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Enregistrement 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 sur OpenAlexaboutno aff
Sa’ed H. Zyoud

Notice bibliographique

RevueJournal of Cosmetic Dermatology · 2024
Typeletter
Langueen
DomaineMedicine
ThématiqueHair Growth and Disorders
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésHair lossAlopecia areataPlatelet-rich plasmaMedicineDermatologyHair growthHair transplantationInternal medicinePlateletPhysiology

Résumé

récupéré en direct d'OpenAlex

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]).

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesBibliométrie
Catégories consensuellesBibliométrie
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,684
Score d'incertitude au seuil0,949

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0620,079
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,024
Tête enseignante GPT0,369
Écart entre enseignants0,345 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2024
Routes d'admission1
Résumé présentoui

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