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in category pathology

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

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

Multiplex staining by sequential immunostaining and antibody removal on routine tissue sections
3,065 downloads bioRxiv 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.

22: Placental pathology in COVID-19
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Posted 12 May 2020

Placental pathology in COVID-19
2,890 downloads medRxiv pathology

Elisheva D Shanes, Leena B. Mithal, Sebastian Otero, Hooman A Azad, Emily S. Miller, Jeffery A. Goldstein

Objectives: To describe histopathologic findings in the placentas of women with COVID-19 during pregnancy. Methods: Pregnant women with COVID-19 delivering between March 18, 2020 and May 5, 2020 were identified. Placentas were examined and compared to historical controls and women with placental evaluation for a history of melanoma. Results: 16 placentas from patients with SARS-CoV-2 were examined (15 with live birth in the 3rd trimester 1 delivered in the 2nd trimester after intrauterine fetal demise). Compared to controls, third trimester placentas were significantly more likely to show at least one feature of maternal vascular malperfusion (MVM), including abnormal or injured maternal vessels, as well as delayed villous maturation, chorangiosis, and intervillous thrombi. Rates of acute and chronic inflammation were not increased. The placenta from the patient with intrauterine fetal demise showed villous edema and a retroplacental hematoma. Conclusions: Relative to controls, COVID-19 placentas show increased prevalence of features of maternal vascular malperfusion (MVM), a pattern of placental injury reflecting abnormalities in oxygenation within the intervillous space associated with adverse perinatal outcomes. Only 1 COVID-19 patient was hypertensive despite the association of MVM with hypertensive disorders and preeclampsia. These changes may reflect a systemic inflammatory or hypercoagulable state influencing placental physiology.

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

Multiscale three-dimensional pathology findings of COVID-19 diseased lung using high-resolution cleared tissue microscopy
2,871 downloads bioRxiv 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.

24: Performance characteristics of the ID NOW COVID-19 assay: A regional health care system experience
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Posted 05 Jun 2020

Performance characteristics of the ID NOW COVID-19 assay: A regional health care system experience
2,809 downloads medRxiv pathology

Mohiedean Ghofrani, Mary T Casas, Robert K Pelz, Catherine Kroll, Natalie Blum, Scott D Foster

Objectives: We compared the Abbott ID NOW COVID-19 point-of-care test (POCT) with polymerase chain reaction (PCR)-based methods to assess the claimed sensitivity and specificity of POCT and to optimize test utilization in our regional health care system. Methods: Assuming PCR to be the gold standard, we used a convenience sampling of mostly symptomatic COVID-19 suspect hospital patients who had already been tested for internal validation and guideline development purposes by both PCR and POCT to calculate the sensitivity and specificity of POCT with Clopper-Pearson 95% confidence intervals (CI). Results: During the study period, 113 paired patient samples met eligibility criteria. The sensitivity of POCT in this population was calculated to be 94.1% [CI 71.31-99.85%] and the specificity was 99.0% [CI 94.33-99.97%]. Conclusions: Based on the lower sensitivity of POCT and the estimated prevalence of COVID-19 in our symptomatic and asymptomatic hospital patients, we recommend a two-pronged testing approach in which COVID-19 suspect patients are tested by the more sensitive PCR, while asymptomatic patients with a low pre-test probability of infection are tested with POCT supplemented by PCR confirmation of positive results. Furthermore, isolation decisions should not be based on POCT results alone.

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

Low-cost, sub-micron resolution, wide-field computational microscopy using opensource hardware
2,660 downloads bioRxiv 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.

26: Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks
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Posted 14 Dec 2017

Pediatric Bone Age Assessment Using Deep Convolutional Neural Networks
2,643 downloads bioRxiv 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.

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

Interpretable multimodal deep learning for real-time pan-tissue pan-disease pathology search on social media
2,636 downloads bioRxiv 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

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

Using Machine Learning to Parse Breast Pathology Reports
2,445 downloads bioRxiv 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.

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

Three-Dimensional Histology of Whole Zebrafish by Sub-Micron Synchrotron X-ray Micro-Tomography
2,413 downloads bioRxiv 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.

30: Analytical performance of lateral flow immunoassay for SARS-CoV-2 exposure screening on venous and capillary blood samples
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Posted 18 May 2020

Analytical performance of lateral flow immunoassay for SARS-CoV-2 exposure screening on venous and capillary blood samples
2,385 downloads medRxiv pathology

Margaret A Black, Guomiao Shen, Xiaojun Feng, Wilfredo Garcia Beltran, Yang Feng, Varshini Vasudevaraja, Douglas Allison, Lawrence H Lin, Tatyana Gindin, Michael Astudillo, Diane Yang, Mandakolathur Murali, A. John Iafrate, George Jour, Paolo Cotzia, Matija Snuderl

Objectives: Numerous serologic immunoassays have been launched to detect antibodies to SARS-CoV-2, including rapid tests. Here, we validate use of a lateral flow immunoassay (LFI) intended for rapid screening and qualitative detection of anti-SARS-CoV-2 IgM and IgG in serum, plasma, and whole blood, and compare results with ELISA. We also seek to establish the value of LFI testing on blood obtained from a capillary blood sample. Methods: Samples collected by venous blood draw and capillary finger stick were obtained from patients with SARS-CoV-2 detected by RT-qPCR and control patients negative for SARS-CoV-2. Samples were tested with the 2019-nCoV IgG/IgM Detection Kit (Colloidal Gold) lateral flow immunoassay, and antibody calls were compared with results obtained by ELISA. Results: The Biolidics LFI kit shows clinical sensitivity of 92% at 7 days after PCR diagnosis of SARS-CoV-2 on venous blood. Test specificity was 92% for IgM and 100% for IgG. There was no significant difference in detecting IgM and IgG with Biolidics LFI and ELISA at D0 and D7 (p=1.00), except for detection of IgM at D7 (p=0.04). Finger stick whole blood of SARS-CoV-2 patients showed 93% sensitivity for antibody detection. Conclusions: Clinical performance of Biolidics 2019-nCoV IgG/IgM Detection Kit (Colloidal Gold) is comparable to ELISA and showed consistent results across different sample types. Furthermore, we show that capillary blood obtained by finger stick shows similar sensitivity for detecting anti-SARS-CoV-2 IgM and IgG antibodies as venous blood samples. This provides an opportunity for decentralized rapid testing in the community and may allow point-of-care and longitudinal self-testing for the presence of anti-SARS-CoV-2 antibodies.

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

Classifying Non-Small Cell Lung Cancer Histopathology Types and Transcriptomic Subtypes using Convolutional Neural Networks
2,245 downloads bioRxiv 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.

32: Significant expression of FURIN and ACE2 on oral epithelial cells may facilitate the efficiency of SARS-CoV-2 entry
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Posted 18 Apr 2020

Significant expression of FURIN and ACE2 on oral epithelial cells may facilitate the efficiency of SARS-CoV-2 entry
2,114 downloads bioRxiv pathology

Mei Zhong, Bing-peng Lin, Hong-bin Gao, Andrew J Young, Xin-hong Wang, Chang Liu, Kai-bin Wu, Ming-xiao Liu, Jian-ming Chen, Jiang-yong Huang, Learn-han Lee, Cui-ling Qi, Lin-hu Ge, Li-jing Wang

Background: Leading to a sustained epidemic spread with >200,000 confirmed human infections, including >100,000 deaths, COVID-19 was caused by SARS-CoV-2 and resulted in acute respiratory distress syndrome (ARDS) and sepsis, which brought more challenges to the patient's treatment. The S-glycoprotein, which recognized as the key factor for the entry of SARS-CoV-2 into the cell, contains two functional domains: an ACE2 receptor binding domain and a second domain necessary for fusion of the coronavirus and cell membranes. FURIN activity, exposes the binding and fusion domains, is essential for the zoonotic transmission of SARS-CoV-2. Moreover, it has been reported that ACE2 is likely to be the receptor for SARS-CoV-2. In addition, FURIN enzyme and ACE2 receptor were expressed in airway epithelia, cardiac tissue, and enteric canals, which considered as the potential target organ of the virus. However, report about the expression of FURIN and ACE2 in oral tissues was limited. Methods: In order to investigate the potential infective channel of new coronavirus in oral cavity, we analyze the expression of ACE2 and FURIN that mediate the new coronavirus entry into host cells in oral mucosa using the public single-cell sequence datasets. Furthermore, immunohistochemical staining experiment was performed to confirm the expression of ACE2 and FURIN in the protein level. Results: The bioinformatics results indicated the differential expression of ACE2 and FURIN on epithelial cells of different oral mucosal tissues and the proportion of FURIN-positive cells was obviously higher than that of ACE2-positive cells. IHC experiments revealed that both the ACE2-positive and FURIN-positive cells in the target tissues were mainly positioned in the epithelial layers, partly expressed in fibroblasts, which further confirm the bioinformatics results. Conclusions: Based on these findings, we speculated that SARS-CoV-2 could effectively invade oral mucosal cells though two possible routes: binding to the ACE2 receptor and fusion with cell membrane activated by FURIN protease. Our results indicated that oral mucosa tissues are susceptible to SARS-CoV-2, which provides valuable information for virus-prevention strategy in clinical care as well as daily life. ### Competing Interest Statement The authors have declared no competing interest.

33: Patterns of recurrence after curative-intent surgery for pancreas cancer reinforce the importance of locoregional control and adjuvant chemotherapy.
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Posted 28 Feb 2018

Patterns of recurrence after curative-intent surgery for pancreas cancer reinforce the importance of locoregional control and adjuvant chemotherapy.
2,078 downloads bioRxiv pathology

Rohan Munir, Kjetil Soreide, Rajan Ravindran, James J Powell, Ewen M. Harrison, Anya Adair, Stephen J. Wigmore, Rowan W. Parks, O James Garden, Lorraine Kirkpatrick, Lucy R Wall, Alan Christie, Ian Penman, Norma McAvoy, Vicki Save, Alan Stockman, David Worrall, Hamish Ireland, Graeme Weir, Neil Masson, Chris Hay, James-Gordon Smith, DJ Mole

Introduction: The pattern of recurrence after surgical excision of pancreas cancer may guide alternative pre-operative strategies to either detect occult disease or need for chemotherapy. This study investigated patterns of recurrence after pancreatic surgery. Methods: Recurrence patterns were described in a series of resected pancreas cancers over a 2-year period and recurrence risk expressed as odds ratio (OR) with 95% confidence interval (C.I.). Survival was displayed by Kaplan-Meier curves. Results: Of 107 pancreas resections, 69 (65%) had pancreatic cancer. R0 resection was achieved in 21 of 69 (30.4%). Analysis was based on 66 patients who survived 30 days after surgery with median follow up 21 months. Recurrence developed in 41 (62.1%) patients with median time to first recurrence of 13.3 months (interquartile range 6.9, 20.8 months). Recurrence site was most frequently locoregional (n=28, 42%), followed by liver (n=23, 35%), lymph nodes (n=21, 32%), and lungs (n=13, 19%). In patients with recurrence, 9 of 41 had single site recurrence; the remaining 32 patients had more than one site of recurrence. Locoregional recurrence was associated with R+ resection (53% vs 25% for R+ vs R0, respectively; OR 3.5, 95% C.I. 1.1-11.2; P=0.034). Venous invasion was associated with overall recurrence risk (OR 3.3, 95% C.I. 1.1-9.4; P=0.025). In multivariable analysis, R-stage and adjuvant chemotherapy predicted longer survival. Discussion: The predominant locoregional recurrence pattern, multiple sites of recurrence and a high R+ resection rate reflect the difficulty in achieving initial local disease control.

34: Human-level recognition of blast cells in acute myeloid leukemia with convolutional neural networks
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Posted 28 Feb 2019

Human-level recognition of blast cells in acute myeloid leukemia with convolutional neural networks
2,078 downloads bioRxiv pathology

Christian Matek, Simone Schwarz, Karsten Spiekermann, Carsten Marr

Reliable recognition of malignant white blood cells is a key step in the diagnosis of hematologic malignancies such as Acute Myeloid Leukemia. Microscopic morphological examination of blood cells is usually performed by trained human examiners, making the process tedious, time-consuming and hard to standardise. We compile an annotated image dataset of over 18,000 white blood cells, use it to train a convolutional neural network for leukocyte classification, and evaluate the network's performance. The network classifies the most important cell types with high accuracy. It also allows us to decide two clinically relevant questions with human-level performance, namely (i) if a given cell has blast character, and (ii) if it belongs to the cell types normally present in non-pathological blood smears. Our approach holds the potential to be used as a classification aid for examining much larger numbers of cells in a smear than can usually be done by a human expert. This will allow clinicians to recognize malignant cell populations with lower prevalence at an earlier stage of the disease.

35: Causes of Death and Comorbidities in Patients with COVID-19
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Posted 17 Jun 2020

Causes of Death and Comorbidities in Patients with COVID-19
2,025 downloads medRxiv pathology

Sefer Elezkurtaj, Selina Greuel, Jana Ihlow, Edward Michaelis, Philip Bischoff, Catarina Alisa Kunze, Bruno Valentin Sinn, Manuela Gerhold, Kathrin Hauptmann, Barbara Ingold-Heppner, Florian Miller, Hermann Herbst, Victor Max Corman, Hubert Martin, Frank L. Heppner, David Horst

Infection by the new corona virus strain SARS-CoV-2 and its related syndrome COVID-19 has caused several hundreds of thousands of deaths worldwide. Patients of higher age and with preexisting chronic health conditions are at an increased risk of fatal disease outcome. However, detailed information on causes of death and the contribution of comorbidities to death yet is missing. Here, we report autopsy findings on causes of death and comorbidities of 26 decedents that had clinically presented with severe COVID-19. We found that septic shock and multi organ failure was the most common immediate cause of death, often due to suppurative pulmonary infection. Respiratory failure due to diffuse alveolar damage presented as the most immediate cause of death in fewer cases. Several comorbidities, such as hypertension, ischemic heart disease, and obesity were present in the vast majority of patients. Our findings reveal that causes of death were directly related to COVID-19 in the majority of decedents, while they appear not to be an immediate result of preexisting health conditions and comorbidities. We therefore suggest that the majority of patients had died of COVID-19 with only contributory implications of preexisting health conditions to the mechanism of death.

36: Two distinct immunopathological profiles in lungs of lethal COVID-19
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Posted 19 Jun 2020

Two distinct immunopathological profiles in lungs of lethal COVID-19
1,933 downloads medRxiv pathology

Ronny Nienhold, Yari Ciani, Viktor H Koelzer, Alexandar Tzankov, Jasmin D Haslbauer, Thomas Menter, Nathalie Schwab, Maurice Henkel, Angela Frank, Veronika Zsikla, Niels Willi, Werner Kempf, Thomas Hoyler, Mattia Barbareschi, Holger Moch, Markus Tolnay, Gieri Cathomas, Francesca Demichelis, Tobias Junt, Kirsten D Mertz

Coronavirus Disease 19 (COVID-19) is a respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has grown to a worldwide pandemic with substantial mortality. Immune mediated damage has been proposed as a pathogenic factor, but immune responses in lungs of COVID-19 patients remain poorly characterized. Therefore we conducted transcriptomic, histologic and cellular profiling of post mortem COVID-19 (n=34 tissues from 16 patients) and normal lung tissues (n=9 tissues from 6 patients). Two distinct immunopathological reaction patterns of lethal COVID-19 were identified. One pattern showed high local expression of interferon stimulated genes (ISGhigh) and cytokines, high viral loads and limited pulmonary damage, the other pattern showed severely damaged lungs, low ISGs (ISGlow), low viral loads and abundant infiltrating activated CD8+ T cells and macrophages. ISGhigh patients died significantly earlier after hospitalization than ISGlow patients. Our study may point to distinct stages of progression of COVID-19 lung disease and highlights the need for peripheral blood biomarkers that inform about patient lung status and guide treatment.

37: Luigi: Large-scale histopathological image retrieval system using deep texture representations
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Posted 22 Jun 2018

Luigi: Large-scale histopathological image retrieval system using deep texture representations
1,680 downloads bioRxiv pathology

Daisuke Komura, Keisuke Fukuta, Ken Tominaga, Akihiro Kawabe, Hirotomo Koda, Ryohei Suzuki, Hiroki Konishi, Toshikazu Umezaki, Tatsuya Harada, Shumpei Ishikawa

Background: As a large number of digital histopathological images have been accumulated, there is a growing demand of content-based image retrieval (CBIR) in pathology for educational, diagnostic, or research purposes. However, no CBIR systems in digital pathology are publicly available. Results: We developed a web application, the Luigi system, which retrieves similar histopathological images from various cancer cases. Using deep texture representations computed with a pre-trained convolutional neural network as an image feature in conjunction with an approximate nearest neighbor search method, the Luigi system provides fast and accurate results for any type of tissue or cell without the need for further training. In addition, users can easily submit query images of an appropriate scale into the Luigi system and view the retrieved results using our smartphone application. The cases stored in the Luigi database are obtained from The Cancer Genome Atlas with rich clinical, pathological, and molecular information. We tested the Luigi system and the smartphone application by querying typical cancerous regions from four cancer types, and confirmed successful retrieval of relevant images with both applications. Conclusions: The Luigi system will help students, pathologists, and researchers easily retrieve histopathological images of various cancers similar to those of the query image. Luigi is freely available at https://luigi-pathology.com/.

38: Modeling of fibrotic lung disease using 3D organoids derived from human pluripotent stem cells
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Posted 03 Feb 2019

Modeling of fibrotic lung disease using 3D organoids derived from human pluripotent stem cells
1,661 downloads bioRxiv pathology

Hans-Willem Snoeck, Alexandros Strikoudis, Lucas Loffredo, Ya-Wen Chen

Idiopathic pulmonary fibrosis (IPF) is an intractable interstitial lung disease for which no curative treatment is available except for lung transplantation. Its pathogenesis is unclear, but a role for injury to type 2 alveolar epithelial cells is hypothesized. Recessive mutations in some, but not all genes implicated in Hermansky-Pudlak Syndrome (HPS) cause HPS-associated interstitial pneumonia (HPSIP), a clinical entity similar to IPF. We previously reported that mutation in HPS1 in embryonic stem cells-derived 3D lung organoids caused fibrotic changes. Here we show that introduction of all HPS mutations associated with HPSIP (HPS1, 2 and 4) promote fibrosis in lung organoids, while mutation in HSP8, which is not associated with HPSIP, does not. Furthermore, genome-expression analysis of epithelial cells derived from these organoids revealed significant overlap with similar analyses of both affected and unaffected lung tissue of non-HPS IPF patients. Importantly, this analysis showed upregulation of interleukin-11 in HPS-mutant fibrotic organoids and in fibrotic and unaffected lung tissue from IPF patients. Furthermore, IL-11 induced fibrosis in WT organoids, while its deletion prevented fibrosis in fibrotic HPS4-mutant organoids, suggesting IL-11 as a therapeutic target in IPF and HPSIP. hPSC-derived 3D lung organoids are therefore a valuable resource to model fibrotic lung disease.

39: Detection of SARS-CoV-2 RNA by direct RT-qPCR on nasopharyngeal specimens without extraction of viral RNA
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Posted 23 Apr 2020

Detection of SARS-CoV-2 RNA by direct RT-qPCR on nasopharyngeal specimens without extraction of viral RNA
1,648 downloads medRxiv pathology

Mohammad Rubayet Hasan, Faheem Mirza, Hamad Al-Hail, Sathyavathi Sundararaju, Thabisile Xaba, Muhammad Iqbal, Hashim Alhussain, Hadi Mohamad Yassine, Andres Perez Lopez, Patrick Tang

To circumvent the limited availability of RNA extraction reagents, we aimed to develop a protocol for direct RT-qPCR to detect SARS-CoV-2 in nasopharyngeal swabs without RNA extraction. Nasopharyngeal specimens positive for SARS-CoV-2 and other coronaviruses collected in universal viral transport (UVT) medium were pre-processed by several commercial and laboratory-developed methods and tested by RT-qPCR assays without RNA extraction using different RT-qPCR master mixes. The results were compared to that of standard approach that involves RNA extraction. Incubation of specimens at 65{degrees}C for 10 minutes along with the use of TaqPath 1-Step RT-qPCR Master Mix provides higher analytical sensitivity for detection of SARS-CoV-2 RNA than many other conditions tested. The optimized direct RT-qPCR approach demonstrated a limit of detection of 6.6x103 copy/ml and high reproducibility (co-efficient of variation = 1.2%). In 132 nasopharyngeal specimens submitted for SARS-CoV-2 testing, the sensitivity, specificity and accuracy of our optimized approach were 95%, 99% and 98.5%, respectively, with reference to the standard approach. Also, the RT-qPCR CT values obtained by the two methods were positively correlated (Pearson correlation coefficient r=0.6971, p=0.0013). The rate of PCR inhibition by the direct approach was 8% compared to 9% by the standard approach. Our simple approach to detect SARS-CoV-2 RNA by direct RT-qPCR may help laboratories continue testing for the virus despite reagent shortages or expand their testing capacity in resource limited settings.

40: Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline
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Posted 29 Oct 2018

Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline
1,646 downloads bioRxiv pathology

Ziqi Tang, Kangway V. Chuang, Charles DeCarli, Lee-Way Jin, Laurel Beckett, Michael J. Keiser, Brittany N Dugger

Neuropathologists assess vast brain areas to identify diverse and subtly-differentiated morphologies. Standard semi-quantitative scoring approaches, however, are coarse-grained and lack precise neuroanatomic localization. We report a proof-of-concept deep learning pipeline identifying specific neuropathologies--amyloid plaques and cerebral amyloid angiopathy--in immunohistochemically-stained archival slides. Using automated segmentation of stained objects and a cloud-based interface, we annotated >70,000 plaque candidates from 43 whole slide images (WSIs) to train and evaluate convolutional neural networks. Networks achieved strong plaque classification on a 10-WSI hold-out set (0.993 and 0.743 areas under the receiver operating characteristic and precision recall curve, respectively). Prediction confidence maps visualized morphology distributions for WSIs at high resolution. Resulting plaque-burden scores correlated well with established semi-quantitative scores on a 30-WSI blinded hold-out. Finally, saliency mapping demonstrated that networks learned patterns agreeing with accepted pathologic features. This scalable means to augment a neuropathologist's ability may suggest a route to neuropathologic deep phenotyping.

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