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Systems Medicine: Physiological Circuits and the Dynamics of Disease
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The SR model was applied to invertebrates to explain rapid shifts in hazard curves in Drosophila and scaling of survival curves in C. elegans.
“It was applied to invertebrates to explain rapid shifts in hazard curves in Drosophila and scaling of survival curves in Caenorhabditis elegans2,19.”
From A damage accumulation model identifies distinct aging regimes across species (2026) · cites this paper as [2]
Experiments indicate that damage accelerates with age.
“Mathematically, if damage did not saturate, that is, if removal was −βx, the steady-state damage would rise linearly with age, whereas experiments indicate that damage accelerates with age2,19,20.”
From A damage accumulation model identifies distinct aging regimes across species (2026) · cites this paper as [2]
The SR model explains many quantitative patterns of aging including Gompertz and Weibull hazard curves, distributions of human frailty index, disease incidence curves and heritability of lifespan.
“The SR model explains many quantitative patterns of aging including Gompertz and Weibull hazard curves19, distributions of human frailty index2, disease incidence curves31 and heritability of lifespan32.”
From A damage accumulation model identifies distinct aging regimes across species (2026) · cites this paper as [2]
In the quasi-steady-state regime, the hazard curve resembles a Gompertz exponential rise over several decades that slows down at very old ages beyond the median lifespan (Fig. 2k).
“The hazard curve resembles Gompertz exponential rise over several decades which slows down at very old ages beyond the median lifespan (Fig. 2k)2,19,29.”
From A damage accumulation model identifies distinct aging regimes across species (2026) · cites this paper as [2]
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Hallmarks of aging: An expanding universe
López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M., & Kroemer, G. (2023) · Cell · doi:10.1016/j.cell.2022.11.001 · Cited by 6,549
Senolytic CAR T cells reverse senescence-associated pathologies
Amor, C., Feucht, J., Leibold, J., Ho, Y.-J., Zhu, C., Alonso-Curbelo, D., Mansilla-Soto, J., Boyer, J. A., Li, X., Giavridis, T., et al. (2020) · Nature · doi:10.1038/s41586-020-2403-9 · Cited by 1,098
Bergman, R. N., Prager, R., Volund, A., & Olefsky, J. M. (1987) · Journal of Clinical Investigation · doi:10.1172/jci112886 · Cited by 772
Cellular senescence: a key therapeutic target in aging and diseases
Zhang, L., Pitcher, L. E., Yousefzadeh, M. J., Niedernhofer, L. J., Robbins, P. D., & Zhu, Y. (2022) · Journal of Clinical Investigation · doi:10.1172/jci158450 · Cited by 631
Somatic mutation rates scale with lifespan across mammals
Cagan, A., Baez-Ortega, A., Brzozowska, N., Abascal, F., Coorens, T. H. H., Sanders, M. A., Lawson, A. R. J., Harvey, L. M. R., Bhosle, S., Jones, D., et al. (2022) · Nature · doi:10.1038/s41586-022-04618-z · Cited by 481
The distribution of cellular turnover in the human body
Sender, R., & Milo, R. (2021) · Nature Medicine · doi:10.1038/s41591-020-01182-9 · Cited by 420
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A damage accumulation model identifies distinct aging regimes across species
Raz, N., Yang, Y., Pridham, G., Shenhar, B., Frenkel, S., Levy, T., Tlusty, T., Mayo, A., & Alon, U. (2026) · Nature Aging · doi:10.1038/s43587-026-01138-7 · Cited by 7
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