Safe Biomarker Dynamics
The goal of this website is to develop
advanced dynamics and control technologies to enhance healthy human
lifespan and longevity. It can be very healthy when biomarker dynamics
stay within safe thresholds. On the other hand, exeeding safe biomarker
thresholds can be disasterous. Identifying and managing very
specific individually high biomarker dynamics can reduce the risks of
unsafe biomarker levels and extend human lifespan.
The noisy nature of lifestyle/biomarker measurements can be challenging.
Filtered data may be used to estimate the trends and cross-correlations
may be used to judge the probability distribution of the estimates. The
dynamics and control process being developed is like the linear or
polynomail regression. It will be referred here as the spectral
regression.
Most may recall perfect health irrespective of lifestyle in their 20's.
This may be attributed to low biomarker dynamics despite lifestyle
variations and other biomarker perturbaitons. There appears to be an
increase in biomarker dynamics as age progresses. Well researched
calorie restriction is known to extend human lifespan.
Author's lifestyle biomarker data
indicate the health benefits of calorie restriction to reduced biomarker
dynamics. The logged time-series data also show similar low biomarker
dynamics under certain high-fat
and certain low-fat dietary
lifestyles. This may explain the evolution of the various dietary
lifestyles ranging from low-fat to high-fat.
Figure above shows the body weight gain-loss tendency to dietary fat
ratios. Many may notice a strong correlation of dietary fat ratio to
other health conditions. While the fat ratio related biomarker dynamics
appear universal, be cautioned that unique individual biomarker dynamics
may also need to be addressed to attain low biomarker dynamics. The
timescale of body weight dynamics is about 10 days in high fat (keto
or atkins) lifestyle. The timescale of body weight dynamics on the
low-fat (vegan, starchy) is
much slower, about 6 months or longer. Again, the timescale of the
dynamics is shorter on high-fat and longer on the low-fat. The timescale
aspect is emphasized because the steady state settling to low biomarker
dynamics may not be observed wihtout the necessary patience on the order
of the required timescale. Even though this approach may look difficult
and challenging, its implementation reduces to conventional digital
control of real-time biomarker measuerments.
Monitoring the dynamics correlation of inflammation, immunity and
recovery from injury may help quantitatively prevent rapid deterioration
even under serious health conditions. We welcome website visitor and
user data contributinos to test out the processing technologies being
developed.
This technology is at a conceptual stage and the ideas are still being
developed. Howver, the data processing to extract dynamic model and
cross-correlations already exist. Anyone that already has time-series
logs at any diet/lifestyle websites may freely have them converted to
dynamic cross correlation models by us. These dynamics models provide
the differential equations and cross-correlations provide the
probability distributions. This model may be used to plan a steepest
descent strategy to a healthier lifespan. This can also provide answers
to comparative MIMO effectiveness of lifestyle to biomarkers within the
specified probabilit distribution.
Caution: Everyone is responsible for their own choice of lifestyle or trying out any of the lifestyle/biomarker dynamics mentioned in this website. We hope to provide processed interpretations of logged data in the form of least squares differential equation (curve fits) with error covariances. These differential equations may be used to identify and gently manage problem biomarker dynamics. Data processing is purely quantitative. It can handle lifestyle or biomarker components that relate to physical/mental/social/spiritual/environmental/etc. as long as they are all time-series data. It does not matter if data is labeled as blood pressure, blood sugar, biomarker 1, biomarker n, ambient temperature, humidity, or stock price. Linearly transformed data should still produce the same results. However, non-linear transformations or other data pre-processing may not produce expected results. With the general lack of dynamics and control application to biomarker dynamics, a low dynamics through small variations in lifestyle facilitated and confirmed by differential equations with known error distributions may be prudent. We hope to provide premium data processing soon. We plan to provide auto/cross covariances and differential equations among the concurrent time-series that may be shared with personal/professional/premium healthcare, nutrition, fitness, and any other services to manage and evaluate lifestyle choices.
Comments:Date: 2024-09-06 17:00:45
Name: Rgmxay
how to get rumalaya without a prescription - oral shallaki cheap amitriptyline 50mg
Date: 2024-09-08 20:05:52
Name: Tizkrd
mestinon 60mg brand - mestinon generic how to get azathioprine without a prescription
Date: 2024-09-13 17:37:54
Name: Qaqong
order voveran without prescription - nimodipine canada buy nimotop without a prescription
Date: 2024-09-14 16:38:06
Name: Udvdxj
buy generic ozobax over the counter - piroxicam 20 mg canada feldene where to buy
Add your comment:
Created: 1-1-2020, Updated: 6-24-2024