Evidence map›Paper›PMID 20617148›Full record

ReviewJournal of biomedicine & biotechnology2010

High throughput T epitope mapping and vaccine development.

Giuseppina Li Pira, Federico Ivaldi, Paolo Moretti, Fabrizio Manca

Abstract readReview
In one paragraph

Review in Journal of biomedicine & biotechnology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

33 citing papers in PubMed.

  1. Trial
  2. Article
  3. Review
  4. Article
  5. Review
  6. Article
  7. Article
  8. Frontiers in immunology · 2022
    Article
  9. Article
  10. Article
  11. Article
  12. Review
  13. Article
  14. CD8F1000Research · 2018
    Review
  15. Immune Control ofFrontiers in immunology · 2018
    Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Giuseppina Li PiraLaboratory of Cellular Immunology, Advanced Biotechnology Center, 16132 Genoa, Italy. lipira@email.it
Federico Ivaldi
Paolo Moretti
Fabrizio Manca

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mapping of antigenic peptide sequences from proteins of relevant pathogens recognized by T helper (Th) and by cytolytic T lymphocytes (CTL) is crucial for vaccine development. In fact, mapping of T-cell epitopes provides useful information for the design of peptide-based vaccines and of peptide libraries to monitor specific cellular immunity in protected individuals, patients and vaccinees. Nevertheless, epitope mapping is a challenging task. In fact, large panels of overlapping peptides need to be tested with lymphocytes to identify the sequences that induce a T-cell response. Since numerous peptide panels from antigenic proteins are to be screened, lymphocytes available from human subjects are a limiting factor. To overcome this limitation, high throughput (HTP) approaches based on miniaturization and automation of T-cell assays are needed. Here we consider the most recent applications of the HTP approach to T epitope mapping. The alternative or complementary use of in silico prediction and experimental epitope definition is discussed in the context of the recent literature. The currently used methods are described with special reference to the possibility of applying the HTP concept to make epitope mapping an easier procedure in terms of time, workload, reagents, cells and overall cost.

Indexed as

Epitopes, T-LymphocyteModels, ImmunologicalVaccinesAnimalsEpitope MappingHigh-Throughput Screening AssaysHumansMiceEpitopes, T-LymphocyteVaccines

Identifiers

PMID20617148
PMCPMC2896667

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.