Facial aging (&skin aging) - how good of a proxy is it for regular aging?

[Alex K Chen]

https://www.rapamycin.news/t/three-dimensional-analysis-of-modeled-facial-aging/4674/10 [warning - PDFs that unnecessarily autodownload]

https://www.rapamycin.news/t/lure-hsu-asian-woman-aging-well-latest-videos/6530/9
 

[originally inspired by "ageless looks" on longecity.org]

==

https://clinicalepigeneticsjournal.biomedcentral.com/articles/10.1186/s13148-023-01590-x

has some EWMR stuff, tho idk if it is the best-written?

=========

Figure 2 shows the change in the skin structure on the surface because of such a simulation: first is a smooth structure, corresponding to the initial “young” age with the distribution p(x); next is a non-smooth structure of “older” age with the distribution q(x).

Figure 2. The distribution of the spatial density of cells and its change with time. The Python code for the production of this figure is outlined in the supplemental IPYNB file.

In Figure 1 and Figure 2, we can see chaotization in the disturbance of skin smoothness, which in the first case is represented as entropy fluctuations, and in the second case as changes in skin thickness in different skin areas. This chaotization corresponds to the growth of Kullback–Leibler entropy and the growth of statistical entropy in the movement from S_young (young age) to S_old (old age) in Figure 1. Using Kullback–Leibler entropy, p(x) can be regarded as the baseline distribution of skin density at a young age, and q(x) as the distribution at an older age.

 

The above Formula (3) for the Kullback–Leibler entropy allows us to estimate the increase in entropy when simulating skin aging. We simulated changes in the skin structure and calculated the corresponding Kullback–Leibler entropy as a function of the number of iterations (see Figure 3).

Figure 3. Changes in the skin structure with age along an arbitrary line (in conventional units), and the calculated corresponding Kullback–Leibler entropy. (a) Profiles q₁(x) and q₂(x) are two broken lines (after a number of iterations); (b) the change in Kullback–Leibler entropy with time, along the x-axis, after a number of iterations. The Python code for the production of this figure is outlined in the supplemental IPYNB file.

Figure 3b shows how the modulus of the difference between the Kullback–Leibler entropy and the unit changes as iterations are performed. This quantity, although with known fluctuations, increases, which characterizes the aging of the system. Such calculations can be made for the configuration of cells in some organ, or in the organism as a whole. At a young age, a certain configuration is fixed. At a later age, it changes (while the cells are repeatedly replaced); the comparison of the mentioned profiles will give an estimate of entropy.

The distribution change with time presented in Figure 2 shows the “stochastization” of spatial regions, which is equivalent to the greater independence of one area from another (decrease in cooperativity); thus, one can expect an increase in the calculated combinatorial entropy as well (see Appendix A).

Edited February 29 by InquilineKea