ArticleFrontiers in public health2022
The landscape of photoaging: From bench to bedside in a bibliometric analysis.
Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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.
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.
Who cites it
2 citing papers in PubMed, 2 citations in OpenAlex.
- A Bibliometric Analysis of the Top 100 Most-Cited Articles on Skin Photoaging.Journal of cosmetic dermatology · 2025Article
- Cellular Senescence and Anti-Aging Strategies in Aesthetic Medicine: A Bibliometric Analysis and Brief Review.Clinical, cosmetic and investigational dermatology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Bibliometric software exists as a platform providing multiple algorithms to process the data to suffice diverse goals. Interpretation of the result must be based on insight into the meaning of the original data and the algorithm used. Medical Subject Headings (MeSH) terms represent the macro-level meaning of topics, keywords that commonly reflect the micro-level aspects. Objective: This study attempts to investigate the landscape of photoaging in the recent two decades by using bibliometric analysis. Methods: Published studies of photoaging were obtained from PubMed and Web of Science Core Collection (WoSCC) from 2000 to 2020. Basic bibliometric information was generated by WoSCC. Major MeSH terms were performed in cluster analysis and displayed as a hierarchical form to induce knowledge structure, detection algorithm on keywords was presented as a timeline form to obtain hotspots, and institutional clusters were labeled with keywords to achieve institutional characteristics. Results: A total of 2,727 and 2,705 studies were identified in PubMed and WoSCC, respectively. The number of photoaging-related studies at 3-year intervals grew steadily. The studies were performed in about 80 countries/regions. The highly frequent major MeSH terms were distributed in seven clusters, reflecting the etiology, pathophysiology, treatment, and prevention of photoaging. The hotspots changed as time went on, and the hotspots in recent 5 years were mitogen-activated protein kinase (MAPK), nuclear factor erythroid-derived 2-like 2 (Nrf2), and antioxidant activity. The highly productive institutions labeling in the top four clusters were Seoul National University, University of Michigan, China Medical University, and Harvard University, with corresponding keywords of UVB, retinoic acid, Nrf2, and rejuvenation. Conclusions: This study built a knowledge structure of pathophysiology, treatment and prevention of photoaging, and identified recent hotspots of MAPK, Nrf2, and antioxidant activity. We provide a landscape of photoaging, from the bench (pathophysiology) to bedside (treatment, prevention), and pave the way for future research.
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What OpenQuestion holds
Registered trials
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.