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2,209 results for “biopsy”
Skin-blubber biopsy samples and associated demographic data collected from cetaceans encountered along the Western Antarctic Peninsula (WAP), 2010 – 2024
Baleen whale populations in the Southern Ocean are recovering after intense commercial whaling in the 20th century. Along the Western Antarctic Peninsula (WAP), this recovery is occurring in one of the planet's most rapidly changing marine ecosystems. Understanding how climate-driven changes influence the population dynamics of whales in this region is critical for understanding what conservation and management actions must be prioritized to maintain the structure and function of this marine ecosystem. To begin understanding the dynamics of whale recovery under continued environmental change, we need to study these whales' demography and population dynamics. As part of our annual sampling surveys for cetaceans along the WAP through the PAL LTER program, we actively collect remote non-lethal skin-blubber biopsy samples and have developed one of the most extensive tissue archives in the Southern Ocean. With these samples, we conduct a series of demographic and physiological measurements. Using the skin portion of the biopsy sample, we isolate nuclear and mitochondrial DNA (mtDNA) to develop a DNA profile for each sample, including genetic sex, a microsatellite genotype, and a mtDNA haplotype. These profiles are used to compare sex ratios of the population, determine individual recaptures through genotype analysis, and better understand population mixing. Using the blubber portion of the biopsy sample, we isolate endocrine markers (e.g., progesterone and cortisol) to monitor population pregnancy rates and stress levels. This data represents some of the first non-lethal quantitative observations of the demography and population dynamics of recovering whale populations in the Antarctic and provides a critical reference point for future work as the Antarctic climate continues to change and populations continue to recover from whaling.
Histopathology data of bone marrow biopsies (HistBMP or HistMNIST)
<p><strong>Data information</strong></p> <p>We prepared a dataset basing on histopathological images freely available on-line (http://www.enjoypath.com/). We selected 16 patients (patient IDs: 272, 274, 283, 289, 290, 291, 292, 295, 297, 298, 299). Each histopathological image represents a bone marrow biopsy. Diagnoses of the chosen cases were associated with different kinds of cancer (e.g., lymphoma, leukemia) or anemia. All original images were taken using HE, 40×, and each image was of size 336 × 448.</p> <p><strong>Data preparation</strong></p> <p>The original RGB representation was transformed to gray scale. Further, we divided each image into small patches of size 28 × 28. Eventually, we picked 10 patients for training, 3 patients for validation and 3 patients for testing, which resulted in 6,800 training images, 2,000 validation images and 2,000 test images. The selection of patients was performed in such a fashion that each dataset contained representative images with different diagnoses and amount of fat.</p> <p>Since the small patches resemble a widely-used benchmark in machine learning/AI community called MNIST, the dataset is referred to as HistMNIST. </p> <p><strong>First usage</strong></p> <p>The dataset was used to train deep generative models (VAEs):</p> <ul> <li>Tomczak, J. M., & Welling, M. (2016). Improving variational auto-encoders using householder flow. <em>arXiv preprint arXiv:1611.09630</em>.</li> </ul>
Data for methylome sequencing: Enriching and Profiling Methylomes for Tumor Classification and Liquid Biopsies
<p>We benchmarked and demonstrated the versatility of FLEXseq (Fragment Ligation EXclusive methylation sequencing) across different sample types: genomic DNA from the K562 (leukemia) cell line, DNA mix-in titrations of four immune cell types (B cells, T cells, monocytes, and neutrophils), DNA titrations of three cancer cell lines (breast invasive carcinoma [BRCA], colon adenocarcinoma [COAD], and glioblastoma [GBM]) mixed with those four immune cell mixtures separately, input titrations of cell-free (cf) DNA from one plasma sample and DNA from formalin-fixed paraffin-embedded (FFPE) tissues, cfDNA from 106 cerebrospinal fluids (CSF) and 42 other body fluids, and DNA from 37 FFPE tissues.</p> <p>We sequenced all the samples mentioned above using FLEXseq. Paired-end reads were quality and length trimmed with cutadapt version 3.5, and all high-quality sequencing reads were then aligned to the hg38 reference genome using Bismark v0.23.0. We then filtered out reads with unmethylated cytosine in the non-CpG context with filter_non_conversion function. Next, we used the bismark_methylation_extractor function to extract the methylation calls (removing single-nucleotide polymorphisms [SNP]).</p> <p>We also used the bam2pat function from wgbs_tools, to convert bam files into .pat files for deconvolution, keeping reads covering at least three CpG sites. The .pat files preserve fragment-level data and were de-identified by removing SNPs using the mask_pat function. </p> <p>We used CNVkit (v0.9.10) to analyze and visualize genome-wide copy numbers. Our inputs into CNVkit were Bismark/Bowtie 2 aligned BAM files deduplicated by Bismark based on end positions and fragment lengths. We then generated log2copy ratio plots for all body fluid and FFPE samples based on the pooled reference and visualized them across all bins using the DNAcopy R package.</p> <p> </p>
Data on the detection of clinically significant prostate cancer by magnetic resonance imaging (MRI)-guided targeted and systematic biopsy
<p>This is a dataset from the original publication “Reasons for missing clinically significant prostate cancer by targeted magnetic resonance imaging/ultrasound fusion-guided biopsy”. From 01/2014 to 04/2019 a sample collective of 785 patients with 3T multiparametric magnetic resonance imaging (mp-MRI) of the prostate and subsequent combined systematic biopsy (SB) and magnetic resonance imaging/ultrasound (US) fusion-guided biopsy (TB) was retrospectively analyzed. Prostate carcinoma (PCa) detection by TB and/or additional SB was analyzed.</p>
DEcancer: Proteomics Datasets of Liquid Biopsy Samples
<p>Two datasets used to build the DEcancer pipelines from open access data. The tables in the cancerseek folder are uploaded for convenience and can also be found from the supplementary data of <a href="https://doi.org/10.1016/j.isci.2019.04.035">Early Cancer Detection from Multianalyte Blood Test Results</a> paper. The nanoparticles dataset is processed with the Perseus software on the <a href="http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD017052">original data</a> from Blume <em>et al's</em> Rapid, deep and precise profiling of the plasma proteome with multi-nanoparticle protein corona. These are distinct datasets in that the nanoparticles is a cohort of patients from a high dimensional, low sample size proteomics dataset.</p>
RNA-Seq and proteomics of Crohn's disease: Terminal ileum of inflamed and non inflamed paired tissue biopsy
<p>The cause of chronic inflammation manifestations such as inflammatory bowel disease (IBD) is still not yet fully understood. Evidence however points to the convergence of environmental factors, genetic factors, intestinal microbiota and the immune response at the intestinal epithelial surface that consequently leads to a breakdown of barrier function in IBD. An increasing number of theories implicate a dysfunctional intestinal epithelial surface in the pathogenesis of IBD and progression to complicated disease, which contributes significantly to the IBD burden. As yet, the significance of the intestinal epithelial surface in IBD manifestation and progression has not been explored using a systems biology approach, that compares the mucosa of healthy patients to those with IBD. In the context of dyregulation of the epithelial barrier, there is a scarcity of publications that explore the contribution of proteins- the effectors of the cells and RNA message in the pathways affected by CD and in particular the localization of inflammatory response. We aim to evaluate the functional pathways of chronic inflammation at the intestinal epithelial surface using a combination of transcriptomic and proteomic analyses on intestinal epithelial tissue samples from paired inflamed and non-inflammed Crohn's disease (CD) and healthy control subjects. Concordance of several biological pathways from both data sets was found to be altered in CD patients' epithelia when compared to healthy controls. This information could be helpful in identifying novel therapeutic targets that aim to restore barrier function at the intestinal epithelial surface and to guide therapy.</p>
Prediction of pCR by Preoperative Biopsy in Breast Cancer With cCR After Neoadjuvant Chemotherapy.
ClinicalTrials.gov study NCT03273426. IPD Sharing: YES. Countries: 1. Publications: 1.
RNA-Seq and proteomics of Crohn's disease: Terminal ileum of inflamed and non inflamed paired tissue biopsy
Open the record for dataset details and reuse information.
Hybrid nanoplasmonic porous biomaterial scaffold for liquid biopsy diagnostics using extracellular vesicles: data and media
<p>Raw datasets and media accompanying the manuscript: <strong>Hybrid nanoplasmonic porous biomaterial scaffold for liquid biopsy diagnostics using extracellular vesicles</strong>, published in ACS Sensors</p>
DICOM converted annotations for the Prostate-MRI-US-Biopsy collection
<p>This dataset contributes DICOM-converted annotations to the publicly available National Cancer Institute Imaging Data Commons [1] Prostate-MRI-US-Biopsy collection (<a href="https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=Community&collection_id=prostate_mri_us_biopsy">https://portal.imaging.datacommons.cancer.gov/explore/filters/?collection_id=Community&collection_id=prostate_mri_us_biopsy</a>). Prostate-MRI-US-Biopsy collection was initially released by The Cancer Imaging Archive (TCIA) [2,3,4]. While the images in this collection are stored in the standard DICOM format, the collection is also accompanied by 1017 semi-automatic segmentations of the prostate and 1317 manual segmentations of target lesions in the STL format. Although STL is a common and practical format for 3D printing, it is not interoperable with many visualization and analysis tools commonly used in medical imaging research and does not provide any standard means to communicate metadata, among other limitations.</p><p>This dataset contains segmentations of the prostate and target lesions harmonized into DICOM representation. Specifically, we created DICOM Encapsulated 3D Manufacturing Model objects (M3D modality) that includes the original STL content enriched with the DICOM metadata. Furthermore, we created an alternative encoding of the surface segmentations by rasterizing them and saving the result as a DICOM Segmentation object (SEG modality). As a result, the contributed DICOM objects can be stored in any DICOM server that supports those objects (including Google Healthcare DICOM stores), and the DICOM Segmentations can be visualized using off-the-shelf tools, such as OHIF Viewer.</p><p>Conversion from STL to DICOM M3D modality was performed using PixelMed toolkit (<a href="https://www.pixelmed.com/dicomtoolkit.html">https://www.pixelmed.com/dicomtoolkit.html</a>). Conversion from STL to DICOM SEG was done in 2 steps. We used Slicer (<a href="https://www.slicer.org/">https://www.slicer.org/</a>) to rasterize the surface segmentation to the matrix of the segmented image, which were next converted to DICOM SEGs using dcmqi (<a href="https://github.com/QIICR/dcmqi">https://github.com/QIICR/dcmqi</a>) [5]. Resulting objects were validated using dicom3tools dciodvfy (<a href="https://www.dclunie.com/dicom3tools.html">https://www.dclunie.com/dicom3tools.html</a>). Details describing the conversion process as well as the details on how to access the encapsulated STL content from the DICOM m3D files are provided in this GitHub repository: <a href="https://github.com/ImagingDataCommons/prostate_mri_us_biopsy_dcm_conversion">https://github.com/ImagingDataCommons/prostate_mri_us_biopsy_dcm_conversion</a>.</p><p>Specific files included in the record are:</p><ol><li><strong>Prostate-MRI-US-Biopsy-DICOM-Annotations.zip</strong>: DICOM M3D and SEG files, organized into the folder hierarchy following this pattern: Prostate-MRI-US-Biopsy/%PatientID/%StudyInstanceUID/%SeriesNumber-%Modality-%SeriesDescription.dcm</li><li><strong>referenced_images_sorted-idc_file_manifest.s5cmd</strong>: IDC manifest for downloading the T2W MRI images corresponding to the annotations. To download the files in this manifest, first install s5cmd (<a href="https://github.com/peak/s5cmd">https://github.com/peak/s5cmd</a>), and run the following command: s5cmd --no-sign-request --endpoint-url https://s3.amazonaws.com run referenced_images_sorted-idc_file_manifest.s5cmd. Files will be organized in the Prostate-MRI-US-Biopsy/%PatientID/%StudyInstanceUID/ folder hierarchy upon download.</li></ol><p><strong>References</strong></p><p>[1] Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S., Aerts, H. J. W. L., Homeyer, A., Lewis, R., Akbarzadeh, A., Bontempi, D., Clifford, W., Herrmann, M. D., Höfener, H., Octaviano, I., Osborne, C., Paquette, S., Petts, J., Punzo, D., Reyes, M., Schacherer, D. P., Tian, M., White, G., Ziegler, E., Shmulevich, I., Pihl, T., Wagner, U., Farahani, K. & Kikinis, R. NCI Imaging Data Commons. <i>Cancer Res.</i> <strong>81,</strong> 4188–4193 (2021). doi: <a href="https://dx.doi.org/10.1158/0008-5472.CAN-21-0950">10.1158/0008-5472.CAN-21-0950</a>. </p><p>[2] Natarajan, S., Priester, A., Margolis, D., Huang, J., & Marks, L. (2020). Prostate MRI and Ultrasound With Pathology and Coordinates of Tracked Biopsy (Prostate-MRI-US-Biopsy) (version 2) [Data set]. The Cancer Imaging Archive. DOI: <a href="https://doi.org/10.7937/TCIA.2020.A61IOC1A">10.7937/TCIA.2020.A61IOC1A</a></p><p>[3] Sonn GA, Natarajan S, Margolis DJ, MacAiran M, Lieu P, Huang J, Dorey FJ, Marks LS. Targeted biopsy in the detection of prostate cancer using an office based magnetic resonance ultrasound fusion device. Journal of Urology 189, no. 1 (2013): 86-91. DOI: <a href="https://doi.org/10.1016/j.juro.2012.08.095">10.1016/j.juro.2012.08.095</a> </p><p>[4] Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: <a href="https://doi.org/10.1007/s10278-013-9622-7">10.1007/s10278-013-9622-7</a></p><p>[5] Herz, C., Fillion-Robin, J.-C., Onken, M., Riesmeier, J., Lasso, A., Pinter, C., Fichtinger, G., Pieper, S., Clunie, D., Kikinis, R. & Fedorov, A. dcmqi: An Open Source Library for Standardized Communication of Quantitative Image Analysis Results Using DICOM. <i>Cancer Res. </i><strong>77,</strong> e87–e90 (2017). DOI: <a href="https://dx.doi.org/10.1158/0008-5472.CAN-17-0336">10.1158/0008-5472.CAN-17-0336</a>.</p>
GlioHyper: Glioma Biopsy Hyperspectral Dataset
<p>A dataset of glioma biopsies which were examined using hyperspectral imaging. The details of the acquisition protocol and description of the data are provided in the accompaniying paper:</p> <p>"A transportable hyperspectral imaging setup based on fast, high-density spectral scanning for in situ quantitative biochemical mapping of fresh tissue biopsies", Luca Giannoni et. al.</p> <p> </p>
Ultrasound-Guided Trans-Uterine Cavity Core Needle Biopsy of Uterine Myometrial Tumors - Diagnostics 2022, 12(6), 1348
<p>Ultrasound-guided trans-uterine cavity core needle biopsy of myometrial tumors to differentiate sarcoma from a benign lesion. </p> <p>This is a supplementary Video to the article published in <em>Diagnostics</em> <strong>2022</strong>, <em>12</em>(6), 1348.</p> <p><a href="https://doi.org/10.3390/diagnostics12061348">https://doi.org/10.3390/diagnostics12061348</a></p>
Dataset related to the article "Binary classification of copy number alteration profiles in liquid biopsy with potential clinical impact in advanced NSCLC"
<p>This record contains original data used in the article "Binary classification of copy number alteration profiles in liquid biopsy with potential clinical impact in advanced NSCLC" to develop a linear support vector machine (SVM) classifier to predict chromosomal instability. </p> <p>We retrospectively evaluated the results of plasma NGS analysis performed at our Institution by using the AVENIO ctDNA Expanded Kit, a panel of 77 genes, which detects the major classes of genetic alterations. Binary classification, into “stable” (SCP) or “unstable” (UCP) chromosomal profiles, was initially performed by visual inspection of individual CNV alteration profiles by two independent professionals of our group. Then we decided to implement a support vector machine (SVM) classifier to automatically classify CNV profiles as SCP or UCP, beyond operators’ experience. We considered the segmented log2 ratios (.cns) files provided by the CNV kit software and computed three features (Segments, Size, Chromosomes). An alteration (“occurrence of instability”) in the CNV profile was defined each time we found a DNA segment of any size with absolute value of the log2 copy ratio exceeding a fixed cut-off. Two different cut-off values on log2 copy ratio were examined: 0.1 and 0.2. Once the cut-off was defined, three features were considered as covariates in the SVM classifier: 1) number of altered segments (Segments), 2) total length of altered regions (Size) and 3) number of affected chromosomes (Chromosomes).</p> <p>The “dataset_0.1.txt” and “dataset_0.2.txt” files are the original data matrices obtained by considering a cut-off of 0.1 and 0.2, respectively, on the absolute value of the log2 copy ratio.</p> <p>Rows represent available samples in our study (n=177). Columns contain the following variables: anonymized sample IDs (Sample), the class, “stable” or “unstable”, as assigned by two independent professionals of our group (Class), the corresponding binary label (Label: 0 for “stable”, 1 for “unstable”), the three features used as covariates in the SVM classifier and computed as described above (Segments, Size, Chromosomes).</p> <p><br>For the detailed results of our work, please refer to the full article.</p>
Data Set available for Liquid Biopsy of CML Diagnosis
<p><em><span>Chronic Myeloid Leukemia, CML, is caused in patients with myeloproliferative disorders by the presence of BCR::ABL1. The research discussed the integration of microfluidic chips with biosensors for the purpose of detecting biomarkers in peripheral blood thereby showing the raw experimental data set avaialble, liquid bipsy sample set available and Calculation for methods and data Simulation along with graphs, which could obviate the need for painful biopsies, thereby improving diagnostic accuracy and accessibility.</span></em></p>
IMC dataset: duodenal biopsies of COVID-19 and Control patients
<p>An Imaging Mass Cytometry dataset containing .mcd files of duodenal biopsies from COVID-19 and Control patients. The peer-reviewed publication for this dataset has been published in Mucosal Immunology, and can be accessed here: https://www.nature.com/articles/s41385-021-00437-z. Please cite this when using the dataset.</p>
Dataset related to article "Archival skin biopsy specimens as a tool for miRNA-based diagnosis: Technical and post-analytical considerations".
<p>microRNA raw data</p>
Data from: A comparison of non-destructive visceral swab and tissue biopsy sampling methods for genotyping-by-sequencing in the freshwater mussel Fusconaia askewi
<p>Limiting harm to organisms via genetic sampling is an important consideration for rare species. Nondestructive sampling techniques have been developed to address this issue in freshwater mussels. Two methods, visceral swabbing and tissue biopsies, have proven to be effective for DNA sampling, though it is unclear as to which method is preferable for genotyping-by-sequencing (GBS). Tissue biopsies may cause undue stress and damage to organisms, while visceral swabbing potentially reduces the chance of such harm. Our study compared the efficacy of these two DNA sampling methods for generating GBS data for the Unionid freshwater mussel, Texas Pigtoe (<em>Fusconaia askewi</em>). Our results find both methods generate quality sequence data, though some considerations are in order. Tissue biopsies produced significantly higher DNA concentrations and larger numbers of reads when compared to swabs, though there was no significant association between starting DNA concentration and number of reads generated. Swabbing produced greater sequence depth (more reads per sequence) while tissue biopsies revealed greater coverage across the genome (at lower sequence depth). Patterns of genomic variation as characterized in principal component analyses were similar regardless of the sampling method, suggesting that the less invasive swabbing is a viable option for producing quality GBS data in these organisms.</p>
Dataset for: Estimating pregnancy rate from blubber progesterone levels of a blindly biopsied beluga population poses methodological, analytical and statistical challenges
<p class="MsoBodyText"><span>Beluga (<em>Delphinapterus leucas</em>) from the St. Lawrence Estuary, Canada, have been declining since the early 2000s, suggesting recruitment issues as a result of low fecundity, abnormal abortion rates or poor calf or juvenile survival. Pregnancy is difficult to observe in cetaceans, making the ground-truthing of pregnancy estimates in wild individuals challenging. Blubber progesterone concentrations were contrasted among 62 SLE beluga with a known reproductive state (i.e., pregnant, resting, parturient, and lactating females), that were found dead in 1997–2019. The suitability of a threshold obtained from decaying carcasses to assess reproductive state and pregnancy rate of freshly-dead or free-ranging and blindly-sampled beluga was examined using three statistical approaches and two datasets (135 freshly-harvested carcasses in Nunavik, and 65 biopsy-sampled SLE beluga). Progesterone concentrations in decaying carcasses were considerably higher in known-pregnant (mean </span><span>±</span><span> sd: </span><span>365 </span><span>±</span><span> 244 ng g<sup>-1</sup> of tissue) </span><span>than resting (</span><span>3.1 </span><span>±</span><span> 4.5 ng g<sup>-1</sup> of tissue</span><span>) or lactating (</span><span>38.4 </span><span>±</span><span> 100 ng g<sup>-1</sup> of tissue</span><span>) females. An approach based on statistical mixtures of distributions and a logistic regression was compared to the commonly-used, fixed threshold approach (here, 100 ng g<sup>-1</sup>) for discriminating pregnant from non-pregnant females. The error rate for classifying individuals of known reproductive status was the lowest for the fixed threshold and logistic regression approaches, but the mixture approach required limited <em>a priori</em> knowledge for clustering individuals of unknown pregnancy status. Mismatches in assignations occurred at lipid content <10% of sample weight. Our results emphasize the importance of reporting lipid contents and progesterone concentrations in both units (ng g<sup>-1 </sup>of tissue and ng g<sup>-1</sup> of lipid) when sample mass is low. By highlighting ways to circumvent potential biases in field sampling associated with capturability of different segments of a population, this study also enhances the usefulness of the technique for estimating pregnancy rate of free-ranging population. </span></p>
Multimodal Fusion of Liquid Biopsy and CT Enhances Differential Diagnosis of Early-stage Lung Adenocarcinoma
<p>This research explores the potential of multimodal fusion for the differential diagnosis of early-stage lung adenocarcinoma (LUAD) (tumor sizes < 2 cm). It combines liquid biopsy biomarkers, specifically extracellular vesicle long RNA (evlRNA) and the computed tomography (CT) attributes.</p><p>It includes 4 files:</p><p>1. gene expression matrix : gene expression matrix for all samples (normalized read counts) .</p><p>2. evlRNA Rad features: 6 image features, 17 evlRNA features and labels</p><p>3. differentially expressed genes: Differentially expressed genes in LUAD patients compared with Benign individuals.</p><p>4. raw data: raw data used to for figure and table.</p>
Evaluation of the Bio-Seal Biopsy Track Plug
ClinicalTrials.gov study NCT00527722. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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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.