Evidence map›Paper›PMID 29268541›Full record

ReviewJournal of thoracic disease2017

Molecular targets in aortic aneurysm for establishing novel management paradigms.

Chengkai Hu, Kai Zhu, Jun Li, Chunsheng Wang, Lao Lai

Open access · hybridAbstract readReview
In one paragraph

Review in Journal of thoracic disease, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.6field-weighted citation impact, top 27% 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

4 citing papers in PubMed, 8 citations in OpenAlex.

  1. Article
  2. Using Molecular Targets to Predict and Treat Aortic Aneurysms.Reviews in cardiovascular medicine · 2022
    Review
  3. PARP-1 (Poly[ADP-Ribose] Polymerase-1).Hypertension (Dallas, Tex. : 1979) · 2018
    Article
  4. 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

5 authors at 3 institutions in 1 country.

Chengkai HuDepartment of Cardiac Surgery, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
Kai ZhuDepartment of Cardiac Surgery, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
Jun LiDepartment of Cardiac Surgery, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
Chunsheng WangDepartment of Cardiac Surgery, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
Lao LaiDepartment of Cardiac Surgery, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
The First Affiliated Hospital, Sun Yat-sen University · CNFudan University · CNShanghai Institute of Hematology · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aortic aneurysm (AA) is a lethal disease and presents a large challenge for surgeons in the clinic. Although surgical management remains the major choice of AA, operative mortality remains high. With advances in understanding of the mechanisms of AAs, molecular targets, such as matrix metalloproteinases (MMPs), D-dimer, and inflammation markers, including C-reactive protein, interleukins and phagocytes, are important in the pathology of development of AA. These markers may become important for improving the diagnostic quality and provide more therapeutic choices for treatment of AA. Although these new markers require long-term trials before they can be translated into the clinic, they can still be helpful in determining new directions. The main aim of this review is to discuss the current findings of molecular targets in progression of AA and discuss the potential application of these new targets for managing this disease.

Indexed as

Aortic aneurysm (AA)inflammationmolecular targetsnanomedicineremodeling

Identifiers

PMID29268541
PMCPMC5721044
OpenAlexW2767551758

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.