. Results: Across all eye specimens, immunohistochemical analysis revealed expression of ACE2 in the conjunctiva, limbus, and cornea, with especially prominent staining in the superficial conjunctival and corneal epithelial surface. Surgical conjunctival specimens also showed expression of ACE2 in the conjunctival epithelium, especially prominent in the superficial epithelium, as well as the substantia propria. All eye and conjunctival specimens also expressed TMPRSS2. Finally, western blot analysis of protein lysates from human corneal epithelium obtained during refractive surgery confirmed expression of ACE2 and TMPRSS2. Conclusions: Together, these results indicate that ocular surface cells including conjunctiva are susceptible to infection by SARS-CoV-2, and could therefore serve as a portal of entry as well as a reservoir for person-to-person transmission of this virus. This highlights the importance of safety practices including face masks and ocular contact precautions in preventing the spread of COVID-19 disease. ### Competing Interest Statement The authors have declared no competing interest.

2: Complete genome characterisation of a novel coronavirus associated with severe human respiratory disease in Wuhan, China
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Posted to bioRxiv 25 Jan 2020

Complete genome characterisation of a novel coronavirus associated with severe human respiratory disease in Wuhan, China
11,803 downloads pathology

Fan Wu, Su Zhao, Bin Yu, Yan-Mei Chen, Wen Wang, Yi Hu, Zhi-Gang Song, Zhao-Wu Tao, Jun-Hua Tian, Yuan-Yuan Pei, Ming-Li Yuan, Yu-Ling Zhang, Fa-Hui Dai, Yi Liu, Qi-Min Wang, Jiao-Jiao Zheng, Lin Xu, Edward C. Holmes, Yong-Zhen Zhang

Emerging and re-emerging infectious diseases, such as SARS, MERS, Zika and highly pathogenic influenza present a major threat to public health[1][1]–[3][2]. Despite intense research effort, how, when and where novel diseases appear are still the source of considerable uncertainly. A severe respiratory disease was recently reported in the city of Wuhan, Hubei province, China. At the time of writing, at least 62 suspected cases have been reported since the first patient was hospitalized on December 12nd 2019. Epidemiological investigation by the local Center for Disease Control and Prevention (CDC) suggested that the outbreak was associated with a sea food market in Wuhan. We studied seven patients who were workers at the market, and collected bronchoalveolar lavage fluid (BALF) from one patient who exhibited a severe respiratory syndrome including fever, dizziness and cough, and who was admitted to Wuhan Central Hospital on December 26th 2019. Next generation metagenomic RNA sequencing[4][3] identified a novel RNA virus from the family Coronaviridae designed WH-Human-1 coronavirus (WHCV). Phylogenetic analysis of the complete viral genome (29,903 nucleotides) revealed that WHCV was most closely related (89.1% nucleotide similarity similarity) to a group of Severe Acute Respiratory Syndrome (SARS)-like coronaviruses (genus Betacoronavirus , subgenus Sarbecovirus ) previously sampled from bats in China and that have a history of genomic recombination. This outbreak highlights the ongoing capacity of viral spill-over from animals to cause severe disease in humans. [1]: #ref-1 [2]: #ref-3 [3]: #ref-4

3: H&E-stained Whole Slide Image Deep Learning Predicts SPOP Mutation State in Prostate Cancer
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Posted to bioRxiv 17 Jul 2016

H&E-stained Whole Slide Image Deep Learning Predicts SPOP Mutation State in Prostate Cancer
6,945 downloads pathology

Andrew J. Schaumberg, Mark A. Rubin, Thomas J. Fuchs

A quantitative model to genetically interpret the histology in whole microscopy slide images is desirable to guide downstream immunohistochemistry, genomics, and precision medicine. We constructed a statistical model that predicts whether or not SPOP is mutated in prostate cancer, given only the digital whole slide after standard hematoxylin and eosin [H&E] staining. Using a TCGA cohort of 177 prostate cancer patients where 20 had mutant SPOP, we trained multiple ensembles of residual networks, accurately distinguishing SPOP mutant from SPOP non-mutant patients (test AUROC=0.74, p=0.0007 Fisher's Exact Test). We further validated our full metaensemble classifier on an independent test cohort from MSK-IMPACT of 152 patients where 19 had mutant SPOP. Mutants and non-mutants were accurately distinguished despite TCGA slides being frozen sections and MSK-IMPACT slides being formalin-fixed paraffin-embedded sections (AUROC=0.86, p=0.0038). Moreover, we scanned an additional 36 MSK-IMPACT patient having mutant SPOP, trained on this expanded MSK-IMPACT cohort (test AUROC=0.75, p=0.0002), tested on the TCGA cohort (AUROC=0.64, p=0.0306), and again accurately distinguished mutants from non-mutants using the same pipeline. Importantly, our method demonstrates tractable deep learning in this "small data" setting of 20-55 positive examples and quantifies each prediction's uncertainty with confidence intervals. To our knowledge, this is the first statistical model to predict a genetic mutation in cancer directly from the patient's digitized H&E-stained whole microscopy slide. Moreover, this is the first time quantitative features learned from patient genetics and histology have been used for content-based image retrieval, finding similar patients for a given patient where the histology appears to share the same genetic driver of disease i.e. SPOP mutation (p=0.0241 Kost's Method), and finding similar patients for a given patient that does not have have that driver mutation (p=0.0170 Kost's Method).

4: High multiplex, digital spatial profiling of proteins and RNA in fixed tissue using genomic detection methods
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Posted to bioRxiv 22 Feb 2019

High multiplex, digital spatial profiling of proteins and RNA in fixed tissue using genomic detection methods
5,813 downloads pathology

Christopher R. Merritt, Giang T Ong, Sarah Church, Kristi Barker, Gary Geiss, Margaret Hoang, Jaemyeong Jung, Yan Liang, Jill McKay-Fleisch, Karen Nguyen, Kristina Sorg, Isaac Sprague, Charles Warren, Sarah Warren, Zoey Zhou, Daniel R. Zollinger, Dwayne L. Dunaway, Gordon B. Mills, Joseph M. Beechem

We have developed Digital Spatial Profiling (DSP), a non-destructive method for high-plex spatial profiling of proteins and RNA, using oligonucleotide detection technologies with unlimited multiplexing capability. The key breakthroughs underlying DSP are threefold: (1) multiplexed readout of proteins/RNA using oligo-tags; (2) oligo-tags attached to affinity reagents (antibodies/RNA probes) through a photocleavable (PC) linker; (3) photocleaving light projected onto the tissue sample to release PC-oligos in any spatial pattern. Here we show precise analyte reproducibility, validation, and cellular resolution using DSP. We also demonstrate biological proof-of-concept using lymphoid, colorectal tumor, and autoimmune tissue as models to profile immune cell populations, stroma, and cancer cells to identify factors specific for the diseased microenvironment. DSP utilizes the unlimited multiplexing capability of modern genomic approaches, while simultaneously providing spatial context of protein and RNA to examine biological questions based on analyte location and distribution.

5: Are pangolins the intermediate host of the 2019 novel coronavirus (2019-nCoV) ?
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Posted to bioRxiv 20 Feb 2020

Are pangolins the intermediate host of the 2019 novel coronavirus (2019-nCoV) ?
5,370 downloads pathology

Ping Liu, Jing-Zhe Jiang, Xiu-Feng Wan, Yan Hua, Xiaohu Wang, Fanghui Hou, Jing Chen, Jiejian Zou, Jinping Chen

The outbreak of 2019-nCoV pneumonia (COVID-19) in the city of Wuhan, China has resulted in more than 60,000 laboratory confirmed cases, and recent studies showed that 2019-nCoV (SARS-CoV-2) could be of bat origin but involve other potential intermediate hosts. In this study, we assembled the genomes of coronaviruses identified in sick pangolins. The molecular and phylogenetic analyses showed that pangolin Coronaviruses (pangolin-CoV) are genetically related to both the 2019-nCoV and bat Coronaviruses but do not support the 2019-nCoV arose directly from the pangolin-CoV. Our study also suggested that pangolin be natural host of Betacoronavirus, with a potential to infect humans. Large surveillance of coronaviruses in pangolins could improve our understanding of the spectrum of coronaviruses in pangolins. Conservation of wildlife and limits of the exposures of humans to wildlife will be important to minimize the spillover risks of coronaviruses from wild animals to humans.

6: Diabetic Retinopathy detection through integration of Deep Learning classification framework
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Posted to bioRxiv 27 Nov 2017

Diabetic Retinopathy detection through integration of Deep Learning classification framework
5,089 downloads pathology

Alexander Rakhlin

This document represents a brief account of ongoing project for Diabetic Retinopathy Detection (DRD) through integration of state-of the art Deep Learning methods. We make use of deep Convolutional Neural Networks (CNNs), which have proven revolutionary in multiple fields of computer vision including medical imaging, and we bring their power to the diagnosis of eye fundus images. For training our models we used publicly available Kaggle data set. For testing we used portion of Kaggle data withheld from training and Messidor-2 reference standard. Neither withheld Kaggle images, nor Messidor-2 were used for training. For Messidor-2 we achieved sensitivity 99%, specificity 71%, and AUC 0.97. These results close to recent state-of-the-art models trained on much larger data sets and surpass average results of diabetic retinopathy screening when performed by trained optometrists. With continuous development of our Deep Learning models we expect to further increase the accuracy of the method and expand it to cataract and glaucoma diagnostics.

7: Pangolin homology associated with 2019-nCoV
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Posted to bioRxiv 20 Feb 2020

Pangolin homology associated with 2019-nCoV
4,903 downloads pathology

Tao Zhang, Qunfu Wu, Zhigang Zhang

To explore potential intermediate host of a novel coronavirus is vital to rapidly control continuous COVID-19 spread. We found genomic and evolutionary evidences of the occurrence of 2019-nCoV-like coronavirus (named as Pangolin-CoV) from dead Malayan Pangolins. Pangolin-CoV is 91.02% and 90.55% identical at the whole genome level to 2019-nCoV and BatCoV RaTG13, respectively. Pangolin-CoV is the lowest common ancestor of 2019-nCoV and RaTG13. The S1 protein of Pangolin-CoV is much more closely related to 2019-nCoV than RaTG13. Five key amino-acid residues involved in the interaction with human ACE2 are completely consistent between Pangolin-CoV and 2019-nCoV but four amino-acid mutations occur in RaTG13. It indicates Pangolin-CoV has similar pathogenic potential to 2019-nCoV, and would be helpful to trace the origin and probable intermediate host of 2019-nCoV.

8: Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis
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Posted to bioRxiv 05 Feb 2018

Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis
4,300 downloads pathology

Alexander Rakhlin, Alexey A. Shvets, Vladimir I. Iglovikov, Alexandr A. Kalinin

Breast cancer is one of the main causes of cancer death worldwide. Early diagnostics significantly increases the chances of correct treatment and survival, but this process is tedious and often leads to a disagreement between pathologists. Computer-aided diagnosis systems showed potential for improving the diagnostic accuracy. In this work, we develop the computational approach based on deep convolution neural networks for breast cancer histology image classification. Hematoxylin and eosin stained breast histology microscopy image dataset is provided as a part of the ICIAR 2018 Grand Challenge on Breast Cancer Histology Images. Our approach utilizes several deep neural network architectures and gradient boosted trees classifier. For 4-class classification task, we report 87.2% accuracy. For 2-class classification task to detect carcinomas we report 93.8% accuracy, AUC 97.3%, and sensitivity/specificity 96.5/88.0% at the high-sensitivity operating point. To our knowledge, this approach outperforms other common methods in automated histopathological image classification. The source code for our approach is made publicly available at https://github.com/alexander-rakhlin/ICIAR2018.

9: Prognostic biomarkers in oral squamous cell carcinoma: a systematic review
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Posted to bioRxiv 16 Jul 2017

Prognostic biomarkers in oral squamous cell carcinoma: a systematic review
3,386 downloads pathology

César Rivera, Ana Karina de Oliveira, Rute Alves Pereira e Costa, Tatiane De Rossi, Adriana Franco Paes Leme

Over the years, several tumor biomarkers have been suggested to foresee the prognosis of oral squamous cell carcinoma (OSCC) patients. Here, we present a systematic review to identify, evaluate and summarize the evidence for OSCC reported markers. Eligible studies were identified through a literature search of MEDLINE/PubMed until January 2016. We included primary articles reporting overall survival, disease-free survival and cause-specific survival as outcomes. Our findings were analysed using REporting recommendations for tumor MARKer prognostic studies (REMARK), QuickGo tool and SciCurve trends. We found 41 biomarkers, mostly proteins evaluated by immunohistochemistry. The selected studies are of good quality, although, any study referred to a sample size determination. Considering the lack of follow-up studies, the molecules are still potential biomarkers. Further research is required to validate these biomarkers in well designed clinical cohort-based studies.

10: Analytical Validation of a COVID-19 qRT-PCR Detection Assay Using a 384-well Format and Three Extraction Methods
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Posted to bioRxiv 05 Apr 2020

Analytical Validation of a COVID-19 qRT-PCR Detection Assay Using a 384-well Format and Three Extraction Methods
3,340 downloads pathology

Andrew C. Nelson, Benjamin Auch, Matthew Schomaker, Daryl M. Gohl, Patrick Grady, Darrell Johnson, Robyn Kincaid, Kylene E Karnuth, Jerry Daniel, Jessica K Fiege, Elizabeth J Fay, Tyler Bold, Ryan A Langlois, Kenneth B. Beckman, Sophia Yohe

The COVID-19 global pandemic is an unprecedented health emergency. Insufficient access to testing has hampered effective public health interventions and patient care management in a number of countries. Furthermore, the availability of regulatory-cleared reagents has challenged widespread implementation of testing. We rapidly developed a qRT-PCR SARS-CoV-2 detection assay using a 384-well format and tested its analytic performance across multiple nucleic acid extraction kits. Our data shows robust analytic accuracy on residual clinical biospecimens. Limit of detection sensitivity and specificity was confirmed with currently available commercial reagents. Our methods and results provide valuable information for other high-complexity laboratories seeking to develop effective, local, laboratory-developed procedures with high-throughput capability to detect SARS-CoV-2.

11: Adversarial childhood events are associated with Sudden Infant Death Syndrome (SIDS): an ecological study
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Posted to bioRxiv 07 Jun 2018

Adversarial childhood events are associated with Sudden Infant Death Syndrome (SIDS): an ecological study
3,308 downloads pathology

Eran Elhaik

Sudden Infant Death Syndrome (SIDS) is the most common cause of postneonatal infant death. The allostatic load hypothesis posits that SIDS is the result of perinatal cumulative painful, stressful, or traumatic exposures that tax neonatal regulatory systems. To test it, we explored the relationships between SIDS and two common stressors, male neonatal circumcision (MNC) and prematurity, using latitudinal data from 15 countries and over 40 US states during the years 1999-2016. We used linear regression analyses and likelihood ratio tests to calculate the association between SIDS and the stressors. SIDS prevalence was significantly and positively correlated with MNC and prematurity rates. MNC explained 14.2% of the variability of SIDS's male bias in the US, reminiscent of the Jewish myth of Lilith, the killer of infant males. Combined, the stressors increased the likelihood of SIDS. Ecological analyses are useful to generate hypotheses but cannot provide strong evidence of causality. Biological plausibility is provided by a growing body of experimental and clinical evidence linking adversary preterm and early-life events with SIDS. Together with historical evidence, our findings emphasize the necessity of cohort studies that consider these environmental stressors with the aim of improving the identification of at-risk infants and reducing infant mortality.

12: Pericyte-specific vascular expression of SARS-CoV-2 receptor ACE2 – implications for microvascular inflammation and hypercoagulopathy in COVID-19
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Posted to bioRxiv 12 May 2020

Pericyte-specific vascular expression of SARS-CoV-2 receptor ACE2 – implications for microvascular inflammation and hypercoagulopathy in COVID-19
3,169 downloads pathology

Liqun He, Maarja Andaloussi Mäe, Lars Muhl, Ying Sun, Riikka Pietilä, Khayrun Nahar, Elisa Vázquez Liébanas, Malin Jonsson Fagerlund, Anders Oldner, Jianping Liu, Guillem Genové, Lei Zhang, Yuan Xie, Stefanos Leptidis, Giuseppe Mocci, Simon Stritt, Ahmed Osman, Andrey Anisimov, Karthik Amudhala Hemanthakumar, Markus Räsänen, Olivier Mirabeau, Emil Hansson, Johan Björkegren, Michael Vanlandewijck, Klas Blomgren, Taija Mäkinen, Xiao-Rong Peng, Thomas D. Arnold, Kari Alitalo, Lars I Eriksson, Urban Lendahl, Christer Betsholtz

Accumulating clinical observations implicate vascular inflammation as an underlying cause of coagulopathy in severely ill COVID-19 patients and it was recently suggested that SARS-CoV-2 virus particles infect endothelial cells. Here, we show that endothelial cells do not express angiotensin-converting enzyme-2 (ACE2), the SARS-CoV-2 receptor. Instead, pericytes and microvascular smooth muscle cells express ACE2 in an organotypic manner. Pericyte deficiency leads to increased endothelial expression and release of Von Willebrand factor and intravascular platelet and fibrin aggregation, suggesting that pericytes limit endothelial pro-thrombotic responses. That pericytes and not endothelial cells express ACE2 may provide important clues to the pathology of COVID-19, as pericytes are normally shielded behind an endothelial barrier and may get infected only when this barrier is compromised by COVID-19 risk factors. ### Competing Interest Statement C.B. is a consultant for AstraZeneca BioPharmaceuticals R&D. X.-R. P. is an employee of AstraZeneca BioPharmaceuticals R&D.

13: Multiplex staining by sequential immunostaining and antibody removal on routine tissue sections
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Posted to bioRxiv 11 Jun 2017

Multiplex staining by sequential immunostaining and antibody removal on routine tissue sections
2,848 downloads pathology

Maddalena Maria Bolognesi, Marco Manzoni, Carla Rossana Scalia, Stefano Zannella, Francesca Maria Bosisio, Mario Faretta, Giorgio Cattoretti

Multiplexing (mplx), labeling for multiple immunostains the very same cell or tissue section in situ, has raised considerable interest. The methods proposed include the use of labelled primary antibodies, spectral separation of fluorochromes, bleaching of the fluorophores or chromogens, blocking of previous antibody layers, all in various combinations. The major obstacles to the diffusion of this technique are high costs in custom antibodies and instruments, low throughput, scarcity of specialized skills or facilities. We have validated a method based on common primary and secondary antibodies and diffusely available fluorescent image scanners. It entails rounds of four-color indirect immunofluorescence, image acquisition and removal (stripping) of the antibodies, before another stain is applied. The images are digitally registered and the autofluorescence is subtracted. Removal of antibodies is accomplished by disulphide cleavage and a detergent or by a chaotropic salt treatment, this latter followed by antigen refolding. More than thirty different antibody stains can be applied to one single section from routinely fixed and embedded tissue. This method requires a modest investment in hardware and materials and uses freeware image analysis software. Mplx on routine tissue sections is a high throughput tool for in situ characterization of neoplastic, reactive, inflammatory and normal cells.

14: Multiscale three-dimensional pathology findings of COVID-19 diseased lung using high-resolution cleared tissue microscopy
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Posted to bioRxiv 17 Apr 2020

Multiscale three-dimensional pathology findings of COVID-19 diseased lung using high-resolution cleared tissue microscopy
2,646 downloads pathology

Guang Li, Sharon E Fox, Brian Summa, Bihe Hu, Carola Wenk, Aibek Akmatbekov, Jack L Harbert, Richard S. Vander Heide, J. Quincy Brown

The study of pulmonary samples from individuals who have died as a direct result of COVID-19 infection is vital to our understanding of the pathogenesis of this disease. Histopathologic studies of lung tissue from autopsy of patients with COVID-19 specific mortality are only just emerging. All existing reports have relied on traditional 2-dimensional slide-based histological methods for specimen preparation. However, emerging methods for high-resolution, massively multiscale imaging of tissue microstructure using fluorescence labeling and tissue clearing methods enable the acquisition of tissue histology in 3-dimensions, that could open new insights into the nature of SARS-Cov-2 infection and COVID-19 disease processes. In this article, we present the first 3-dimensional images of lung autopsy tissues taken from a COVID-19 patient, including 3D "virtual histology" of cubic-millimeter volumes of the diseased lung, providing unique insights into disease processes contributing to mortality that could inform frontline treatment decisions. ### Competing Interest Statement JQB is a co-founder and shareholder in Instapath Inc. No other authors declare competing interests.

15: Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks
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Posted to bioRxiv 14 Dec 2017

Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks
2,504 downloads pathology

Vladimir I. Iglovikov, Alexander Rakhlin, Alexandr A. Kalinin, Alexey A. Shvets

Skeletal bone age assessment is a common clinical practice to diagnose endocrine and metabolic disorders in child development. In this paper, we describe a fully automated deep learning approach to the problem of bone age assessment using data from the 2017 Pediatric Bone Age Challenge organized by the Radiological Society of North America. The dataset for this competition is consisted of 12.6k radiological images. Each radiograph in this dataset is an image of a left hand labeled by the bone age and the sex of a patient. Our approach utilizes several deep neural network architectures trained end-to-end. We use images of whole hands as well as specific parts of a hand for both training and inference. This approach allows us to measure the importance of specific hand bones for the automated bone age analysis. We further evaluate performance of the method in the context of skeletal development stages. Our approach outperforms other common methods for bone age assessment.

16: Low-cost, sub-micron resolution, wide-field computational microscopy using opensource hardware
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Posted to bioRxiv 02 Nov 2018

Low-cost, sub-micron resolution, wide-field computational microscopy using opensource hardware
2,438 downloads pathology

Tomas Aidukas, Regina Eckert, Andrew R Harvey, Laura Waller, Pavan C. Konda

The revolution in low-cost consumer photography and computation provides fertile opportunity for a disruptive reduction in the cost of biomedical imaging. Conventional approaches to low-cost microscopy are fundamentally restricted, however, to modest field of view (FOV) and/or resolution. We report a low-cost microscopy technique, implemented with a Raspberry Pi single-board computer and color camera combined with Fourier ptychography (FP), to computationally construct 25-megapixel images with sub-micron resolution. New image-construction techniques were developed to enable the use of the low-cost Bayer color sensor, to compensate for the highly aberrated re-used camera lens and to compensate for misalignments associated with the 3D-printed microscope structure. This high ratio of performance to cost is of particular interest to high-throughput microscopy applications, ranging from drug discovery and digital pathology to health screening in low-income countries. 3D models and assembly instructions of our microscope are made available for open source use.

17: Interpretable multimodal deep learning for real-time pan-tissue pan-disease pathology search on social media
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Posted to bioRxiv 21 Aug 2018

Interpretable multimodal deep learning for real-time pan-tissue pan-disease pathology search on social media
2,372 downloads pathology

Andrew J. Schaumberg, Wendy C. Juarez-Nicanor, Sarah J. Choudhury, Laura G. Pastrián, Bobbi S. Pritt, Mario Prieto Pozuelo, Ricardo Sotillo Sánchez, Khanh Ho, Nusrat Zahra, Betul Duygu Sener, Stephen Yip, Bin Xu, Srinivas Rao Annavarapu, Aurélien Morini, Karra A. Jones, Kathia Rosado-Orozco, Sanjay Mukhopadhyay, Carlos Miguel, Hongyu Yang, Yale Rosen, Rola H. Ali, Olaleke O. Folaranmi, Jerad M. Gardner, Corina Rusu, Celina Stayerman, John Gross, Dauda E. Suleiman, S. Joseph Sirintrapun, Mariam Aly, Thomas J. Fuchs

Pathologists are responsible for rapidly providing a diagnosis on critical health issues. Challenging cases benefit from additional opinions of pathologist colleagues. In addition to on-site colleagues, there is an active worldwide community of pathologists on social media for complementary opinions. Such access to pathologists worldwide has the capacity to improve diagnostic accuracy and generate broader consensus on next steps in patient care. From Twitter we curate 13,626 images from 6,351 tweets from 25 pathologists from 13 countries. We supplement the Twitter data with 113,161 images from 1,074,484 PubMed articles. We develop machine learning and deep learning models to (i) accurately identify histopathology stains, (ii) discriminate between tissues, and (iii) differentiate disease states. Area Under Receiver Operating Characteristic is 0.805-0.996 for these tasks. We repurpose the disease classifier to search for similar disease states given an image and clinical covariates. We report precision@k=1 = 0.7618±0.0018 (chance 0.397±0.004, mean±stdev). The classifiers find texture and tissue are important clinico-visual features of disease. Deep features trained only on natural images (e.g. cats and dogs) substantially improved search performance, while pathology-specific deep features and cell nuclei features further improved search to a lesser extent. We implement a social media bot (@pathobot on Twitter) to use the trained classifiers to aid pathologists in obtaining real-time feedback on challenging cases. If a social media post containing pathology text and images mentions the bot, the bot generates quantitative predictions of disease state (normal/artifact/infection/injury/nontumor, pre-neoplastic/benign/ low-grade-malignant-potential, or malignant) and lists similar cases across social media and PubMed. Our project has become a globally distributed expert system that facilitates pathological diagnosis and brings expertise to underserved regions or hospitals with less expertise in a particular disease. This is the first pan-tissue pan-disease (i.e. from infection to malignancy) method for prediction and search on social media, and the first pathology study prospectively tested in public on social media. We will share data through [pathobotology.org][1]. We expect our project to cultivate a more connected world of physicians and improve patient care worldwide. [1]: http://pathobotology.org

18: Three-Dimensional Histology of Whole Zebrafish by Sub-Micron Synchrotron X-ray Micro-Tomography
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Posted to bioRxiv 25 Aug 2018

Three-Dimensional Histology of Whole Zebrafish by Sub-Micron Synchrotron X-ray Micro-Tomography
2,320 downloads pathology

Yifu Ding, Daniel J. Vanselow, Maksim A. Yakovlev, Spencer R. Katz, Alex Y Lin, Darin P Clark, Phillip Vargas, Xuying Xin, Jean E Copper, Victor A Canfield, Khai C Ang, Yuxin Wang, Xianghui Xiao, Francesco De Carlo, Damian B. van Rossum, Patrick La Rivière, Keith C. Cheng

Histological studies providing cellular insights into tissue architecture have been central to biological discovery and remain clinically invaluable today. Extending histology to three dimensions would be transformational for research and diagnostics. However, three-dimensional histology is impractical using current techniques. We have customized sample preparation, synchrotron X-ray tomographic parameters, and three-dimensional image analysis to allow for complete histological phenotyping using whole larval and juvenile zebrafish. The resulting digital zebrafish can be virtually sectioned and visualized in any plane. Whole-animal reconstructions at subcellular resolution also enable computational characterization of the zebrafish nervous system by region-specific detection of cell nuclei and quantitative assessment of individual phenotypic variation. Three-dimensional histological phenotyping has potential use in genetic and chemical screens, and in clinical and toxicological tissue diagnostics.

19: Using Machine Learning to Parse Breast Pathology Reports
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Posted to bioRxiv 10 Oct 2016

Using Machine Learning to Parse Breast Pathology Reports
2,283 downloads pathology

Adam Yala, Regina Barzilay, Laura Salama, Molly Griffin, Grace Sollender, Aditya Bardia, Constance Lehman, Julliette M Buckley, Suzanne B Coopey, Fernanda Polubriaginof, Judy E Garber, Barbara L Smith, Michele A Gadd, Michelle C Specht, Thomas M Gudewicz, Anthony Guidi, Alphonse Taghian, Kevin S Hughes

Purpose: Extracting information from Electronic Medical Record is a time-consuming and expensive process when done manually. Rule-based and machine learning techniques are two approaches to solving this problem. In this study, we trained a machine learning model on pathology reports to extract pertinent tumor characteristics, which enabled us to create a large database of attribute searchable pathology reports. This database can be used to identify cohorts of patients with characteristics of interest. Methods: We collected a total of 91,505 breast pathology reports from three Partners hospitals: Massachusetts General Hospital (MGH), Brigham and Womens Hospital (BWH), and Newton Wellesley Hospital (NWH), covering the period from 1978 to 2016. We trained our system with annotations from two datasets, consisting of 6,295 and 10,841 manually annotated reports. The system extracts 20 separate categories of information, including atypia types and various tumor characteristics such as receptors. We also report a learning curve analysis to show how much annotation our model needs to perform reasonably. Results: The model accuracy was tested on 500 reports that did not overlap with the training set. The model achieved accuracy of 90% for correctly parsing all carcinoma and atypia categories for a given patient. The average accuracy for individual categories was 97%. Using this classifier, we created a database of 91,505 parsed pathology reports. Conclusions: Our learning curve analysis shows that the model can achieve reasonable results even when trained on a few annotations. We developed a user-friendly interface to the database that allows physicians to easily identify patients with target characteristics and export the matching cohort. This model has the potential to reduce the effort required for analyzing large amounts of data from medical records, and to minimize the cost and time required to glean scientific insight from this data.

20: Classifying Non-Small Cell Lung Cancer Histopathology Types and Transcriptomic Subtypes using Convolutional Neural Networks
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Posted to bioRxiv 25 Jan 2019

Classifying Non-Small Cell Lung Cancer Histopathology Types and Transcriptomic Subtypes using Convolutional Neural Networks
2,110 downloads pathology

Kun-Hsing Yu, Feiran Wang, Gerald J. Berry, Christopher Ré, Russ B. Altman, Michael P. Snyder, Isaac S. Kohane

Non-small cell lung cancer is a leading cause of cancer death worldwide, and histopathological evaluation plays the primary role in its diagnosis. However, the morphological patterns associated with the molecular subtypes have not been systematically studied. To bridge this gap, we developed a quantitative histopathology analytic framework to identify the gene expression subtypes of non-small cell lung cancer objectively. We processed whole-slide histopathology images of lung adenocarcinoma (n=427) and lung squamous cell carcinoma patients (n=457) in The Cancer Genome Atlas. To establish neural networks for quantitative image analyses, we first build convolutional neural network models to identify tumor regions from adjacent dense benign tissues (areas under the receiver operating characteristic curves (AUC) > 0.935) and recapitulated expert pathologists' diagnosis (AUC > 0.88), with the results validated in an independent cohort (n=125; AUC > 0.85). We further demonstrated that quantitative histopathology morphology features identified the major transcriptomic subtypes of both adenocarcinoma and squamous cell carcinoma (P < 0.01). Our study is the first to classify the transcriptomic subtypes of non-small cell lung cancer using fully-automated machine learning methods. Our approach does not rely on prior pathology knowledge and can discover novel clinically-relevant histopathology patterns objectively. The developed procedure is generalizable to other tumor types or diseases.

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