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Record W4402809130 · doi:10.3389/fneur.2024.1413762

Mapping the research of mitochondria and Parkinson’s disease: a bibliometric analysis

2024· review· en· W4402809130 on OpenAlexaboutno aff
Yan-Jun Chen, Ming-Rong Xie, Shengqiang Zhou, Fang Liu

Bibliographic record

VenueFrontiers in Neurology · 2024
Typereview
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
FundersChina Academy of Chinese Medical SciencesNatural Science Foundation of Hunan Province
KeywordsLibrary scienceBibliometricsCitationGerontologyWeb of scienceImpact factorMedicinePolitical sciencePathologyComputer scienceLawMeta-analysis

Abstract

fetched live from OpenAlex

Background Parkinson’s disease (PD) is a chronic, progressive neurodegenerative disorder primarily affecting the elderly. Relevant studies suggest a significant connection between the mitochondria and PD. Publications exploring this connection have steadily increased in recent years. This study employs a bibliometric approach to comprehensively analyze the current status and future directions of the research on mitochondria and PD. Method We retrieved data from the Web of Science database and used CiteSpace, VOSviewer, and “Bibliometrix” software to visually analyze various aspects of the research field. These aspects included the number of published papers, contributing countries and institutions, authors, publishing journals, cited references, and keywords. Results Our analysis identified a total of 3,291 publications involving 14,670 authors from 2,836 organizations across 78 countries. The publication volume exhibited a continuous upward trend from 1999 to 2023. The United States emerged as the leading force in this research area, contributing the highest number of high-quality publications. Notably, the United States collaborated extensively with Germany and the United Kingdom. The University of Pittsburgh stood out as the most prolific institution. Harvard University had the highest academic influence and closely cooperated with the University of Pittsburgh, Juntendo University, and McGill University. Dr. Hattori Nobutaka was identified as the most prolific author, while Dr. Youle, Richard J emerged as the most influential author based on the highest average citation frequency. The Journal of Neurochemistry was the most published journal. The most co-cited paper was titled “Hereditary early-onset Parkinson’s disease caused by mutations in PINK1.” The major keywords included oxidative stress, alpha-synuclein, pink1, mitophagy, and mitochondrial dysfunction. Mitofusin 2, ubiquitin, and mitochondrial quality control have been identified as new research hotspots in recent years. Conclusion Mitochondria-PD research is experiencing a steady increase in activity, fueled by increasing close collaboration between countries and different institutions. However, there is a need to further strengthen collaboration and communication between developed and developing nations. Current research has focused on the specific mechanisms of mitochondrial dysfunction and their relationship with PD. Mitofusin 2, ubiquitin, and mitochondrial quality control are positioned to be the hotspots and future research directions.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.992
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.037
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.1810.227
Science and technology studies0.0010.001
Scholarly communication0.0060.003
Open science0.0010.003
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.102
GPT teacher head0.381
Teacher spread0.279 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designMeta-analysis
DomainEvaluation
GenreReview

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

Citations2
Published2024
Admission routes1
Has abstractyes

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