Guadalupe Gutiérrez-Esparza 1,2,* ,†, Mireya Martínez-García 3,† , Manlio F. Márquez-Murillo 2 , Malinalli Brianza-Padilla 3 , Enrique Hernández-Lemus 4,5,* and Luis M. Amezcua-Guerra 3,*
1 “Researcher for Mexico” Program under SECIHTI, Secretariat of Sciences, Humanities, Technology, and Innovation, Mexico City 08400, Mexico
2 Division of Diagnostic and Treatment Services, National Institute of Cardiology Ignacio Chávez, Mexico City 04510, Mexico; 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。
3 Department of Immunology, National Institute of Cardiology Ignacio Chávez, Mexico City 04510, Mexico; 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。 (M.M.-G.); 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。 (M.B.-P.)
4 Computational Genomics Division, National Institute of Genomic Medicine, Mexico City 14610, Mexico
5 Center for Complexity Sciences, Universidad Nacional Autónoma de México, Mexico City 04510, Mexico
*Correspondence: 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。 (G.G.-E.); 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。 (E.H.-L.); 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。 (L.M.A.-G.)
† These authors contributed equally to this work.
Academic Editor: Motoyuki Iemitsu Received: 11 February 2025 Revised: 3 March 2025 Accepted: 6 March 2025 Published: 17 March 2025
Citation: Gutiérrez-Esparza, G.; Martínez-García, M.; Márquez Murillo, M.F.; Brianza-Padilla, M.; Hernández-Lemus, E.; Amezcua Guerra, L.M. Tlalpan 2020 Case Study: Enhancing Uric Acid Level Prediction with Machine Learning Regression and Cross-Feature Selection. Nutrients 2025, 17, 1052. https://doi.org/ 10.3390/nu17061052
Copyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/)
Abstract: Background/Objectives: Uric acid is a key metabolic byproduct of purine degradation and plays a dual role in human health. At physiological levels, it acts as an antioxidant, protecting against oxidative stress. However, excessive uric acid can lead to hyperuricemia, contributing to conditions like gout, kidney stones, and cardiovascular diseases. Emerging evidence also links elevated uric acid levels with metabolic disorders, including hypertension and insulin resistance. Understanding its regulation is crucial for preventing associated health complications. Methods: This study, part of the Tlalpan 2020 project, aimed to predict uric acid levels using advanced machine learning algorithms. The dataset included clinical, anthropometric, lifestyle, and nutritional characteristics from a cohort in Mexico City. We applied Boosted Decision Trees (Boosted DTR), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), and Shapley Additive Explanations (SHAP) to identify the most relevant variables associated with hyperuricemia. Feature engineering techniques improved model performance, evaluated using Mean Squared Error (MSE), Root-Mean-Square Error (RMSE), and the coefficient of determination (R²). Results: Our study showed that XGBoost had the highest accuracy for anthropometric and clinical predictors, while CatBoost was the most effective at identifying nutritional risk factors. Distinct predictive profiles were observed between men and women. In men, uric acid levels were primarily influenced by renal function markers, lipid profiles, and hereditary predisposition to hyperuricemia, particularly paternal gout and diabetes. Diets rich in processed meats, high-fructose foods, and sugary drinks showed stronger associations with elevated uric acid levels. In women, metabolic and cardiovascular markers, family history of metabolic disorders, and lifestyle factors such as passive smoking and sleep quality were the main contributors. Additionally, while carbohydrate intake was more strongly associated with uric acid levels in women, fructose and sugary beverages had a greater impact in men. To enhance model robustness, a cross-feature selection approach was applied, integrating top features from multiple models, which further improved predictive accuracy, particularly in gender-specific analyses. Conclusions: These findings provide insights into the metabolic, nutritional characteristics, and lifestyle determinants of uric acid levels, supporting targeted public health strategies for hyperuricemia prevention.
Keywords: uric acid; regression-based machine learning; feature selection; feature engineering; Mexico City; Tlalpan 2020 cohort
Zhi‑cheng Yang1,3, He Lin1 , Guo‑jun Liu2 , Hui Pan1 , Jun‑lu Zhu3 , Xiao‑hong Zhang3 , Feng Gao2 , Zhong Wang2 and Zhi‑hao Wang
*Correspondence: Zhi‑hao Wang 该Email地址已收到反垃圾邮件插件保护。要显示它您需要在浏览器中启用JavaScript。
1 Department of Geriatric Medicine & Laboratory of Gerontology and Anti‑Aging Research, Qilu Hospital of Shandong University, Jinan 250012, Shandong, China
2 Shandong Qilu Stem Cell Engineering Co., Ltd, Jinan 250012, Shandong, China
3 School of Nursing and Rehabilitation, Shandong University, Jinan 250012, Shandong, China
© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Abstract
Background Non-healing pressure ulcers impose heavy burdens on patients and clinicians. Cord blood mononu‑ clear cells (CB-MNCs) are a novel type of tissue repair seed cells. However, their clinical application is restricted by low retention and survival rates post-transplantation. This study aims to investigate the role of thermo-sensitive chitosan/ hydroxyethyl cellulose/glycerophosphate (CS/HEC/GP) hydrogel encapsulated CB-MNCs in pressure ulcer wound
Methods Pressure ulcers were induced on the backs of aged mice. After construction and validation of the charac‑ terization of thermo-sensitive CS/HEC/GP hydrogel, CB-MNCs are encapsulated in the hydrogel, called CB-MNCs@ CS/HEC/GP which was locally applied to the mouse wounds. Mouse skin tissues were harvested for histological and molecular biology analyses.
Results CB-MNCs@CS/HEC/GP therapy accelerated pressure ulcer wound healing, attenuated inflammatory responses, promoted cell proliferation, angiogenesis, and collagen synthesis. Further investigation revealed that CB MNCs@CS/HEC/GP exerted therapeutic effects by promoting changes in cell types, including fibroblasts, endothelial cells, keratinocytes, and smooth muscle cells.
Conclusion CB-MNCs@CS/HEC/GP enhanced the delivery efficiency of CB-MNCs, preserved the cell viability, and contributed to pressure ulcer wound healing. Thus, CB-MNCs@CS/HEC/GP represents a novel therapeutic approach for skin regeneration of chronic wounds.
Keywords Wound healing, Aged, Pressure ulcers, Cord blood mononuclear cells, Thermo-sensitive hydrogel
Authors
Maria Hendrika van Zuilen
Preetha Kamath
Joy E. Schank
Catherine R. Ratliff
David Strider
Matthew Regulski
Barbara C. Zeiger
Vita Boyar
伤口世界平台生态圈，以“关爱人间所有伤口患者”为愿景，连接、整合和拓展线上和线下的管理慢性伤口的资源，倡导远程、就近和居家管理慢性伤口，解决伤口专家的碎片化时间的价值创造、诊疗经验的裂变复制、和患者的就近、居家和低成本管理慢性伤口的问题。
2019广东省医疗行业协会伤口管理分会年会
扫一扫了解详情:
任何关于疾病的建议都不能替代执业医师的面对面诊断。所有门诊时间仅供参考，最终以医院当日公布为准。
网友、医生言论仅代表其个人观点，不代表本站同意其说法，请谨慎参阅，本站不承担由此引起的法律责任。
