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Cardiac biomarkers are enzymes, proteins, and hormones released into the blood when cardiac cells are injured. They are powerful tools for triaging.
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Psychoneuroimmunology (PNI) is a multidisciplinary field that examines how psychological factors, particularly stress, interact with the immune system and impact physical health. Research in PNI has shown that chronic or traumatic stress can disrupt both the hypothalamic-pituitary-adrenal axis and the sympathetic nervous system. These disruptions contribute to serious health conditions, including cardiovascular diseases.
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The Effect of Aging on Tissues01:19

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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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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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Multimodal Computational Approach for Forecasting Cardiovascular Aging Based on Immune and Clinical-Biochemical

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Summary

This study developed a novel model using machine learning to predict cardiovascular disease risk by analyzing clinical, immunological, and biochemical markers. The findings aid in early detection and personalized prevention strategies for cardiovascular aging.

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Area of Science:

  • Cardiology
  • Immunology
  • Biochemistry
  • Data Science

Background:

  • Cardiovascular disease (CVD) risk prediction is enhanced by integrating clinical, immunological, and biochemical markers.
  • A wide array of biomarkers including immune cells (e.g., CD14, CD16), cytokines (e.g., IL-10), inflammatory markers (e.g., CRP), and organ function indices (e.g., GFR, NT-proBNP) were analyzed.
  • Clinical factors such as arterial hypertension, diabetes mellitus, and lifestyle choices were also incorporated.

Purpose of the Study:

  • To develop an innovative predictive model for cardiovascular disease (CVD) risk.
  • To enable early detection of predisposition to CVDs and their complications.
  • To create personalized recommendations for CVD prevention and management.

Main Methods:

  • Utilized mathematical modeling and machine learning techniques for risk prediction.
  • Included 52 patients aged 65 and older.
  • Employed numerical methods like Runge-Kutta, Adams-Bashforth, and backward-directed Euler for accurate modeling of biomarker dynamics.

Main Results:

  • HLA-DR (50%), CD14 (41%), and CD16 (38%) demonstrated the strongest association with aging processes.
  • Body Mass Index (BMI) correlated with placental growth factor (PGF) (37%).
  • Glomerular filtration rate (GFR) showed positive association with physical activity (47%), while superoxide dismutase (SOD) activity showed negative correlation (48%), indicating reduced antioxidant defense.

Conclusions:

  • The study improves the accuracy of cardiovascular risk prediction.
  • Results facilitate personalized recommendations for preventing and managing cardiovascular disease.
  • The integrated approach highlights the importance of diverse biomarkers in assessing cardiovascular health.