Evidence map›Paper›PMID 38362371›Full record

ReviewClinical and experimental vaccine research2024

Humanized mouse model for vaccine evaluation: an overview.

Shivani Kaushik, Lata Kumari, Rakesh Kumar Deepak

Open access · diamondAbstract readReview
In one paragraph

Review in Clinical and experimental vaccine research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.9field-weighted citation impact, top 15% of its field
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

4 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Review
  3. Review
  4. Review
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

3 authors at 1 institution in 1 country.

Shivani KaushikAll India Institute of Medical Sciences, New Delhi, India.ORCID https://orcid.org/0009-0009-3188-149X
Lata KumariAll India Institute of Medical Sciences, New Delhi, India.ORCID https://orcid.org/0009-0002-6502-1884
Rakesh Kumar DeepakAll India Institute of Medical Sciences, New Delhi, India.ORCID https://orcid.org/0000-0002-8355-3019
All India Institute of Medical Sciences · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Animal models are essential in medical research for testing drugs and vaccines. These models differ from humans in various respects, so their results are not directly translatable in humans. To address this issue, humanized mice engrafted with functional human cells or tissue can be helpful. We propose using humanized mice that support the engraftment of human hematopoietic stem cells (HSCs) without irradiation to evaluate vaccines that influence patient immunity. For infectious diseases, several types of antigens and adjuvants have been developed and evaluated for vaccination. Peptide vaccines are generally used for their capability to fight cancer and infectious diseases. Evaluation of adjuvants is necessary as they induce inflammation, which is effective for an enhanced immune response but causes adverse effects in some individuals. A trial can be done on humanized mice to check the immunogenicity of a particular adjuvant and peptide combination. Messenger RNA has also emerged as a potential vaccine against viruses. These vaccines need to be tested with human immune cells because they work by producing a particular peptide of the pathogen. Humanized mice with human HSCs that can produce both myeloid and lymphoid cells show a similar immune response that these vaccines will produce in a patient.

Indexed as

Hematopoietic stem cellsHumanized miceImmune response antigensmRNA vaccinesPeptide vaccines

Identifiers

PMID38362371
PMCPMC10864885
OpenAlexW4392830455

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.