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1,200 results for “Perfusate”
Enhanced perfusion following exposure to radiotherapy: a theoretical investigation (revised manuscript data)
<p>Dataset and software supporting the revised manuscript: "Enhanced perfusion following exposure to radiotherapy: a theoretical investigation." See the enclosed README and manuscript for further information.</p>
Rapid microfluidic perfusion system enables controlling dynamics of intracellular pH regulated by Na+/H+ exchanger NHE1
<p>Dataset for manuscript titled "Rapid microfluidic perfusion system enables controlling dynamics of intracellular pH regulated by Na+/H+ exchanger NHE1."</p> <p>Version 2: additional data for Figure 2(e) and (f).</p>
Treatment with 3-aminobenzamide during ex vivo lung perfusion of damaged rat lungs reduces graft injury and dysfunction after transplantation
<p>Ex vivo lung perfusion (EVLP) with pharmacological reconditioning may increase donor lung utilization for transplantation (LTx). 3-Aminobenzamide (3-AB), an inhibitor of poly (ADP-ribose) polymerase (PARP), reduces ex vivo lung injury in rat lungs damaged by warm ischemia (WI). Here we determined the effects of 3-AB reconditioning on graft outcome after LTx. Three groups of donor lungs were studied: Control (Ctrl): 1 hour WI + 3 hours cold ischemia (CI) + LTx; EVLP: 1 hour WI + 3 hours EVLP + LTx; EVLP + 3-AB: 1 hour WI + 3 hours EVLP + 3-AB (1 mg<sup>.</sup>mL<sup>−1</sup>) + LTx. Two hours after LTx, we determined lung graft compliance, edema, histology, neutrophil counts in bronchoalveolar lavage (BAL), mRNA levels of adhesion molecules within the graft, as well as concentrations of interleukin-6 and 10 (IL-6, IL-10) in BAL and plasma. 3-AB reconditioning during EVLP improved compliance and reduced lung edema, neutrophil infiltration, and the expression of adhesion molecules within the transplanted lungs. 3-AB also attenuated the IL-6/IL-10 ratio in BAL and plasma, supporting an improved balance between pro- and anti-inflammatory mediators. Thus, 3-AB reconditioning during EVLP of rat lung grafts damaged by WI markedly reduces inflammation, edema, and physiological deterioration after LTx, supporting the use of PARP inhibitors for the rehabilitation of damaged lungs during EVLP.</p>
Effects of cold or warm ischemia and ex-vivo lung perfusion on the release of damage associated molecular patterns and inflammatory cytokines in experimental lung transplantation
<p>Lung transplantation (LTx) is associated with sterile inflammation, possibly related to the release of damage associated molecular patterns (DAMPs) by injured allograft cells. We have measured cellular damage and the release of DAMPs and cytokines in an experimental model of LTx after cold or warm ischemia and examined the effect of pretreatment with ex-vivo lung perfusion (EVLP).</p>
BRAIN CT PERFUSION IN ACUTE ISCHEMIC STROKE PATIENTS IN THE EARLY TIME WINDOW
Open the record for dataset details and reuse information.
Gene expression profiling of normothermic machine perfusion of human livers
<p>Normothermic machine perfusion (NMP) has been successfully implemented in clinical routine of liver transplantation over the past years. However, little is known about the mechanisms how NMP impacts on the transcriptome of a human donor liver. We herein examined gene expression profiles in transplanted and non-transplanted livers over NMP time. 50 livers subjected to NMP were included in this study. 30 were transplanted after a maximum of 20 hours (h) perfusion, while 15 were discarded due to poor performance. Biopsies were collected before NP (PRE), 1h, 6h, 12h, 20h of NMP and after reperfusion. Next-generation sequencing was applied in liver biopsies to assess differential gene expression over perfusion time. Perfusate samples were collected regularly to monitor liver function. Significantly differentially expressed genes between each timepoint and PRE (LIVER_NMP_TIMEPOINT_PRE_DEG.xlsx) as well as between non-transplanted livers (NTP) and transplanted livers (TP) (LIVER_NMP_NTP_TP_DEG.xlsx) were determined using the R package DESeq2 and included in this dataset.</p>
CD44 Blocking Antibody perfusion tracking dataset
<p>This dataset contains tracking results of different combinations of CD44 antibody-blocked AsPC1 and MiaPaca cells perfused on CD44 antibody-blocked endothelial monolayers under physiological flow speeds.</p> <p>Videos were recorded using a Nikon Eclipse Ti2-E microscope and 20x objective.</p> <p>Perfused cells from the generated videos were segmented using custom-trained Stardist models. Tracking was performed using TrackMate, and tracking results were analyzed using a custom CellTracksColab notebook. </p> <p>The dataset here contains the CSV files generated by TrackMate (Track and Spots information), the tracking data stored in the CellTracksColab format (Analysis.zip), and the analysis output used in the paper (Analysis.zip). </p> <h3> Specifications</h3> <ul> <li> <p>Sample information</p> </li> <ul> <li> <p>AsPC1 and MiaPaca cells perfused on HUVEC cells under physiological flow speeds: 400 µm/s (p1), 200 µm/s (p2), 100 µm/s (p3) and 400 µm/s (p4). </p> </li> <li> <p>CD44 antibody blocking of PDACs, HUVECs, or both prior to perfusion</p> </li> </ul> <li> <p>Imaging specs</p> </li> <ul> <li> <p>Microscope: Nikon Eclipse Ti2-E, 20x objective</p> </li> <li> <p>Data Type: Brightfield microscopy images (16-bit)</p> </li> <li> <p>Image Size: 1024 x 1022 pixels (Pixel size: 650 nm)</p> </li> <li> <p>Recording speed 25 frames/s</p> </li> </ul> <li> <p>DL models:</p> </li> <ul> <li> <p>Cancer cells: <a href="https://doi.org/10.5281/zenodo.10572122">https://doi.org/10.5281/zenodo.10572122</a> </p> </li> <li> <p>Neutrophils: <a href="https://doi.org/10.5281/zenodo.10572231">https://doi.org/10.5281/zenodo.10572231</a></p> </li> <li> <p>Mononucleated cells: <a href="https://doi.org/10.5281/zenodo.10572200">https://doi.org/10.5281/zenodo.10572200</a></p> </li> <li> <p>Model Training and predictions: Conducted using ZeroCostDL4Mic (<a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist">https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki</a>)</p> </li> </ul> <li> <p>Tracking parameters (TrackMate):</p> </li> <ul> <li> <p>Detection: label detector</p> </li> <li> <p>Tracking: Simple LAP detector: Linking max distance: 20 px; Gap-closing max distance: 20 px; Gap-closing max frame gap: 4. </p> </li> <li> <p>Track filtering: min number of spots in the tracks 11.79 </p> </li> </ul> <li> <p>Tracking analysis</p> </li> <ul> <li> <p>Tracks were analyzed using a customized CellTracksColab notebook (<a href="https://github.com/CellMigrationLab/PDAC_DL/tree/main/CellTracksColab">https://github.com/CellMigrationLab/PDAC_DL/tree/main/CellTracksColab</a>)</p> </li> </ul> </ul> <h3>Contents of the repository</h3> <ul> <li> <p>Analysis_AsPC1.zip dataset</p> </li> <li> <p>Analysis_Miapaca.zip dataset</p> </li> <li> <p>As_blockboth.zip dataset</p> </li> <li> <p>As_ctrlblock.zip dataset</p> </li> <li> <p>As_HUblock.zip dataset</p> </li> <li> <p>As_TCblock.zip dataset</p> </li> <li> <p>Mia_blockboth.zip</p> </li> <li> <p>Mia_ctrlblock.zip</p> </li> <li> <p>Mia_HUblock.zip</p> </li> <li> <p>Mia_TCblock.zip</p> </li> </ul> <p><strong> </strong></p> <div> <h3>Reference</h3> <div><strong>Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers</strong></div> </div> <div>Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet</div> <div>bioRxiv 2024.09.30.615654; doi: <a href="https://www.biorxiv.org/content/10.1101/2024.09.30.615654v1">https://doi.org/10.1101/2024.09.30.615654</a></div> <p> </p>
HUVEC CD44 siRNA perfusion tracking dataset
<p>This dataset contains tracking results of AsPC1 and MiaPaca cells perfused on CD44 siRNA-silenced endothelial monolayers under physiological flow speeds.</p> <p>Videos were recorded using a Nikon Eclipse Ti2-E microscope and 20x objective.</p> <p>Perfused cells from the generated videos were segmented using custom-trained Stardist models. Tracking was performed using TrackMate, and tracking results were analyzed using a custom CellTracksColab notebook. </p> <p>The dataset here contains the CSV files generated by TrackMate (Track and Spots information), the tracking data stored in the CellTracksColab format (Analysis.zip), and the analysis output used in the paper (Analysis.zip). </p> <h3>Specifications</h3> <ul> <li> <p>Sample information</p> </li> <ul> <li> <p>AsPC1 and MiaPaca cells perfused on HUVEC cells under physiological flow speeds: 400 µm/s (p1), 200 µm/s (p2), 100 µm/s (p3) and 400 µm/s (p4). </p> </li> <li> <p>CD44 siRNA silencing of the HUVEC monolayer</p> </li> </ul> <li> <p>Imaging specs</p> </li> <ul> <li> <p>Microscope: Nikon Eclipse Ti2-E, 20x objective</p> </li> <li> <p>Data Type: Brightfield microscopy images (16-bit)</p> </li> <li> <p>Image Size: 1024 x 1022 pixels (Pixel size: 650 nm)</p> </li> <li> <p>Recording speed 25 frames/s</p> </li> </ul> <li> <p>DL models:</p> </li> <ul> <li> <p>Cancer cells: <a href="https://doi.org/10.5281/zenodo.10572122">https://doi.org/10.5281/zenodo.10572122</a> </p> </li> <li> <p>Neutrophils: <a href="https://doi.org/10.5281/zenodo.10572231">https://doi.org/10.5281/zenodo.10572231</a></p> </li> <li> <p>Mononucleated cells: <a href="https://doi.org/10.5281/zenodo.10572200">https://doi.org/10.5281/zenodo.10572200</a></p> </li> <li> <p>Model Training and predictions: Conducted using ZeroCostDL4Mic (<a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist">https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki</a>)</p> </li> </ul> <li> <p>Tracking parameters (TrackMate):</p> </li> <ul> <li> <p>Detection: label detector</p> </li> <li> <p>Tracking: Simple LAP detector: Linking max distance: 20 px; Gap-closing max distance: 20 px; Gap-closing max frame gap: 4. </p> </li> <li> <p>Track filtering: min number of spots in the tracks 11.79 </p> </li> </ul> <li> <p>Tracking analysis</p> </li> <ul> <li> <p>Tracks were analyzed using a customized CellTracksColab notebook (<a href="https://github.com/CellMigrationLab/PDAC_DL/tree/main/CellTracksColab">https://github.com/CellMigrationLab/PDAC_DL/tree/main/CellTracksColab</a>)</p> </li> </ul> </ul> <h3>Contents of the repository</h3> <ul> <li> <p>Analysis.zip</p> </li> <li> <p>As_HUsi1.zip dataset</p> </li> <li> <p>As_HUsi2.zip dataset</p> </li> <li> <p>As_HUsi3.zip dataset</p> </li> <li> <p>As_HUsiCtrl.zip dataset</p> </li> <li> <p>Mia_HUsi1.zip dataset</p> </li> <li> <p>Mia_HUsi2.zip dataset</p> </li> <li> <p>Mia_HUsi3.zip dataset</p> </li> <li> <p>Mia_HUsiCtrl.zip dataset</p> </li> </ul> <div> <h3>Reference</h3> <div><strong>Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers</strong></div> </div> <div>Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet</div> <div>bioRxiv 2024.09.30.615654; doi: <a href="https://www.biorxiv.org/content/10.1101/2024.09.30.615654v1">https://doi.org/10.1101/2024.09.30.615654</a></div>
PDAC cells CD44 siRNA perfusion tracking dataset
<p>This dataset contains tracking results of CD44 siRNA-silenced AsPC1, and MiaPaca cells perfused on endothelial monolayers under physiological flow speeds.</p> <p>Videos were recorded using a Nikon Eclipse Ti2-E microscope and 20x objective.</p> <p>Perfused cells from the generated videos were segmented using custom-trained Stardist models. Tracking was performed using TrackMate. Tracking results were analyzed using a custom CellTracksColab notebook. </p> <p>The dataset here contains the CSV files generated by TrackMate (Track and Spots information), the tracking data stored in the CellTracksColab format (Analysis.zip), and the analysis output used in the paper (Analysis.zip). <strong> </strong></p> <h3>Specifications</h3> <ul> <li> <p>Sample information</p> </li> <ul> <li> <p>AsPC1 and MiaPaca cells perfused on HUVEC cells under physiological flow speeds: 400 µm/s (p1), 200 µm/s (p2), 100 µm/s (p3) and 400 µm/s (p4). </p> </li> <li> <p>CD44 siRNA silencing of PDACs prior to perfusion</p> </li> </ul> <li> <p>Imaging specs</p> </li> <ul> <li> <p>Microscope: Nikon Eclipse Ti2-E, 20x objective</p> </li> <li> <p>Data Type: Brightfield microscopy images (16-bit)</p> </li> <li> <p>Image Size: 1024 x 1022 pixels (Pixel size: 650 nm)</p> </li> <li> <p>Recording speed 25 frames/s</p> </li> </ul> <li> <p>DL models:</p> </li> <ul> <li> <p>Cancer cells: <a href="https://doi.org/10.5281/zenodo.10572122">https://doi.org/10.5281/zenodo.10572122</a> </p> </li> <li> <p>Neutrophils: <a href="https://doi.org/10.5281/zenodo.10572231">https://doi.org/10.5281/zenodo.10572231</a></p> </li> <li> <p>Mononucleated cells: <a href="https://doi.org/10.5281/zenodo.10572200">https://doi.org/10.5281/zenodo.10572200</a></p> </li> <li> <p>Model Training and predictions: Conducted using ZeroCostDL4Mic (<a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist">https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki</a>)</p> </li> </ul> <li> <p>Tracking parameters (TrackMate):</p> </li> <ul> <li> <p>Detection: label detector</p> </li> <li> <p>Tracking: Simple LAP detector: Linking max distance: 20 px; Gap-closing max distance: 20 px; Gap-closing max frame gap: 4. </p> </li> <li> <p>Track filtering: min number of spots in the tracks 11.79 </p> </li> </ul> <li> <p>Tracking analysis</p> </li> <ul> <li> <p>Tracks were analyzed using a customized CellTracksColab notebook (<a href="https://github.com/CellMigrationLab/PDAC_DL/tree/main/CellTracksColab">https://github.com/CellMigrationLab/PDAC_DL/tree/main/CellTracksColab</a>)</p> </li> </ul> </ul> <h3><strong> </strong>Contents of the repository</h3> <ul> <li> <p>Analysis.zip</p> </li> <li> <p>As_TCsi1.zip dataset</p> </li> <li> <p>As_TCsi2.zip dataset</p> </li> <li> <p>As_TCsi3.zip dataset</p> </li> <li> <p>As_TCsiCtrl.zip dataset</p> </li> <li> <p>Mia_TCsi1.zip dataset</p> </li> <li> <p>Mia_TCsi2.zip dataset</p> </li> <li> <p>Mia_TCsi3.zip dataset</p> </li> <li> <p>Mia_TCsiCtrl.zip dataset</p> </li> </ul> <div> <h3>Reference</h3> <div><strong>Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers</strong></div> </div> <div>Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet</div> <div>bioRxiv 2024.09.30.615654; doi: <a href="https://www.biorxiv.org/content/10.1101/2024.09.30.615654v1">https://doi.org/10.1101/2024.09.30.615654</a></div>
Hyaluronidase treatment perfusion tracking dataset
<p>This dataset contains tracking results of different combinations of Hyaluronidase-treated AsPC1 and MiaPaca cells perfused on Hyaluronidase-treated endothelial monolayer under physiological flow speeds.</p> <p>Videos were recorded using a Nikon Eclipse Ti2-E microscope and 20x objective.</p> <p>Perfused cells from the generated videos were segmented using custom-trained Stardist models. Tracking was performed using TrackMate, and tracking results were analyzed using a custom CellTracksColab notebook. </p> <p>The dataset here contains the CSV files generated by TrackMate (Track and Spots information), the tracking data stored in the CellTracksColab format (Analysis.zip), and the analysis output used in the paper (Analysis.zip). </p> <h3> Spesifications</h3> <ul> <li> <p>Sample information</p> </li> <ul> <li> <p>AsPC1 and MiaPaca cells perfused on HUVEC cells under physiological flow speeds: 400 µm/s (p1), 200 µm/s (p2), 100 µm/s (p3) and 400 µm/s (p4). </p> </li> <li> <p>Hyaluronidase treatment PDACs or HUVECs prior to perfusion</p> </li> </ul> <li> <p>Imaging specs</p> </li> <ul> <li> <p>Microscope: Nikon Eclipse Ti2-E, 20x objective</p> </li> <li> <p>Data Type: Brightfield microscopy images (16-bit)</p> </li> <li> <p>Image Size: 1024 x 1022 pixels (Pixel size: 650 nm)</p> </li> <li> <p>Recording speed 25 frames/s</p> </li> </ul> <li> <p>DL models:</p> </li> <ul> <li> <p>Cancer cells: <a href="https://doi.org/10.5281/zenodo.10572122">https://doi.org/10.5281/zenodo.10572122</a> </p> </li> <li> <p>Neutrophils: <a href="https://doi.org/10.5281/zenodo.10572231">https://doi.org/10.5281/zenodo.10572231</a></p> </li> <li> <p>Mononucleated cells: <a href="https://doi.org/10.5281/zenodo.10572200">https://doi.org/10.5281/zenodo.10572200</a></p> </li> <li> <p>Model Training and predictions: Conducted using ZeroCostDL4Mic (<a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki/Stardist">https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki</a>)</p> </li> </ul> <li> <p>Tracking parameters (TrackMate):</p> </li> <ul> <li> <p>Detection: label detector</p> </li> <li> <p>Tracking: Simple LAP detector: Linking max distance: 20 px; Gap-closing max distance: 20 px; Gap-closing max frame gap: 4. </p> </li> <li> <p>Track filtering: min number of spots in the tracks 11.79 </p> </li> </ul> <li> <p>Tracking analysis</p> </li> <ul> <li> <p>Tracks were analyzed using a customized CellTracksColab notebook (<a href="https://github.com/CellMigrationLab/PDAC_DL/tree/main/CellTracksColab">https://github.com/CellMigrationLab/PDAC_DL/tree/main/CellTracksColab</a>)</p> </li> </ul> </ul> <h3>Contents of the repository</h3> <ul> <li> <p>Analysis.zip dataset</p> </li> <li> <p>As_ctrl.zip</p> </li> <li> <p>As_HUdigestion.zip</p> </li> <li> <p>As_TCdigestion.zip</p> </li> <li> <p>Mia_ctrl.zip</p> </li> <li> <p>Mia_HUdig.zip</p> </li> <li> <p>Mia_TCdig.zip</p> </li> </ul> <p><strong> </strong></p> <div> <h3>Reference</h3> <div><strong>Fast label-free live imaging reveals key roles of flow dynamics and CD44-HA interaction in cancer cell arrest on endothelial monolayers</strong></div> </div> <div>Gautier Follain, Sujan Ghimire, Joanna W. Pylvänäinen, Monika Vaitkevičiūtė, Diana Wurzinger, Camilo Guzmán, James RW Conway, Michal Dibus, Sanna Oikari, Kirsi Rilla, Marko Salmi, Johanna Ivaska, Guillaume Jacquemet</div> <div>bioRxiv 2024.09.30.615654; doi: <a href="https://www.biorxiv.org/content/10.1101/2024.09.30.615654v1">https://doi.org/10.1101/2024.09.30.615654</a></div> <p> </p>
Data file: Equal performance of HTK-based and UW-based perfusion solutions in sub-normothermic liver machine perfusion
Open the record for dataset details and reuse information.
Dataset for Subnormothermic ex vivo lung perfusion attenuates ischemia reperfusion injury from donation after circulatory death donors
<p>Use of normothermic <i>ex vivo</i> lung perfusion (EVLP) was adopted in clinical practice to assess the quality of marginal donor lungs. Subnormothermic perfusion temperatures are in use among other solid organs to improve biochemical, clinical and immunological parameters. In a rat EVLP model of donation after circulatory death (DCD) lung donors, we tested the effect of four subnormothermic EVLP temperatures that could further improve organ preservation. Warm ischemic time was of 2 hours. EVLP time was of 4 hours. Lung physiological data were recorded and metabolic parameters were assessed. Lung oxygenation at 21°C and 24°C were significantly improved whereas pulmonary vascular resistance and edema formation at 21°C EVLP were significantly worsened when compared to 37°C EVLP. The perfusate concentrations of potassium ions and lactate exiting the lungs with 28°C EVLP were significantly lower whereas sodium and chlorine ions with 32°C EVLP were significantly higher when compared to 37°C EVLP. Also compared to 37°C EVLP, the pro-inflammatory chemokines MIP2, MIP-1α, GRO-α, the cytokine IL-6 were significantly lower with 21°C, 24°C and 28°C EVLP, the IL-18 was significantly lower but only with 21°C EVLP and IL-1β was significantly lower at 21°C and 24°C EVLP. Compared to the 37°C EVLP, the lung tissue ATP content after 21°C, 24°C and 28°C EVLP were significantly higher, the carbonylated protein content after 28°C EVLP was significantly lower and we measured significantly higher myeloperoxidase activities in lung tissues with 21°C, 24°C and 32°C. The 28°C EVLP demonstrated acceptable physiological variables, significantly higher lung tissue ATP content and decreased tissue carbonylated proteins with reduced release of pro-inflammatory cytokines. In conclusion, the 28°C EVLP is a non inferior setting in comparison to the clinically approved 37°C EVLP and significantly improve biochemical, clinical and immunological parameters and may reduce I/R injuries of DCD lung donors.</p>
Radiomic and Artificial Intelligence Analysis with Textural Metrics, Morphological and Dynamic Perfusion Features Extracted by Dynamic Contrast-Enhanced Magnetic Resonance Imaging in the Classification of Breast Lesions
<p>We uploaded the 15 morphological features of 91 samples of 85 patients analyzed in the manuscript: Fusco, Roberta, Adele Piccirillo, Mario Sansone, Vincenza Granata, Paolo Vallone, Maria L. Barretta, Teresa Petrosino, Claudio Siani, Raimondo Di Giacomo, Maurizio Di Bonito, Gerardo Botti, and Antonella Petrillo. 2021. "Radiomic and Artificial Intelligence Analysis with Textural Metrics, Morphological and Dynamic Perfusion Features Extracted by Dynamic Contrast-Enhanced Magnetic Resonance Imaging in the Classification of Breast Lesions" Applied Sciences 11, no. 4: 1880. https://doi.org/10.3390/app11041880</p>
Diffusion and perfusion imaging in rectal cancer restaging
<p>I uploaded the images of the manuscript: Diffusion and perfusion imaging in rectal cancer restaging</p>
Code for manuscript "Analysis of dynamic susceptibility contrast perfusion MRI using physics-informed deep learning"
<p>This is the processing code and in vivo statistics for the manuscript "Analysis of dynamic susceptibility contrast perfusion MRI using physics-informed deep learning".</p>
Correlation between LDH/PDH activities ratio and tissue pH in the perfused mouse heart – a potential non-invasive indicator of cardiac pH provided by hyperpolarized magnetic resonance
<p>Primary data for DOI: 10.1002/nbm.4444</p> <p>NMR in Biomedicine. 2021;34:e4444.</p> <p>Title: Correlation between LDH/PDH activities ratio and tissue pH in the perfused mouse heart – a potential non-invasive indicator of cardiac pH provided by hyperpolarized magnetic resonance</p> <p>Authors: David Shaul, Assad Azar, Gal Sapir, Sivaranjan Uppala, Atara Nardi-Schreiber, Ayelet Gamliel, Jacob Sosna, J. Moshe Gomori, and Rachel Katz-Brull</p> <p> </p> <p>These primary datasets contain data presented in the above publication and consist of:</p> <p>1. <sup>31</sup>P-NMR spectra</p> <p>2. Hyperpolarized <sup>13</sup>C-NMR spectra</p> <p>Please consult the Archive Guide.</p>
Pancreatic Perfusion Using Secretin and MRI
ClinicalTrials.gov study NCT02458118. IPD Sharing: NO. Countries: 1. Publications: 12.
Hypothermic Oxygenated Perfusion for Extended Criteria Donors in Liver Transplantation (HOPExt)
ClinicalTrials.gov study NCT03929523. IPD Sharing: NO. Countries: 1. Publications: 2.
Phase I Trial of Continuous Hyperthermic Peritoneal Perfusion (CHPP) With Cisplatin Plus Early Postoperative Intraperitoneal Paclitaxel and 5-FU for Peritoneal Carcinomatosis
ClinicalTrials.gov study NCT00001569. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Effectiveness Study of Single Photon Emission Computed Tomography (SPECT) Versus Positron Emission Tomography (PET) Myocardial Perfusion Imaging
ClinicalTrials.gov study NCT00976053. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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.