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Record W4400057243 · doi:10.1111/jocd.16441

Global research trends and hotspots in the application of platelet‐rich plasma to hair growth from 2006 to 2023: Bibliometric and visual analysis

2024· letter· en· W4400057243 on OpenAlexaboutno aff
Sa’ed H. Zyoud

Bibliographic record

VenueJournal of Cosmetic Dermatology · 2024
Typeletter
Languageen
FieldMedicine
TopicHair Growth and Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsHair lossAlopecia areataPlatelet-rich plasmaMedicineDermatologyHair growthHair transplantationInternal medicinePlateletPhysiology

Abstract

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I read with great interest the systematic review “The role of platelet-rich plasma in androgenetic alopecia: A systematic review” by Donnelly et al.1 in the Journal of Cosmetic Dermatology. Platelet-rich plasma (PRP) was first described in hematology as a small volume of plasma with a higher concentration of platelets than peripheral blood and was first used in the 1970s as a transfusion product to treat thrombocytopenia. PRP is now widely used in sports medicine, regenerative medicine, esthetic medicine, and hair loss treatments due to its high concentration of growth factors and cytokines, which promote wound healing and tissue restoration.1, 2 Since 2006, researchers have investigated the use of PRP for treating alopecia. PRP is a promising therapeutic option for hair loss, including androgenetic alopecia (aGA) and female pattern hair loss, either alone or in combination with traditional therapies or hair transplantation. Furthermore, PRP is considered a safe, effective, and steroid-sparing option for treating alopecia areata.2 The numerous advantages of bibliometric analysis include quantitative assessment, trend identification, and research performance and quality evaluation.3 This strategy has been shown to be successful in dermatology research and in general PRP research.4, 5 Despite recent bibliometric data on published dermatology research, no thorough evaluation of PRP therapy for hair regrowth has been performed. The main goals are to identify potential avenues for future research and to obtain a greater understanding of the evolution of PRP therapy for hair regrowth. In addition to closing current research gaps, academics working in this field will find great use for this analysis. Furthermore, it can be combined with a high-value knowledge structure to guide researchers' scientific research directions. While many databases are used globally for evaluation research, the Scopus database was selected for its recognized reliability in conducting bibliometric analyses. An established resource for locating biomedical research, including MEDLINE documents, is Scopus, the largest abstract and citation database of peer-reviewed research literature in the world. We used the key terms “Platelet-rich plasma for hair growth” and their synonyms because our research focused specifically on PRP and hair growth rather than related topics. Data mining was carried out on 24 May 2024. These terms were sourced from two sources: (1) keywords found in previous research and (2) the PubMed Medical Subject Headings (MeSH) term list. The main theme of this study was journal publications containing “platelet-rich plasma and hair,” which were identified based on a search of titles and abstracts over a period of 17 years, from 2006 to 2023. The analysis focused mainly on the frequencies and percentages of publications by document type, country, journal, and institute. VOSviewer software (www.vosviewer.com, Van Eck & Waltman version 1.6.20) was used to create a visual representation of a term co-occurrence map and overlay visualization, involving only terms that appeared in the title and abstract at least 10 times under binary counting. The terms with the highest relevance scores were used to create a term map for the visualization of networks. The algorithm ensured that terms that co-occurred more frequently had larger bubbles, and terms with high similarity were located close to each other. Between 2006 and 2023, a total of 464 papers on the use of PRP for hair growth were published. Of these, 291 (62.72%) were original research papers, 126 (27.16%) were reviews, and 27 (5.82%) were letters. There was a notable increase in publications after 2015, with the number of articles on PRP for hair growth increasing significantly in the last decade (R2 = 0.8597; p = 0.001). Before 2015, the annual average number of publications related to the use of PRP for hair growth was approximately 3 per year. However, since 2015, this number has grown significantly, averaging approximately 49 documents per year, as shown in Figure 1. The United States led in the number of publications with 145 (31.25%), followed by India with 59 (12.72%), China with 49 (10.56%), and Italy with 41 (8.84%). Notable institutions that contributed to this research included the University of Toronto and Università degli Studi di Roma Tor Vergata, each with 16 publications (3.45%). The major journals in this field included the Journal of Cosmetic Dermatology, with 51 (10.99%) publications; Dermatologic Surgery, with 29 (6.25%); and Dermatologic Therapy, with 19 (4.09%). The article by Li et al.,6 published in Dermatologic Surgery, was the most cited study, with 271 citations. The effects of PRP on hair growth were investigated using both in vivo and in vitro models. Activated PRP increased the proliferation of dermal papilla (DP) cells and stimulated the extracellular signal-regulated kinase (ERK) and Akt signaling pathways. Furthermore, fibroblast growth factor 7 (FGF-7) and beta-catenin, both of which are potent stimuli for hair growth, were upregulated in DP cells. Compared with control mice, mice injected with activated PRP showed a faster transition from the telogen to anagen phase. This research supports the potential clinical application of autologous PRP and its secretory factors to promote hair growth. According to their average frequency in all publications, the keywords were divided into several colors (see Figure 2B). Keywords from more recent studies (post-2020) are indicated in yellow, while keywords from earlier research (pre-2020) are indicated in blue. The keywords associated with the categories “PRP efficacy in alopecia treatment” and “evidence-based guidelines and systematic reviews” showed prominent themes between 2020 and 2023, indicating their possible importance for further investigation. In contrast, research on “mechanisms of action” appears to have received more attention before 2020. In conclusion, there was a noticeable increase in publications on PRP for hair growth research between 2006 and 2023. Most related research has focused on the mechanism of action of PRP, how well it works to treat different types of alopecia, and the need for standardized protocols. The leading countries in this field are the United States, India, China, and Italy. Importantly, in recent years, there has been a shift toward examining the clinical efficacy of PRP and creating evidence-based guidelines. Although PRP shows great promise in treating hair loss, there are still issues to be resolved, including heterogeneity in the data, the absence of large-scale trials, and the lack of standardized protocols. To fully realize the therapeutic potential of PRP for alopecia, improve patient outcomes, and refine treatment protocols as hot topics, more research is necessary to address these challenges and advance the field. S.Z., the sole author, read and approved the final manuscript. The author thanks An-Najah National University for all its administrative assistance during the implementation of the project. The English language of some sentences in this manuscript was edited by American Journal Experts (AJE) AI for digital editing. No support was received for conducting this study. The author declares that he has no competing interests. Given that this was a bibliometric study without human participation, there was no need for ethical approval. All the data generated or analyzed during this study are included in this published article. In addition, other datasets used during the current study are available from the author upon reasonable request ([email protected]).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesBibliometrics
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.684
Threshold uncertainty score0.949

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0620.079
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.369
Teacher spread0.345 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Cosmetic DermatologySame topicHair Growth and DisordersFrench-language works237,207