Evidence map›Paper›PMID 32188748›Full record

ArticlemSphere2020

mSphere of Influence: Predicting Immune Responses and Susceptibility to Influenza Virus-May the Data Be with You.

Irene Ramos

Abstract read
In one paragraph

Article in mSphere, 2020. 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
–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

2 citing papers in PubMed.

  1. Review
  2. 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

1 author.

Irene RamosDepartment of Neurology, Icahn School of Medicine at Mount Sinai, New York, New York, USA irene.ramos-lopez@mssm.edu.ORCID 0000-0002-0223-0120

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Irene Ramos works in the field of immunology to viral infections. In this mSphere of Influence article, she reflects on how "Global analyses of human immune variation reveal baseline predictors of postvaccination responses" by Tsang et al. (Cell 157:499-513, 2014, https://doi.org/10.1016/j.cell.2014.03.031) and "A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection" by Fourati et al. (Nat Commun 9:4418, 2018, https://doi.org/10.1038/s41467-018-06735-8) made an impact on her by highlighting the importance of data science methods to understand virus-host interactions.

Indexed as

ImmunityDisease SusceptibilityHost Microbial InteractionsHumansInfluenza, Humanantibodiesdata scienceinfluenza vaccinesinfluenza viruspredictive modelingtranscriptomics

Identifiers

PMID32188748
PMCPMC7082138

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.