Evidence map›Paper›PMID 30504893›Full record

ArticleScientific reports2018

Identification of Immune Signatures of Novel Adjuvant Formulations Using Machine Learning.

Sidhartha Chaudhury, Elizabeth H Duncan, Tanmaya Atre, Casey K Storme, Kevin Beck, Stephen A Kaba, David E Lanar, Elke S Bergmann-Leitner

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing 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

24 citing papers in PubMed.

  1. Trial
  2. Machine Learning in Nonhuman Primate Models of Infectious Diseases: Current Applications and Future Perspectives.Journal of the American Association for Laboratory Animal Science : JAALAS · 2026
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  20. SIMON: Open-Source Knowledge Discovery Platform.Patterns (New York, N.Y.) · 2021
    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

8 authors.

Sidhartha ChaudhuryBiotechnology High Performance Computing Software Applications Institute, Telemedicine and Advanced Technology Research Center, U.S. Army Medical Research and Materiel Command, Fort Detrick, MD, USA. sidhartha.chaudhury.civ@mail.mil.ORCID http://orcid.org/0000-0001-9513-6688
Elizabeth H DuncanMalaria Vaccine Branch, US Military Malaria Research Program, Walter Reed Army Institute of Research, Silver Spring, MD, USA.
Tanmaya AtreMalaria Vaccine Branch, US Military Malaria Research Program, Walter Reed Army Institute of Research, Silver Spring, MD, USA.
Casey K StormeMalaria Vaccine Branch, US Military Malaria Research Program, Walter Reed Army Institute of Research, Silver Spring, MD, USA.
Kevin BeckMiltenyi Biotec Inc., San Diego, CA, USA.
Stephen A KabaMalaria Vaccine Branch, US Military Malaria Research Program, Walter Reed Army Institute of Research, Silver Spring, MD, USA.
David E LanarMalaria Vaccine Branch, US Military Malaria Research Program, Walter Reed Army Institute of Research, Silver Spring, MD, USA.
Elke S Bergmann-LeitnerMalaria Vaccine Branch, US Military Malaria Research Program, Walter Reed Army Institute of Research, Silver Spring, MD, USA. elke.s.bergmann-leitner.civ@mail.mil.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Adjuvants have long been critical components of vaccines, but the exact mechanisms of their action and precisely how they alter or enhance vaccine-induced immune responses are often unclear. In this study, we used broad immunoprofiling of antibody, cellular, and cytokine responses, combined with data integration and machine learning to gain insight into the impact of different adjuvant formulations on vaccine-induced immune responses. A Self-Assembling Protein Nanoparticles (SAPN) presenting the malarial circumsporozoite protein (CSP) was used as a model vaccine, adjuvanted with three different liposomal formulations: liposome plus Alum (ALFA), liposome plus QS21 (ALFQ), and both (ALFQA). Using a computational approach to integrate the immunoprofiling data, we identified distinct vaccine-induced immune responses and developed a multivariate model that could predict the adjuvant condition from immune response data alone with 92% accuracy (p = 0.003). The data integration also revealed that commonly used readouts (i.e. serology, frequency of T cells producing IFN-γ, IL2, TNFα) missed important differences between adjuvants. In summary, broad immune-profiling in combination with machine learning methods enabled the reliable and clear definition of immune signatures for different adjuvant formulations, providing a means for quantitatively characterizing the complex roles that adjuvants can play in vaccine-induced immunity. The approach described here provides a powerful tool for identifying potential immune correlates of protection, a prerequisite for the rational pairing of vaccines candidates and adjuvants.

Indexed as

Machine LearningAdjuvants, ImmunologicAnimalsCD4-Positive T-LymphocytesCD8-Positive T-LymphocytesCytokinesDose-Response Relationship, ImmunologicImmunity, CellularLiposomesMacaca mulattaVaccinesAdjuvants, ImmunologicCytokinesLiposomesVaccines

Identifiers

PMID30504893
PMCPMC6269591

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Registered trials

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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.