A bibliometric analysis of research trends in mesenchymal stem cell therapy for neonatal bronchopulmonary dysplasia: 2004–2024
Bibliographic record
Abstract
Introduction: Bronchopulmonary dysplasia (BPD) is a chronic lung disease predominantly affecting preterm infants, often requiring mechanical ventilation and supplemental oxygen. The pathogenesis of BPD involves a combination of genetic susceptibility and environmental insults, such as oxidative stress and mechanical ventilation. Mesenchymal stem cells (MSCs) have emerged as a promising therapeutic option for BPD due to their immunomodulatory, anti-inflammatory, and regenerative properties. This study aims to perform a bibliometric analysis of the publication landscape surrounding MSC therapy for BPD to identify research trends, collaborative networks, influential research clusters, and emerging research frontiers from 2004 to 2024. Methods: A bibliometric analysis was conducted using the Web of Science Core Collection (WoSCC) as the primary database due to its comprehensive citation indexing and standardized metadata. To ensure data integrity, we included publications from January 2004 (when the first relevant MSC studies for BPD began appearing) to November 2024. The search query combined terms related to BPD and MSCs, focusing on English-language articles and reviews. After retrieval, data were cleaned through duplicate removal and relevance verification processes. Quantitative analysis was performed on publication counts, authors, journals, institutions, and countries. Visual analysis tools, VOSviewer ( 1) and CiteSpace ( 2), were employed to map collaboration networks and identify research clusters through co-citation and co-occurrence analyses. Statistical validation of bibliometric distributions was conducted using Bradford's law and Price's law. Citation metrics were normalized by publication year to account for citation accumulation bias. Results: < 0.01), with a compound annual growth rate of 18.2%. The United States was the leading contributor (131 publications, 37.1%), followed by China (72 publications, 20.4%) and Canada (54 publications, 15.3%). Network analysis identified five distinct collaborative clusters, with limited cross-cluster collaboration. Citation analysis, normalized for publication age, revealed that the American Journal of Respiratory and Critical Care Medicine had the highest field-weighted citation impact (3.8). Keyword co-occurrence analysis demonstrated a significant shift from whole-cell therapies to extracellular vesicle research after 2018, with "microvesicles" and "exosomes" emerging as high-intensity burst terms (burst strength >5.0). The co-citation analysis identified three primary research clusters: stem cell therapy mechanisms (42.3% of citations), respiratory physiology and pathology (38.1%), and clinical neonatology (19.6%). Conclusion: This bibliometric analysis maps the evolving landscape of MSC therapy research for BPD over the past two decades, revealing distinct research clusters with limited cross-disciplinary integration. Our findings demonstrate a clear shift from whole-cell MSC investigations toward MSC-derived exosomes as a cell-free therapeutic approach, particularly since 2018. Despite the growing body of preclinical evidence, visualization of publication patterns reveals significant gaps between laboratory findings and clinical applications, with only 8.2% of publications reporting clinical outcomes. The analysis further highlights geographical imbalances in research contributions and collaborative networks, suggesting opportunities for broader international engagement. These findings provide a foundation for directing future research efforts toward addressing knowledge gaps, particularly in understanding precise mechanisms of action and establishing standardized clinical protocols.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.186 | 0.284 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".