Realistic challenges and path options for the high-quality development of smart stadiums in China by Shaoquan Chu

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The relationship between the expression of serum asprosin and miR-21 in patients with osteoporosis and delayed healing after OVCF surgery by Gaoming Song, Silong Lin, Xianqi Zhang, Hong Pan

“…A nomogram model was constructed to predict delayed healing, and its predictive value was evaluated using the receiver operating curve (ROC) analysis. …”
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An endoscopic ultrasound-based interpretable deep learning model and nomogram for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer by Nan Yi, Shuangyang Mo, Yan Zhang, Qi Jiang, Yingwei Wang, Cheng Huang, Shanyu Qin, Haixing Jiang

“…The retained nonzero coefficient features were subsequently applied to develop predictive eight DL models based on distinct machine learning algorithms. …”
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Machine learning model for differentiating malignant from benign thyroid nodules based on the thyroid function data by Quan Zhou, Lihua Zhang, Nan Xiang, Lele Zhang, Fuqiang Ma, Fengchang Yu, Shenhui Lv, Zhilin Lu, He-Rong Mao

“…FT4, TPOAB and FT3 were validated as the top three features in the Gradient Boosting model.Conclusions This study innovatively developed a predictive model for benign and malignant TNs based on the Gradient Boosting Decision Tree algorithm. …”
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LYN and CYBB are pivotal immune and inflammatory genes as diagnostic biomarkers in recurrent spontaneous abortion by Zhuna Wu, Qiuya Lin, Zhimei Zhou, Yajing Xie, Li Huang, Liying Sheng, Qirong Shi, Yumin Ke

“…The compositional patterns of the infiltration of 22 immune cell types in RSA were analyzed via the CIBERSORT algorithm.ResultWe identified 403 differentially expressed genes (DEGs) and 7 DIIRGs between RSA endometrium and Non-RSA endometrium. …”
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Advancing a machine learning-based decision support tool for pre-hospital assessment of dyspnoea by emergency medical service clinicians: a retrospective observational study by Wivica Kauppi, Henrik Imberg, Johan Herlitz, Oskar Molin, Christer Axelsson, Carl Magnusson

“…The gradient boosting algorithm had the overall best performance, with excellent calibration of the predictions, and consistently showed higher sensitivity to detect SAE than the other methods. …”
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Deep MALDI-MS spatial omics guided by quantum cascade laser mid-infrared imaging microscopy by Lars Gruber, Stefan Schmidt, Thomas Enzlein, Huong Giang Vo, Tobias Bausbacher, James Lucas Cairns, Yasemin Ucal, Florian Keller, Martina Kerndl, Denis Abu Sammour, Omar Sharif, Gernot Schabbauer, Rüdiger Rudolf, Matthias Eckhardt, Stefania Alexandra Iakab, Laura Bindila, Carsten Hopf

“…QCL-MIR imaging-guided MSI allowed for unequivocal on-tissue elucidation of 157 sulfatides selectively accumulating in kidneys of arylsulfatase A-deficient mice used as ground truth concept and provided chemical rationales for improvements to ion mobility prediction algorithms. Using this workflow, we characterized sclerotic spinal cord lesions in mice with experimental autoimmune encephalomyelitis (EAE), a model of multiple sclerosis, and identified upregulation of inflammation-related ceramide-1-phosphate and ceramide phosphatidylethanolamine as markers of white matter lipid remodeling. …”
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Quantitative Analysis of Structural Parameters Importance of Helical Temperature Microfiber Sensor by Artificial Neural Network by Juan Liu, Minghui Chen, Hang Yu, Jinjin Han, Hongyi Jia, Zhili Lin, Zhijun Wu, Jixiong Pu, Xining Zhang, Hao Dai

“…With the assistance of the evaluation algorithms based on the well-performed backpropagation neural network (BPNN), we quantitatively analyze the importance of the structural parameters of the supported helical microfiber (HMF) temperature sensor. …”
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Multimodal marvels of deep learning in medical diagnosis using image, speech, and text: A comprehensive review of COVID-19 detection by Md Shofiqul Islam, Khondokar Fida Hasan, Hasibul Hossain Shajeeb, Humayan Kabir Rana, Md. Saifur Rahman, Md. Munirul Hasan, AKM Azad, Ibrahim Abdullah, Mohammad Ali Moni

“…We explore the architecture of deep learning models, emphasising their data-specific structures and underlying algorithms. Subsequently, we compare different deep learning strategies utilised in COVID-19 analysis, evaluating them based on methodology, data, performance, and prerequisites for future research. …”
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Incorporating Deep Learning Into Hydrogeological Modeling: Advancements, Challenges, and Future Directions by Zhenxue Dai, Chuanjun Zhan, Huichao Yin, Junjun Chen, Lulu Xu, Yuzhou Xia, Songlin Yang, Wei Chen, Mingxu Cao, Zhengyang Du, Xiaoying Zhang, Bicheng Yan, Yue Ma, Hao Wang, Farzad Moeini, Mohamad Reza Soltanian, Hung Vo Thanh, Kenneth C. Carroll

“…Deep learning (DL) has emerged as a promising tool, offering significant improvements in accuracy and efficiency for tasks such as time series prediction, spatial data analysis, and inverse modeling. …”
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Machine learning-based brain magnetic resonance imaging radiomics for identifying rapid eye movement sleep behavior disorder in Parkinson’s disease patients by Yandong lian, Yibin Xu, Linlin Hu, Yuguo Wei, Zhaoge Wang

“…Additionally, multi-factor logistic regression analysis identified clinical predictors associated with PD-RBD, and these clinical features were integrated with the radiomics signatures to develop predictive models using various machine learning algorithms. …”
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OntoCommerce: Incorporating Ontology and Sequential Pattern Mining for Personalized E-Commerce Recommendations by Ghulam Mustafa, Naveed Ahmad Jhamat, Zeeshan Arshad, Nadia Yousaf, Md. Nazmul Abdal, Mohammed Maray, Dokhyl Alqahtani, Mohamad Amir Merhabi, Muhammad Abdul Aziz, Touseef Ahmad

“…This ontology is then utilized to calculate customer preference similarities and generate predictions for the intended customer. The SPM algorithm is applied to the results of collaborative filtering to generate personalized recommendations for the customer. …”
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AIpollen: An Analytic Website for Pollen Identification Through Convolutional Neural Networks by Xingchen Yu, Jiawen Zhao, Zhenxiu Xu, Junrong Wei, Qi Wang, Feng Shen, Xiaozeng Yang, Zhonglong Guo

“…For the optimization algorithm, we opted for the Adam optimizer and utilized the cross-entropy loss function. …”
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Evaluating natalizumab first-line and later-line use in multiple sclerosis: a US claims database analysis by Robin L. Avila, Nicole S. Croteau, Fei Tang, Jason C. Simeone, Khalil Jomaa, Boyang Bian, Mattia Gianinazzi

“…Relapses were identified using a validated claims-based algorithm, and time to first relapse was analyzed using Kaplan–Meier methods. …”
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Evaluating the Pattern of Forest Cover Changes Using Fuzzy Object-Oriented Techniques (Case Study: Kaleybar County) by Majid Pishnamaz Ahmadi, keyvan Mohammadzadeh, seyed Asadolah Hejazi

“…The results were then calculated and finalized in ArcGIS software after accurate evaluation. 3-Results and Discussion In this research, images in 200 scales sorted consecutively from 1 to 200 were segmented using low to high multi-functional hierarchical segmentation approach with shape coefficient of 0.4 and compression coefficient of 0.5 in order to construct LV graphs and the appropriate scales for image segmentation were determined using the plotted graphs. By predicting the appropriate scale for creating image units using the algorithm (ESP), the scale of 15 with coefficients of shape and compression 0.3 and 0.5 respectively was scaled as the appropriate scale for extraction of Landsat 5 and 7 satellite images, and the scale of 130 with shape coefficient of 0.4 and compression coefficient of 0.5 was chosen as the appropriate scale for Landsat 8 satellite OLI images. …”
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Transforming urinary stone disease management by artificial intelligence-based methods: A comprehensive review by Anastasios Anastasiadis, Antonios Koudonas, Georgios Langas, Stavros Tsiakaras, Dimitrios Memmos, Ioannis Mykoniatis, Evangelos N. Symeonidis, Dimitrios Tsiptsios, Eliophotos Savvides, Ioannis Vakalopoulos, Georgios Dimitriadis, Jean de la Rosette

“…The main subjects were related to the detection of urinary stones, the prediction of the outcome of conservative or operative management, the optimization of operative procedures, and the elucidation of the relation of urinary stone chemistry with various factors. …”
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Rethinking the Paradigm of Using Ps for Diagnosing Compartment Syndrome by Yasser Bouklouch, BSc, MPH, July Agel, MA, ATC, William T. Obremskey, MD, MPH, MMHC, Andrew H. Schmidt, MD, Kathy Liu, MB, ChB, Jerald R. Westberg, MPH, Matthew Zakariah, BSc, Eli Bunzel, MD, Greer Henry, MSc, Andres Fidel Diaz, MD, Thierry Bégué, MD, Mitchell Bernstein, MD, Edward J. Harvey, MDCM, MSc

“…The combinations were tested for predictive power using 2 machine learning algorithms. …”
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In silico assessment of biocompatibility and toxicity: molecular docking and dynamics simulation of PMMA-based dental materials for interim prosthetic restorations by Ravinder S. Saini, Rayan Ibrahim H. Binduhayyim, Vishwanath Gurumurthy, Abdulkhaliq Ali F. Alshadidi, Shashit Shetty Bavabeedu, Rajesh Vyas, Doni Dermawan, Punnoth Poonkuzhi Naseef, Seyed Ali Mosaddad, Artak Heboyan

“…The HADDOCK standalone version was utilized for docking calculations, employing a Lamarckian genetic algorithm to explore the conformational space of ligand-receptor interactions. …”
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Molecular profiles of tumor contrast enhancement: A radiogenomic analysis in anaplastic gliomas by Xing Liu, Yiming Li, Zhiyan Sun, Shaowu Li, Kai Wang, Xing Fan, Yuqing Liu, Lei Wang, Yinyan Wang, Tao Jiang

“…Gene set enrichment analysis (GSEA), significant analysis of microarray, generalized linear models, and Least absolute shrinkage and selection operator algorithm were used to explore radiogenomic and prognostic signatures of AG patients. …”
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Hot deformation physical mechanisms and a unified constitutive model of a solid solution Ti55511 alloy deformed in the two-phase region by Huijie Zhang, Y.C. Lin, Gang Su, Yongfu Xie, Wei Qiu, Ningfu Zeng, Song Zhang, Guicheng Wu

“…Material constants are determined using a genetic algorithm (GA), and the experimental data align well with the predicted data. …”
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