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Uncovering glycolysis-driven molecular subtypes in diabetic nephropathy: a WGCNA and machine learning approach for diagnostic precision

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BIOLOGY DIRECT 2025年第1期20卷 1-16页

作者： Fan, Chenglong Yang, Guanglin Li, Cheng Cheng, Jiwen Chen, Shaohua Mi, Hua Guangxi Med Univ Affiliated Hosp 1 Dept Urol Nanning 530021 Guangxi Peoples R China Guangxi Med Univ Canc Hosp Dept Urol Nanning 530000 Guangxi Peoples R China

IntroductionDiabetic nephropathy (DN) is a common diabetes-related complication with unclear underlying pathological mechanisms. Although recent studies have linked glycolysis to various pathological states, its role in DN remains largely *** this study, the expression patterns of glycolysis-related genes (GRGs) were first analyzed using the GSE30122, GSE30528, and GSE96804 datasets, followed by an evaluation of the immune landscape in DN. An unsupervised consensus clustering of DN samples from the same dataset was conducted based on differentially expressed GRGs. The hub genes associated with DN and glycolysis-related clusters were identified via weighted gene co-expression network analysis (WGCNA) and machine learning algorithms. Finally, the expression patterns of these hub genes were validated using single-cell sequencing data and quantitative real-time polymerase chain reaction (qRT-PCR).ResultsEleven GRGs showed abnormal expression in DN samples, leading to the identification of two distinct glycolysis clusters, each with its own immune profile and functional pathways. The analysis of the GSE142153 dataset showed that these clusters had specific immune characteristics. Furthermore, the Extreme Gradient Boosting (XGB) model was the most effective in diagnosing DN. The five most significant variables, including GATM, PCBD1, F11, HRSP12, and G6PC, were identified as hub genes for further investigation. Single-cell sequencing data showed that the hub genes were predominantly expressed in proximal tubular epithelial cells. In vitro experiments confirmed the expression pattern in *** study provides valuable insights into the molecular mechanisms underlying DN, highlighting the involvement of GRGs and immune cell infiltration.

关键词： Diabetic nephropathy Glycolysis Glycolysis-related genes machine learning algorithm Hub genes

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Enhancing digital health services: A machine learning approach to personalized exercise goal setting

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DIGITAL HEALTH 2024年 10卷

作者： Fang, Ji Lee, Vincent C. S. Ji, Hao Wang, Haiyan Southeast Univ Tranportat Coll Southeast Univ Nanjing 211189 Peoples R China Monash Univ Dept Data Sci & Artificial Intelligence Melbourne Australia Hangzhou Med Coll Hangzhou Peoples R China

Background The utilization of digital health has increased recently, and these services provide extensive guidance to encourage users to exercise frequently by setting daily exercise goals to promote a healthy lifestyle. These comprehensive guides evolved from the consideration of various personalized behavioral factors. Nevertheless, existing approaches frequently neglect the users' dynamic behavior and the changing in their health *** This study aims to fill this gap by developing a machine learning algorithm that dynamically updates auto-suggestion exercise goals using retrospective data and realistic behavior *** We conducted a methodological study by designing a deep reinforcement learning algorithm to evaluate exercise performance, considering fitness-fatigue effects. The deep reinforcement learning algorithm combines deep learning techniques to analyze time series data and infer user's exercise behavior. In addition, we use the asynchronous advantage actor-critic algorithm for reinforcement learning to determine the optimal exercise intensity through exploration and exploitation. The personalized exercise data and biometric data used in this study were collected from publicly available datasets, encompassing walking, sports logs, and *** In our study, we conducted the statistical analyses/inferential tests to compare the effectiveness of machine learning approach in exercise goal setting across different exercise goal-setting strategies. The 95% confidence intervals demonstrated the robustness of these findings, emphasizing the superior outcomes of the machine learning *** Our study demonstrates the adaptability of machine learning algorithm to users' exercise preferences and behaviors in exercise goal setting, emphasizing the substantial influence of goal design on service effectiveness.

关键词： Digital health service exercise goal setting changing exercise behavior machine learning algorithm internet of things technologies fitness-fatigue effect exercise performance

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A machine learning-based forecasting model for personal maximum allowable exposure time under extremely hot environments

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SUSTAINABLE CITIES AND SOCIETY 2024年 101卷

作者： Choi, Yujin Seo, Seungwon Lee, Junsoo Kim, Tae Wan Koo, Choongwan Incheon Natl Univ Div Architecture & Urban Design Incheon 22012 South Korea

As global warming leads to an increase in the frequency and intensity of heatwaves, protecting outdoor workers from heat-related illnesses becomes paramount. To address this challenge, this study aimed to forecast personal maximum allowable exposure time by considering individual differences under extremely hot environments. To enhance the prediction accuracy of the proposed approach, a machine learning-based error correction model was developed in conjunction with the PHS_HR method (i.e., predicted heat strain index using real-time heart rate), reflecting dynamic changes in personal biometric characteristics (e.g., heart rate, body fat percentage, etc.) as well as environmental conditions and exposure time. Among the developed models, the multi-layer perceptron (MLP) algorithm demonstrated a high degree of reliability in forecasting personal maximum allowable exposure time, achieving a mean absolute error (MAE) of 0.19 minutes (11 seconds), compared to the existing PHS_HR method (MAE: 5.05 minutes). This study highlights the potential benefits of data-driven computational methods in occupational health and safety management. By providing more accurate predictions of personal maximum allowable exposure time under extremely hot environments, the risk of heat-related illnesses outdoors can be effectively mitigated. As heatwaves become more prevalent, the proposed approach offers a more valuable tool to ensure safer work environments.

关键词： Maximum allowable exposure time extremely hot environment construction workers continuous work time predicted heat strain machine learning algorithm

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Forecasting personal heat strain under extremely hot environments: Utilizing feature importance in machine learning

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartE期133卷

作者： Seo, Seungwon Choi, Yujin Koo, Choongwan Incheon Natl Univ Div Architecture & Urban Design Incheon 22012 South Korea

Since the frequency and intensity of heatwaves are expected to increase due to global warming, it is crucial to establish a simplified approach to preventing personal heat-related illnesses as occupational hazard under extremely hot environments, by taking into account individual differences in heat strain. In light of this, this study proposed a simplified forecast model for personal heat strain under extremely hot environments by utilizing feature importance in machine learning, focused on enhancing its field applicability. Using the 1417 records gathered from the experiments under extremely hot conditions, the simplified model was developed by utilizing four types of machine learning algorithms (i.e., random forest, extreme gradient boosting, support vector regression, and multi-layer perceptron). As a result, the proposed simplified models with four principal features (i.e., dry-bulb temperature, radiant temperature, relative humidity, and percentage body fat) were found to be the most reliable, resulting in a small difference of 0.047 degrees C compared to the reference model. Since the principal biometric features can be easily measured by a simple and low-cost body composition test (but not with the physical measurement of personal core body temperature) before they are put into the field, the proposed approach will aid in providing more systematic and efficient management system for proactively preventing personal heat-related illnesses. Furthermore, it is expected that the field applicability of the proposed approach can be continuously enhanced by utilizing the accumulated big data gathered from the field-based living-lab projects over a lengthy period.

关键词： Personal heat strain Extremely hot environments Forecasting model Principal biometric features machine learning algorithm Field applicability

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Active learning on stacked machine learning techniques for predicting compressive strength of alkali-activated ultra-high-performance concrete

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ARCHIVES OF CIVIL AND MECHANICAL ENGINEERING 2024年第1期25卷 1-35页

作者： Kazemi, Farzin Shafighfard, Torkan Jankowski, Robert Yoo, Doo-Yeol Gdansk Univ Technol Fac Civil & Environm Engn Ul G Narutowicza 11-12 PL-80233 Gdansk Poland Univ Naples Federico II Sch Polytech & Basic Sci Dept Struct Engn & Architecture I-80125 Naples Italy Polish Acad Sci Inst Fluid Flow Machinery Generala Jozefa Fiszera 14 PL-80231 Gdansk Poland Yonsei Univ Dept Architecture & Architectural Engn 50 Yonsei Ro Seoul 03722 South Korea

Conventional ultra-high performance concrete (UHPC) has excellent development potential. However, a significant quantity of CO2 is produced throughout the cement-making process, which is in contrary to the current worldwide trend of lowering emissions and conserving energy, thus restricting the further advancement of UHPC. Considering climate change and sustainability concerns, cementless, eco-friendly, alkali-activated UHPC (AA-UHPC) materials have recently received considerable attention. Following the emergence of advanced prediction techniques aimed at reducing experimental tools and labor costs, this study provides a comparative study of different methods based on machine learning (ML) algorithms to propose an active learning-based ML model (AL-Stacked ML) for predicting the compressive strength of AA-UHPC. A data-rich framework containing 284 experimental datasets and 18 input parameters was collected. A comprehensive evaluation of the significance of input features that may affect compressive strength of AA-UHPC was performed. Results confirm that AL-Stacked ML-3 with accuracy of 98.9% can be used for different general experimental specimens, which have been tested in this research. Active learning can improve the accuracy up to 4.1% and further enhance the Stacked ML models. In addition, graphical user interface (GUI) was introduced and validated by experimental tests to facilitate comparable prospective studies and predictions.

关键词： machine learning algorithm Alkali-activated ultra-high performance concrete Active learning algorithm Compressive strength Hyperparameter optimization

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Readmission Risk Prediction After Total Hip Arthroplasty Using machine learning and Hyperparameter Optimized with Bayesian Optimization

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2025年第2期16卷 887-898页

作者： Purbasari, Intan Yuniar Bayuseno, Athanasius Priharyoto Isnanto, R. Rizal Winarni, Tri Indah Diponegoro Univ Sch Postgrad Studies Doctoral Program Informat Syst Semarang Indonesia Univ Pembangunan Nas Vet Jawa Timur Fac Comp Sci Dept Informat Surabaya Indonesia Diponegoro Univ Fac Engn Dept Mech Engn Semarang Indonesia Diponegoro Univ Fac Engn Dept Comp Engn Semarang Indonesia Diponegoro Univ Fac Med Dept Anat Semarang Indonesia Univ Diponegoro Undip Biomech Engn & Res Ctr UBM ERC Semarang Indonesia

machine learning techniques are increasingly used in orthopaedic surgery to assess risks such as length of stay, complications, infections, and mortality, offering an alternative to traditional methods. However, model performance varies depending on private institutional data, and optimizing hyperparameters for better predictions remains a challenge. This study incorporates automatic hyperparameter tuning to improve readmission prediction in orthopaedics using a public medical dataset. Bayesian Optimization was applied to optimize hyperparameters for seven machine learning algorithms- Extreme Gradient Boosting, Stochastic Gradient Boosting, Random Forest, Support Vector machine, Decision Tree, Neural Network, and Elastic-net Penalized Logistic Regression- predicting readmission risk after Total Hip Arthroplasty (THA). Data from the MIMIC-IV database, including 1,153 THA patients, was used. Model performance was evaluated using Precision, Recall, and AUC-ROC, comparing optimized algorithms to those without hyperparameter tuning from previous studies. The optimized Extreme Gradient Boosting algorithm achieved the highest AUC-ROC of 0.996, while other models also showed improved accuracy, precision, and recall. This research successfully developed and validated optimized machine learning models using Bayesian Optimization, enhancing readmission prediction following THA based on patient demographics and preoperative diagnosis. The results demonstrate superior performance compared to prior studies that either lacked hyperparameter optimization or relied on exhaustive search methods.

关键词： Total hip arthroplasty orthopaedic surgery Bayesian Optimization machine learning algorithm hyperparameter optimization

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The Association of Elevated Depression Levels and Life's Essential 8 on Cardiovascular Health With Predicted machine learning Models and Interpretations: Evidence From NHANES 2007-2018

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DEPRESSION AND ANXIETY 2025年第1期2025卷 8865176页

作者： Wu, Zhixing Xu, Pengyuan Zhai, Yali Mahe, Jinli Guo, Kai Olawole, Wuraola Zhu, Jiahao Han, Jin Bai, Guannan Zhang, Lin Monash Univ Sch Publ Hlth & Prevent Med Melbourne Australia Columbia Univ Mailman Sch Publ Hlth Biostatist Track New York NY USA Monash Univ Sch Engn Melbourne Australia Inner Mongolia Univ Sci & Technol Sch Publ Hlth Baotou Inner Mongolia Peoples R China Inner Mongolia Univ Sci & Technol Baotou Med Coll Baotou Inner Mongolia Peoples R China Johns Hopkins Univ Sch Nursing Baltimore MD USA Harbin Med Univ Dept Outpatient Chemotherapy Affiliated Hosp Harbin Peoples R China New York Univ Shanghai Div Arts & Sci Shanghai Peoples R China New York Univ Shanghai Ctr Global Hlth Equ Shanghai Peoples R China Univ New South Wales Black Dog Inst Sydney Australia Zhejiang Univ Sch Med Childrens Hosp Hangzhou Peoples R China Monash Univ Suzhou Ind Pk Monash Res Inst Sci & Technol Suzhou 215000 Peoples R China

Background and Objective: The association between depression severity and cardiovascular health (CVH) represented by Life's Essential 8 (LE8) was analyzed, with a novel focus on ranked levels and different ages. machine learning (ML) algorithms were also selected aimed at providing predictions to suggest practical recommendations for public awareness and clinical ***: We included 21,279 eligible participants from the National Health and Nutrition Examination Survey (NHANES) 2007-2018. Weighted ordinal logistic regression (LR) was utilized with further sensitivity and dose-response analysis, and ML algorithms were analyzed with SHapley Additive exPlanations (SHAP) applied to make interpretable results and ***: Our studies demonstrated an inverse relationship between LE8 and elevated depressive levels, with robustness confirmed through subgroup and interaction analysis. Age-specific findings revealed middle-aged and older adults (aged 40-60 and over 60) which showed higher depresion severity, highlighting the need for greater awareness and targeted interventions. Eight ML algorithms were selected to provide predictive results, and further SHAP would become ideal supplement to increase model ***: Our studies demonstrated a negative association between LE8 and elevated depressive levels and provided a suite of ML predictive models, which would generate recommendations toward clinical implications and subjective interventions.

关键词： age-specific analysis cardiovascular health cardiovascular risks depression depressive symptoms Life's Essential 8 lifestyle patterns machine learning algorithm predictive and association models psychological health

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Reliability of noninvasive hyperspectral tongue diagnosis for menstrual diseases using machine learning method

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SCIENTIFIC REPORTS 2025年第1期15卷 1-13页

作者： Liang, Aohui Ge, Jiaming Liu, Zhaowei Han, Xiangli Hou, Songtao Li, Gang Liu, Ming Zhao, Jing Tianjin Univ Tradit Chinese Med Coll Pharmaceut Engn Tradit Chinese Med Tianjin 301617 Peoples R China Tianjin Univ Tradit Chinese Med Tianjin Key Lab Intelligent TCM Diag & Treatment T Tianjin 301617 Peoples R China Tianjin Univ TCM Dept Geriatr Teaching Hosp 4 Tianjin 300450 Peoples R China Tianjin Univ TCM Dept Proctol Teaching Hosp 4 Tianjin 300450 Peoples R China Tianjin Univ State Key Lab Precis Measurement Technol & Instrum Tianjin 300072 Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Inst Biomed Engn Tianjin 300192 Peoples R China

The outward appearance of human tongue can reflect changes in blood circulation caused by pathological states, and it has been used as an assisted method for clinical diseases diagnosis for thousands of years in China. The purpose of this study is to observe the changes in the tongue of patients with menstrual-related diseases in hyperspectral imaging and to explore the value of hyperspectral tongue imaging combining with machine learning algorithm (HSI-ML) in the diagnosis of menstrual diseases (MD). Hyperspectral tongue images are collected from 429 patients with five different kinds of MD and 52 participants with normal menstruation. Tongue coating and tongue body spectral characteristics are extracted and used as model input variables to investigate the influence on the modeling *** (Norm), first derivative (1st D), second derivative (2nd D), savitzky-golay smoothing (S-G), multiplicative scatter correction (MSC), and standard normal variate transformation (SNV) are used as preprocessing method. Four model algorithms, k-nearest neighbor (KNN), random forest (RF), support vector machines (SVM) and artificial neural network(ANN) are used and compared. Experimental results show that patients of each MD group exhibit significantly lower spectral reflectance of tongue coating and tongue body (P < 0.05) than participants of normal menstruation group. And the modeling results indicate that the "2nd D + S-G + ANN" identification model based on tongue body spectral characteristics yields the optimal performance. Specifically, its accuracy, macro-precision, macro-recall, and macro-F1 score are 0.9729, 0.9697, 0.9703, and 0.97, respectively. It indicates that HSI-ML method with hyperspectral tongue images can provide a rapid and noninvasive detection method for MD screening.

关键词： Menstrual diseases Tongue Hyperspectral imaging machine learning algorithm Screening

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machine learning-assisted nanosensor arrays: An efficiently high-throughput food detection analysis

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TRENDS IN FOOD SCIENCE & TECHNOLOGY 2024年 149卷

作者： Li, Yuechun Zhang, Wenrui Cui, Zhaowen Shi, Longhua Shang, Yiwen Ji, Yanwei Wang, Jianlong Northwest A&F Univ Coll Food Sci & Engn 22 Xinong Rd Yangling 712100 Shaanxi Peoples R China

Background: How to timely identify the food quality through a low-cost, easy operation, and high-throughput way is a milestone protects for food industry, especially in resource-limited area. Nanosensors by integrating with biomolecules (such as antibodies and aptamers) have emerged substitutes for the standard equipment analysis in the large-scale screening. However, the expensive cost of biomolecules, the "lock-key" combination unable to solve some problems (such as food freshness), and emerging food risks have strictly their development in food industry. Additionally, nanosensors without biomolecules are easily suffered from the non-specific interference, making the detection results unreliable. Therefore, some studies have concentrated on the sensor array by using nanomaterials as receptors to solve abovementioned problems, which is based on the multiple signal responses to generate the distinctive fingerprint for each analyte. Scope and approach: This review comprehensively discussed the machine learning-assisted nanosensor arrays for the efficiently high-throughput food detection analysis, which mainly concludes candidates for nanosensor arrays, commonly used machine learning algorithms, and the application in food applications (such as foodborne hazards, food components, food freshness, food origin, and food adulteration). Additionally, we have proposed the challenges and prospects of machine learning-assisted nanosensor arrays in food applications to bridge the gap of current development bottleneck. Key findings and conclusions: Therefore, machine learning-assisted nanosensor arrays for the efficiently highthroughput detection analysis in food industry are proposed.

关键词： machine learning algorithm Nanosensor array Food quality Food high-throughput detection

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machine learning analysis of cervical balance in early-onset scoliosis post-growing rod surgery: a case-control study

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SCIENTIFIC REPORTS 2025年第1期15卷 1-11页

作者： Han, Bo Hai, Junrui Jonathan Pan, Aixing Wang, Yingjie Hai, Yong Capital Med Univ Beijing Jishuitan Hosp Beijing Peoples R China Princeton Int Sch Math & Sci 19 Lambert Dr Princeton NJ 08540 USA Capital Med Univ Beijing Chao Yang Hosp Dept Orthoped Surg GongTiNanLu 8 Beijing 100020 Peoples R China China Univ Petr East China Coll Control Sci & Engn Qingdao 266580 Peoples R China Capital Med Univ Joint Lab Res & Treatment Spinal Cord Injury Spina Beijing Peoples R China Capital Med Univ Clin Ctr Spinal Deform Beijing Peoples R China

We aimed to analyze the cervical sagittal alignment change following the growing rod treatment in early-onset scoliosis (EOS) and identify the risk factors of sagittal cervical imbalance after growing-rod surgery of machine learning. EOS patients from our centre between 2007 and 2019 were retrospectively reviewed. Radiographic parameters include the cervical lordosis (CL), T1 slope, C2-C7 sagittal vertical axis (C2-7 SVA), primary curve Cobb angle, thoracic kyphosis (TK), C7-S1 sagittal vertical axis (C7-S1 SVA) and proximal junctional angle (PJA) were evaluated preoperatively, postoperatively and at the final follow-up. The parameters were analyzed using a t-test and chi 2 test. The machine learning methodology of a sparse additive machine (SAM) was applied to identify the risk factors that caused the cervical imbalance. 138 patients were enrolled in this study (96 male and 42 female). The mean thoracic curve Cobb angle was 67.00 +/- 22.74 degrees. The mean age at the first operation was 8.5 +/- 2.6yrs. The mean follow-up was 38.48 +/- 10.87 months. CL, T1 slope, and C2-7 SVA increased significantly in the final follow-up compared with the pre-operative data. (P < 0.05). The CL and T1 slope increased more significantly in the group of patients who had proximal junctional kyphosis (PJK) compared with the patients without PJK (P < 0.05). The location of the upper instrumented vertebrae (UIV) and single/dual growing rod had no significant influence on the sagittal cervical parameters (P > 0.05). According to the SAM analysis of machine learning algorithms, Postoperative PJK, more improvement of kyphosis, and T1 slope angle were identified as the risk factors of cervical sagittal imbalance during the treatment of growing rod surgery. The growing rod surgery in EOS significantly affected the cervical sagittal alignment. Postoperative PJK and more improvement of kyphosis and T1 slope angle would lead to a higher incidence of cervical sagittal imbalance.

关键词： Cervical alignment Early-Onset Scoliosis Growing rod Proximal junctional kyphosis machine learning algorithm

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