Association of physical activity with increased PI3K and Akt mRNA levels in adipose tissues of obese and non-obese adults

Montazeri, Marzieh; Zarkesh, Maryam; Zadeh‐Vakili, Azita; Khalili, Davood; Movahedi, Monireh; Khalaj, Alireza

doi:10.1038/s41598-023-36365-0

Cited by 1 publication

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References 51 publications

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“…The distribution and volume of adipose tissue are influenced by a complex interplay of physiological, psychological, and clinical factors 6 – 8 . Two distinct types of adipose tissue—subcutaneous adipose tissue (SAT) and VAT—differ anatomically and functionally within the human body 9 , 10 . Cavitation therapy, utilizing low-frequency, high-energy ultrasound (US), has emerged as a promising non-invasive technique for reducing abdominal fat and enhancing body contouring.…”

Section: Introductionmentioning

confidence: 99%

Employing machine learning for enhanced abdominal fat prediction in cavitation post-treatment

Abdel Hady,

Mabrouk,

Abd El-Hafeez

2024

Sci Rep

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This study investigates the application of cavitation in non-invasive abdominal fat reduction and body contouring, a topic of considerable interest in the medical and aesthetic fields. We explore the potential of cavitation to alter abdominal fat composition and delve into the optimization of fat prediction models using advanced hyperparameter optimization techniques, Hyperopt and Optuna. Our objective is to enhance the predictive accuracy of abdominal fat dynamics post-cavitation treatment. Employing a robust dataset with abdominal fat measurements and cavitation treatment parameters, we evaluate the efficacy of our approach through regression analysis. The performance of Hyperopt and Optuna regression models is assessed using metrics such as mean squared error, mean absolute error, and R-squared score. Our results reveal that both models exhibit strong predictive capabilities, with R-squared scores reaching 94.12% and 94.11% for post-treatment visceral fat, and 71.15% and 70.48% for post-treatment subcutaneous fat predictions, respectively. Additionally, we investigate feature selection techniques to pinpoint critical predictors within the fat prediction models. Techniques including F-value selection, mutual information, recursive feature elimination with logistic regression and random forests, variance thresholding, and feature importance evaluation are utilized. The analysis identifies key features such as BMI, waist circumference, and pretreatment fat levels as significant predictors of post-treatment fat outcomes. Our findings underscore the effectiveness of hyperparameter optimization in refining fat prediction models and offer valuable insights for the advancement of non-invasive fat reduction methods. This research holds important implications for both the scientific community and clinical practitioners, paving the way for improved treatment strategies in the realm of body contouring.

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