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Background: Accurate diagnosis of Parkinson's disease (PD) is challenging due to its diverse manifestations. Machine learning (ML) algorithms can improve diagnostic precision, but their generalizability across medical centers in China is underexplored. Objective: To assess the accuracy of an ML algorithm for PD diagnosis, trained and tested on data from different medical centers in China. Methods: A total of 1656 participants were included, with 1028 from Beijing (training set) and 628 from Fuzhou (external validation set). Models were trained using the least absolute shrinkage and selection operator–logistic regression (LASSO-LR), decision tree (DT), random forest (RF), eXtreme gradient boosting (XGboost), support vector machine (SVM), and k-nearest neighbor (KNN) techniques. Hyperparameters were optimized using five-fold cross-validation and grid search techniques. Model performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, accuracy, sensitivity (recall), specificity, precision, and F1 score. Variable importance was assessed for all models. Results: SVM demonstrated the best differentiation between healthy controls (HCs) and PD patients (AUC: 0.928, 95% CI: 0.908–0.947; accuracy: 0.844, 95% CI: 0.814–0.871; sensitivity: 0.826, 95% CI: 0.786–0.866; specificity: 0.861, 95% CI: 0.820–0.898; precision: 0.849, 95% CI: 0.807–0.891; F1 score: 0.837, 95% CI: 0.803–0.868) in the validation set. Constipation, olfactory decline, and daytime somnolence significantly influenced predictability. Conclusion: We identified multiple pivotal variables and SVM as a precise and clinician-friendly ML algorithm for prediction of PD in Chinese patients. [ABSTRACT FROM AUTHOR]

Background: Hypertrophic scar (HS) is characterized by the hyperproliferation of fibroblasts and the excessive deposition of extracellular matrix (ECM). Thrombospondin 1 (THBS1) is a component of the ECM, which has been implicated in HS formation. Objectives: This study aimed to explore whether miR-375/miR-5691 could modulate HS formation by targeting THBS1. Methods: The expression levels of miR-375/miR-5691/THBS1 in HS and normal skin tissues were measured by quantitative reverse transcription-polymerase chain reaction. 3-(4,5)-dimethylthiahiazo (-z-y1)-2,5-di-phenytetrazoliumromide and Western blot assays were performed on fibroblasts isolated from HS tissues (HSFBs) to determine cell proliferation and the expression levels of proliferating cell nuclear antigen (PCNA), apoptosis-related proteins (caspase3/9, cleaved caspase3/9, Bax, and Bcl-2), and ECM-related proteins. The binding sites between THBS1 and miR-375/miR-5691 were predicted by the TargetScan. Dual-luciferase reporter and anti-Ago2 immunoprecipitation assays were applied to confirm the interactions between THBS1 and miR-375/miR-5691. Results: The expression levels of both miR-375 and miR-5691 were downregulated in HS tissues and HSFBs, which were negatively correlated with THBS1 expression levels. The overexpression of miR-375/miR-5691 inhibited cell proliferation and ECM production, and promoted apoptosis of HSFBs, while silencing of miR-375/miR-5691 led to an opposite result. In the mechanism analysis, THBS1 was confirmed as the direct target gene of miR-375/miR-5691. Furthermore, rescue experiments showed that the suppressed growth of HSFBs and ECM production induced by silencing of THBS1 was reversed by miR-375/miR-5691 inhibitors. Conclusion: MiR-375/miR-5691 was downregulated in HS tissues, and it could suppress the hyperproliferation and ECM production of HSFBs by targeting THBS1. [ABSTRACT FROM AUTHOR]

This paper proposes a new electrical discharge machining (EDM) process evaluation method, which is a combination of decision-making trial and evaluation laboratory (DEMATEL) and analytic network process (ANP). The green manufacturing theory is integrated with the traditional EDM evaluation system to establish a new comprehensive evaluation system based on five perspectives of green manufacturing theory. This method offers a new approach for EDM process evaluation and the promotion of environment and economic aspects. An exact numerical example is given to demonstrate the validity and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Increased data monitoring enables the energy-efficient operation of air-conditioning systems via data-mining. The latter is projected to have lesser consumption but more comprehensive diagnosis than traditional methods. Following the companion paper that proposed a systematic method for energy-saving potential calculations via data-mining, this article presents a detailed case study in an ice-storage air-conditioning system by employing the proposed method. Raw data were preprocessed prior to recognizing the constant- and variable-speed devices in the system. Classification and regression tree algorithms were utilized to identify the operating modes of the system. The regression models between the energy-consumption and operating-state parameters of the nine pumps and two chillers were fitted. Furthermore, the constraints pertaining to system operation were summarized. From the results, the particle swarm optimization method was applied to elucidate the benchmark energy cost and the consequent cost savings potential. The cost savings potential for the chiller plant room during the investigation duration of 59 d reached as high as 24.03%. The case study demonstrates the feasibility, effectiveness, and stability of the systematic approach. Further studies can facilitate the development of corresponding control strategies based on the potential analysis results, to investigate better optimization algorithm, and visualize the analysis process. [ABSTRACT FROM AUTHOR]

Air-conditioning systems contribute the most to energy consumption among building equipment. Hence, energy saving for air-conditioning systems would be the essence of reducing building energy consumption. The conventional energy-saving diagnosis method through observation, test, and identification (OTI) has several drawbacks such as time consumption and narrow focus. To overcome these problems, this study proposed a systematic method for energy-saving diagnosis in air-conditioning systems based on data mining. The method mainly includes seven steps: (1) data collection, (2) data preprocessing, (3) recognition of variable-speed equipment, (4) recognition of system operation mode, (5) regression analysis of energy consumption data, (6) constraints analysis of system running, and (7) energy-saving potential analysis. A case study with a complicated air-conditioning system coupled with an ice storage system demonstrated the effectiveness of the proposed method. Compared with the traditional OTI method, the data-mining-based method can provide a more comprehensive analysis of energy-saving potential with less time cost, although it strongly relies on data quality in all steps and lacks flexibility for diagnosing specific equipment for energy-saving potential analysis. The results can deepen the understanding of the operating data characteristics of air-conditioning systems. [ABSTRACT FROM AUTHOR]

Ischemic stroke (IS) has a high recurrence rate. Machine learning (ML) models have been developed based on single-modal biochemical tests, and imaging data have been used to predict stroke recurrence. However, the prediction accuracy of these models is not sufficiently high. Therefore, this study aimed to collect biochemical detection and magnetic resonance imaging (MRI) data to establish a dataset and propose a high-performance heterogeneous multimodal IS recurrence prediction model based on deep learning. This is a retrospective cohort study. Data were retrospectively collected from 634 IS patients in Zhuhai, China, a 12-month follow-up was conducted to determine stroke recurrence. We propose the ischemic stroke multi-group learning (ISGL) model, an integrated model for predicting the recurrence risk of multimodal IS in patients, based on a capsule neural network and a linear support vector machine (SVM). Two capsule neural network prediction models based on T1 and T2 signals in the MRI data and a SVM prediction model based on biochemical test data were established. Finally, a vote was conducted on the final judgment of the integrated model. The ISGL model was compared with 6 classical ML and deep learning models: k-nearest neighbors, SVM, logistic regression, random forest, eXtreme Gradient Boosting, and visual geometry group. The results revealed that the accuracy, specificity, sensitivity and the area under the curve of the ISGL model were 95%, 96%, 94%, and 95%, respectively. Among the comparison models, the visual geometry group method exhibited the best performance, but it much lower than those of the ISGL model. Analysis of the importance of biochemical test data revealed that low-density lipoprotein, smoking, and heart disease history were the positively correlated factors, and total cholesterol, high-density lipoprotein, and diabetes were and the negatively correlated factors. This study proposes the ISGL model can be used simultaneously with MRI and biochemical data to predict IS recurrence. This combination resulted in higher rate of performance than that of the other ML models. Additionally, this study found related risk factors affected recurrence, which can be used to intervene in high-risk patients' recurrence as early as possible and promote the development of secondary prevention of stroke., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)