Evidence map›Paper›PMID 35847779›Full record

ReviewFrontiers in medicine2022

Toward Molecular Stratification and Precision Medicine in Systemic Sclerosis.

Maria Noviani, Vasuki Ranjani Chellamuthu, Salvatore Albani, Andrea Hsiu Ling Low

Open access · goldAbstract readReview
In one paragraph

Review in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
1.3field-weighted citation impact, top 20% 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

5 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Review
  5. CD8International journal of molecular sciences · 2022
    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

4 authors at 3 institutions in 1 country.

Maria NovianiDepartment of Rheumatology and Immunology, Singapore General Hospital, Singapore, Singapore.
Vasuki Ranjani ChellamuthuTranslational Immunology Institute, SingHealth Duke-NUS Academic Medical Centre, Singapore, Singapore.
Salvatore AlbaniDuke-National University of Singapore Medical School, Singapore, Singapore.
Andrea Hsiu Ling LowDepartment of Rheumatology and Immunology, Singapore General Hospital, Singapore, Singapore.
SingHealth Duke-NUS Academic Medical Centre · SGNational University of Singapore · SGSingapore General Hospital · SG

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Systemic sclerosis (SSc), a complex multi-systemic disease characterized by immune dysregulation, vasculopathy and fibrosis, is associated with high mortality. Its pathogenesis is only partially understood. The heterogenous pathological processes that define SSc and its stages present a challenge to targeting appropriate treatment, with differing treatment outcomes of SSc patients despite similar initial clinical presentations. Timing of the appropriate treatments targeted at the underlying disease process is critical. For example, immunomodulatory treatments may be used for patients in a predominantly inflammatory phase, anti-fibrotic treatments for those in the fibrotic phase, or combination therapies for those in the fibro-inflammatory phase. In advancing personalized care through precision medicine, groups of patients with similar disease characteristics and shared pathological processes may be identified through molecular stratification. This would improve current clinical sub-setting systems and guide personalization of therapies. In this review, we will provide updates in SSc clinical and molecular stratification in relation to patient outcomes and treatment responses. Promises of molecular stratification through advances in high-dimensional tools, including omic-based stratification (transcriptomics, genomics, epigenomics, proteomics, cytomics, microbiomics) and machine learning will be discussed. Innovative and more granular stratification systems that integrate molecular characteristics to clinical phenotypes would potentially improve therapeutic approaches through personalized medicine and lead to better patient outcomes.

Indexed as

molecularmulti-omic analysesprecision medicinestratificationsystemic sclerosis

Identifiers

PMID35847779
PMCPMC9279904
OpenAlexW4283761529

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.