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Aging01:26

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Aging is a complex biological phenomenon influenced by various processes that affect cellular and systemic functions. Several prominent theories attempt to explain its mechanisms, highlighting cellular limitations, oxidative damage, and hormonal changes as central factors in aging.
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The cellular clock theory posits that the human lifespan is closely tied to the finite capacity of cells to divide, a phenomenon governed by telomeres, which are protective caps at the ends of...
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Several body functions deteriorate with age. The external signs of aging are easily identifiable. For example, the skin becomes dry, less elastic, and thins out, forming wrinkles. The skin of the face begins to appear looser due to a decrease in the levels of elastic and collagen fibers in the connective tissue. Additionally, melanin production in the hair follicle decreases with age, resulting in gray hair. Moreover, the senses of sight and hearing decline, so glasses and hearing aids may...
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Mitochondria are eukaryotic cellular organelles that are known to produce energy through a process called oxidative phosphorylation. Besides their primary function, mitochondria are involved in various cellular processes, including cell growth, differentiation, signaling, metabolism, and senescence. Age-related changes cause a decline in mitochondrial quality and integrity due to increased mitochondrial mutations and oxidative damage. Thus, aging can severely impact mitochondrial functions,...
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A natural language processing-driven map of the aging research landscape.

Jose Perez-Maletzki1,2, Jorge Sanz-Ros3

  • 1Universidad Europea de Valencia, Faculty of Health Sciences, Department of Physiotherapy, Nutrition and Sports Sciences, Valencia 46010, España.

Aging
|November 26, 2025
PubMed
Summary

This study used natural language processing (NLP) and machine learning (ML) to map aging research trends. Findings reveal a shift from basic biology to clinical studies, particularly neurodegenerative disorders, with a notable gap between biology of aging (BoA) and clinical applications.

Keywords:
agingliterature miningnatural language processingsynthesistopic modelling

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Area of Science:

  • Gerontology and Biomedical Informatics
  • Computational Biology
  • Translational Medicine

Background:

  • Aging research has expanded exponentially, necessitating advanced synthesis tools beyond traditional reviews.
  • The complexity of aging literature requires unbiased, data-driven approaches for comprehensive analysis.
  • Understanding the evolution and thematic landscape of aging research is crucial for future directions.

Purpose of the Study:

  • To analyze the thematic evolution of aging research over a century using advanced NLP and ML techniques.
  • To identify key research clusters, shifting priorities, and translational gaps in the field.
  • To provide a scalable, data-driven alternative to conventional narrative reviews in aging research.

Main Methods:

  • Analysis of 461,789 aging research abstracts published between 1925 and 2023.
  • Integration of Latent Dirichlet Allocation (LDA), TF-IDF, dimensionality reduction, and clustering algorithms.
  • Utilized natural language processing (NLP) and machine learning (ML) for thematic landscape delineation.

Main Results:

  • A significant shift in aging research focus from early cellular/molecular mechanisms to recent clinical studies, especially neurodegenerative disorders.
  • Identification of a persistent conceptual divide between the biology of aging (BoA) and clinical research domains.
  • Discovery of distinct research clusters, including potentially overlooked biological processes, and mapping of interconnections.

Conclusions:

  • Aging research priorities have shifted towards clinical applications, highlighting translational gaps.
  • NLP and ML offer a powerful, scalable method for synthesizing vast scientific literature.
  • The identified thematic landscape and interconnections can guide future research directions in gerontology.