Evidence map›Paper›PMID 25788109›Full record

ArticleDrug delivery and translational research2011

Novel antigen delivery technologies: a review.

Deepika Jain, Vikas Jain, Ranjit Singh

Open access · bronzeAbstract read
PubMed Publisher
In one paragraph

Article in Drug delivery and translational research, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.3field-weighted citation impact, top 43% 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

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Immune cell engineering: opportunities in lung cancer therapeutics.Drug delivery and translational research · 2020
    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.

Deepika JainSchool of Pharmaceutical Sciences, Shobhit University, NH-58, Modipuram, Meerut, Uttar Pradesh, 250110, India.
Vikas Jain
Ranjit Singh
Shobhit University · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Over the past few years, new insights into immunobiology and delivery systems have allowed the development of better vaccines and for a wider range of diseases. Currently available vaccines represent outstanding success story in modern medicine and have had a dramatic effect on morbidity and mortality worldwide. Conventional vaccines have been based on live attenuated, or killed, viruses or bacteria, or recombinant proteins from these organisms. The design of live attenuated vaccines depended to some extent on serendipity and resulted in low success rates. Both live attenuated and killed vaccines require handling of live pathogens and are associated with safety problems. Despite the success of vaccines, there is a clear need for novel antigen delivery technologies to improve vaccine efficacy and safety. Antigen stability, safety, and immunogenicity are the key hurdles in development of novel antigen delivery technologies. Nowadays, various novel drug delivery systems are becoming one of the fastest growing sectors in the pharmaceutical and biotechnological industries. Delivery of vaccines via oral, intranasal, transcutaneous, and intradermal routes will decrease the risk of needle-borne diseases and may eliminate the need for trained personnel and sterile equipment. Currently, various techniques involving DNA vaccines, adjuvants, nanoparticles, liposome, microneedle, and NanoMAP technology are being developed and evaluated. This review focuses on the current development of some novel vaccine delivery systems and will explore the non-parenteral routes of vaccine administrations.

Identifiers

PMID25788109
OpenAlexW2084789140

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.