Development of explainable artificial intelligence based machine learning model for predicting 30-day hospital readmission after renal transplantation by Nasser Alnazari, Omar Ibrahim Alanazi, Muath Owaidh Alosaimi, Ziyad Mohamed Alanazi, Ziyad Mohammed Alhajeri, Khaled Mohammed Alhussaini, Abdulkarim Mekhlif Alanazi, Ahmed Y. Azzam

“…Our methodology included a four-stage machine learning pipeline: data processing, feature preparation, model development using stratified 5-fold cross-validation, and clinical validation. Multiple algorithms were evaluated, with gradient boosting demonstrating superior performance. …”
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Identification and Validation of Key Genes Involved in the Coupling of Mitochondria-Associated Endoplasmic Reticulum Membrane in Hemorrhoidal Disease by Mao L, Rao Z, Wang Y, Yang J, He J, Zheng Z, Chen L

“…Additionally, CIBERSORT algorithms were utilized to evaluate immune cell infiltration abundance, differences, and correlations in the samples. …”
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Neurofibromatosis-Noonan syndrome: a prospective monocentric study of 26 patients and literature review by Didier Bessis, Dominique Vidaud, Pierre Meyer, Laurence Pacot, de La Villeon G, Adeline Alice Bonnard, Yline Capri, Christine Coubes, Fanchon Herman, Didier Lacombe, Nicolas Molinari, Laura Poujade, Agathe Roubertie, Julien Van Gils, Alain Verloes, David Geneviève, Hélène Cavé, Marjolaine Willems

“…Secondary objectives include evaluating inter-rater diagnostic agreement among experienced clinicians and assessing the utility of deep-learning algorithms (Face2Gene® [F2G]). …”
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A gene signature related to programmed cell death to predict immunotherapy response and prognosis in colon adenocarcinoma by Lei Zheng, Jia Lu, Dalu Kong, Yang Zhan

“…Immune infiltration of the samples was evaluated using CIBERSORT and Microenvironment Cell Populations (MCP)-counter algorithms. …”
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Machine learning-based diagnostic and prognostic models for breast cancer: a new frontier on the clinical application of natural killer cell-related gene signatures in precision me... by Yutong Fang, Rongji Zheng, Yefeng Xiao, Qunchen Zhang, Junpeng Liu, Jundong Wu

“…We constructed ML-based diagnostic models using 12 algorithms and evaluated their performance for identifying the optimal ML diagnostic model. …”
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Linking Immunological Parameters and Recovery of Patient’s Motor and Cognitive Functions In The Acute Period of Ischemic Stroke by A. M. Tynterova, N. N. Shusharina, A. M. Golubev, E. M. Moiseeva, L. S. Litvinova

“…Objective. To evaluate the relationship between immunological parameters and functional outcome in patients with varying severity of ischemic stroke based on statistical methodology.Materials and methods. …”
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Exploring the Ethical Challenges of Conversational AI in Mental Health Care: Scoping Review by Mehrdad Rahsepar Meadi, Tomas Sillekens, Suzanne Metselaar, Anton van Balkom, Justin Bernstein, Neeltje Batelaan

“…The following 10 themes were distinguished: (1) safety and harm (discussed in 52/101, 51.5% of articles); the most common topics within this theme were suicidality and crisis management, harmful or wrong suggestions, and the risk of dependency on CAI; (2) explicability, transparency, and trust (n=26, 25.7%), including topics such as the effects of “black box” algorithms on trust; (3) responsibility and accountability (n=31, 30.7%); (4) empathy and humanness (n=29, 28.7%); (5) justice (n=41, 40.6%), including themes such as health inequalities due to differences in digital literacy; (6) anthropomorphization and deception (n=24, 23.8%); (7) autonomy (n=12, 11.9%); (8) effectiveness (n=38, 37.6%); (9) privacy and confidentiality (n=62, 61.4%); and (10) concerns for health care workers’ jobs (n=16, 15.8%). …”
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Comprehensive integration of diagnostic biomarker analysis and immune cell infiltration features in sepsis via machine learning and bioinformatics techniques by Liuqing Yang, Liuqing Yang, Liuqing Yang, Rui Xuan, Rui Xuan, Rui Xuan, Dawei Xu, Dawei Xu, Dawei Xu, Aming Sang, Aming Sang, Aming Sang, Jing Zhang, Jing Zhang, Jing Zhang, Yanfang Zhang, Xujun Ye, Xinyi Li, Xinyi Li, Xinyi Li

“…CIBERSORT was utilized to evaluate the inflammatory and immunological condition of sepsis. …”
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An improved hybrid approach involving deep learning for urban greening tree species classification with Pléiades Neo 4 imagery—A case study from Nanjing, Eastern China by Min Sun, Stephane G.P. Debulois, Zhengnan Zhang, Xiaolei Cui, Zhili Chen, Mingshi Li

“…The distribution of urban tree species shapes spatial differences in urban eco-environmental benefits and liveability. …”
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Can Stereoscopic Density Replace Planar Density for Forest Aboveground Biomass Estimation? A Case Study Using Airborne LiDAR and Landsat Data in Daxing’anling, China by Xuan Mu, Dan Zhao, Zhaoju Zheng, Cong Xu, Jinchen Wu, Ping Zhao, Xiaomin Li, Yong Pang, Yujin Zhao, Tianyu An, Yuan Zeng, Bingfang Wu

“…Forest aboveground biomass (AGB) is a key indicator for evaluating carbon sequestration capacity and forest productivity. …”
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Intelligent multi-modeling reveals biological relationships and adaptive phenotypes for dairy cow adaptation to climate change by Robson Mateus Freitas Silveira, Angela Maria de Vasconcelos, Concepta McManus, Luiz Paulo Fávero, Iran José Oliveira da Silva

“…In this study, we develop a systematic methodology with multivariate models and machine learning algorithms to (i) model complex patterns of relationships or multi-phenotypic differences between the thermal environment and thermoregulatory, hormonal, biochemical, hematological and productive responses; and (ii) identify potential associations among biological relationships that may underlie shared and specific phenotypic patterns of adaptive responses. …”
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ImmuProgML: machine learning-based dissection of cancer-immune dynamics during tumor progression to improve immunotherapy by Hanxiao Zhou, Lan Mei, Qianyi Lu, Yakun Zhang, Yue Sun, Caiyu Zhang, Han Jiang, Jiajun Zhou, Xia Li, Yunpeng Zhang, Shangwei Ning

“…We introduced the DNEX score, which combines expression changes with immunotherapy-driven network topologies, and employed machine learning algorithms for prognostic and immunotherapy response predictions. …”
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Trends in the epidemiology of diabetic retinopathy in Russian Federation according to the Federal Diabetes Register (2013–2016) by Dmitry V. Lipatov, Olga K. Vikulova, Anna V. Zheleznyakova, Mikhail А. Isakov, Elena G. Bessmertnaya, Anna A. Tolkacheva, Timofey A. Chistyakov, Marina V. Shestakova, Ivan I. Dedov

“…Results: In 2016 the DR prevalence in RF was T1 38,3%, T2 15,0%, with marked interregional differences: 2,6–66,1%, 1,1–46,4%, respectively. …”
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Clinical characteristics, prognosis, and predictive modeling in class IV ± V lupus nephritis by Anjing Wang, Anjing Wang, Yunlong Qin, Yunlong Qin, Yan Xing, Zixian Yu, Liuyifei Huang, Jinguo Yuan, Yueqing Hui, Mei Han, Guoshuang Xu, Jin Zhao, Shiren Sun

“…The prognostic model was developed using machine learning algorithms and Cox regression. The model’s performance was evaluated in terms of discrimination, calibration, and risk classification using the concordance index (C-index), integrated brier score (IBS), net reclassification index (NRI), and integrated discrimination improvement (IDI), respectively.ResultsA total of 313 patients were enrolled for this study, including 156 class IV and 157 class IV+V LN. …”
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Efficiency and safety of the Russian-made KERATOLINK device used to treat patients with stage I–II keratoconus and pellucid marginal corneal degeneration by A. T. Khandzhyan, E. N. Iomdina, A. V. Ivanova, A. S. Sklyarova, N. V. Khodzhabekyan, I. V. Manukyan

“…The analysis of various UVCL programs revealed no difference in the recovery period and showed comparable clinical and functional results. …”
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Root-Zone Salinity in Irrigated Arid Farmland: Revealing Driving Mechanisms of Dynamic Changes in China’s Manas River Basin over 20 Years by Guang Yang, Xuejin Qiao, Qiang Zuo, Jianchu Shi, Xun Wu, Alon Ben-Gal

“…The approach demonstrated high predictive accuracy (<i>R</i>² = 0.96 ± 0.01, root mean squared error <i>RMSE</i> = 0.19 ± 0.03 g kg⁻¹, maximum absolute error <i>MAE</i> = 0.14 ± 0.02 g kg⁻¹) in evaluating <i>SSC</i> drivers. Factors such as initial <i>SSC</i>, crop type distribution, duration of film mulched drip irrigation implementation, normalized difference vegetation index (NDVI), irrigation amount, and actual evapotranspiration (<i>ET_a</i>), with mean (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mfenced close="|" open="|"><mrow><mrow><mi>SHAP</mi><mo> </mo><mi>value</mi></mrow></mrow></mfenced></mrow></semantics></math></inline-formula>) ≥ 0.02 g kg⁻¹, were found to be more closely correlated with root-zone <i>SSC</i> variations than other factors. …”
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A Framework for High-Spatiotemporal-Resolution Soil Moisture Retrieval in China Using Multi-Source Remote Sensing Data by Zhuangzhuang Feng, Xingming Zheng, Xiaofeng Li, Chunmei Wang, Jinfeng Song, Lei Li, Tianhao Guo, Jia Zheng

“…Four machine learning and deep learning algorithms are applied, including Random Forest Regression (RFR), Extreme Gradient Boosting (XGBoost), Long Short-Term Memory (LSTM) networks, and Ensemble Learning (EL). …”
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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.

“…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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