Integrative genomic analysis and diagnostic modeling of osteoporosis: unraveling the interplay of autophagy, osteogenesis, adipogenesis, and immune infiltration by Lin-Jing Han, Jian-Zong Zhu, Jian-Zong Zhu, Hong-Cai Liu, Xiao-Sheng Lin, Xiao-Sheng Lin, Shu-Zhong Yang

“…The single-sample gene-set enrichment analysis (ssGSEA) was used to assess immune cell infiltration; the CIBERSORT algorithm was used to evaluate immune cells within the different subtypes of OP.ResultsThe study identified 1,297 DEGs, with 14 DEGs related to autophagy, osteogenesis, and adipogenesis (AP&OG&AGRDEGs) showing significant expression differences between OP and control groups, including seven upregulated and seven downregulated genes (p-value < 0.05). …”
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Development and Validation of a Radiomics Nomogram Based on Magnetic Resonance Imaging and Clinicoradiological Factors to Predict HCC TACE Refractoriness by Dong Y, Hu J, Meng X, Yang B, Peng C, Zhao W

“…YuHan Dong,1 Jihong Hu,2 Xuerou Meng,2 Bin Yang,3 Chao Peng,1 Wei Zhao1 1Medical Imaging Department, The First Affiliated Hospital of Kunming Medical University, Kunming, 650032, People’s Republic of China; 2Department of Interventional Radiology, The First Affiliated Hospital of Kunming Medical University, Kunming, 650032, People’s Republic of China; 3Medical Imaging Center, The First Hospital of Kunming, Kunming, 650051, People’s Republic of ChinaCorrespondence: Wei Zhao, Medical Imaging Department, The First Affiliated Hospital of Kunming Medical University, Kunming, 650032, People’s Republic of China, Email kyyyzhaowei@foxmail.com Chao Peng, Medical Imaging Department, The First Affiliated Hospital of Kunming Medical University, Kunming, 650032, People’s Republic of China, Email 609101429@qq.comPurpose: This study constructs a predictive model for hepatocellular carcinoma (HCC) transarterial chemoembolization (TACE) refractoriness using a machine learning (ML) algorithm and verifies the predictive performance of different algorithms.Patients and Methods: Clinical and magnetic resonance imaging (MRI) data of 131 patients (48 with TACE refractoriness) who underwent repeated TACE treatment for HCC were retrospectively collected. …”
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Association of microtubule-based processes gene expression with immune microenvironment and its predictive value for drug response in oestrogen receptor-positive breast cancer by Zhenfeng Huang, Minghui Zhang, Nana Zhang, Mengyao Zeng, Yao Qian, Meng Zhu, Xiangyan Meng, Ming Shan, Guoqiang Zhang, Feng Liu

“…Prognostic risk models were developed via random forest, support vector machines and the least absolute shrinkage and selection operator algorithm. Single-cell analysis revealed differences in the expression levels of key genes among various cell types. …”
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Constructing a predictive model for acute mastitis in lactating women based on machine learning by Liujing Zhu, Zuyan Huang, Yan Chen, Guangqiu Li, Liwen Liu

“…Prediction models were established using four different ML algorithms. Through analysis, when comparing the four distinct ML models on the test set, the MLP model performed optimally across various evaluation metrics, including the highest area under the receiver operating characteristic (ROC) curve (AUROC) (0.898), sensitivity (0.820), test specificity (0.863), and F1 score (0.849), with an accuracy of 0.840. …”
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Association between international normalized ratio-to-albumin ratio and mortality in critically ill patients with gastrointestinal bleeding: a retrospective MIMIC-IV database study by Xingyi Yang, Shasha Ying, Lihong Lv, Yishu Ji, Jiaqian Ying, Huyan Ke

“…This finding was corroborated by significant differences in KM survival curves (log-rank P < 0.001). …”
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AI-based body composition analysis of CT data has the potential to predict disease course in patients with multiple myeloma by Franz Wegner, Malte Maria Sieren, Hanna Grasshoff, Lennart Berkel, Christoph Rowold, Marcel Philipp Röttgerding, Soleiman Khalil, Sam Mogadas, Felix Nensa, René Hosch, Gabriela Riemekasten, Anna Franziska Hamm, Nikolas von Bubnoff, Jörg Barkhausen, Roman Kloeckner, Cyrus Khandanpour, Theo Leitner

“…Abstract The aim of this study was to evaluate the benefit of a volumetric AI-based body composition analysis (BCA) algorithm in multiple myeloma (MM). …”
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Machine learning based on pangenome-wide association studies reveals the impact of host source on the zoonotic potential of closely related bacterial pathogens by Cheng Han, Shiying Lu, Pan Hu, Jiang Chang, Deying Zou, Feng Li, Yansong Li, Qiang Lu, Honglin Ren

“…Integrating these genes into an ML model based on the support vector machine (SVM) algorithm allows us to predict the zoonotic potential of various Brucella strains with high accuracy. …”
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The magnetic permeability signature in high-frequency electromagnetic data modeling: a case study for GPR approximation by Alejandra I. Sánchez, Luis A. Gallardo

“…In this paper, we analyze the distinctive effect of magnetic permeability on the radar signal. To evaluate the transit of an electromagnetic wave, we developed a finite-difference time-domain (FDTD) algorithm that accounts for ε−,μ−, and σ− heterogeneities. …”
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How Students Behave while Solving Critical Thinking Tasks in an Unconstrained Online Environment: Insights from Process Mining by Anastasia Beliaeva

“…The findings of the work were gained on generalised behaviour patterns from the process mining algorithm deployed on two groups of students (63 low performing and 45 high performing students). …”
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External validation of and improvement upon a model for the prediction of placenta accreta spectrum severity using prospectively collected multicenter ultrasound data by Magdalena Kolak, Stephen Gerry, Hubert Huras, Ammar Al Naimi, Karin A. Fox, Thorsten Braun, Vedran Stefanovic, Heleen vanBeekhuizen, Olivier Morel, Alexander Paping, Charline Bertholdt, Pavel Calda, Zdenek Lastuvka, Andrzej Jaworowski, Egle Savukyne, Sally Collins, IS‐PAS group

“…Abstract Introduction This study aimed to validate the Sargent risk stratification algorithm for the prediction of placenta accreta spectrum (PAS) severity using data collected from multiple centers and using the multicenter data to improve the model. …”
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Impact of uncertainty quantification through conformal prediction on volume assessment from deep learning-based MRI prostate segmentation by Marius Gade, Kevin Mekhaphan Nguyen, Sol Gedde, Alvaro Fernandez-Quilez

“…The relative volume difference (RVD) was used to evaluate the PV calculation accuracy, and the Wilcoxon Signed-Rank Test was used to assess statistical differences. …”
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Effect of a health education program on reduction of pediculosis in school girls at Amphoe Muang, Khon Kaen Province, Thailand. by Manachai Yingklang, Chatchawan Sengthong, Ornuma Haonon, Rungtiwa Dangtakot, Porntip Pinlaor, Chulaporn Sota, Somchai Pinlaor

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Altered diversity and composition of gut microbiota in Korean children with food allergy by Minyoung Jung, Ji Young Lee, Sukyung Kim, Jeongmin Song, Sehun Jang, Sanghee Shin, Min Hee Lee, Mi Jin Kim, Jiwon Kim, Han Byul Lee, Yeonghee Kim, Kangmo Ahn, Minji Kim, Jihyun Kim

“…Bacterial species richness, intracommunity diversity, and intergroup dissimilarity were evaluated. Functional profiles were predicted using Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) and the Minimal Set of Pathways (MinPath) algorithm. …”
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Integrated single cell and bulk RNA sequencing analyses reveal the impact of tryptophan metabolism on prognosis and immunotherapy in colon cancer by Yanyan Hu, Ximo Xu, Hao Zhong, Chengshen Ding, Sen zhang, Wei Qin, Enkui Zhang, Duohuo Shu, Mengqin Yu, Naijipu Abuduaini, Xiao Yang, Bo Feng, Jianwen Li

“…The Oncopredict algorithm facilitated the identification of sensitive chemotherapeutic agents, while the immune escape score was employed to evaluate the immunotherapy response across risk groups. …”
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Semiautomated Three-Dimensional Landmark Placement on Knee Models Is a Reliable Method to Describe Bone Shape and Alignment by Nancy Park, B.S., Johannes Sieberer, M.Sc., Armita Manafzadeh, Ph.D., Rieke-Marie Hackbarth, Shelby Desroches, B.S., Rithvik Ghankot, B.S., John Lynch, Ph.D., Neil A. Segal, M.D., Joshua Stefanik, Ph.D., David Felson, M.D., John P. Fulkerson, M.D.

“…Purpose: To assess the inter- and intrarater reliability of 21 anatomical landmarks initially placed with an artificial intelligence algorithm and then manually verified with human input. …”
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Diagnostic potential of salivary microbiota in persistent pulmonary nodules: identifying biomarkers and functional pathways using 16S rRNA sequencing and machine learning by Xiao Zeng, Qiong Ma, Chun-Xia Huang, Jun-Jie Xiao, Xi Fu, Yi-Feng Ren, Yu-Li Qu, Hong-Xia Xiang, Mao Lei, Ru-Yi Zheng, Yang Zhong, Ping Xiao, Xiang Zhuang, Feng-Ming You, Jia-Wei He

“…Seven advanced machine learning algorithms (logistic regression, support vector machine, multi-layer perceptron, naïve Bayes, random forest, gradient boosting decision tree, and LightGBM) were utilized to evaluate performance and identify key microorganisms, with fivefold cross-validation employed to ensure robustness. …”
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Comparative assessment of efficacy and toxicity of R-DA-EPOCH and R-mNHL-BFM-90 induction courses in the treatment of patients with diffuse large B-cell lymphoma with poor prognost... by M. O. Bagova, A. U. Magomedova, S. K. Kravchenko, Ya. K. Mangasarova, O. V. Margolin, E. S. Nesterova, L. G. Gorenkova, A. E. Misyurina, E. A. Fastova, F. E. Babaeva, K. A. Sychevskaya, S. M. Kulikov, Yu. A. Chabaeva, V. G. Savchenko

“…Despite the frequency of occurrence, there is no single algorithm for treating DLBCL patients with poor prognostic factors. …”
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Development of a Conditional Generative Adversarial Network Model for Television Spectrum Radio Environment Mapping by Oluwatobi Emmanuel Dare, Kennedy Okokpujie, Emmanuel Adetiba, Olabode Idowu-Bismark, Abdultaofeek Abayomi, Raymond Jules Kala, Emmanuel Owolabi, Udeme Christopher Ukpong

“…The model performance was evaluated using mean square error (MSE) and mean absolute error (MAE). 12 different experiments were carried out varying the training parameters of the CGAN architecture to obtain an optimal model. …”
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An Upscaling-Based Strategy to Improve the Ephemeral Gully Mapping Accuracy by Solmaz Fathololoumi, Daniel D. Saurette, Harnoordeep Singh Mann, Naoya Kadota, Hiteshkumar B. Vasava, Mojtaba Naeimi, Prasad Daggupati, Asim Biswas

“…Employing the Random Forest (RF) algorithm, this study executed three distinct strategies for EGs identification. …”
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Prediction of induction chemotherapy efficacy in patients with locally advanced nasopharyngeal carcinoma using habitat subregions derived from multi-modal MRI radiomics by Mulan Pan, Lu Lu, Xingyu Mu, Xingyu Mu, Guanqiao Jin

“…The K-means clustering algorithm was utilized to segment the tumor into five distinct habitat subregions based on imaging features. …”
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