Evidence map›Paper›PMID 29549634›Full record

ReviewAdvances in experimental medicine and biology2017

Naïve Human Antibody Libraries for Infectious Diseases.

Soo Khim Chan, Anizah Rahumatullah, Jing Yi Lai, Theam Soon Lim

Abstract readReview
In one paragraph

Review in Advances in experimental medicine and biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

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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.

Soo Khim ChanInstitute for Research in Molecular Medicine, Universiti Sains Malaysia, Minden, Penang, Malaysia.
Anizah RahumatullahInstitute for Research in Molecular Medicine, Universiti Sains Malaysia, Minden, Penang, Malaysia.
Jing Yi LaiInstitute for Research in Molecular Medicine, Universiti Sains Malaysia, Minden, Penang, Malaysia.
Theam Soon LimInstitute for Research in Molecular Medicine, Universiti Sains Malaysia, Minden, Penang, Malaysia. theamsoon@usm.my.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Many countries are facing an uphill battle in combating the spread of infectious diseases. The constant evolution of microorganisms magnifies the problem as it facilitates the re-emergence of old infectious diseases as well as promote the introduction of new and more deadly variants. Evidently, infectious diseases have contributed to an alarming rate of mortality worldwide making it a growing concern. Historically, antibodies have been used successfully to prevent and treat infectious diseases since the nineteenth century using antisera collected from immunized animals. The inherent ability of antibodies to trigger effector mechanisms aids the immune system to fight off pathogens that invades the host. Immune libraries have always been an important source of antibodies for infectious diseases due to the skewed repertoire generated post infection. Even so, the role and ability of naïve antibody libraries should not be underestimated. The naïve repertoire has its own unique advantages in generating antibodies against target antigens. This chapter will highlight the concept, advantages and application of human naïve libraries as a source to isolate antibodies against infectious disease target antigens.

Indexed as

Cell Surface Display TechniquesPeptide LibraryAnimalsAntibodies, MonoclonalAnti-Infective AgentsCommunicable DiseasesHost-Pathogen InteractionsHumansAntibodies, MonoclonalAnti-Infective AgentsPeptide LibraryInfectious diseasesMonoclonal antibodiesNaïve antibody libraryPhage display

Identifiers

PMID29549634
PMCPMC7120739

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.