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7,459 results for “kidney”
Full summary statistics of mixQTL for GTEx v8 Kidney_Cortex
The mixQTL method is described in paper doi.org/10.1101/2020.04.22.050666. Please cite the original paper if using the data.
Raw data: Redox protein Memo1 coordinates FGF23-driven signaling and small Rho-GTPases in the kidney
<p>This dataset contains the raw data for "Redox protein Memo1 coordinates FGF23-driven signaling and small Rho-GTPases in the kidney". We investigated the role of Memo1 redox function in FGF23-driven receptor tyrosine kinase signaling in the kidney.</p> <p> </p> <p>Raw data which are not included here can be found using the following accession codes:</p> <p>RNAseq data: NCBI SRA, Accession: PRJNA672305 </p> <p>LC-MS/MS kidney data: proteomeXchange accession PXD022342 </p> <p>LC-MS/MS recombinant protein data: proteomeXchange PXD022382 </p>
Immunofluorescence staining of a human kidney (#1, tumor area) obtained by MELC
<p>19 marker MELC run in a human peri-tumor kidney sample (#1). Each image shows the same field of view, sequentially stained with the depicted fluorescence labelled antibodies. Images contain 2024 x 2024 pixels and are generated using an inverted wide field fluorescence microscope with a 20 x objective, a lateral resolution of 325 nm and an axial resolution above 5 µm. Scale bar 100 µm.</p> <p>All raw images were aligned based on the reference phase contrast image taken at the beginning of measurement. Each fluorescence MELC image was processed by background subtraction and illumination correction, based on bleaching images. Then an “Extended Depth of Field” algorithm was applied to the 3D fluorescence stack in each cycle. Images were at last normalized in ImageJ, where a rolling ball algorithm was used for background estimation, edges were removed (accounting for the maximum allowed shift during the autofocus procedure) and fluorescence intensities were stretched to the full intensity range (16 bit -> 2<sup>16</sup>).</p>
Immune repertoire profiling reveals that clonally expanded B and T cells infiltrating diseased human kidneys can also be tracked in the blood
<p>Recent advances in high-throughput sequencing allow for the competitive analysis of the human B and T cell immune repertoire. In this study we compared Immunoglobulin and T cell receptor repertoires of lymphocytes found in kidney and blood samples of 10 patients with various renal diseases based on next-generation sequencing data.</p>
Reference model and embedding for human kidney endothelial cell mapping
<p>Reference model and embedding for human kidney endothelial cell mapping</p><p>The reference model serves as a basis for the mapping of new data to the HLCA using scArches (Lotfollahi et al., https://doi.org/10.1038/s41587-021-01001-7). </p>
Figure 1 in Morphological, histological and molecular characteristics of Myxobolus spp. (Cnidaria: Myxozoa) infecting the kidney of silver carp in Lake Taihu
Figure 1. Spores of Myxobolus lieni (Nie & Li, 1973) (A–B) and M. varius (Achmerov, 1960) (C–D) from Hypophthalmichthys molitrix, line drawings. Scale bars = 2 μm.
Figure 2 in Morphological, histological and molecular characteristics of Myxobolus spp. (Cnidaria: Myxozoa) infecting the kidney of silver carp in Lake Taihu
Figure 2. Spores of Myxobolus lieni (Nie & Li, 1973) (A–B) and M. varius (Achmerov, 1960) (C–D) from Hypophthalmichthys molitrix, digitized images. Scale bars = 10 μm.
Figure 3 in Morphological, histological and molecular characteristics of Myxobolus spp. (Cnidaria: Myxozoa) infecting the kidney of silver carp in Lake Taihu
Figure 3. Histopathological sections of Hypophthalmichthys molitrix kidney infected by Myxobolus spp. A–C. M. lieni (Nie & Li, 1973), in the renal tubules; D. M. varius (Achmerov, 1960), in the renal interstitium. Arrows indicate the plasmodia which contains 2–4 mature myxospores. Scale bars = 10 μm.
Figure 4 in Morphological, histological and molecular characteristics of Myxobolus spp. (Cnidaria: Myxozoa) infecting the kidney of silver carp in Lake Taihu
Figure 4. Bayesian inference trees constructed with the SSU rDNA sequences. Numbers near the nodes shows the posterior probability and bootstrap values of BI and maximum likelihood (ML), respectively. Information of GenBank accession number, infection site, host and locality follows the species name. Abbreviations: B—brain; E—encephalocoele; F—fin; G—gills; GA—gill arch; GL— capillary network of the gill lamellae; H—heart; I—intestine; K—kidney; M—mesentery; MP- palate of the mouse; MC—muscle cells; SB—swim bladder; UB—urinary bladder.
Pretrained network for segmentation of kidneys and exophytic cysts in subjects with autosomal dominant polycystic kidney disease (ADPKD)
<p>This pretrained network based on the 3D U-Net is for segmentation of kidneys and exophytic cysts in subjects with autosomal dominant polycystic kidney disease (ADPKD). The network was trained with 157 (including 53 cases with exophytic cysts) subjects with ADPKD. The details of trained dataset and the performance of the network will be updated after our manuscript is accepted for publication.</p>
Parallel generation of extensive vascular networks with application to an archetypal human kidney model
<p>Given the relevance of the inextricable coupling between microcirculation and physiology, and the relation to organ function and disease progression, the construction of synthetic vascular networks for mathematical modelling and computer simulation is becoming an increasingly broad field of research. Building vascular networks that mimic in-vivo morphometry is feasible through algorithms such as constrained constructive optimisation (CCO) and variations. Nevertheless, these methods are limited by the maximum number of vessels to be generated due to the whole network update required at each vessel addition. In this work, we propose a CCO-based approach endowed with a domain decomposition strategy to concurrently create vascular networks. The performance of this approach is evaluated by analysing the agreement with the sequentially generated networks and studying the scalability when building vascular networks up to 200,000 vascular segments. Finally, we apply our method to vascularise a highly complex geometry corresponding to the cortex of a prototypical human kidney. The technique presented in this work enables the automatic generation of extensive vascular networks, removing the limitation from previous works. Thus, we can extent vascular networks (e.g., obtained from medical images) to pre-arteriolar level, yielding patient-specific whole-organ vascular models with an unprecedented level of detail.</p>
OCT porcine kidney dataset for percutaneous nephrostomy guidance
<h2><strong>Code</strong> [<a href="https://github.com/thepanlab/FOCT_kidney" target="_blank" rel="noopener">GitHub</a>] | <strong>Publication</strong> [<a href="https://doi.org/10.1364/BOE.421299" target="_blank" rel="noopener">Biomedical Optics Express'21</a>]</h2> <h3>Abstract</h3> <p>Percutaneous renal access is the critical initial step in many medical settings. In order to obtain the best surgical outcome with minimum patient morbidity, an improved method for access to the renal calyx is needed. In our study, we built a forward-view optical coherence tomography (OCT) endoscopic system for percutaneous nephrostomy (PCN) guidance. Porcine kidneys were imaged in our experiment to demonstrate the feasibility of the imaging system. Three tissue types of porcine kidneys (renal cortex, medulla, and calyx) can be clearly distinguished due to the morphological and tissue differences from the OCT endoscopic images. To further improve the guidance efficacy and reduce the learning burden of the clinical doctors, a deep-learning-based computer aided diagnosis platform was developed to automatically classify the OCT images by the renal tissue types. Convolutional neural networks (CNN) were developed with labeled OCT images based on the ResNet34, MobileNetv2 and ResNet50 architectures. Nested cross-validation and testing was used to benchmark the classification performance with uncertainty quantification over 10 kidneys, which demonstrated robust performance over substantial biological variability among kidneys. ResNet50-based CNN models achieved an average classification accuracy of 82.6%±3.0%. The classification precisions were 79%±4% for cortex, 85%±6% for medulla, and 91%±5% for calyx and the classification recalls were 68%±11% for cortex, 91%±4% for medulla, and 89%±3% for calyx. Interpretation of the CNN predictions showed the discriminative characteristics in the OCT images of the three renal tissue types. The results validated the technical feasibility of using this novel imaging platform to automatically recognize the images of renal tissue structures ahead of the PCN needle in PCN surgery.</p> <h3>Description</h3> <p>The dataset contains OCT images of 10 porcine kidneys from three tissues: cortex, medulla, and pelvis calyx. There is 1000 images per tissues/per kidney. More information about the dataset can be found in the paper: <a href="https://doi.org/10.1364/BOE.421299">https://doi.org/10.1364/BOE.421299</a></p> <p>The repository that processed this dataset can be found at <a href="https://github.com/thepanlab/FOCT_kidney">https://github.com/thepanlab/FOCT_kidney</a></p> <p> </p>
Supporting data for "Chromatin conformation and histone modification profiling across human kidney anatomic regions"
<p>Here we deposit supporting data for our manuscript entitled "Chromatin conformation and histone modification profiling across human kidney anatomic regions".</p> <ul> <li>donor_info.pdf: Additional clinical information of the donor involved in the study</li> <li>Large zip files with names starting as "hic": Juicer Hi-C outputs aligned with hg38 genome <ul> <li>Note: hg19 alignment outputs are available at GEO.</li> </ul> </li> <li>hg19_loop_domain.zip: Hi-C chromatin contact domain finding results with Arrowhead and chromatin loop finding results with HiCCUPs (implemented in Juicer tools), aligned with hg19 genome</li> <li>hg38_loop_domain.zip: Hi-C chromatin contact domain finding results with Arrowhead and chromatin loop finding results with HiCCUPs (implemented in Juicer tools), aligned with hg38 genome</li> <li>CUTRUN_peak_hg19.tar.gz: CUT&RUN peaks.stringent.bed data outputs generated by hg19 alignment</li> <li>CUTRUN_peak_hg38.tar.gz: CUT&RUN peaks.stringent.bed data outputs generated by hg38 alignment</li> <li>CUTRUN_bigwig_hg38.tar.gz: CUT&RUN bigwig outputs generated by hg38 alignment <ul> <li>Note: hg19 alignment outputs are available at GEO.</li> </ul> </li> <li>CUTRUN_overlapped_peaks.xlsx: Overlaps between H3K27me3 and H3K4me3 in each anatomical region. Peaks were loaded from ‘.peaks.stringent.bed’ files in R and converted to GRanges objects using package ‘GenomicRanges’. R function ‘intersect’ was used to calculate the overlap between two GRanges objects, represented as each row in the table. <ul> <li>Here we list the details of overlaps between H3K27me3 and H3K4me3 in each anatomical region, as well as overlaps for each of the histone markers across anatomical regions.</li> </ul> </li> </ul>
Unveiling Pathophysiological Insights: Serum Metabolic Dysregulation in Acute Respiratory Distress Syndrome Patients with Acute Kidney Injury
<p>The uploaded data is an Excel sheet obtained after performing the binning of 1H CPMG NMR spectra acquired using 800 MHz NMR on the serum samples of ARDS patients and ARDS with AKI patients. </p>
Figure 4 in Anatomical and morphological study of the kidneys of the breeding emu (Dromaius novaehollandiae)
Figure 4. Kidney histology sections in emu (Dromaius novaehollandiae). a, b, c, and d – H&E staining.a - Transverse section showing C - renal cortex; MC - medullary cone; MG - mammalian glomerulus; RG - reptilian glomerulus. b - Renal cortex; P - proximal tubule; D - distal tubule. c - Medullary cone; CD - collecting duct; arrow - thick limb of Henle's loop. d - Transverse section though the area around the intralobular vein - V; C - renal cortex; P - proximal tubule; MC - medulla cone; CD - collecting duct. e and f - PAS staining. e - Transverse section showing C - renal cortex; MC - medullary cone; MG - mammalian glomerulus; RG - reptilian glomerulus; CD - collecting duct; arrow - brush border of the proximal tubule. f - Renal cortex; D - distal tubule; U - urinary space; arrow - brush border of the proximal tubule.
Figure 1 in Anatomical and morphological study of the kidneys of the breeding emu (Dromaius novaehollandiae)
Figure 1. Healthy adult emus (Dromaius novaehollandiae) at the experimental farm of the Department of Poultry and Ornamental Bird Breeding, West Pomeranian University of Technology in Szczecin.
Figure 3 in Anatomical and morphological study of the kidneys of the breeding emu (Dromaius novaehollandiae)
Figure 3. Kidneys of emu (Dromaius novaehollandiae). A - Right kidney. B - Left kidney: 1 - cranial division; 2 - middle division; 3 - caudal division.
Figure 1 in Histological and histochemical study on the mesonephric kidney of Pelophylax bedriagae (Anura: Ranidae)
Figure 1. Light microscopic view of the kidney of P. bedriagae. A) Simple squamous epithelium of the parietal layer of Bowman's capsule (black arrow), podocytes in the visceral layer of Bowman's capsule (red arrow), Bowman's space (*), glomerulus (G). B) Proximal tubule (PT), distal tubule (DT), glomerulus (G). C) Collecting duct (ellipse), melanomacrophages in the kidney parenchyma (black arrow). D) The localization of HA mainly in the interstitium surrounding the collecting ducts.
FIGURE 5 in Morphological and histometric features of the caudal kidney in piranha Pygocentrus nattereri (Characiformes: Serrasalmidae)
FIGURE 5 | Box plot (median, 25 and 75th quartile) and lines inside represent the median for the area of the Bowman' space, renal capsule and glomerulus of female and male of Pygocentrus nattereri. The points represent outliers and (*) show differences between female and male = p <0.05.
FIGURE 3 in Morphological and histometric features of the caudal kidney in piranha Pygocentrus nattereri (Characiformes: Serrasalmidae)
FIGURE 3 | Histological features of renal corpuscles and lymphohematopoietic region of caudal kidney in Pygocentrus nattereri. A. Renal corpuscle possesses podocytes (P) around of glomerular capillaries; Ep = nuclei of parietal squamous cell layer covering the capsule; erythrocytes (E) are found into the capillaries spread along to mesangial cells (M); note bacteria internalized by a macrophage (arrow); HE, scale bar = 10 µm. B. Mesangial (M), basal membrane visceral (BMv) and parietal (BMp), positively reacted for PAS reaction; scale bar = 10 µm; C. Renal corpuscle demonstrating negative reaction for collagenous fibers; glomerular space (GS); erythrocyte (E) cytoplasm stained of yellow in MT stain; scale bar = 10 µm. D. A general view of lymphohematopoietic tissue; granulocytes showing a pronounced eosinophilic cytoplasm and tend to form little cellular niches whereas agranulocytes are randomly distributed in cordonal clusters (arrowhead); both cell categories common primordial lineages which displays larger basophilic nuclei (arrows); HE, scale bar = 50; E. Rodlet cells are often found thorough lymphohematopoietic tissue (arrows); HE, scale bar = 10 µm.
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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.