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Related Concept Videos

Renewal of Skin Epidermal Stem Cells01:12

Renewal of Skin Epidermal Stem Cells

The skin is divided into epidermis, dermis, and hypodermis, the skin's outermost, middle, and inner layers. The human epidermal layer regularly undergoes renewal, where old, dead cells are replaced by new cells. Epidermal stem cells or EpiSCs divide and differentiate to restore the lost cells. For the renewal process, some EpiSCs continuously self-renew. In contrast, few others differentiate into transit-amplifying cells, which later form prickle or spinous cells, followed by granular cells,...
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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.
Cellular Clock Theory
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The biological clock is involved in many aspects of regulating complex physiology in all animals. It was in 1935 when German zoologists, Hans Kalmus and Erwin Bünning, discovered the existence of circadian rhythm in Drosophila melanogaster. However, the internal molecular mechanisms behind the circadian clock remained a mystery until 1984, when Jeffrey C. Hall, Michael Rosbash, and Michael W. Young discovered the expression of the Per gene oscillating over a 24-hour cycle. In subsequent years,...
Circadian Rhythms and Gene Regulation02:19

Circadian Rhythms and Gene Regulation

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The Effect of Aging on Tissues

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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Related Experiment Video

Updated: May 28, 2026

Measuring Single-Cell Aging with an Imaging-based Biomarker of Chromatin and Epigenetic Aging
09:10

Measuring Single-Cell Aging with an Imaging-based Biomarker of Chromatin and Epigenetic Aging

Published on: January 30, 2026

Decoding Skin Aging Through Transcriptomic Clocks: Gene Expression Signatures, Associated Pathways, and Explainable

Vasiliki Kefala1, Vasiliki-Sofia Grech1, Niki Tertipi1

  • 1Department of Biomedical Sciences, School of Health and Care Sciences, University of West Attica, GR-12243 Athens, Greece.

Genes
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

Transcriptomic aging clocks offer a dynamic way to measure skin aging beyond chronological age. These gene-expression models reveal key aging processes like senescence and inflammation.

Keywords:
agingaging clocksbulk RNAseqexplainable AIfibroblastsgene expressionmachine learningmulti-omicssingle cell RNAseqskintranscriptomics

Related Experiment Videos

Last Updated: May 28, 2026

Measuring Single-Cell Aging with an Imaging-based Biomarker of Chromatin and Epigenetic Aging
09:10

Measuring Single-Cell Aging with an Imaging-based Biomarker of Chromatin and Epigenetic Aging

Published on: January 30, 2026

Area of Science:

  • Dermatology
  • Genomics
  • Computational Biology

Background:

  • Skin aging is a complex process influenced by intrinsic and extrinsic factors.
  • Chronological age is an insufficient measure of skin's biological aging state.
  • Molecular biomarkers are needed to accurately assess skin aging variability.

Purpose of the Study:

  • To review transcriptomic aging clocks in human skin.
  • To emphasize gene-expression signatures, biological pathways, and computational modeling.
  • To evaluate advancements in machine learning and artificial intelligence for skin aging.

Main Methods:

  • Targeted literature search in PubMed and IEEE Xplore.
  • Focus on gene-expression profiles and computational models.
  • Analysis of transcriptomic frameworks like SkinAGE.

Main Results:

  • Transcriptomic models capture coordinated changes in senescence, DNA damage, inflammation, and ECM remodeling.
  • Gene-expression approaches quantify dynamic and biologically relevant skin aging states.
  • Machine learning, including deep learning, enhances aging clock accuracy and interpretability.

Conclusions:

  • Transcriptomic aging clocks provide dynamic and personalized insights into skin aging.
  • Explainable AI is crucial for understanding and trusting these models.
  • Future research will integrate multi-omics data and digital twins for predictive skin aging models.