Human adaptation to cold and warm climatic conditions: A comparison between two geographically diverse Indigenous populations

Ghosh, Sudipta

doi:10.1002/ajhb.23932

American J Hum Biol

2023

DOI: 10.1002/ajhb.23932

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Human adaptation to cold and warm climatic conditions: A comparison between two geographically diverse Indigenous populations

Sudipta Ghosh

Abstract: ObjectivesThe present study aims to compare body adiposity and blood pressure (BP) in two climatically and ethnically diverse populations, examining whether thermoregulatory adaptive mechanism may protect Indigenous populations from exhibiting adverse consequences of increased adiposity.MethodsA cross sectional sample of 404 subjects, of which 200 were Monpa and 204 were Santhal, from two ethnically and geographically distinct populations of India were studied. Body mass index (BMI; kg/m2), fat mass (FM; kg), … Show more

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2024

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Cited by 3 publications

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Exploration of Geographical Environmental Factors Influencing Regional Population Mortality Patterns in China

Li,

2024

American J Hum Biol

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ObjectivesThe regional population mortality patterns in China exhibit substantial geographical distribution characteristics. This paper aims to explore the impact and mechanisms of geographical environmental factors on regional population mortality patterns.MethodsThis study first utilized the data from China's Seventh Population Census to obtain mortality patterns for the 31 provincial‐level administrative regions. Subsequently, a functional regression method was employed to explore the geographical environmental driving factors of regional mortality patterns.ResultsThe study provides a detailed explanation of the mechanisms and marginal contributions of key geographical environmental factors at different age groups.Conclusions(1) The impact of geographical environmental factors on mortality patterns shows distinct phased characteristics. Mortality patterns before the age of 40 years are hardly influenced by geographical environmental factors, with a noticeable impact beginning at ages 40–69 years and reaching the maximum influence after the age of 70 years. (2) In mortality patterns at ages 40–69 years, average altitude have the most substantial impact, followed by extreme low‐temperature days and PM2.5 concentration. In mortality patterns at ages 70–94 years, high‐temperature days have the greatest influence, followed by the impact of SO2 concentration. (3) In comparisons based on gender, socioeconomic factors, and geographical environmental factors, gender and urban–rural differences have the most significant impact on regional population mortality patterns, followed by the influence of other socioeconomic factors, with geographical environmental factors having a relatively smaller impact.

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Exploration of Geographical Environmental Factors Influencing Regional Population Mortality Patterns in China

Li,

2024

American J Hum Biol

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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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Utilizing machine learning to analyze trunk movement patterns in women with postpartum low back pain

A. Abdel Hady,

Abd El-Hafeez

2024

Sci Rep

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This paper presents an analysis of trunk movement in women with postnatal low back pain using machine learning techniques. The study aims to identify the most important features related to low back pain and to develop accurate models for predicting low back pain. Machine learning approaches showed promise for analyzing biomechanical factors related to postnatal low back pain (LBP). This study applied regression and classification algorithms to the trunk movement proposed dataset from 100 postpartum women, 50 with LBP and 50 without. The Optimized optuna Regressor achieved the best regression performance with a mean squared error (MSE) of 0.000273, mean absolute error (MAE) of 0.0039, and R2 score of 0.9968. In classification, the Basic CNN and Random Forest Classifier both attained near-perfect accuracy of 1.0, the area under the receiver operating characteristic curve (AUC) of 1.0, precision of 1.0, recall of 1.0, and F1-score of 1.0, outperforming other models. Key predictive features included pain (correlation of -0.732 with flexion range of motion), range of motion measures (flexion and extension correlation of 0.662), and average movements (correlation of 0.957 with flexion). Feature selection consistently identified pain, flexion, extension, lateral flexion, and average movement as influential across methods. While limited to this initial dataset and constrained by generalizability, machine learning offered quantitative insight. Models accurately regressed (MSE < 0.01, R2 > 0.95) and classified (accuracy > 0.94) trunk biomechanics distinguishing LBP. Incorporating additional demographic, clinical, and patient-reported factors may enhance individualized risk prediction and treatment personalization. This preliminary application of advanced analytics supported machine learning's potential utility for both LBP risk determination and outcome improvement. This study provides valuable insights into the use of machine learning techniques for analyzing trunk movement in women with postnatal low back pain and can potentially inform the development of more effective treatments.Trial registration: The trial was designed as an observational and cross-section study. The study was approved by the Ethical Committee in Deraya University, Faculty of Pharmacy, (No: 10/2023). According to the ethical standards of the Declaration of Helsinki. This study complies with the principles of human research. Each patient signed a written consent form after being given a thorough description of the trial. The study was conducted at the outpatient clinic from February 2023 till June 30, 2023.

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Human adaptation to cold and warm climatic conditions: A comparison between two geographically diverse Indigenous populations

Cited by 3 publications

References 56 publications

Exploration of Geographical Environmental Factors Influencing Regional Population Mortality Patterns in China

Exploration of Geographical Environmental Factors Influencing Regional Population Mortality Patterns in China

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

Utilizing machine learning to analyze trunk movement patterns in women with postpartum low back pain

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