INTERNATIONAL FORUM “OLD AND NEW MEDIA: ALONG THE PATH TOWARDS A NEW AESTHETICS” / МЕЖДУНАРОДНЫЙ ФОРУМ «СТАРЫЕ И НОВЫЕ МЕДИА: ПУТИ К НОВОЙ ЭСТЕТИКЕ»... by BOGATYRYOVA ELENA A.

“…Along with the formulation of the enumerated questions, the presentations of the participants of the conference concretized the conclusions about the influence of new media on the cognitive capabilities of man, his means of action, “new plasticity” and a new way of living. Notice was made of alarming tendencies of transforming consciousness into that of “gamers,” reacting in correlations with certain algorithms, and a transference from discourse and substantiations towards reactions and evaluations. …”
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Identification of Ferroptosis‐Related Gene in Age‐Related Macular Degeneration Using Machine Learning by Meijiang Zhu, Jing Yu

“…Differentially expressed genes (DEGs) were selected and intersected with genes from the ferroptosis database to obtain differentially expressed ferroptosis‐associated genes (DEFGs). Machine learning algorithms were employed to screen diagnostic genes. …”
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Identification of Serum miRNAs as Effective Diagnostic Biomarkers for Distinguishing Primary Central Nervous System Lymphoma from Glioma by Pei-pei Si, Xiao-hui Zhou, Zhen-zhen Qu

“…Candidate miRNAs were identified through SVM-RFE analysis and LASSO model. ROC assays were operated to determine the diagnostic value of serum miRNAs in distinguishing PCNSL from glioma. …”
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Mapping the digital silk road: evolution and strategic shifts in Chinese social media marketing (2015–2025) by Xinrui Liang, Wan Mohd Hirwani Wan Hussain, Mohammed R. M. Salem

“…Following Arksey and O’Malley’s five-stage scoping framework, 3,710 records from Web of Science and Scopus were screened, yielding 41 peer-reviewed studies. Results indicate a transition from search-based behaviour to AI-facilitated impulse purchasing, enabled by algorithmic recommendations, parasocial influencer relations, and livestream commerce. …”
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Mechanism and relevance of necroptosis to immune microenvironment of periodontitis: A pilot study by ZHENG Zhanglong, LI Jia, JIANG Jirui, SHAN Zhengnan, LI Shengjiao

“…[Objective:] To explore the effect and mechanism of necroptosis on the immune microenvironment of periodontitis. [Methods:] We screened out the differentially expressed necroptosis-related genes in periodontitis, first calculated the hub genes through machine learning algorithms, and constructed a diagnostic model. …”
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The two ends of the spectrum: comparing chronic schizophrenia and premorbid latent schizotypy by actigraphy by Szandra László, Ádám Nagy, József Dombi, Emőke Adrienn Hompoth, Emese Rudics, Zoltán Szabó, András Dér, András Búzás, Zsolt János Viharos, Anh Tuan Hoang, Vilmos Bilicki, István Szendi

“…By applying model-explaining tools to the well-performing models, we could conclude the movement patterns and characteristics of the groups. …”
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Deciphering the role of cuproptosis in the development of intimal hyperplasia in rat carotid arteries using single cell analysis and machine learning techniques by Miao He, Hui Chen, Zhengli Liu, Boxiang Zhao, Xu He, Qiujin Mao, Jianping Gu, Jie Kong

“…Methods: We downloaded single-cell sequencing and bulk transcriptome data from the GEO database to screen for copper-growth-associated genes (CAGs) using machine-learning algorithms, including Random Forest and Support Vector Machine. …”
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Identification of M2 macrophage-related genes associated with diffuse large B-cell lymphoma via bioinformatics and machine learning approaches by Jiayi Zhang, Zhixiang Jia, Jiahui Zhang, Xiaohui Mu, Limei Ai

“…Using the Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine Recursive Feature Elimination (SVM-RFE), and Random Forest (RF) algorithms, we screened for seven potential diagnostic biomarkers with strong diagnostic capabilities: SMAD3, IL7R, IL18, FAS, CD5, CCR7, and CSF1R. …”
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Integrating equity, diversity, and inclusion throughout the lifecycle of artificial intelligence for healthcare: a scoping review. by Ting Wang, Elham Emami, Dana Jafarpour, Raymond Tolentino, Genevieve Gore, Samira Abbasgholizadeh Rahimi

“…Previous research has shown that AI models improve when socio-demographic factors such as gender and race are considered. …”
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Identification of novel gut microbiota-related biomarkers in cerebral hemorrhagic stroke by Fengli Ye, Huili Li, Hongying Li, Xiue Mu

“…Functional enrichment, gene set enrichment analysis (GSEA), and protein–protein interaction (PPI) analyses were performed. Hub genes were screened using LASSO, RandomForest, and SVM-RFE algorithms. …”
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Identification of markers correlating with mitochondrial function in myocardial infarction by bioinformatics. by Wenlong Kuang, Jianwu Huang, Yulu Yang, Yuhua Liao, Zihua Zhou, Qian Liu, Hailang Wu

“…The 10 MI-related hub MitoDEGs were then obtained by eight different algorithms. Immunoassays showed a significant increase in monocyte macrophage and T cell infiltration. …”
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Locating and quantifying CH₄ sources within a wastewater treatment plant based on mobile measurements by J. Yang, Z. Xu, Z. Xia, Z. Xia, X. Pei, Y. Yang, B. Qiu, B. Qiu, S. Zhao, S. Zhao, Y. Zhang, Y. Zhang, Z. Wang, Z. Wang

“…We utilized a multi-source Gaussian plume model combined with a genetic algorithm inversion framework, designed to locate major sources within the plant and quantify the corresponding <span class="inline-formula">CH₄</span> emission fluxes. …”
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Identification of aging-related biomarkers and immune infiltration analysis in renal stones by integrated bioinformatics analysis by Yuanzhao Wang, Nana Chen, Bangqiu Zhang, Pingping Zhuang, Bingtao Tan, Changlong Cai, Niancai He, Hao Nie, Songtao Xiang, Chiwei Chen

“…Using logistic regression, SVM, and LASSO regression algorithms, a successful early-diagnosis model for RS was developed, yielding 7 key genes: CNR1, KIT, HTR2A, DES, IL33, UCP2, and PPT1. …”
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Identification of potential metabolic biomarkers and immune cell infiltration for metabolic associated steatohepatitis by bioinformatics analysis and machine learning by Haoran Xie, Junjun Wang, Qiuyan Zhao

“…Protein-Protein Interaction (PPI) network and machine learning algorithms, including Least Absolute Shrinkage and Selection Operator (LASSO) regression, Support Vector Machine-Recursive Feature Elimination (SVM-RFE), and Random Forest (RF), were applied to screen for signature MRDEGs. …”
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Shared pathogenic mechanisms linking obesity and idiopathic pulmonary fibrosis revealed by bioinformatics and in vivo validation by Linjie Chen, Haojie Chen, Zinan Chen, Kunyi Zhang, Hongsen Zhang, Jiahe Xu, Tongsheng Chen

“…Functional enrichment (GO/KEGG), protein-protein interaction (PPI) networks, and machine learning algorithms were applied to screen hub genes, validated by ROC curves. …”
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Prediction and validation of anoikis-related genes in neuropathic pain using machine learning. by Yufeng He, Ye Wei, Yongxin Wang, Chunyan Ling, Xiang Qi, Siyu Geng, Yingtong Meng, Hao Deng, Qisong Zhang, Xiaoling Qin, Guanghui Chen

“…We also used rats to construct an NP model and validated the analyzed hub genes using hematoxylin and eosin (H&E) staining, real-time polymerase chain reaction (PCR), and Western blotting assays.…”
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Prognostic, oncogenic roles, and pharmacogenomic features of AMD1 in hepatocellular carcinoma by Youliang Zhou, Yi Zhou, Jiabin Hu, Yao Xiao, Yan Zhou, Liping Yu

“…Univariate Cox regression analysis and Pearson correlation were used to screen for AMD1-related genes (ARGs). Multidimensional bioinformatic algorithms were utilized to establish a risk score model for ARGs. …”
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Identification of glucocorticoid-related genes in systemic lupus erythematosus using bioinformatics analysis and machine learning. by Yinghao Ren, Weiqiang Chen, Yuhao Lin, Zeyu Wang, Weiliang Wang

“…Furthermore, we utilized least absolute shrinkage and selection operator (LASSO) regression and Random Forest (RF) algorithms to screen for hub genes. We then validated the expression of these hub genes and constructed nomograms for further validation. …”
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DEPDC1B, CDCA2, APOBEC3B, and TYMS are potential hub genes and therapeutic targets for diagnosing dialysis patients with heart failure by Wenwu Tang, Wenwu Tang, Zhixin Wang, Xinzhu Yuan, Liping Chen, Haiyang Guo, Zhirui Qi, Ying Zhang, Xisheng Xie

“…In addition, we further explored potential mechanism and function of hub genes in HF of patients with MHD through GSEA, immune cell infiltration analysis, drug analysis and establishment of molecular regulatory network.ResultsTotally 23 candidate genes were screened out by overlapping 673 differentially expressed genes (DEGs) and 147 key module genes, of which four hub genes (DEPDC1B, CDCA2, APOBEC3B and TYMS) were obtained by two machine learning algorithms. …”
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Exploration of biomarkers for predicting the prognosis of patients with diffuse large B-cell lymphoma by machine-learning analysis by Shifen Wang, Hong Tao, Xingyun Zhao, Siwen Wu, Chunwei Yang, Yuanfei Shi, Zhenshu Xu, Dawei Cui

“…Moreover, four hub genes (CXCL9, CCL18, C1QA and CTSC) were significantly screened from the three datasets using RF algorithms. …”
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