Evidence map›Paper›PMID 36818649›Full record

ArticleFrontiers in molecular neuroscience2023

microRNAs profiling of small extracellular vesicles from midbrain tissue of Parkinson's disease.

Zhengzhe Li, Dongdong Chen, Renjie Pan, Yanbiao Zhong, Tianyu Zhong, Zhigang Jiao

Open access · goldAbstract read
In one paragraph

Article in Frontiers in molecular neuroscience, 2023. 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
0.9field-weighted citation impact, top 26% 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, 6 citations in OpenAlex.

  1. Article
  2. Review
  3. 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

6 authors at 2 institutions in 1 country.

Zhengzhe LiThe First School of Clinical Medicine, Gannan Medical University, Ganzhou, China.
Dongdong ChenThe First School of Clinical Medicine, Gannan Medical University, Ganzhou, China.
Renjie PanThe First School of Clinical Medicine, Gannan Medical University, Ganzhou, China.
Yanbiao ZhongDepartment of Rehabilitation Medicine, First Affiliated Hospital of Gannan Medical University, Ganzhou, China.
Tianyu ZhongThe First School of Clinical Medicine, Gannan Medical University, Ganzhou, China.
Zhigang JiaoThe First School of Clinical Medicine, Gannan Medical University, Ganzhou, China.
Gannan Medical University · CNFirst Affiliated Hospital of Gannan Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Small extracellular vesicles (sEVs) are generated by all types of cells during physiological or pathological conditions. There is growing interest in tissue-derived small extracellular vesicles (tdsEVs) because they can be isolated from a single tissue source. Knowing the representation profile of microRNA (miRNA) in midbrain tissue-derived sEVs (bdsEVs) and their roles is imperative for understanding the pathological mechanism and improving the diagnosis and treatment of Parkinson's disease (PD). bdsEVs from a rat model of PD and a sham group were separated and purified using ultracentrifugation, size-exclusion chromatography (SEC), and ultrafiltration. Then, miRNA profiling of bdsEVs in both groups was performed using next-generation sequencing (NGS). The expression levels of 180 miRNAs exhibited significant differences between the two groups, including 114 upregulated and 66 downregulated genes in bdsEVs of PD rats compared with the sham group (

Indexed as

biomarkersmicroRNAsmidbrain tissueParkinson’ diseasesmall extracellular vesicles

Identifiers

PMID36818649
PMCPMC9935574
OpenAlexW4319162082

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.