Evidence map›Paper›PMID 37994407›Full record

ArticleRenal failure2023

Global biomarkers trends in acute kidney injury: a bibliometric analysis.

Fan Fan, Peifeng Xu

Abstract read
In one paragraph

Article in Renal failure, 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
–field-weighted citation impact
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.

  1. Article
  2. Article
  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

2 authors.

Fan FanDepartment of General Medicine, The Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China.
Peifeng XuDepartment of General Medicine, The Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesAcute kidney injury (AKI) is a common global condition with high morbidity and mortality rates. Biomarkers can aid in the diagnosis, prediction, intervention, and outcome assessment of AKI. This study aimed to summarize the current research status and identify research hotspots for AKI biomarkers using bibliometric analysis.

methodsRelevant original English language articles were retrieved from the Science Citation Index Expanded of the Web of Science Core Collection database, from inception to 31 December 2022. Full records and related cited references from all the documents were collected and analyzed.

resultsA total of 16368 authors from 3379 institutions in 83 countries/regions contributed to 2916 documents that were published in 712 academic journals. Annual publication output followed exponential growth since 2008. The United States, the University of Pittsburgh, and the American Journal of Physiology-Renal Physiology were the most productive countries, institutions, and journals in terms of research outputs, respectively. The area of interest has shifted from neutrophil gelatinase-associated lipocalin, cell cycle, and tubular damage toward sepsis and COVID-19. Apoptosis, inflammation, and chronic kidney disease have become popular in recent years, and studies on ferroptosis, machine learning, COVID-19, and renal fibrosis will be the focus of future research. IMPLICATIONS: This bibliometric study suggests that future research on AKI biomarkers would focus on ferroptosis, renal fibrosis and COVID-19. Artificial intelligence, such as machine learning, maybe the most promising direction for the discovery and validation of AKI biomarkers.

Indexed as

Acute Kidney InjuryCOVID-19Artificial IntelligenceBibliometricsBiomarkersFibrosisHumansBiomarkersAcute kidney injurybibliometric analysisbiomarkercitespaceVOSviewer

Identifiers

PMID37994407
PMCPMC11001340

What OpenQuestion holds

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Registered trials

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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.