医用射频皮肤美容与治疗专家共识

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28 10月 2024

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Author : 伤口世界

Experts consensus on the application of medical radiofrequency incosmetic dermatology and treatment
中华医学会皮肤性病学分会皮肤激光医疗美容学组
中华医学会皮肤激光技术应用研究中心
中国医师协会美容与整形医师分会激光学组
中华医学会医学美学与美容学分会激光美容学组、皮肤美容学组
【关键词] 射频，医用;皮肤美容治疗;共识
[中图分类号] R454.1 R751.05 [文献标识码]A [文章编号] 1674-1293(2021)04-0193-05

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APOE ε4–associated heterogeneity of neuroimaging biomarkers across the Alzheimer’s disease continuum 2025-08-22 00:00

Jason Mares1,2 Gautam Kumar1,3,4 Anurag Sharma1,3 Sheina Emrani5 Laura Beth McIntire6 Jia Guo7,8 Vilas Menon1,2 Tal Nuriel1,3 for the Alzheimer’s Disease Neuroimaging Initiative

1 Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, New York, New York, USA

2 Department of Neurology, Columbia University, New York, New York, USA

3 Department of Pathology and Cell Biology, Columbia University, New York, New York, USA

4 Department of Neurobiology, University of Maryland, Baltimore, Maryland, USA

5 Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA

6 Lipidomics and Biomarker Discovery Lab, Brain Health Imaging Institute, Department of Radiology, Weill Cornell Medicine, New York, New York, USA

7 Department of Psychiatry, Columbia University, New York, New York, USA

8 Zuckerman Institute, Columbia University, New York, New York, USA

Correspondence

Tal Nuriel, Taub Institute for Research on Alzheimer’s Disease and the Aging Brain, Columbia University, 630 W. 168th St., P&S 12-420E, New York, NY 10032, USA. Email: 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。 The Alzheimer’s Disease Neuroimaging Initiative: Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data, but did not participate in analysis or writing of this report.

Funding information

NIA, Grant/Award Numbers: K01 AG061264, R01 AG070202, R01 AG078800, R01 AG066831, U19 AG024904 This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

© 2025 The Author(s). Alzheimer’s & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer’s Association

Abstract

INTRODUCTION: While the role of apolipoprotein E (APOE) ε4 in Alzheimer’s dis ease (AD) susceptibility has been studied extensively, much less is known about the differences in disease presentation in APOE ε4 carriers versus non-carriers.

METHODS: To help elucidate these differences, we performed a broad analysis com paring the regional levels of six different neuroimaging biomarkers in the brains of APOE ε4 carriers versus non-carriers who participated in the Alzheimer’s Disease Neuroimaging Initiative (ADNI).

RESULTS:We observed significant APOE ε4–associated heterogeneity in regional amy loid beta deposition, tau accumulation, glucose uptake, brain volume, cerebral blood flow, and white matter hyperintensities within each AD diagnostic group. We also observed important APOE ε4–associated differences in cognitively unimpaired indi viduals who converted to mild cognitive impairment/AD versus those who did not

DISCUSSION: This observed heterogeneity in neuroimaging biomarkers between APOE ε4 carriers versus non-carriers may have important implications regarding the prevention, diagnosis, and treatment of AD in different subpopulations.

KEYWORDS

Alzheimer’s disease, Alzheimer’s Disease Neuroimaging Initiative, apolipoprotein E, biomarkers, heterogeneity, neuroimaging

Highlights

∙ An extensive study was performed on the apolipoprotein E (APOE) ε4–associated heterogeneity in neuroimaging biomarkers from the Alzheimer’s Disease Neu roimaging Initiative.

∙ Robust APOE ε4–associated increases in amyloid beta (Aβ) deposition throughout the brain, in every diagnostic group, were observed.

∙ APOE ε4–associated increases in tau pathology, decreases in glucose uptake, and increases in brain atrophy, which expand in regional scope and magnitude with disease progression, were observed.

∙ Significant sex- and age-related differences in APOE ε4–associated neuroimaging biomarker heterogeneity, with overall increases in pathological presentation in female APOE ε4 carriers, were observed.

∙ Regional differences in Aβ deposition, tau accumulation, glucose uptake, ventricle size, and white matter hyperintensities were observed in cognitively normal partic ipants who converted to mild cognitive impairment/Alzheimer’s disease, which may hold potential predictive value.
Nutritional Interventions for Pressure Ulcer Prevention in Hip Fracture Patients: A Systematic Review and Meta-Analysis of Controlled Trials 2025-08-21 00:00

Jose M. Moran 1,* , Laura Trigo-Navarro 2 , Esther Diestre-Morcillo 3 , Elena Pastor-Ramon 4 and Luis M. Puerto-Parejo 5

1 Nursing and Occupational Therapy College, University of Extremadura, 10001 Caceres, Spain

2 Área de Salud de Badajoz, Supervisora del Bloque Quirúrgico, Hospital Materno Infantil de Badajoz, Calle Violeta 3, 06010 Badajoz, Spain; 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 Área de Salud de Badajoz, Banco de Sangre, Hospital Universitario de Badajoz, Av. de Elvas, s/n, 06080 Badajoz, Spain

4 Biblioteca Virtual de ciencias de la Salud de las Illes Balears (Bibliosalut), Ctra. De Valldemossa, 79, mòdul L+1, 07120 Palma, Spain; 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。

5 Gerencia del Área de Salud de Badajoz, Supervisor del Área de Investigación, Proyectos y Gestión, Av. de Huelva, 8, 06005 Badajoz, Spain; 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。

* 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。

Academic Editor: Yi-Chia Huang

Received: 22 January 2025

Revised: 6 February 2025

Accepted: 8 February 2025

Published: 11 February 2025

Citation: Moran, J.M.; Trigo-Navarro, L.; Diestre-Morcillo, E.; Pastor-Ramon, E.; Puerto-Parejo, L.M. Nutritional Interventions for Pressure Ulcer Prevention in Hip Fracture Patients: A Systematic Review and Meta-Analysis of Controlled Trials. Nutrients 2025, 17, 644. https://doi.org/10.3390/ nu17040644

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/Objective: Pressure ulcers represent a significant complication in patients with reduced mobility, such as those recovering from hip fractures. In the present study, we aimed to comprehensively assess the impact of oral nutritional interventions on the development of pressure ulcers in hip fracture patients via a systematic review and meta analysis of controlled studies evaluating the effectiveness of oral nutritional supplements compared with standard care. Methods: In accordance with PRISMA standards, this systematic review and meta-analysis of controlled studies evaluated the effectiveness of any type of oral nutritional supplements compared with standard care in hip fracture patients. The risk of bias was evaluated using the Cochrane ROB2 tool for randomized controlled trials and the ROBINS-1 tool for nonrandomized trials. Results: Fourteen studies (10 randomized controlled trials and 4 controlled trials) published since 1990 (n = 1648) were included. Oral nutritional supplementation was associated with a statistically significant decrease in the odds ratio of developing pressure ulcers in hip fracture patients (OR 0.54, 95% CI: 0.40–0.73, p < 0.001). Conclusions: The incidence and evolution of pressure ulcers can be improved by oral dietary supplementation in patients who have undergone hip fracture surgery. Accordingly, we propose that oral nutritional supplementation should be considered an essential component of comprehensive post-hip-fracture care.

Keywords: hip fracture; pressure ulcers; oral nutritional supplement; pressure sores; meta-analysis; wound healing; nutritional intervention
Tlalpan 2020 Case Study: Enhancing Uric Acid Level Prediction with Machine Learning Regression and Cross-Feature Selection 2025-08-20 00:00

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

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主任医师、教授、博导，南方医科大学第三附属医院（广东省骨科医院）院长

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主任医师、教授、博导，南方医科大学第三附属医院（广东省骨科医院）院长

医用射频皮肤美容与治疗专家共识

伤口世界

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