19 Rapamycin and Longevity – Part 3: Continued Talk on Rapamycin’s Mechanism and Dietary Sources

This third part of our series delves deeper into Rapamycin's mechanism of action and investigates its presence in dietary sources. In this episode, you'll discover: - A refined explanation of Rapamycin's mechanism using a non-linear mathematical model (the logistic equation) to account for the limitations of cellular growth inhibition. - How Rapamycin's influence on mTOR and cellular processes translates to promoting longevity. - A scientific exploration of various food sources, including potatoes, carrots, cheese, and fermented products, to determine their Rapamycin content. - The challenges associated with detecting Rapamycin in food due to its low concentration. - The exciting potential of future research into how everyday foods might influence our health and longevity. This video is perfect for: - Anyone curious about the science behind aging and longevity. - Viewers interested in Rapamycin's potential as an anti-aging intervention. - Individuals who enjoy learning about the intricate workings of cellular processes. Want to delve deeper into the math? A Python code snippet is included at the end of this description to help you visualize the differential equations involved in Rapamycin's mechanism. Timeline 00:04 Intro 00:38 Recap and Finalizing the Mechanism of Rapamycin 03:42 Dietary Sources of Rapamycin 05:00 TRACE AMOUNTS: 1-100 ppm (per billion). 05:48 Outro Subscribe to our channel for more insightful videos on the science of longevity! P.S. Leave a comment below if you have any questions about the video or the included Python code snippet. Python Code Snippet (for further exploration): The description includes the provided Python code snippet without modification. It explains the purpose of the code and highlights the equation representing cellular growth inhibition. import numpy as np import matplotlib.pyplot as plt # Define the time variable t = np.linspace(0, 10, 400) # Define the differential equations with arbitrary constants k1, k2, k3, k4 = 0.5, 0.3, 0.4, 0.2 a, b = 0.6, 0.1 alpha, beta = 0.7, 0.2 # Arbitrary functions to simulate the differential equations C = k1 * np.exp(-k2 * t) # Simulate the first equation M = np.exp(-k3 * t) # Simulate the second equation G = (a - b * M) * M # Simulate the third equation, which should be highlighted H = alpha * (1 - M) - beta * np.exp(-t) # Simulate the fourth equation # Plot the curves plt.figure(figsize=(10, 6)) plt.plot(t, C, label=r'$\frac{dC}{dt} = k_1 \cdot R \cdot F - k_2 \cdot C$', color='blue') plt.plot(t, M, label=r'$\frac{dM}{dt} = -k_3 \cdot C \cdot M + k_4 \cdot M_{\text{inactive}}$', color='green') plt.plot(t, G, label=r'$\frac{dG}{dt} = (a - b \cdot M) \cdot M$', color='red', linewidth=2.5) plt.plot(t, H, label=r'$\frac{dH}{dt} = \alpha \cdot (1 - M) - \beta \cdot H$', color='purple') # Highlight the third equation's curve plt.plot(t, G, color='red', linewidth=3, label=r'$\frac{dG}{dt} = (a - b \cdot M) \cdot M$ (Highlighted)') # Adding labels and title plt.xlabel('Time (t)') plt.ylabel('Values') plt.title('Curves of Four Differential Equations in Rapamycin’s Mechanism of Action') plt.legend(loc='upper right') # Show the plot plt.grid(True) plt.show() Longevity #HealthyAging #Inflammation #Inflammaging #Interleukin11 #trametinib #rapamycin #metformin #MetabolicHealth #AntiAging #Healthspan #Lifespan #ERK #AMPK #mTORC1 #lysosomes #microbiome #Type_2_Diabetes #T2D #fasting #CancerTreatment #LongevityResearch #MEKPathway #KinaseInhibitors #AntiAging #MedicalResearch #ChronicInflammation #cellgrowth Disclaimer: This information is intended for general knowledge and informational purposes only, and does not constitute medical advice.