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329 results for “histopathology”
Fig. 2 in The natural interaction between Myotis nigricans (Schinz, 1821) and its trematodes: A histopathological analysis
Fig. 2. The non-parasitized gallbladder of Myotis nigricans. (A) Detail of the gallbladder mucosa epithelium formed by simple epithelium that varies from cubic to cylindrical. (B) Folds and pleats in gallbladder with mucosa epithelium. (C) General view of gallbladder isthmus. (D) Details of gallbladder isthmus and bile duct segment.
Fig. 4 in Histopathology of gills, kidney and liver of a Neotropical fish caged in an urban stream
Fig. 4. Photomicrographs of the liver of P. lineatus caged in Cambé stream. a) normal hepatic tissue, showing hepatocytes with granular cytoplasm (*) and central and round nucleus (arrow); b) hepatocytes with irregular shaped nucleus (black arrows), eosinophilic granules in the cytoplasm (arrowheads) and nuclear hypertrophy (*); c) bile stagnation (arrows); d) nuclear degeneration (arrows) and cytoplasmic degeneration (*); e) melanomacrophages aggregate, close to a vessel (white arrow) and cytoplasmic vacuolation (*); f) hepatic tissue showing focal necrosis (white arrow). Scale bar 10 mm, H.E.
Fig. 3 in Histopathology of gills, kidney and liver of a Neotropical fish caged in an urban stream
Fig. 3. Photomicrographs of the kidney of P. lineatus caged in Cambé stream. a) normal renal corpuscle showing the glomerulus and the Bowman's space well defined (arrow), proximal tubules (*), distal tubules (arrowheads); b) glomerular expansion and absence of the Bowman's space (arrow) and tubule cells with hypertrophied nucleus (arrowheads); c) tubule starting the regeneration process (white arrow), occlusion of the tubular lumen (black arrows) and cloudy swelling degeneration (*); d) detail of 2 tubules with hyaline droplets degeneration (*). Scale bar 10 mm, H.E.
Fig. 1 in Histopathology of gills, kidney and liver of a Neotropical fish caged in an urban stream
Fig. 1. Map showing the region of Londrina city (Paraná State), where the in situ tests were carried out at the reference site (Apertados stream), and the sites at Cambé stream (A, B and C).
Figure 1 in Histopathological aspects in ripe fruits of Tahiti lime Citrus citrus x latifolia (Rutaceae) affected by phytophagous mites
Figure 1. Macroscopic and microscopic lesions on the pericarps of healthy Tahiti lime fruits affected by phytophagous mites. A–C. Healthy fruit and tissue. A. Pericarp surface; B. Pericarp surface viewed under scanning electron microscopy (SEM). Stomata can be observed; C. Cross section of the pericarp, showing the exocarp (Safranina- Alcian blue). D–F. Fruit and tissues affected by Polyphagotarsonemus latus. D. Lesions on the pericarp; E. Detail of the lesions on the pericarp surface (white arrow) (SEM); F. Cross section of the pericarp, showing the lesion affecting the exocarp and the formation of the peridermis (Safranin-Alcian blue). G–I. Fruit and tissues affected by Phyllocoptruta oleivora. G. Lesions on the pericarp; H. Detail of the lesions on the pericarp surface (white arrow) (SEM); I. Cross section of the pericarp, showing the lesion affecting the exocarp and the formation of peridermis and melanin deposits (Safranin-Alcian blue); J–L. Fruit and tissues affected by Schizotetranychus hindustanicus. J. Lesions on the pericarp. Mites nests (white arrows) can be observed; K. Mites nests in SEM (white arrow). Spider web of nest formation can be clearly seen; L. Cross section of the pericarp, showing the lesion affecting the exocarpal layers (white arrow) (Safranin-Alcian blue). CU: cuticle; EN: stomata; IEX: inner exocarp; ME: melanin; OEX:
Figure 3 in Histopathological aspects in ripe fruits of Tahiti lime Citrus citrus x latifolia (Rutaceae) affected by phytophagous mites
Figure 3. Histochemical tests applied to Tahiti lime pericarps affected by phytophagous mites (Cross sections). A–C. Pericarps affected by Polyphagotarsonemus latus, Phyllocoptruta oleivora and Schizotetranychus hindustanicus respectively. A dark brown stain can be observed due to the accumulation of polyphenols in the OEX: exocarpal layers (white arrows, Fast Blue B); D–F. Pericarps affected by Po. latus, Ph. Oleivora and S. hindustanicus respectively. A magenta stain can be observed due to the accumulation of lignin in the exocarpal layers (white arrows, Phloroglucinol acid); G–I. Pericarps affected by Po. latus, Ph. Oleivora and S. hindustanicus respectively. Callose was not detected in the exocarpal layers (Lacmoid). Melanin deposits were observed; J–L. Pericarps affected by Po. latus, Ph. Oleivora and S. hindustanicus respectively. Primary walls stain magenta and no protein or starch granules were detected (PAS-Amidoblack); M–O. Pericarps affected by Po. latus, Ph. Oleivora and S. hindustanicus respectively. Primary walls are stained purple, lignified tissues blue-green, and polyphenols brown or black (Toluidine Blue). Schizotetranychus hindustanicus lesions are restricted to feeding zones below nests (Fig. 3O) (White arrows). Peridermis formation was observed in all cases (Fig. 3A–O). CU: cuticle; IEX: inner exocarp; ME: Melanin; OEX: outer exocarp; PP: primary walls; PR: Peridermis.
Figure 2. A–E in Histopathological aspects in ripe fruits of Tahiti lime Citrus citrus x latifolia (Rutaceae) affected by phytophagous mites
Figure 2. A–E. Histochemical tests applied to healthy Tahiti lime pericarps (cross sections). A. Polyphenols Test (Fast Blue B). No positive reaction for polyphenols is observed in the exocarpal layers; B. The reaction for lignin detection (phloroglucinol acid) is negative in the exocarpal layers, but it is positive in lignified tissues (reddish staining) such as the fruit xylem (detail, white arrow); C. Callose reaction (Lacmoid) is negative in the exocarpal layers, but positive (blue staining) in fruit phloem (detail, arrow heads); D. PAS-Amidoblack test, the primary walls of the exocarpal layers and starch granules are stained magenta; E. Toluidine Blue stain, the primary walls of the exocarpal layers stain violet and cuticle dark blue. CU: cuticle; GA: starch granules; IEX: inner exocarp; OEX: outer exocarp; PP: primary walls.
2 million histopathology stain vectors including Hematoxylin & Eosin color variations
<p>The database includes the stain vectors including color variations of over 2 million patches.</p> <p>The database is adopted in "Data-driven color augmentation for H&E stained images in computational pathology", (https://www.sciencedirect.com/science/article/pii/S2153353922007830) to check is the color variation of an augmentated sample is acceptable or not. During the training, the color variation of an augmented sample is compared with the variations included in the database. If N variations from the database are found within a radius R from the components of the augmented sample, the augmented sample is considered acceptable (in terms of color variations); otherwise, it is discarded.</p> <p>The database is stored in a .pickle file, including vectors with six elements: the RGB components of Hematoxylin and Eosin, for every patch. Double entries are removed from the database. Code to import and to extend database with new data is available here: https://github.com/ilmaro8/Data_Driven_Color_Augmentation</p> <p>Stain vectors are collected from six private and public sources, to ensure that color variations can cover the variability of H&E-stained tissues: TCGA, ExaMode colon dataset, Camelyon, Puerta del Mar, Clinic, CAD.</p>
Training data for the "Computational textural mapping harmonises sampling variation and reveals multidimensional histopathological fingerprints"
<p>There are two ZIP-files consisting of small histological image tiles that have been used to detect and quantify distinct tissue textures and lymphocyte proportions from H&E-stained clear cell renal cell carcinoma (KIRC) digital tissue sections of the Cancer Genome Atlas (TCGA) image archive and the Helsinki dataset.</p> <p>The <strong>tissue_classification </strong>file contains 300x300px tissue texture image tiles (n=52,713) representing renal cancer (“cancer”; n=13,057, 24.8%); normal renal (“normal”; n=8,652, 16.4%); stromal (“stroma”; n= 5,460, 10.4%) including smooth muscle, fibrous stroma and blood vessels; red blood cells (“blood”; n=996, 1.9%); empty background (“empty”; n=16,026, 30.4%); and other textures including necrotic, torn and adipose tissue (“other”; n=8,522, 16.2%). Image tiles have been randomly selected from the TCGA-KIRC WSI and the Helsinki datasets.</p> <p>The <strong>binary_lymphocytes </strong>file contains mostly 256x256px-sized but also smaller image tiles of Low (n=20,092, 80.1%) or High (n=5,003, 19.9%) lymphocyte density (n=25,095). Image tiles have been randomly selected from the TCGA-KIRC WSI dataset.</p> <p>All accuracy of all annotations have been double-checked. However, the classification between multiple tissue textures or lymphocyte density can be sometimes ambiguous.</p> <p>The deep learning model parameters trained with the ResNet-18 infrastructure for (1) lymphocyte and (2) texture classification are named as (1) <strong>resnet18_binary_lymphocytes.pth</strong> and (2) <strong>resnet18_tissue_classification.pth</strong>. Codes and instructions to use these are found in <a href="https://github.com/vahvero/RCC_textures_and_lymphocytes_publication_image_analysis">https://github.com/vahvero/RCC_textures_and_lymphocytes_publication_image_analysis</a>.</p> <p> </p> <p>If you use either work, please cite the publication by Brummer O et al (1) AND the TCGA Research Network (2):<br><strong>(1) </strong><strong>Brummer, O., Pölönen, P., Mustjoki, S. <em>et al.</em> Computational textural mapping harmonises sampling variation and reveals multidimensional histopathological fingerprints. <em>Br J Cancer</em> 129, 683–695 (2023). </strong><a href="https://doi.org/10.1038/s41416-023-02329-4">https://doi.org/10.1038/s41416-023-02329-4</a></p> <p><strong>(2) The results shown here are in whole or part based upon data generated by the TCGA Research Network: </strong><strong><a href="https://www.cancer.gov/tcga">https://www.cancer.gov/tcga</a></strong><strong>.</strong></p>
Strong histopathological relevance patches for MSI vs. MSS classification
<p>This is a subset of dataset "Histological images for MSI vs. MSS classification in gastrointestinal cancer, FFPE samples" CRC_DX[1]. For more details, please visit [1]. After a fused distillation, a neural network based framework will vote for the candidates for the histopathological strong relevance patches. We incorporate the morphology knowledge from pathologists to sort out the most representative samples and tag them to this subdata.</p> <p>The dataset has two labels i.e., MSIMUT_strong and MSS_strong.</p> <p>MSIMUT_strong : 28 307 patches.</p> <p>MSS_strong : 27 146 patches.</p> <p>[1] Histological images for MSI vs. MSS classification in gastrointestinal cancer, FFPE samples. https://doi.org/10.5281/zenodo.2530835</p>
Figure 1 in A histopathological study on the freshwater fish species chub (Squalius cephalus) in the Karasu River, Turkey
Figure 1. Map of Erzurum city and the basin of the Karasu River showing the three sampling sites.
PathoEye: a deep learning framework for histopathological image analysis of skin tissue
<p>This dataset comprises two parts: one consisting of skin pathology slices near the epidermal layer of elderly individuals under sunlight exposure conditions, and the other comprising skin pathology slices near the epidermal layer of young individuals under the same conditions. Each type of image comes in two sizes: one is 128×128 pixels, and the other is 512×512 pixels.</p>
Pan-Cancer-Nuclei-Seg-DICOM: DICOM converted Dataset of Segmented Nuclei in Hematoxylin and Eosin Stained Histopathology Images
<div> <p>This dataset corresponds to a collection of images and/or image-derived data available from National Cancer Institute <a href="https://portal.imaging.datacommons.cancer.gov/">Imaging Data Commons (IDC)</a> [1]. This dataset was converted into DICOM representation and ingested by the IDC team. You can explore and visualize the corresponding images using IDC Portal here: <a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=Pan-Cancer-Nuclei-Seg-DICOM" target="_blank" rel="noopener">Pan-Cancer-Nuclei-Seg-DICOM</a>. You can use the manifests included in this Zenodo record to download the content of the collection following the <strong>Download instructions</strong> below.</p> <h3>Collection description</h3> </div> <div> <div>This collection contains automatic nucleus segmentation data of 5,060 whole slide tissue images of 10 cancer types earlier published in [2] (<a href="https://doi.org/10.7937/TCIA.2019.4A4DKP9U">https://doi.org/10.7937/TCIA.2019.4A4DKP9U</a>) stored in DICOM Bulk Annotation and DICOM Segmentation formats.</div> <div> </div> <div>DICOM Bulk Annotation nuclei annotations are stored as closed polygons along with the area of each nuclei. DICOM Segmentation version contains binary segmentations obtained by rasterizing the polygon contours. </div> <div> </div> <div>The annotations correspond to digital pathology images from the TCGA-BLCA,TCGA-BRCA,TCGA-CESC,TCGA-COAD,TCGA-GBM,TCGA-LUAD,TCGA-LUSC,TCGA-PAAD,TCGA-PRAD,TCGA-READ,TCGA-SKCM,TCGA-STAD,TCGA-UCEC,TCGA-UVM collections available in NCI Imaging Data Commons.</div> <div> </div> <div>To learn how these files are organized and how to access the content programmatically, see this documentation page: <a href="https://highdicom.readthedocs.io/en/latest/ann.html">https://highdicom.readthedocs.io/en/latest/ann.html</a>.</div> <div> </div> <div>Conversion of the nuclei segmentations from the original format into DICOM ANN and SEG representations was done using the code available in <a href="https://doi.org/10.5281/zenodo.13871765">10.5281/zenodo.10632181</a>.</div> <div> </div> <div>Annotations corresponding to this container ID in the source failed to convert due to the pixel matrix being too large to store: <code>TCGA-OL-A66K-01Z-00-DX1</code></div> <div> </div> <div>The following container IDs from the source annotations have failed due to inability to find the annotated images using the container IDs:</div> <div> <pre><code>TCGA-CU-A3QU-01Z-00-DX1 TCGA-A2-A0D1-01Z-00-DX1 TCGA-AQ-A1H2-01Z-00-DX1 TCGA-AQ-A1H2-01Z-00-DX1 TCGA-AQ-A1H3-01Z-00-DX1 TCGA-AQ-A1H3-01Z-00-DX1 TCGA-BH-A0B2-01Z-00-DX1 TCGA-E2-A15E-01Z-00-DX1 TCGA-E2-A1IP-01Z-00-DX1 TCGA-F4-6857-01Z-00-DX1 TCGA-12-0773-01Z-00-DX4 TCGA-35-3621-01Z-00-DX1 TCGA-49-4486-01Z-00-DX1 TCGA-33-4587-01Z-00-DX1 TCGA-D9-A1X3-01Z-00-DX1 TCGA-D9-A1X3-01Z-00-DX2 TCGA-D9-A4Z6-01Z-00-DX1 TCGA-EE-A17Y-01Z-00-DX1 TCGA-EE-A29R-01Z-00-DX1 TCGA-EE-A2A0-01Z-00-DX1 TCGA-EE-A2MS-01Z-00-DX1 TCGA-ER-A199-01Z-00-DX1 TCGA-ER-A1A1-01Z-00-DX1 TCGA-ER-A2NC-01Z-00-DX1 TCGA-FS-A1Z7-06Z-00-DX10 TCGA-FS-A1Z7-06Z-00-DX11 TCGA-FS-A1Z7-06Z-00-DX12 TCGA-FS-A1Z7-06Z-00-DX13 TCGA-FS-A1ZN-01Z-00-DX10 TCGA-FS-A1ZN-01Z-00-DX11 TCGA-FS-A1ZW-06Z-00-DX10 TCGA-FS-A1ZW-06Z-00-DX11 TCGA-GN-A261-01Z-00-DX1 TCGA-GN-A266-01Z-00-DX1 TCGA-GN-A268-01Z-00-DX1 TCGA-GN-A26A-01Z-00-DX1 TCGA-XV-AB01-01Z-00-DX1 TCGA-AJ-A23O-01Z-00-DX1 TCGA-AP-A056-01Z-00-DX1 TCGA-BK-A139-01Z-00-DX1 TCGA-E6-A1M0-01Z-00-DX1</code></pre> </div> <div> <h3>Files included</h3> <p>A manifest file's name indicates the IDC data release in which a version of collection data was first introduced. For example, <code>pan_cancer_nuclei_seg_dicom-collection_id-idc_v19-aws.s5cmd</code> corresponds to the annotations for th eimages in the <code>collection_id</code> collection introduced in IDC data release v19. DICOM Binary segmentations were introduced in IDC v20. If there is a subsequent version of this Zenodo page, it will indicate when a subsequent version of the corresponding collection was introduced.</p> <p>For each of the collections, the following manifest files are provided:</p> <ol> <li><code>pan_cancer_nuclei_seg_dicom-<collection_id>-idc_v20-aws.s5cmd</code>: manifest of files available for download from public IDC Amazon Web Services buckets</li> <li><code>pan_cancer_nuclei_seg_dicom-<collection_id>-idc_v20-gcs.s5cmd</code>: manifest of files available for download from public IDC Google Cloud Storage buckets</li> <li><code>pan_cancer_nuclei_seg_dicom-<collection_id>-idc_v20-dcf.dcf</code>: Gen3 manifest (for details see <a href="../records/Gen3%20manifest%20documentation">https://learn.canceridc.dev/data/organization-of-data/guids-and-uuids</a>)</li> </ol> <p>Note that manifest files that end in <code>-aws.s5cmd</code> reference files stored in Amazon Web Services (AWS) buckets, while <code>-gcs.s5cmd</code> reference files in Google Cloud Storage. The actual files are identical and are mirrored between AWS and GCP.</p> <h3>Download instructions</h3> <p>Each of the manifests include instructions in the header on how to download the included files.</p> <p>To download the files using <code>.s5cmd</code> manifests:</p> <ol> <li>install <a href="https://github.com/imagingdatacommons/idc-index" target="_blank" rel="noopener">idc-index</a> package: <code>pip install --upgrade idc-index</code></li> <li>download the files referenced by manifests included in this dataset by passing the <code>.s5cmd</code> manifest file: <code>idc download manifest.s5cmd</code></li> </ol> <p>To download the files using <code>.dcf</code> manifest, see manifest header.</p> <h3>Acknowledgments</h3> <p>Imaging Data Commons team has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l.</p> <h3>References</h3> </div> </div> <div>[1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., Bridge, C., Herrmann, M. D., Homeyer, A., Lewis, R., Aerts, H. J. W. L., Krishnaswamy, D., Thiriveedhi, V. K., Ciausu, C., Schacherer, D. P., Bontempi, D., Pihl, T., Wagner, U., Farahani, K., Kim, E. & Kikinis, R. National cancer institute imaging data commons: Toward transparency, reproducibility, and scalability in imaging artificial intelligence. Radiographics 43, (2023).</div> <div> </div> <div>[2] Hou, L., Gupta, R., Van Arnam, J. S., Zhang, Y., Sivalenka, K., Samaras, D., Kurc, T., & Saltz, J. H. (2019). Dataset of Segmented Nuclei in Hematoxylin and Eosin Stained Histopathology Images of 10 Cancer Types [Data set]. The Cancer Imaging Archive. https://doi.org/10.7937/TCIA.2019.4A4DKP9U</div>
Melanoma Histopathology Dataset with Tissue and Nuclei Annotations
<h2><strong>Description</strong>:</h2> <p>This dataset is designed for development of deep learning models for segmentation of nuclei and tissue in melanoma H&E stained histopathology. Existing nuclei segmentation models that are trained on non-melanoma specific datasets have low performance due to the ability of melanocytes to mimic other cell types, whereas existing melanoma specific models utilize older, sub-optimal techniques. Moreover, these models do not provide tissue annotations necessary for determining the localization of tumor-infiltrating lymphocytes, which may hold value for predictive and prognostic tasks. To address this, we created a melanoma specific dataset with nuclei and tissue annotations. <br><br></p> <h2><strong>Methodology</strong>:</h2> <p><strong>Sample Collection</strong>:</p> <p>Regions of interest (ROIs) were sampled from H&E stained slides of 103 primary melanoma specimens and 102 metastatic melanoma specimens, scanned using a Hamamatsu scanner at 40× magnification (0.23 μm per pixel). All slides were obtained from regular diagnostic procedures.<br>From each specimen, a 40× magnified ROI of 1024×1024 pixels was selected for annotation. Additionally, a context ROI of 5120×5120 pixels was sampled to provide information about the broader context for the annotation process. Selection was performed by a trained medical expert (M.S.) and subsequently verified by a dermatopathologist (W.B.). Manual ROI selection ensured the inclusion of diverse tissue and nuclei types.</p> <p><strong>Annotation Process</strong>:</p> <ul> <li><strong>Nuclei segmentation<br></strong>Nuclei segmentations were generated using Hover-Net pretrained on the PanNuke dataset. Manual annotation adjustments were performed by author M.S. using QuPath, with the following nuclei categories: tumor, stroma, vascular endothelium, histiocyte, melanophage, lymphocyte, plasma cell, neutrophil, apoptotic cell, and epithelium. All annotations were reviewed and corrected, where needed, by a dermatopathologist (W.B.).</li> <li><strong>Tissue segmentation<br></strong>Tissue segmentations were created manually using QuPath by M.S., with the following categories: tumor, stroma, epidermis, necrosis, blood vessel, and background. Annotations were reviewed and corrected, where needed, by a dermatopathologist (W.B.).</li> </ul> <p><strong>Quality Control</strong>: <br>To assess the reliability of the annotations, intra- and interobserver agreement (by pathologist G.B.) were determined on 12 randomly selected ROIs.</p> <ul> <li><strong>Nuclei segmentation<br></strong>The intraobserver overall precision was 84.89%, with a recall of 86.45%, and an F1 score of 85.66%. Interobserver overall precision was 80.34%, with a recall of 80.62%, and an F1 score of 80.20%. These results are based on the sum of all true positive, false positive, and false negative counts for the 12 ROIs.</li> <li><strong>Tissue segmentation<br></strong>The DICE score was determined on the same 12 randomly selected ROIs. The average intraobserver DICE score was 0.90, and the interobserver DICE score was also 0.90.</li> </ul> <p> </p> <p><strong>Version 3</strong>:<br>Removed sample "training_set_metastatic_roi_103" due to inconsistencies in annotation file.</p> <p><strong>Version 4:<br></strong>Sample training_set_metastatic_roi_088 missed one color annotation for a nuclei_apoptosis in the geojson file rendering it qupath uncompatible. This is fixed in the new version. </p> <p><strong>Version 5:<br></strong>Addition of correct sample of training_set_metastatic_roi_103" after deadline of <a href="https://puma.grand-challenge.org/">panoptic segmentation of nuclei and tissue in advanced melanoma challenge</a> test phase. </p>
Fig 4.A in Study of haematology profile & histopathological changes in di-ammonium phosphate induced climbing perch, Anabas testudineus (Bloch.)
Fig 4.A: Photomicrograph of the normal liver of control fish, Anabas testudineus. H. & E., 100X
His-MMDM: Multi-domain and Multi-omics Translation of Histopathology Images with Diffusion Models
<p>This repository (and several sub-repositories) contains the data for the manuscript "His-MMDM: Multi-domain and Multi-omics Translation of Histopathology Images with Diffusion Models"</p> <p>The current repository contains the de-identified metadata of WSIs. Additionally, it contains Supplementary Data S1-S7.</p> <p>Due to the large size of WSIs, the archives have been divided into several parts to satisfy the size limit of Zenodo.</p> <p>HMU-C dataset:</p> <p>part a: <a href="../doi/10.5281/zenodo.12636965">https://zenodo.org/doi/10.5281/zenodo.12636965</a></p> <p>part b: <a href="../doi/10.5281/zenodo.12705912">https://zenodo.org/doi/10.5281/zenodo.12705912</a></p> <p>HMU-1st dataset:</p> <p>part a: <a href="../doi/10.5281/zenodo.12710399">https://zenodo.org/doi/10.5281/zenodo.12710399</a></p> <p>part b: <a href="../doi/10.5281/zenodo.12723825">https://zenodo.org/doi/10.5281/zenodo.12723825</a></p> <p>part c: <a href="../doi/10.5281/zenodo.12724507">https://zenodo.org/doi/10.5281/zenodo.12724507</a></p> <p>part d: <a href="../doi/10.5281/zenodo.12726785">https://zenodo.org/doi/10.5281/zenodo.12726785</a></p> <p>Please fully download all the parts and concatenate them before extraction.</p> <p>Supplementary Data S1-S7:</p> <p><a href="https://zenodo.org/doi/10.5281/zenodo.16763510">https://zenodo.org/doi/10.5281/zenodo.16763510</a></p>
Figure 3 in Potential histopathological and immunological effects of SARS-CoV-2 on the liver
Figure 3. Immune-mediated liver injury adapted from Spearman et al. (2021).
Figure 4 in Potential histopathological and immunological effects of SARS-CoV-2 on the liver
Figure 4. The major liver histological features adapted from Díaz et al. (2020).
Figure 1 in Potential histopathological and immunological effects of SARS-CoV-2 on the liver
Figure 1. SARS-CoV-2 structure and life cycle adapted from Zhong et al. (2020).
Fig. 13 in Histopathological characterisation of retinal lesions associated to Diplostomum species (Platyhelminthes: Trematoda) infection in polymorphic Arctic charr Salvelinus alpinus
Fig. 13. Diplostomum sp. metacercaria in a choroidal vessel. Scale bar = 200 μm.
ScienceDex guides
Understand access before you commit
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.