Evidence map›Paper›PMID 40890367›Full record

ReviewNature reviews. Rheumatology2025

Immune-cell profiling to guide stratification and treatment of patients with rheumatic diseases.

Deepak A Rao

Abstract readReview
PubMed Publisher
In one paragraph

Review in Nature reviews. Rheumatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. Article
  6. Review
  7. Article
  8. 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

1 author.

Deepak A RaoDivision of Rheumatology, Inflammation, Immunity, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA. darao@bwh.harvard.edu.ORCID http://orcid.org/0000-0001-9672-7746

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Methods for high-dimensional immune-cell profiling have advanced dramatically in the past decade. Studies of tissue and blood samples from patients with rheumatic diseases have revealed stereotyped features of immune dysregulation in individual diseases and in subsets of patients who share diagnosis of a heterogeneous disease. Translating immunological patterns into clinically implementable, actionable biomarkers has the potential to improve detection and quantification of pathological immune activity and selection of appropriate treatments for autoimmune rheumatic diseases. For example, cytometric features can be used to distinguish the various forms of inflammatory arthritis, stratify subsets of patients with rheumatoid arthritis or subsets of patients with systemic lupus erythematosus and predict treatment responses. Cellular immune profiling also enables the identification of specific features of immune dysregulation in individuals with rare, undiagnosed, inflammatory diseases. Several paths might lead to translation of discoveries from broad immune profiling into clinical tests to interrogate immune activation in people with rheumatic diseases.

Indexed as

Rheumatic DiseasesArthritis, RheumatoidBiomarkersHumansLupus Erythematosus, SystemicBiomarkers

Identifiers

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.