Evidence map›Paper›PMID 37298580›Full record

ArticleInternational journal of molecular sciences2023

Quantitative Proteomic and Phosphoproteomic Profiling of Lung Tissues from Pulmonary Arterial Hypertension Rat Model.

Ang Luo, Yangfan Jia, Rongrong Hao, Yafang Yu, Xia Zhou, Chenxin Gu, Meijuan Ren, Haiyang Tang

Open access · goldAbstract read
In one paragraph

Article in International journal of molecular sciences, 2023. 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
0.8field-weighted citation impact, top 25% 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, 3 citations in OpenAlex.

  1. Article
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  4. Review
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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 at 1 institution in 1 country.

Ang LuoCollege of Veterinary Medicine, Northwest A&F University, Xianyang 712100, China.ORCID 0000-0002-7831-6430
Yangfan JiaCollege of Veterinary Medicine, Northwest A&F University, Xianyang 712100, China.
Rongrong HaoCollege of Veterinary Medicine, Northwest A&F University, Xianyang 712100, China.
Yafang YuCollege of Veterinary Medicine, Northwest A&F University, Xianyang 712100, China.
Xia ZhouCollege of Veterinary Medicine, Northwest A&F University, Xianyang 712100, China.
Chenxin GuCollege of Veterinary Medicine, Northwest A&F University, Xianyang 712100, China.
Meijuan RenLife Science Research Core Service, Northwest A&F University, Xianyang 712100, China.
Haiyang TangCollege of Veterinary Medicine, Northwest A&F University, Xianyang 712100, China.ORCID 0000-0001-9537-2891
Northwest A&F University · CN

Funding

National Natural Science Foundation of China 81970052, 82170057 and 8200054
6 · The paper itself

Abstract

Pulmonary arterial hypertension (PAH) is a rare but fatal disease characterized by elevated pulmonary vascular resistance and increased pressure in the distal pulmonary arteries. Systematic analysis of the proteins and pathways involved in the progression of PAH is crucial for understanding the underlying molecular mechanism. In this study, we performed tandem mass tags (TMT)-based relative quantitative proteomic profiling of lung tissues from rats treated with monocrotaline (MCT) for 1, 2, 3 and 4 weeks. A total of 6759 proteins were quantified, among which 2660 proteins exhibited significant changes (

Indexed as

Hypertension, PulmonaryPulmonary Arterial HypertensionAnimalsDisease Models, AnimalFamilial Primary Pulmonary HypertensionLungPhosphopeptidesProteomicsPulmonary ArteryRatsPhosphopeptidesphosphoproteomicproteomicpulmonary arterial hypertension

Identifiers

PMID37298580
PMCPMC10253736
OpenAlexW4379232292

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.