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3,650 results for “antibody”

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zenodo32/100

High prevalence of SARS-CoV-2 antibodies in pregnant women in the inner city of Johannesburg, Gauteng Province, South Africa

<p>Results of population-based age stratified seroepidemiological investigation in South Africa.</p>

opencc-by-4.0Nov 2021View details →
dryad32/100

Source data for: Human monoclonal antibodies against Staphylococcus aureus surface antigens recognize in vitro biofilm and in vivo implant infections

<p class="CxSpFirst">Implant-associated <i>Staphylococcus aureus</i> infections are difficult to treat because of biofilm formation. Bacteria in a biofilm are often insensitive to antibiotics and host immunity. Monoclonal antibodies (mAbs) could provide an alternative approach to improve the diagnosis and potential  treatment of biofilm-related infections. Here we show that mAbs targeting common surface components of <i>S. aureus</i> can recognize clinically relevant biofilm types. The mAbs were also shown to bind a collection of clinical isolates derived from different biofilm-associated infections (endocarditis, prosthetic joint, catheter). We identify two groups of antibodies: one group that uniquely binds <i>S. aureus </i>in biofilm state and one that recognizes <i>S. aureus </i>in both biofilm and planktonic state. Furthermore, we show that a mAb recognizing wall teichoic acid (WTA; clone 4497) specifically localizes to a subcutaneously implanted pre-colonized catheter in mice. In conclusion, we demonstrate the capacity of several human mAbs to detect <i>S. aureus</i> biofilms<i> in vitro</i> and <i>in vivo</i>.</p>

opencc-zeroDec 2021View details →
zenodo32/100

Self-assembling peptide nanofiber HIV vaccine elicits robust vaccine-induced antibody functions and modulates Fc glycosylation.

<p>To develop vaccines for certain key global pathogens such as HIV, it is crucial to elicit both neutralizing and non-neutralizing Fc-mediated effector antibody functions. Clinical evidence indicates that non-neutralizing antibody functions including antibody-dependent cellular cytotoxicity (ADCC) and antibody-dependent cellular phagocytosis (ADCP) contribute to protection against several pathogens. In this study, we demonstrated that conjugation of HIV Envelop (Env) antigen gp120 to a self-assembling nanofiber material named Q11 induced antibodies with higher breadth and functionality when compared to soluble gp120. Immunization with Q11-conjugated gp120 vaccine (gp120-Q11) demonstrated higher tier 1 neutralization, ADCP and ADCC as compared to soluble gp120. Moreover, Q11 conjugation altered the Fc N-glycosylation profile of antigen-specific antibodies, leading to a phenotype associated with increased ADCC in animals immunized with gp120-Q11. Thus, this nanomaterial vaccine strategy can enhance non-neutralizing antibody functions possibly through modulation of IgG Fc N-glycosylation.</p>

opencc-by-4.0Jun 2022View details →
dryad32/100

Data from: Evolution of functional antibodies following acute Epstein-Barr Virus infection

<p>While Epstein-Barr virus causes mostly asymptomatic infection, associated malignancies, and autoimmune and lymphoproliferative diseases occur. To dissect the evolution of humoral immune responses over the course of EBV infection and to gain a better understanding of the potential contribution of antibody (Ab) function to viral control, we comprehensively profiled Ab specificities and Fc-functionalities using systems serology and VirScan. Ab functions against two early (p18 and p47/54) and two latent (gp350/220 and EBNA-1) EBV proteins were overall modest and/or short-lived, differing from humoral responses induced during acute infection by other viruses such as HIV. In the first year post-infection, only p18 elicited robust IgM-driven complement deposition and IgG-driven neutrophil phagocytosis while responses against EBNA-1 were largely Fc-functionally silent and only matured during chronic infection to drive phagocytosis. In contrast, Abs against Influenza virus readily mediated broad Fc-activity in all participants. These data suggest that EBV evades the induction of robust Fc-functional Abs, potentially due to the virus' life cycle, switching from lytic to latent stages during infection. </p>

opencc-zeroAug 2022View details →
zenodo32/100

Huntingtin Q23 with FLAG antibody Grafix in 0-1 percent Glutaraldehyde – 28th November 2017

<p>Huntingtin structure function open lab notebook</p>

opencc-by-4.0Nov 2017View details →
zenodo32/100

OMAP-19 Organ Mapping Antibody Panel (OMAP) for Multiplexed Antibody-Based Imaging of Human Tonsil with IBEX

<p>Representative dataset acquired using the Iterative Bleaching Extends multi-pleXity (IBEX) imaging method described in:</p> <ol> <li>&ldquo;IBEX: A versatile multi-plex optical imaging approach for deep phenotyping and spatial analysis of cells in complex tissues&ldquo;, A. Radtke et al., <em>Proc. Natl. Acad. Sci. USA,</em> 2020, <a href="https://doi.org/10.1073/pnas.2018488117">doi.org/10.1073/pnas.2018488117</a>.</li> <li>"IBEX: an iterative immunolabeling and chemical bleaching method for high-content imaging of diverse tissues", A. J. Radtke, et al.,&nbsp;<em>Nat Protoc</em>, 2022, <a href="https://doi.org/10.1038/s41596-021-00644-9">doi.org/10.1038/s41596-021-00644-9</a>.</li> </ol> <p>This dataset accompanies the <a href="https://www.nature.com/articles/s41592-023-01846-7">Organ Mapping Antibody Panel (OMAP) effort</a> led by the <a href="https://commonfund.nih.gov/HuBMAP">Human BioMolecular Atlas Program</a> detailed <a href="https://humanatlas.io/omap">here</a>.</p> <p>OMAP-19 was designed for IBEX imaging of human FFPE tonsil samples sectioned onto glass slides. The panel consists of 6 cycles of 20 primary antibodies, 5 secondary antibodies, and the nuclear label Hoechst for image alignment and nuclear segmentation. This OMAP provides a spatial context for 10 anatomical structures and at least 16 cell types present in the <a href="https://cdn.humanatlas.io/hra-releases/v2.0/docs/asct-b/asct-b-vh-palatine-tonsil.copy.html">ASCT+B tonsil table</a>. OMAP-19 was designed to examine myeloid subsets (CD11b, CD11c, CD14, CD15, HLA-DR) and angiogenesis (VEGF-A and VEGF-C) in human ovarian cancer biopsies. The human tonsil was used as a positive control for antibody validation and panel development. Antigen retrieval was performed using a pressure cooker (Borg Decloaker BD1000, 110&deg;C for 15 minutes) and a tris-based buffer (pH 9.5). Several custom antibodies were created using commercial labeling kits as indicated in the associated RRID entries for these antibodies. More details on the antigen retrieval protocol and step-by-step application of antibodies can be found on the <a href="https://ibeximagingcommunity.github.io/ibex_imaging_knowledge_base/">IBEX Imaging Community knowlege-base</a>&nbsp;(current version) and on <a href="../records/7693279">Zenodo</a> (last official version).</p> <p>Images were acquired using an inverted Leica TCS SP8 X confocal microscope equipped with a 40X objective (NA 1.3), 4 HyD and 1 PMT detectors, a white light laser that produces a continuous spectral output between&nbsp;470 and 670 nm as well as a 405&nbsp;nm laser. All images were captured at an 16-bit depth, with a line average of 3, and 1024x1024 format with the following pixel dimensions: x (0.379 &micro;m), y (0.379 &micro;m), and z (1 &micro;m). Images were tiled and merged using the LAS X Navigator software.</p> <p>Image supplied as a .ims Imaris Format file and can be opened with the <strong>free</strong>&nbsp;<a href="https://imaris.oxinst.com/imaris-viewer">Imaris Viewer</a>&nbsp;software&nbsp;or <a href="https://imagej.net/software/fiji/">ImageJ/Fiji</a>. Image channel and antibody meta-data supplied as an xlsx file.</p>

opencc-by-4.0May 2024View details →
zenodo32/100

Dataset for Unique and redundant roles of mouse BCMA, TACI, BAFF, APRIL and IL-6 in supporting antibody-producing cells in different tissues

<p>This dataset is related to "Unique and redundant roles of mouse BCMA, TACI, BAFF, APRIL and IL-6 in supporting antibody-producing cells in different tissues" (Eslami M, Schuepbach-Mallpell S, Diana D, Willen L, Kowalczyk-Quintas C, Desponds C, Peter B, Vigolo M, Renevey F, Donz&eacute; O, L&uuml;ther SA, Yalkinoglu &Ouml;, Alouche N, Schneider P).</p>

opencc-by-4.0Jun 2024View details →
zenodo32/100

Immunohistochemistry validation by comparison of two antibodies

<p><strong><span>Supplementary figure 1. Immunohistochemistry validation by comparison of two antibodies. </span></strong><span>The panels show immunostaining results obtained by two independent TFF1 antibodies. Using MSVA-482M, a distinct cytoplasmic staining was seen in surface epithelial cells (but not of glands) in the stomach (A), subsets of goblet cells in the duodenum (B) and the colon (C), a subset of mucinous cells in the submandibulary gland (D), a subset of luminal epithelial cells and intraluminal mucus in the breast (E), a small subset of urothelial cells (mostly umbrella cells) in the renal pelvis (F), a large subset of urothelial cells in an inflamed urinary bladder (G), and in epithelial cells of the gallbladder (H). Using clone EPR3972, a comparable staining was seen in the stomach (a), the duodenum (b), the colon (c), the submandibulary gland (d), the breast (e), the renal pelvis (f), the bladder (g), and the gallbladder (h). The images A-H and a-h are from consecutive tissue sections.</span></p>

opencc-by-4.0Jun 2024View details →
zenodo32/100

Imunnesenescence of antibody repertoire in individuals from endemic areas for infectious diseases

<p>Heavy Chain Antibody Repertoire data in the AIRR format, clonotyped with YClon, of patients diagnosed with COVID-19 and a control group. The sample labels in the paper and the sample labels in this repository are correspondent according to the following table:</p> <table> <tbody> <tr> <td>Sample</td> <td>Deposite_code</td> </tr> <tr> <td>C_01</td> <td>A04</td> </tr> <tr> <td>C_02</td> <td>A20</td> </tr> <tr> <td>C_03</td> <td>A24</td> </tr> <tr> <td>C_04</td> <td>A65</td> </tr> <tr> <td>C_05</td> <td>A66</td> </tr> <tr> <td>C_06</td> <td>A67</td> </tr> <tr> <td>H_NEA_01</td> <td>ID141</td> </tr> <tr> <td>H_NEA_02</td> <td>ID143</td> </tr> <tr> <td>H_NEA_03</td> <td>ID144</td> </tr> <tr> <td>H_NEA_04</td> <td>ID187</td> </tr> <tr> <td>H_NEA_05</td> <td>ID195</td> </tr> <tr> <td>H_NEA_06</td> <td>ID226</td> </tr> <tr> <td>H_NEA_07</td> <td>ID248</td> </tr> <tr> <td>H_NEA_08</td> <td>ID268</td> </tr> <tr> <td>H_NEA_09</td> <td>ID310</td> </tr> <tr> <td>H_NEA_10</td> <td>ID375</td> </tr> <tr> <td>M_EA_01</td> <td>GV43</td> </tr> <tr> <td>M_EA_02</td> <td>GV68</td> </tr> <tr> <td>M_EA_03</td> <td>GV106</td> </tr> <tr> <td>M_EA_04</td> <td>GV144</td> </tr> <tr> <td>M_EA_05</td> <td>GV146</td> </tr> <tr> <td>M_EA_06</td> <td>GV47</td> </tr> <tr> <td>M_EA_07</td> <td>GV50</td> </tr> <tr> <td>M_EA_08</td> <td>GV51</td> </tr> <tr> <td>M_EA_09</td> <td>GV54</td> </tr> <tr> <td>M_EA_10</td> <td>GV92</td> </tr> <tr> <td>M_NEA_01</td> <td>ID094</td> </tr> <tr> <td>M_NEA_02</td> <td>ID117</td> </tr> <tr> <td>M_NEA_03</td> <td>ID124</td> </tr> <tr> <td>M_NEA_04</td> <td>ID131</td> </tr> <tr> <td>M_NEA_05</td> <td>ID132</td> </tr> <tr> <td>M_NEA_06</td> <td>ID155</td> </tr> <tr> <td>M_NEA_07</td> <td>ID240</td> </tr> <tr> <td>M_NEA_08</td> <td>ID244</td> </tr> </tbody> </table>

opencc-by-4.0Jul 2024View details →
zenodo32/100

Bats generate lower affinity, but higher diversity antibody responses compared to mice, an effect that can be manipulated with diet

<p><span>Bats are reservoirs of many zoonotic viruses that are fatal in humans but do not cause disease in bats. Moreover, bats generate low neutralizing antibody titers in response to experimental viral infection, although more robust antibody responses have been observed in wild caught bats during times of food stress. Here we compared the antibody titers and B cell receptor (BCR) diversity of Jamaican fruit bats (<em>Artibeus jamaicensis</em>; JFB) and BALB/c mice generated in response to T-dependent and T-independent antigens. We then manipulated the diet of JFBs and challenged them with H18N11 influenza A-like virus or a replication incompetent Nipah virus VSV (Nipah-riVSV). Under standard housing conditions, JFBs generated a lower avidity antibody response and possessed more BCR mRNA diversity compared to BALB/c mice. However, withholding protein from JFBs improved serum neutralization in response to Nipah-riVSV and improved serum antibody titers specific to H18 but reduced BCR mRNA diversity. </span></p>

opencc-by-4.0May 2024View details →
zenodo32/100

Q fever in Egypt: Epidemiological survey of Coxiella burnetii specific antibodies in cattle, buffaloes, sheep, goats and camels

<p>Dataset complementing the publication &quot;Q&nbsp;fever in Egypt: Epidemiological survey of <em>Coxiella&nbsp;burnetii</em> specific antibodies in cattle, buffaloes, sheep, goats and camels&quot; (PLOS ONE, 2018).</p>

opencc-by-4.0Jan 2018View details →
zenodo32/100

Huntingtin Q23 with FLAG antibody Grafix in 0-1 percent Glutaraldehyde – 26th January 2018

<p>Huntingtin structure-function open lab notebook project.&nbsp;</p>

opencc-by-4.0Jan 2018View details →
zenodo32/100

Finding an antibody to detect EZH1 protein expression – Part 1

<p>Finding an antibody to determine EZH1 expression in AML patient cells.</p>

opencc-by-4.0Jul 2018View details →
zenodo32/100

Screen of ESK1 TCR mimic antibody against a library of HLA-A*02:01 MHC-I peptides

<p>Minigene sequencing of T2 cells sorted for high and low binding to the TCR mimic antibody &quot;ESK1.&quot;</p>

opencc-by-nc-4.0Jul 2018View details →
zenodo32/100

Structure-guided disulfide engineering restricts antibody conformation to elicit TNFR agonism

<p>This dataset contains the input files used to run conventional MD simulations described in 'Structure-guided disulfide engineering restricts antibody conformation to elicit TNFR agonism', as well as the structures extracted from these simulations used in GAJOE and CRYSOL analysis (see sasbdb.org deposited dataset SASDSC7 and SASDSD7).&nbsp;</p>

opencc-by-4.0Jun 2024View details →
zenodo32/100

Fluorescence-activated droplet sequencing (FAD-seq) directly provides sequences of screening hits in antibody discovery

<p>Contains source datas (e.g. uncropped gels, FASTQ nanopore sequences, numeric data) and the python scripts for Fluorescence-activated droplet sequencing (FAD-seq) publication</p>

opencc-by-4.0Aug 2024View details →
zenodo32/100

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.&nbsp;</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).&nbsp;</p> <h3>&nbsp;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 &micro;m/s (p1), 200 &micro;m/s (p2), 100 &micro;m/s (p3) and 400 &micro;m/s (p4).&nbsp;</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>&nbsp;</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.&nbsp;</p> </li> <li> <p>Track filtering: min number of spots in the tracks 11.79&nbsp;</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>&nbsp;</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&nbsp;Follain,&nbsp;Sujan&nbsp;Ghimire,&nbsp;Joanna W.&nbsp;Pylv&auml;n&auml;inen,&nbsp;Monika&nbsp;Vaitkevičiūtė,&nbsp;Diana&nbsp;Wurzinger,&nbsp;Camilo&nbsp;Guzm&aacute;n,&nbsp;James RW&nbsp;Conway,&nbsp;Michal&nbsp;Dibus,&nbsp;Sanna&nbsp;Oikari,&nbsp;Kirsi&nbsp;Rilla,&nbsp;Marko&nbsp;Salmi,&nbsp;Johanna&nbsp;Ivaska,&nbsp;Guillaume&nbsp;Jacquemet</div> <div>bioRxiv&nbsp;2024.09.30.615654;&nbsp;doi:&nbsp;<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>&nbsp;</p>

opencc-by-4.0Aug 2024View details →
zenodo32/100

Efficacy and safety of different monoclonal antibodies for osteoarthritis: a Bayesian network meta-analysis

Open the record for dataset details and reuse information.

opencc-by-4.0Sep 2024View details →
zenodo32/100

Resources of "Evolving antibody response to SARS-CoV-2 antigenic shift from XBB to JN.1"

<p>Resources of the article "Evolving antibody response to SARS-CoV-2 antigenic shift from XBB to JN.1". See https://github.com/yunlongcaolab/SARS-CoV-2-JN.1-mAbs for future updates.</p>

opencc-by-4.0Oct 2024View details →
dryad32/100

Data from: Screening test for neutralizing antibodies against yellow fever virus, based on a flavivirus pseudotype

Given the possibility of yellow fever virus reintroduction in epidemiologically receptive geographic areas, the risk of vaccine supply disruption is a serious issue. New strategies to reduce the doses of injected vaccines should be evaluated very carefully in terms of immunogenicity. The plaque reduction test for the determination of neutralizing antibodies (PRNT) is particularly time-consuming and requires the use of a confinement laboratory. We have developed a new test based on the use of a non-infectious pseudovirus (WN/YF17D). The presence of a reporter gene allows sensitive determination of neutralizing antibodies by flow cytometry. This WN/YF17D test was as sensitive as PRNT for the follow-up of yellow fever vaccinees. Both tests lacked specificity with sera from patients hospitalized for acute Dengue virus infection. Conversely, both assays were strictly negative in adults never exposed to flavivirus infection or vaccination, and in patients sampled some time after acute Dengue infection. This WN/YF17D test will be particularly useful for large epidemiological studies and for screening for neutralizing antibodies against yellow fever virus.

opencc-zeroDec 2016View details →

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International Brain Laboratory public data

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