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329 results for “histopathology”
Figure 3 in Determination of oxidative, genotoxic, and histopathologic effects of metal pollution on the fish fauna inhabiting Karasu River, Turkey
Figure 3. Pyramidal neurons (circles) in normal fish brain section (A. mossulensis) H & E.
Dataset of histopathological image crops from GTEx project
<p>This is a dataset of histological slides from the GTEx project that has been balanced for 3 major factors (organ, sex, and age bracket) that may be useful to train models in supervised or self-supervised modes.</p> <p>Four datasets are avaialble:</p> <ul> <li><code>gtex_histology_balanced_3_slides_200_tiles.tar.gz</code>: Conditioned on the 3 factors, 3 slides were selected per group, and 200 tiles in tissue segmented areas selected randomly per slide.</li> <li><code>gtex_histology_balanced_3_slides_2000_tiles.tar.gz</code>: Conditioned on the 3 factors, 3 slides were selected per group, and 2000 tiles in tissue segmented areas selected randomly per slide.</li> <li><code>gtex_histology_balanced_10_slides_100_tiles.tar.gz</code>: Conditioned on the 3 factors, 10 slides were selected per group (when possible), and 100 tiles in tissue segmented areas selected randomly per slide. This dataset matches closely the "gtex_histology_balanced_3_slides_200_tiles.tar.gz" dataset in total number of tiles.</li> <li><code>gtex_histology_balanced_10_slides_800_tiles.tar.gz</code>: Conditioned on the 3 factors, 10 slides were selected per group (when possible), and 800 tiles in tissue segmented areas selected randomly per slide. This dataset matches closely the "gtex_histology_balanced_3_slides_200_tiles.tar.gz" dataset in total number of tiles.</li> </ul> <p>Each archive file contains the following:</p> <ul> <li><code>slide_annotation.csv</code>: a slide-level annotation of the slides (see below)</li> <li><code>train</code>: a directory with image tiles to be used to train a model</li> <li><code>valid</code>: a directory with image tiles to be used to validate a model</li> </ul> <p>The slide_annotation file contains publicly available information on the slides in addition to 3 columns:</p> <ul> <li>"Tissue_simple": the organ of the slide</li> <li>"split": whether the slide was assign the 'train' or 'valid' split for training. The validation split slides have 1/10th of the tiles from training.</li> <li>"n_tiles": the number of image tiles in the dataset for each slide</li> </ul> <p>Example:</p> <table> <tbody> <tr> <td>Tissue Sample ID</td> <td>Tissue</td> <td>Subject ID</td> <td>Sex</td> <td>Age Bracket</td> <td>Hardy Scale</td> <td>Pathology Categories</td> <td>Pathology Notes</td> <td>Tissue_simple</td> <td>split</td> <td>n_tiles</td> </tr> <tr> <td>GTEX-1128S-1426</td> <td>Esophagus - Mucosa</td> <td>GTEX-1128S</td> <td>female</td> <td>60-69</td> <td>Fast death - natural causes</td> <td> </td> <td>6 pieces, near- total autolysis/mucosa completely sloughed</td> <td>Esophagus</td> <td>train</td> <td>200</td> </tr> <tr> <td>GTEX-113JC-1226</td> <td>Stomach</td> <td>GTEX-113JC</td> <td>female</td> <td>50-59</td> <td>Fast death - natural causes</td> <td> </td> <td>6 pieces, well dissected mucosa; some areas are severely autolyzed</td> <td>Stomach</td> <td>valid</td> <td>20</td> </tr> <tr> <td>GTEX-1192W-2526</td> <td>Muscle - Skeletal</td> <td>GTEX-1192W</td> <td>male</td> <td>60-69</td> <td>Fast death - natural causes</td> <td> </td> <td>2 pieces, ~10-20% interstitial fat, rep foci delineated</td> <td>Muscle</td> <td>train</td> <td>200</td> </tr> <tr> <td>GTEX-1192X-0426</td> <td>Muscle - Skeletal</td> <td>GTEX-1192X</td> <td>male</td> <td>50-59</td> <td>Slow death</td> <td> </td> <td>2 pieces, 5-10% interstitial fat, rep. foci delineated</td> <td>Muscle</td> <td>valid</td> <td>20</td> </tr> <tr> <td>GTEX-11DXX-1326</td> <td>Stomach</td> <td>GTEX-11DXX</td> <td>female</td> <td>60-69</td> <td>Ventilator case</td> <td>gastritis</td> <td>6 pieces, mild chronic active gastritis</td> <td>Stomach</td> <td>train</td> <td>200</td> </tr> </tbody> </table> <p>Inside <code>train</code> and <code>valid</code> and JPEG files named with the following convention: <code><Tissue Sample ID>.<Tissue_simple>.<Sex>.<Age Bracket>.<Y position>.<X position>.jpg</code> such that the origin of the crops can be traced and the file name serve as a direct class label if desired.</p> <p>Examples: "GTEX-ZYT6-1326.Pancreas.male.30-39.47492.16064.jpg", "GTEX-WWYW-2726.Ovary.female.50-59.5024.15008.jpg.</p>
"COVID toes": a meta-analysis of case and observational studies on clinical, histopathological and laboratory findings.
<p><strong>Background: </strong>Coronavirus disease (COVID-19) is related to several extrapulmonary disorders; however, little is known about the clinical, laboratory and histopathological characteristics of pernio-like skin lesions associated with COVID-19 infection. <strong>Objective: </strong>To evaluate and summarize the clinical, laboratory and histopathological characteristics of pernio-like lesions reported in the literature. <strong>Methods: </strong>We conducted a search of the PubMed, SciELO and ScienceDirect databases for articles published between 1 January 2020 and 30 November 2020, following the PRISMA recommendations (PROSPERO registration ID: CRD42020225055). The target population was individuals with suspected or laboratory-confirmed COVID-19 with pernio-like lesions. Observational studies, research letters and case/series reports were all eligible for inclusion. Observational studies were evaluated using a random-effects model to calculate the weighted mean prevalence, overall mean and 95% confidence interval. We evaluated case studies using the chi-square test for dichotomous variables and the Mann-Whitney test for continuous variables. <strong>Results:</strong> A total of 187 patients from case reports and 715 patients from 18 observational studies were included. The mean age of patients was 16.6 (14.5–18.8) years. Feet were affected in 91.4% (87.0–94.4%) of patients in observational studies. The proportion of patients with a positive RT-PCR test was less than 15%. Lesion topography and morphology were associated with age. <strong>Conclusion:</strong> Lesions mostly occurred in pediatric patients, and the morphological characteristics tended to differ between pediatric and non-pediatric populations. There is a possible multifactorial component in lesion pathophysiology. The non-positivity of laboratory tests does not exclude an association with COVID-19. Chilblain-like lesions may be a late manifestation of COVID-19.</p>
Control mice (non-infected with S. mansoni parasites) - cage 1 - Liver histopathology data.
<p>The present dataset contains all the histopathology images used to quantify fibrotic areas in liver of control mice (non-infected with S. mansoni parasite). These data are presented in the manuscript entitled "No evidence for schistosome parasite fitness trade-offs in the intermediate and definitive host" (dataset # 10/11).<br> Each folder corresponds to one mouse sample and contain, along with a readme file, all the files used to quantify fibrotic area (_TRICH.czi) and (_HE.czi) files.</p>
A Phase 2 Study With MIP-1404 in Men With High-Risk PC Scheduled for RP and EPLND Compared to Histopathology
ClinicalTrials.gov study NCT01667536. IPD Sharing: Not stated. Countries: 8. Publications: 18.
A Trial of Two Electrosurgical Conizations: Histopathological Analysis of Excision Margins
ClinicalTrials.gov study NCT01929993. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Phase III Study of Florbetaben (BAY94-9172) PET Imaging for Detection/Exclusion of Cerebral β-amyloid Compared to Histopathology
ClinicalTrials.gov study NCT01020838. IPD Sharing: Not stated. Countries: 5. Publications: 2.
Comparison Between Combined Scoring of Bethesda Cytology and TIRADS Ultrasound With Histopathology in Thyroid Nodule
ClinicalTrials.gov study NCT06391021. IPD Sharing: NO. Countries: 1. Publications: 1.
Pilot Study Evaluating the Role of Histopathology Correlation in Treatment Planning
ClinicalTrials.gov study NCT02681614. IPD Sharing: NO. Countries: 1. Publications: 1.
Multi-omic, histopathologic, and clinicopathologic effects of once-weekly oral rapamycin in a naturally occurring feline model of hypertrophic cardiomyopathy: A pilot study
Open the record for dataset details and reuse information.
MIHIC: A multiplex IHC histopathological image classification dataset for lung cancer immune microenvironment quantification
<p>A cohort of 47 TMA sections from 114 patients was collected from Liaoning cancer hospital \& Institute, where each TMA section has the size of 188,416$\times$110,080 pixels (i.e., 42660.87um$\times$24924.15um) at 40$\times$ magnification. TMA sections contain different number of tissue cores, ranging from 28 to 48. After excluding poor quality TMA sections with tissue folding, missing or contamination, there are totally 114 patients. Each patient has tissue cores with 12 different IHC stains, including CD3, CD20, CD34, CD38, CD68, CDK4, cyclin-D1, D2-40, FAP, Ki67, P53, and SMA. Two pathologists have manually labeled clear tissue regions (i.e., without controversy) in TMA sections based on visual examination via Qupath software, where six tissue types including Alveoli, Immune cells, Nerosis, Other, Stroma, Tumor were annotated. Besides the annotated six tissue types, we added one more Background type.</p> <p>To build histological classification models, we split 309,698 image patches in MIHIC dataset into three sets: training, validation and test. Note that image patches extracted from the same annotated tissue region are distributed into the same set, which avoids data leakage during classification model optimization. According to the number of extracted ROIs, train, val and test accounted for 64\%, 16\% and 20\%.</p> <h1>if you use this dataset, please cite:</h1> <pre>@article{wang2024mihic, title={MIHIC: a multiplex IHC histopathological image classification dataset for lung cancer immune microenvironment quantification}, author={Wang, Ranran and Qiu, Yusong and Wang, Tong and Wang, Mingkang and Jin, Shan and Cong, Fengyu and Zhang, Yong and Xu, Hongming}, journal={Frontiers in Immunology}, volume={15}, year={2024}, publisher={Frontiers Media SA} }</pre>
OCELOT: Overlapped Cell on Tissue Dataset for Histopathology
<p>The OCELOT dataset is a histopathology dataset designed to facilitate the development of methods that utilize cell and tissue relationships. The dataset comprises both small and large field-of-view (FoV) patches extracted from digitally scanned whole slide images (WSIs), with overlapping regions. The small and large FoV patches are accompanied by annotations of cells and tissues, respectively. The WSIs are sourced from the publicly available TCGA database and were stained using the H&E method before being scanned with an Aperio scanner.</p> <ul> <li>For more details, please check <a href="https://lunit-io.github.io/research/ocelot_dataset/">https://lunit-io.github.io/research/ocelot_dataset/</a>.</li> <li>Before downloading the dataset, please <strong>carefully read and agree to the Terms and Conditions</strong> at <a href="https://lunit-io.github.io/research/ocelot_tc/">https://lunit-io.github.io/research/ocelot_tc/</a>.</li> <li>If you use the dataset, <strong>please cite</strong><span> the </span><a href="https://openaccess.thecvf.com/content/CVPR2023/html/Ryu_OCELOT_Overlapped_Cell_on_Tissue_Dataset_for_Histopathology_CVPR_2023_paper.html">OCELOT dataset paper</a><span> and the </span><a href="https://www.sciencedirect.com/science/article/abs/pii/S1361841525002981">OCELOT 2023 challenge paper</a><span>.</span></li> </ul> <p>-----------------------------------------------------------------------------------</p> <p><strong>Release note.</strong></p> <p>In version 1.0.1, we exclude four test cases (586, 589, 609, 615) due to under-annotated issue.<br>In version 1.0.0, we include images and annotations of validation and test splits.<br>In version 0.1.2, we modified the coordinates of cell labels to range from 0 to 1023 (-1 from the previous coordinates).<br>In version 0.1.1, we removed non-H&E stained patches from the dataset.</p>
"Histopathology Slide Indexing and Search: Are We There Yet?" - UCLA Test Slides
<p>In-House UCLA test slides used for the case report in "Histopathology Slide Indexing and Search: Are We There Yet?" submitted to NEJM AI.</p>
A Lung Nodule Dataset with Histopathology-based Cancer Type Annotation (DICOM VERSION)
<p>We constructed a groundbreaking lung CT dataset, which includes 330 annotated nodules from 95 patients. It is worth noting that we have integrated the results of patient clinical diagnosis, frozen diagnosis, and pathological diagnosis, supplementing this with labeled lung cancer types on 308 samples containing nodules.</p>
In-depth comparative toxicogenomics of glyphosate and Roundup herbicides: Histopathology, transcriptome and epigenome signatures, and DNA damage
<p><span><span><span><span><span><span><span><span><span><span><span>Whether or not glyphosate activates cellular mechanisms involved in carcinogenesis remains controversial. We tested whether glyphosate and three glyphosate-based commercial herbicide formulations activate mechanisms known to be key characteristics of carcinogens. The mammalian stem cell-based genotoxicity ToxTracker assay showed that the representative EU formulation Roundup MON 52276 and the UK formulation Roundup MON 76473, but not glyphosate and the US Roundup formulation MON 76207, activated oxidative stress and unfolded protein responses. High-throughput molecular profiling of liver function was performed in female Sprague-Dawley rats exposed to glyphosate or MON 52276 (both at 0.5, 50, 175 mg/kg bw/day glyphosate equivalent concentration) for 90 days. Histopathology and serum biochemistry analysis showed that MON 52276 but not glyphosate treatment increased hepatic steatosis and necrosis. MON 52276 and glyphosate altered the expression of genes in liver reflecting TP53 activation by DNA damage and the regulation of circadian rhythms. The most affected genes in liver also had their expression similarly altered in kidneys. Small RNA profiling in liver showed miR-22 and miR-17 had their levels decreased by MON 52276, while mir-30 levels were decreased, whilst miR-10 levels were increased by glyphosate. DNA methylation profiling of liver revealed 5,727 and 4,496 differentially methylated CpG sites between the control and glyphosate and MON 52276 exposed groups of animals respectively. Direct DNA damage measurement by apurinic/apyrimidinic lesion formation in liver was increased with glyphosate exposure. Altogether, our results show that Roundup herbicide formulations are causing more biological changes than glyphosate alone, activating mechanisms involved in cellular carcinogenesis.</span></span></span></span></span></span></span></span></span></span></span></p>
Clinical and histopathological features of 81 cases of canine apocrine gland carcinoma of the anal sac
<p>Canine apocrine gland anal sac adenocarcinoma (AGASAC) is a malignant tumour with variable clinical progression. The objective of this study was to use robust multivariate models, based on models employed in human medical oncology, to establish clinical and histopathological risk factors of poor survival. Clinical data and imaging of 81 cases with AGASAC were reviewed. Tissue was available for histological review and immunohistochemistry in 49 cases. Tumour and lymph node size were determined using the response evaluation criteria in solid tumours system (RECIST). Modelling revealed tumour size over 2 cm, lymph node size grouped in three tiers by the two thresholds 1.6 cm and 5 cm, surgical management, and radiotherapy were independent clinical variables associated with survival, irrespective of tumour stage. Tumour size over 1.3 cm and presence of distant metastasis were independent clinical variables associated with first progression free interval. The presence of the histopathological variables of tumour necrosis, a solid histological pattern, and vascular invasion in the primary tumour were independent risk factors of poor survival. Based upon these independent risk factors, scoring algorithms to predict survival in AGASAC patients are presented.</p>
Histopathological, Oxidative Stress, and DNA Damage Assessment in the Vas Deferens Tissue of the Freshwater Leech Erpobdella johanssoni (Johansson, 1927) Following BTEX Exposure
Open the record for dataset details and reuse information.
HoVer-NeXt: A Fast Nuclei Segmentation and Classification Pipeline for Next Generation Histopathology - Datasets
<p>This repository contains training and validation data for</p> <p><strong>HoVer-NeXt: A Fast Nuclei Segmentation and Classification Pipeline for Next Generation Histopathology </strong></p> <p><strong>Accepted for Oral Presentation at MIDL2024: <a href="https://openreview.net/pdf?id=3vmB43oqIO">https://openreview.net/pdf?id=3vmB43oqIO</a> <br></strong></p> <p><strong>More information and code are available at <a href="https://github.com/digitalpathologybern/hover_next_inference" target="_blank" rel="noopener">https://github.com/digitalpathologybern/hover_next_inference</a></strong></p> <p>Modified Lizard dataset to include mitosis (lizard_mitosis.zip), mitosis dataset (mitosis_ds.zip) and a holdout eosinophil validation set (eos_eval.zip)</p> <p>mitosis_ds.zip also contains the hold-out H&E mitosis test set.</p> <p>The original lizard dataset was createdy by Simon Graham et al. and was shared under CC BY-NC-SA 4.0. The tile-based dataset can be downloaded from <a href="https://conic-challenge.grand-challenge.org/Data/">https://conic-challenge.grand-challenge.org/Data/</a> after registering for the challenge. We modify the dataset by including an additional mitosis class, however note that there are a number of mitosis which are still not (correctly annotated).</p>
His-MMDM: Multi-domain and Multi-omics Translation of Histopathology Images with Diffusion Models
<p>partab of His-MMDM: Multi-domain and Multi-omics Translation of Histopathology Images with Diffusion Models</p>
Data and Codes for Publication: "Sexually Dimorphic Computational Histopathological Signatures Prognostic of Overall Survival in High-Grade Gliomas via Deep Learning"
<p><strong>Data</strong></p> <p>The patches and the associated tumor segmentation labels (expert-vetted) from our analysis are available in Patches.pytable file.<br><br><strong>Codes<br><br></strong>The codes for training tumor segmentation models and conducting survival analysis are available in the following files</p> <ul> <li>ResNet-train: Code to train Resnet18 model for Tumor Segmentation</li> <li>Tumor_Segmentation: Code to segment tumor regions from WSI using ResNet18 model</li> <li>ResNet_Cox_train: Code to train ResNet-Cox model in 5 folds cross-validation setting</li> <li>Evaluate_ResNetCox: Code to evaluate ResNet-Cox model</li> </ul>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.