Find research datasets worth reusing

opencc-by-4.0Oct 2020View details →

Analysis of human humoral responses in a typhoid vaccine efficacy trial used for SIMON analysis

The VAST dataset contains data from 72 individuals enrolled in the clinical study to evaluate humoral responses in a typhoid vaccine efficacy trial in a controlled human Salmonella Typhi infection model (see original publication: <a href="https://doi.org/10.3389/fimmu.2019.02582">https://doi.org/10.3389/fimmu.2019.02582</a>). Only day 0 (day of the challenge) log-transformed data were used in the SIMON analysis, as described in the publication (<a href="https://doi.org/10.1101/2020.08.16.252767">https://doi.org/10.1101/2020.08.16.252767</a>). Individuals were vaccinated with either a purified Vi polysaccharide (Vi-PS) vaccine (35 individuals) or the Vi tetanus toxoid conjugate (Vi-TT) vaccine (37 individuals) one month prior to oral challenge with live Salmonella Typhi. Out of 72 individuals, 26 developed an acute typhoid infection following the challenge.

opencc-by-4.0Jul 2020View details →

Kotliarov 2020 Vaccine Responsiveness PBMC dataset for Besca

Kotliarov, Y., Sparks, R., Martins, A.J. et al. Broad immune activation underlies shared set point signatures for vaccine responsiveness in healthy individuals and disease activity in patients with lupus. Nat Med 26, 618–629 (2020). https://doi.org/10.1038/s41591-020-0769-8.  We reprocessed the dataset using the Besca package (<a href="https://github.com/bedapub/besca">https://github.com/bedapub/besca</a>). The original gene expression data are available from <a href="https://doi.org/10.35092/yhjc.c.4753772">https://doi.org/10.35092/yhjc.c.4753772</a>.

opencc-by-4.0Jun 2021View details →

SARS-CoV-2 mRNA vaccines induce persistent human germinal centre responses

These are the processed BCR repertoire bulk sequencing data described in <a href="https://doi.org/10.1038/s41586-021-03738-2">Turner & O'Halloran et al., Nature, 2021</a> (Fig 3b-d; Extended Data Fig 3; Extended Data Table 6). The corresponding raw sequencing reads are available on SRA under <a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA731610">BioProject PRJNA731610</a>. Summary: Bulk-sorted total plasmablasts from PBMCs and germinal centre B cells at 4 weeks after primary immunization from 3 vaccinees who had no prior history of infection with SARS-CoV-2.  Code: Code along with Docker container for reproducing the NGS data-based figures and analyses in the published paper can be <a href="https://github.com/julianqz/wustl_published/tree/main/nature_2021">found on GitHub</a>. Metadata file: WU368_turner_et_al_nature_2021_meta.tsv Abbreviations: <ul> <li>LN = lymph node</li> <li>PB = plasmablast</li> <li>GC = germinal centre</li> <li>mAb = monoclonal antibody</li> </ul> BCR data file: WU368_turner_et_al_nature_2021_bcr.tsv.gz In addition to the processed bulk sequences, also included are the heavy chains of 37 mAbs that had been validated to be spike-binding and that were used together with the bulk sequences for clonal lineage inference. The mAbs are annotated as "mab" in the "seq_type" column. BCR data column descriptions The columns largely follow the <a href="https://changeo.readthedocs.io/en/stable/standard.html">AIRR-C Rearrangement format</a>. The main deviation is that CDR3s are used, as opposed to IMGT-defined "junctions". Non-standard columns are noted below. <ul> <li>v_call_genotyped: V gene annotation reassigned after individualized genotyping by <a href="https://tigger.readthedocs.io/en/stable/">TIgGER</a></li> <li>germline_[vdj]_call: clonal consensus germline sequence reconstructed via <a href="https://changeo.readthedocs.io/en/stable/methods/germlines.html">`CreateGermlines.py --cloned` using Change-O</a></li> <li>isotype: IGH[ADEGM]</li> <li>cdr3: CDR3 nucleotide sequence</li> <li>cdr3_length: CDR3 nucleotide sequence length</li> <li>cdr3_aa: CDR3 amino acid sequence</li> <li>collapse_count: number of duplicate IMGT-aligned V(D)J sequences that were collapsed by <a href="https://alakazam.readthedocs.io/en/stable/topics/collapseDuplicates/">`alakazam::collapseDuplicates`</a></li> <li>donor: vaccinee</li> <li>sample: sample ID (arbitrary)</li> <li>timepoint: time point at which sample was collected</li> <li>tissue: tissue from which sample was collected</li> <li>sorting: FACS sorting</li> <li>seq_type: sequence type (mAb or bulk)</li> <li>nuc_RS_19_312: number of replacement and silent mutations between IMGT-numbered nucleotide positions 19-312 along IGHV sequences, calculated by <a href="https://shazam.readthedocs.io/en/stable/topics/calcObservedMutations/">`shazam::calcObservedMutations`</a></li> <li>nuc_denom_19_312: number of informative nucleotide positions for counting mutations, excluding non-A/T/G/C positions (such as "N", "-", ".")</li> <li>nuc_RS_freq_19_312: nucleotide-level mutation frequency (= nuc_RS_19_312 / nuc_denom_19_312)</li> </ul>

opencc-by-4.0Mar 2023View details →

Characterization of the anti-spike IgG immune response to COVID-19 vaccines in people with a wide variety of immunodeficiencies

Participants submitted saliva using the OME-505 collection device (OMNIgene Oral, Ottawa, Canada) every two weeks from vaccination through six months post-dose 3 to detect breakthrough SARS-CoV-2 infections. Viral RNA was extracted using the NucliSENS easyMag automated extraction system from 200ul of saliva in stabilizing solution and eluted in a total volume of 50ul. First strand cDNA synthesis was performed from 5ul of eluted RNA using SuperScript IV VILO Master Mix (Thermo Fisher). Positive specimens were then sequenced. Multiplex tiled amplicon libraries were prepared using the Midnight panel and Rapid barcoding kit RBK-004 (Oxford Nanopore technologies) using previously published protocol. Twelve sample pooled libraries were sequenced on a GridION X5 nanopore sequencer using Flongle adapters. After sequencing, raw data were processed using interARTIC to generate consensus sequences and variant calls. SARS-CoV-2 lineages were determined using these consensus sequences and the NextClade and Pangolin platforms.

Fig. 3 in Prairie dog responses to vector control and vaccination during an initial Yersinia pestis invasion

Fig. 3. Predicted re-encounter rates (95% confidence intervals [CIs]) over a single trapping interval (2007–2008) for adult female and male black-tailed prairie dogs inoculated at Conata Basin, South Dakota in 2007 with F1–V fusion protein vaccine or placebo on the no dust and dusted plots (the latter with flea control). Sample sizes are depicted above the 95% CIs.

Fig. 1 in Prairie dog responses to vector control and vaccination during an initial Yersinia pestis invasion

Fig. 1. Categories of flea vector control (deltamethrin dust) and F1–V fusion protein plague vaccination (V = vaccine, P = placebo, N = no inoculation) used for analyses of black-tailed prairie dog annual re-encounter rates (2007–2008 and 2008–2009) at Conata Basin, South Dakota. Annual re-encounter rates were compared for subsets of animals, here each enclosed by unique rectangles. Sample sizes are depicted in subsequent figures with results from multivariate analyses.

Fig. 5 in Prairie dog responses to vector control and vaccination during an initial Yersinia pestis invasion

Fig. 5. Predicted re-encounter rates (95% confidence intervals [CIs]) over a single trapping interval 2007–2008 for non-inoculated adult and juvenile blacktailed prairie dogs on the no dust and dusted plots (the latter with flea control) at Conata Basin, South Dakota. Sample sizes are depicted above the 95% CIs.

Fig. 2 in Prairie dog responses to vector control and vaccination during an initial Yersinia pestis invasion

Fig. 2. Predicted flea parasitism (95% confidence intervals [CIs]) on blacktailed prairie dogs at the no dust and dusted plots, 2007–2008 at Conata Basin, South Dakota (prevalence on the left, intensity on the right). Prairie dog burrows on the dusted plots were treated annually with deltamethrin dust at ~4–6 g per burrow. Model predictions adjust (i.e., control) for year and Julian day (adjusted here as year 2008, and Julian day 212 for prevalence and 200 for intensity). Flea intensity data were log-transformed (log10) for analysis; hence, predicted flea intensity and 95% CIs could extend below 0. Sample sizes are depicted above the 95% CIs.

Fig. 4 in Prairie dog responses to vector control and vaccination during an initial Yersinia pestis invasion

Fig. 4. Predicted re-encounter rates (95% confidence intervals [CIs]) over two trapping intervals (2007–2008 and 2008–2009) for adult female and male black-tailed prairie dogs in Conata Basin, South Dakota inoculated in 2007 or 2008 with F1–V fusion protein vaccine or placebo on the dusted plots (with flea control). Sample sizes are depicted above or below the 95% CIs.

opencc-by-4.0Nov 2021View details →

Pre-vaccination and early B cell signatures of the antibody response to SARS-CoV-2 mRNA vaccine

The data presented in Code repository for Kardava, L., Rachmaninoff, N., Lau, W. W., Buckner, C. M., Trihemasava, K., Blazkova, J., ... & Moir, S. (2022). Early human B cell signatures of the primary antibody response to mRNA vaccination. Proceedings of the National Academy of Sciences, 119(28), e2204607119.<a href="https://www.pnas.org/doi/epdf/10.1073/pnas.2204607119">https://www.pnas.org/doi/epdf/10.1073/pnas.2204607119</a> are made available here. All code to reproduce the figures can be found here: https://github.com/niaid/COVID_Vaccine_Bcells <a href="https://zenodo.org/api/files/f93859d0-b062-4def-8b17-c0f21ee36f09/all_subjects_cd19_positive_and_keys.zip">all_subjects_cd19_positive_and_keys.zip</a> contains a CSV file of all CD19+ cells with flowSOM clusters shown. Accompanying files allow for matching of timepoint and subject information. <a href="https://zenodo.org/api/files/f93859d0-b062-4def-8b17-c0f21ee36f09/All_subjects_FCS_files_deidentified.zip">All_subjects_FCS_files_deidentified.zip</a> contains the raw fcs files and is organized by timepoint and subject.

opencc-by-4.0Jan 2022View details →

Germinal centre-driven maturation of B cell response to SARS-CoV-2 mRNA vaccination

These are the processed BCR repertoire and transcriptomics data described in <a href="https://doi.org/10.1038/s41586-022-04527-1">Kim & Zhou et al., Nature, 2022</a>. The raw sequencing data new to this study are available on SRA under BioProject <a href="https://www.ncbi.nlm.nih.gov/sra/?term=PRJNA777934">PRJNA777934</a>. This study also used BCR repertoire data from <a href="https://doi.org/10.1038/s41586-021-03738-2">Turner & O'Halloran et al., Nature, 2021</a> (<a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA731610">PRJNA731610</a>) and <a href="https://doi.org/10.1016/j.immuni.2021.08.013">Schmitz, Turner & Liu et al., Immunity, 2021</a> (<a href="https://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA741267">PRJNA741267</a>).   Code Code along with Docker containers for reproducing the NGS data-based figures and analyses in the published paper can be <a href="https://github.com/julianqz/wustl_published/tree/main/nature_2022">found on GitHub</a>.   Metadata File: WU368_kim_et_al_nature_2022_meta.tsv Notes: <ul> <li>Sample breakdown by `sequence_type` (132 total) <ul> <li>73 bulk BCR sequencing samples (`bulk`) <ul> <li>57 new</li> <li>5 from <a href="https://doi.org/10.1038/s41586-021-03738-2">Turner & O'Halloran et al., Nature, 2021</a></li> <li>11 from <a href="https://doi.org/10.1016/j.immuni.2021.08.013">Schmitz, Turner & Liu et al., Immunity, 2021</a>.</li> </ul> </li> <li>56 10x Genomics single-cell VDJ + 5' gene expression samples (`tgx`)</li> <li>3 samples from <a href="https://doi.org/10.1038/s41586-021-03738-2">Turner & O'Halloran et al., Nature, 2021</a> (`mab`) corresponding to a total of 37 S-binding mAbs previously reported. These are not the same as the 2099 recombinant mAbs generated in this study (see below).</li> </ul> </li> <li>Sample collection time was originally recorded in days in the `timepoint` column. Timepoints were referenced in weeks in the manuscript, as shown in the `timepoint_ms` column.</li> <li>`bio_rep` and `tech_rep` = biological replicate and technical replicate respectively.</li> </ul> Abbreviations: <ul> <li>LN = lymph node</li> <li>BM = bone marrow</li> <li>PB = plasmablast</li> <li>GC = germinal centre</li> <li>LLPC = long-lived plasma cell</li> <li>NS = no sorting</li> <li>mAb = monoclonal antibody</li> </ul>   Information on the 2099 recombinant mAbs generated in this study File: WU368_kim_et_al_nature_2022_mabs.tsv Notes on columns: <ul> <li>`h_sequence_id` and `l_sequence_id`: Sequence IDs of the heavy and light chains respectively.</li> <li>`elisa`: ELISA results for binding to SARS-CoV-2 S (`TRUE` = positive).</li> </ul>   Processed BCR data - heavy chains File: WU368_kim_et_al_nature_2022_bcr_heavy.tsv Analysis was based on heavy chain-based clonal inference. Notes on columns: The columns largely follow the <a href="https://changeo.readthedocs.io/en/stable/standard.html">AIRR-C Rearrangement format</a>. The main deviation is that CDR3s were used, as opposed to IMGT-defined "junctions". Nonetheless, junction-related columns are included here as some repositories such as <a href="https://gateway.ireceptor.org/login">iReceptor</a> use these. Non-standard columns are noted below. <ul> </ul> <ul> <li>`cell_id`: Only sequences from single-cell samples and the 37 mAbs from Turner & O'Halloran et al., Nature, 2021 have cell IDs following the format `[donor]_[sample]@[id]`. `NA` for bulk sequences.</li> <li>`sequence_id`: Sequence IDs follow the format `[donor]_[sample]@[id]`.</li> <li>`v_call_genotyped`: V gene annotation reassigned after individualized genotyping by <a href="https://tigger.readthedocs.io/en/stable/">TIgGER</a>.</li> <li>`germline_[vdj]_call`: Clonal consensus germline calls after corresponding clonal consensus sequence were reconstructed via <a href="https://changeo.readthedocs.io/en/stable/methods/germlines.html">`CreateGermlines.py --cloned` from Change-O</a>.</li> <li>`isotype`: IGH[ADEGM].</li> <li>`cdr3`: CDR3 nucleotide sequence.</li> <li>`cdr3_length`: CDR3 nucleotide sequence length.</li> <li>`cdr3_aa`: CDR3 amino acid sequence.</li> <li>`collapse_count`: Number of duplicate IMGT-aligned V(D)J sequences that were collapsed by <a href="https://alakazam.readthedocs.io/en/stable/topics/collapseDuplicates/">`alakazam::collapseDuplicates`</a>.</li> <li>`donor`, `timepoint`, `tissue`, `sorting`, `seq_type`: Propagated as is from the metadata file. <ul> <li>In `seq_type`, `tgx` corresponds to 10x Genomics data; `mab` corresponds specifically to the 37 S-binding mAbs from Turner & O'Halloran et al., Nature, 2021.</li> </ul> </li> <li>`timepoint_2`: Same as `timepoint`, except that `d28+d35` and `d201+d208` were treated as `d28` (week 4) and `d201` (week 29) respectively as described in Materials & Methods.</li> <li>`gex_anno`: Cell type identity annotation based on transcriptomic profiles. Mapped from `anno_leiden_0.18` from WU368_kim_et_al_nature_2022_gex_b_cells.h5ad.</li> <li>`compartment`: B cell compartment. <ul> <li>ABC = activated B cell. LNPC = lymph node plasma cell. RMB = resting memory B cell.</li> <li>Minor differences in terminology <ul> <li>The manuscript refers to the memory compartment as MBCs, whereas the terminology used in the data is RMB. As described in Materials & Methods, analysis involving the memory compartment used specifically d201 bulk-sequenced memory sorts from blood. To get these sequences, subset `s_pos_clone`, `seq_type`, `compartment`, and `timepoint_2` to, respectively, `TRUE`, `bulk`, `RMB`, and `d201`. </li> <li>The manuscript uses the term BMPC (bone marrow plasma cell), whereas the data uses the term LLPC.</li> </ul> </li> </ul> </li> <li>`clone_id`: B cell clonal lineage IDs follow the format `[donor]@[id]`.</li> <li>`s_pos_clone`: `TRUE` if a sequence belonged to a B cell clone that was designated as S-binding by virtue of containing one of the recombinant mAbs that tested positive via ELISA or one of the S-binding mAbs from Turner & O'Halloran et al., Nature, 2021.</li> <li>`expressed_id`: mAb IDs for the 2099 recombinant mAbs generated in this study (mapped from `mab_id` from WU368_kim_et_al_nature_2022_mabs.tsv) and the 37 mAbs from Turner & O'Halloran et al., Nature, 2021. `NA` for everything else.</li> <li>`elisa`: ELISA results for binding of recombinant mAbs to SARS-CoV-2 S. `TRUE` if positive. `NA` if not tested.</li> <li>`nuc_RS_19_312`: number of replacement and silent mutations between IMGT-numbered nucleotide positions 19-312 along IGHV sequences, calculated by <a href="https://shazam.readthedocs.io/en/stable/topics/calcObservedMutations/">`shazam::calcObservedMutations`</a>.</li> <li>`nuc_denom_19_312`: number of informative nucleotide positions for counting mutations, excluding non-A/T/G/C positions (such as "N", "-", ".").</li> <li>`nuc_RS_freq_19_312`: nucleotide-level mutation frequency (= nuc_RS_19_312 / nuc_denom_19_312).</li> </ul>   Processed BCR data - light chains File: WU368_kim_et_al_nature_2022_bcr_light.tsv Light chains were not used for heavy chain-based clonal inference or analysis.   Processed transcriptomics data Files: <ul> <li>WU368_kim_et_al_nature_2022_gex_all_cells.h5ad (clustering all cells)</li> <li>WU368_kim_et_al_nature_2022_gex_b_cells.h5ad (re-clustering only the B cells)</li> </ul> Notes: <ul> <li>The `h5ad` files can be imported into <a href="https://scanpy.readthedocs.io/en/stable/index.html">Scanpy</a> as an <a href="https://scanpy.readthedocs.io/en/stable/usage-principles.html#anndata">AnnData object</a>.</li> <li>Each `AnnData` object has 3 `.layers`, each representing a version of the count matrix. <ul> <li>`raw_counts`: Imported from `<a href="https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/6.0/using/aggregate">cellranger aggr</a>` output by `scanpy.read_10x_mtx`.</li> <li>`log_norm`: Log-noramlized expression values outputted by `scanpy.pp.normalize_total` followed by `scanpy.pp.log1p`.</li> <li>`scaled`: The `log_norm` layer scaled to unit variance and zero mean by `scanpy.pp.scale`. </li> </ul> </li> <li>The `gene_name` and `biotype` columns in `.var` were extracted from GENCODE v32 GTF.</li> <li>Columns in `.obs` (each row corresponds to a cell) <ul> <li>`n_feature`: The `n_genes_by_counts` column produced by `scanpy.pp.calculate_qc_metrics`, renamed. The number of genes expressed. This is before subsetting the genes.</li> <li>`n_umi`: The `total_counts` column produced by `scanpy.pp.calculate_qc_metrics`, renamed. The total UMI counts in a cell.</li> <li>`pct_mt`: The `pct_counts_mt` column produced by `scanpy.pp.calculate_qc_metrics`, renamed. The percentage of counts in mitochondrial genes.</li> <li>`n_hkg`: The number of housekeeping genes for which expression was detected.</li> <li>`n_gene_expressed`: The total number of genes for which expression was detected. This is after subsetting the genes.</li> <li>`pre_qc_bcr`: `TRUE` if a cell also had paired BCR data available. Produced by cross-referencing the cellular barcodes in `cell_barcodes.json` outputted by `cellranger vdj`. At this point the BCR data had not gone through the QC process in the BCR processing pipeline (hence `pre_qc`). </li> <li>`leiden_[resolution]`: Cluster assignment by `scanpy.tl.leiden`.</li> <li>`anno_leiden_[resolution]`: Cell type identity annotations based on transcriptomic profiles. This was mapped onto the `gex_anno` column in the processed heavy chain BCR data.</li> </ul> </li> <li>UMAP coordinates can be found in `.obsm["X_umap"]`.</li> <li>`.X` has been set to `None` in order to reduce file size.</li> </ul> In addition, the preprocessed count matrix outputted by `<a href="https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/6.0/using/aggregate">cellranger aggr</a>` is available from <a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE195673">GEO under BioProject PRJNA777934</a>.

opencc-by-4.0Aug 2022View details →

Pre- and post-intervention responses to a knowledge, attitudes, and practices survey for the study, "Disseminating vaccination information in baby soap products increases knowledge and vaccine uptake in central Uganda: A non-randomized controlled trial"

This dataset contains responses to the pre- and post-intervention knowledge, attitudes, and practices surveys utilized for the study, "Disseminating vaccination information in baby soap products increases knowledge and vaccine uptake in central Uganda: A non-randomized controlled trial."

openSep 2024View details →

Interferon-induced activation of dendritic cells and monocytes by yellow fever vaccination correlates with early antibody responses

Bulk RNA-seq analysis of sorted subpopulations isolated from PBMC of yellow fever vaccinees from before and 3, 7, 14 and 28 days after vaccination and single cell RNA-seq analysis of sorted DC and monocytes fractions isolated from PBMC of of yellow fever vaccinees from before and 3 and 7 days after vaccination.

opencc-by-4.0Dec 2022View details →

VIGET: A web portal for study of vaccine-induced host responses based on Reactome pathways and ImmPort data

Host responses to vaccines are complex but important to investigate. To facilitate the study, we have developed a tool called Vaccine Induced Gene Expression Analysis Tool (VIGET), with the aim to provide an interactive online tool for users to efficiently and robustly analyze the host immune response gene expression data collected in the ImmPort database. VIGET allows users to select vaccines, choose ImmPort studies, set up analysis models by choosing confounding variables and two groups of samples having different vaccination times, and then perform differential expression analysis to select genes for pathway enrichment analysis and functional interaction network construction using the Reactome’s web services. VIGET provides features for users to compare results from two analyses, facilitating comparative response analysis across different demographic groups. VIGET uses the Vaccine Ontology (VO) to classify various types of vaccines such as live or inactivated flu vaccines, yellow fever vaccines, etc. Different variables are classified using our Vaccine Investigation Ontology (VIO). To showcase the utilities of VIGET, we conducted a longitudinal analysis of immune responses to yellow fever vaccines and found an intriguing complex activity response pattern of pathways in the immune system annotated in Reactome, demonstrating that VIGET is a valuable web portal that supports effective vaccine response studies using Reactome pathways and ImmPort data. The portal has been deployed at <a href="https://viget.violinet.org/">https://viget.violinet.org/</a>.

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Characterization of the anti-spike IgG immune response to COVID-19 vaccines in people with a wide variety of immunodeficiencies

Research on COVID-19 vaccination in immune-deficient/disordered people (IDP) has primarily focused on cancer and organ transplantation populations. In a prospective cohort of 195 IDP and 35 healthy volunteers, anti-spike IgG was detected in 88% of IDP post-dose 2, increasing to 93% by six months post-dose 3. Despite high seroconversion, median IgG levels for IDP never surpassed 1/3 that of healthy volunteers. IgG binding to Omicron BA.1 was lower than all other variants. Angiotensin-converting enzyme 2 pseudo-neutralization (% inhibition) was only modestly correlated with anti-spike IgG concentration. IgG levels were not significantly altered by participants' use of different mRNA-based vaccines, immunomodulating treatments, and prior SARS-CoV-2 infections. While our data show that three doses of COVID-19 vaccinations induce anti-spike IgG in most IDP, additional doses are needed to achieve the levels of protection in healthy volunteers. Due to the strikingly reduced IgG response to Omicron BA.1, the efficacy of additional vaccinations, including bivalent vaccines, should be studied in this population.

opencc-zeroMar 2023View details →

A Multicenter Study to Assess Response to Influenza Vaccine in Multiple Sclerosis Participants Treated With Ofatumumab

ClinicalTrials.gov study NCT04667117. IPD Sharing: YES. Countries: 1. Publications: 1.

Exploring the Immune Response to SARS-CoV-2 modRNA Vaccines in Patients With Secondary Progressive Multiple Sclerosis (AMA-VACC)

ClinicalTrials.gov study NCT04792567. IPD Sharing: YES. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →

Long Term Immune Memory Responses to HPV Vaccination Following 2 vs 3 Doses of Quad-HPV Vaccine

ClinicalTrials.gov study NCT02968420. IPD Sharing: NO. Countries: 1. Publications: 2.

Genomics and Epigenomics of the Elderly Response to Pneumococcal Vaccines

ClinicalTrials.gov study NCT03104075. IPD Sharing: YES. Countries: 1. Publications: 2.

SARS-CoV-2 Immune Responses After COVID-19 Therapy and Subsequent Vaccine

ClinicalTrials.gov study NCT04952402. IPD Sharing: YES. Countries: 1. Publications: 0.