Evidence map›Paper›PMID 31205825›Full record

ArticlePeerJ2019

Bibliometric analysis of research on the trends in autophagy.

Ting Hong, Xinzhe Feng, Wenwen Tong, Weidong Xu

Open access · goldAbstract read
In one paragraph

Article in PeerJ, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.6field-weighted citation impact, top 17% 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

2 citing papers in PubMed, 17 citations in OpenAlex.

  1. Article
  2. 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

4 authors at 3 institutions in 1 country.

Ting Hong *Department of Endocrinology, Drum Tower Hospital, Nanjing University Medical School, Nanjing, Jiangsu, P.R. China.
Xinzhe Feng *Department of Orthopedics, Changhai Hospital, Second Military Medical University, Shanghai, Shanghai, P.R. China.
Wenwen TongDepartment of Orthopedics, Changhai Hospital, Second Military Medical University, Shanghai, Shanghai, P.R. China.
Weidong XuDepartment of Orthopedics, Changhai Hospital, Second Military Medical University, Shanghai, Shanghai, P.R. China.
Second Military Medical University · CNChanghai Hospital · CNNanjing Drum Tower Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAutophagy is an important mechanism to maintain homeostasis in cells. It has been linked with ageing and many currently incurable diseases, including heart disease, cancer, myopathies, neurodegeneration, and diabetes. Autophagy research is very important for identifying better treatments. This study aimed to explore the hotspots of autophagy research published from different countries, organizations, and authors.

methodsBetween 1962 and 2018, articles published about autophagy were identified in the Web of Science database. The total and annual number of articles, citations, impact factor, Hirsch (H)-index, number of article citations, productive authors, and involved journals were collected for quantitative and qualitative comparisons.

resultsFrom 1962 to 2018, 18,811 autophagy-related articles written in English were published. Most were from China (6,731). The United States dominated in citation frequency (391,030) and h-index (264). Among related journals,

conclusionsPublication of articles about autophagy has increased notably from 1962 to 2018, and has increased annually. The general quality of publications from China is still in need of improvement. Autophagy research has shifted gradually from basic studies to clinical studies in recent years.

Indexed as

AutophagyBibliometricCitationH-indexVOS viewer

Identifiers

PMID31205825
PMCPMC6556104
OpenAlexW2948064752

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.