Selecting patients with sickle cell disease for gene addition or gene editing‐based therapeutic approaches: Report on behalf of a joint EHA Specialized Working Group and EBMT Hemog... by Lucia de Franceschi, Franco Locatelli, David Rees, Christian Chabannon, Jean‐Hugues Dalle, Stefano Rivella, Achille Iolascon, Stephan Lobitz, Miguel R. Abboud, Josu de la Fuente, Pagona Flevari, Emanuele Angelucci, Mariane de Montalembert

“…The decision‐making algorithm was developed by a panel of experts in hemoglobinopathies and/or transplantation chosen by EHA SWG and EBMT, to discuss the selection of SCD patients for gene therapy and draw notes on the related clinical problems.…”
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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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Risk assessment of oral leukoplakia by DNA content enhanced by machine learning models by Matheus Henrique Lopes Dominguete, Vera Cavalcanti de Araújo, Fernanda Viviane Mariano, Carmen Silvia Passos Lima, João Figueira Scarini, Paulo de Camargo Moraes, Victor Angelo Martins Montalli, Larissa Agatti, Zuraiza Mohamad Zaini, Luíza Hellmeister, Marcelo Sperandio

“…A 3-step algorithm using these two parameters to classify lesions into low risk and high risk resulted in 52% positive predictive value (PPV) and 93% negative predictive value (NPV). …”
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Detection of Apple Sucrose Concentration Based on Fluorescence Hyperspectral Image System and Machine Learning by Chunyi Zhan, Hongyi Mao, Rongsheng Fan, Tanggui He, Rui Qing, Wenliang Zhang, Yi Lin, Kunyu Li, Lei Wang, Tie’en Xia, Youli Wu, Zhiliang Kang

“…This study used FHIS combined with machine learning (ML) to predict SC at the apple’s equatorial position. Primary features were extracted using variable importance projection (VIP), the successive projection algorithm (SPA), and extreme gradient boosting (XGBoost). …”
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Race course characteristics are the most important predictors in 48 h ultramarathon running by Beat Knechtle, David Valero, Elias Villiger, Katja Weiss, Pantelis T. Nikolaidis, Lorin Braschler, Rodrigo Luiz Vancini, Marilia Santos Andrade, Ivan Cuk, Thomas Rosemann, Mabliny Thuany

“…A machine learning (ML) model based on the XG Boost algorithm was built to predict running speed from the athlete´s age, gender, country of origin, where the race occurs and race course characteristic such as elevation (flat or hilly) and surface (asphalt, cement, granite, grass, gravel, sand, track, or trail). …”
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Preliminary exploratory study on differential diagnosis between benign and malignant peripheral lung tumors: based on deep learning networks by Yuan Wang, Yuan Wang, Yutong Zhang, Yongxin Li, Tianyu She, Meiqing He, Hailing He, Dong Zhang, Dong Zhang, Jue Jiang

“…DeLong’s test was employed to compare the differences between the groups.ResultsAmong the five models, the one based on the ResNet18 algorithm demonstrated the highest performance. It exhibited statistically significant advantages in predictive accuracy (p < 0.05) compared to the models based on ResNet152, ResNet101, ResNet50, and ResNet34 algorithms. …”
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AI-Powered Identification of Osteoporosis in Dental Panoramic Radiographs: Addressing Methodological Flaws in Current Research by Robert Gaudin, Shankeeth Vinayahalingam, Niels van Nistelrooij, Iman Ghanad, Wolfus Otto, Stephan Kewenig, Carsten Rendenbach, Vasilios Alevizakos, Pascal Grün, Florian Kofler, Max Heiland, Constantin von See

“…Initially, the YOLOv8 object detection model was employed to predict the regions of interest. Subsequently, the predicted regions of interest were extracted from the PRs and processed by the EfficientNet classification model. …”
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Removal mechanism and damage evolution of SiCp/Al composites based on FEM-MD model considering 3D random polyhedral particles in orthogonal cutting by Ming Li, Qingguang Li, Xianchao Pan, Jiaqi Wang, Zixuan Wang, Shengzhi Xu, Yunguang Zhou, Lianjie Ma, Tianbiao Yu

“…Experimental validation confirmed the multi-scale model's predictive accuracy for machining-induced surface damage. …”
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Liquid biopsy-derived extracellular vesicle protein biomarkers for diagnosis and prognostic assessment of lung squamous cell carcinoma by Sheng Ma, Na Zhao, Xin Dong, Yaru Wang, Lei Song, Ruiqi Zheng, Xiaochen Zhi, Congcong Ma, Shujun Cheng, Jie Li, Yutao Liu, Ting Xiao

“…Additionally, a prognostic prediction model for LUSC was developed using a combination of 101 machine learning algorithms. …”
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Revealing the heterogeneity of treatment resistance in less‐defined subtype diffuse large B cell lymphoma patients by integrating programmed cell death patterns and liquid biopsy by Wei Hua, Jie Liu, Yue Li, Hua Yin, Hao‐Rui Shen, Jia‐Zhu Wu, Yi‐Lin Kong, Bi‐Hui Pan, Jun‐Heng Liang, Li Wang, Jian‐Yong Li, Rui Gao, Jin‐Hua Liang, Wei Xu

“…By employing various machine learning algorithms, we pinpointed eight pivotal genes linked to PCD, specifically FLT3, SORL1, CD8A, BCL2L1, COL13A1, MPG, DYRK2 and CAMK2B. …”
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Modifiable Factors and 10‐Year and Lifetime Risk of Cardiovascular Disease in Adults With New‐Onset Diabetes: The Kailuan Cohort Study by Shouling Wu, Yuntao Wu, Yi Ning, Xiang Peng, Haiyan Zhao, Jun Feng, Liming Lin, Chunyu Ruan, Shuohua Chen, Jinwei Tian, Cheng Jin

“…Modifiable risk factors were assessed using the Life's Essential 8 algorithm, including physical activity, nicotine exposure, dietary health, sleep duration, body mass index, blood pressure, blood glucose, and lipid levels. …”
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Deep learning-assisted analysis of biomarker changes after increase of dosing from aflibercept 2 mg to 8 mg in therapy-resistant neovascular age-related macular degeneration by Siegfried Priglinger, Jakob Siedlecki, Benedikt Schworm, Franziska Eckardt, Michael Hafner, Ben Asani, Caspar Liesenhoff, Alexander Kufner, Johannes Benedikt Schiefelbein

“…All patients had previously received aflibercept 2 mg, with an average of 30.7 previous anti-VEGF injections. Significant reductions in intraretinal fluid and fibrovascular pigment epithelial detachment at timepoint V3 were observed. …”
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EEG Microstate Dynamics during Different Physiological Developmental Stages and the Effects of Medication in Schizophrenia by Shihai Ling, Lingyan Du, Xi Tan, Guozhi Tang, Yue Che, Shirui Song

“…After pre-processing, EEG data from different groups were subjected to microstate analysis, and the K-Means clustering algorithm was applied to classify the data into 4 microstates. …”
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Spatio-Temporal Habitat Dynamics of Migratory Small Yellow Croaker (<i>Larimichthys polyactis</i>) in Hangzhou Bay, China by Xiangyu Long, Dong Wang, Pengbo Song, Mengwen Han, Rijin Jiang, Yongdong Zhou

“…We evaluated the performance of eleven modeling algorithms to identify the most accurate model for predicting small yellow croaker distributions. …”
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MicroRNA-1912 regulates cholesterol homeostasis by targeting PCSK9 by Chan Joo Lee, Myeongjune Go, Sae-Bom Jeon, Sun-Ho Lee, Dongkook Min, So Hee Park, Seunghyun Lee, Sahng Wook Park

“…Specific interactions of miR-1912 and miR-224 with the 3′ untranslated region (UTR) of PCSK9 mRNA were confirmed by a luciferase reporter assay and site-directed mutagenesis analysis of the predicted seed region. In transgenic mice expressing liver-specific human PCSK9 with its 3ʹ UTR, administration of miR-1912 mimics resulted in a reduction of plasma total cholesterol levels as determined by fast protein liquid chromatography following hepatic delivery. …”
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Transparent and Robust Artificial Intelligence-Driven Electrocardiogram Model for Left Ventricular Systolic Dysfunction by Min Sung Lee, Jong-Hwan Jang, Sora Kang, Ga In Han, Ah-Hyun Yoo, Yong-Yeon Jo, Jeong Min Son, Joon-myoung Kwon, Sooyeon Lee, Ji Sung Lee, Hak Seung Lee, Kyung-Hee Kim

“…The AiTiALVSD model, based on a deep learning algorithm, was evaluated against echocardiographic ejection fraction values. …”
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Risk factors and an interpretability tool of in-hospital mortality in critically ill patients with acute myocardial infarction by Rui Yang, Tao Huang, Renqi Yao, Di Wang, Yang Hu, Longbing Ren, Shaojie Li, Yali Zhao, Zhijun Dai

“…Conclusion: We built the BRF model and the web-based tool by the model algorithm. The model effect has been verified externally. …”
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A novel molecular classification system based on the molecular feature score identifies patients sensitive to immune therapy and target therapy by Yang Li, Yang Li, Yinan Ding, Yinan Ding, Jinghao Wang, Xiaoxia Wu, Xiaoxia Wu, Dinghu Zhang, Dinghu Zhang, Han Zhou, Han Zhou, Pengfei Zhang, Pengfei Zhang, Guoliang Shao, Guoliang Shao

“…Subsequently, machine learning algorithms were used to predict the classifications and prognoses. …”
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Long-term prognosis of 47 pediatric patients with Blau syndrome in China by Xinwei Shi, Jianghong Deng, Junmei Zhang, Xiaozhen Zhao, Yinan Zhao, Li Li, Fengqiao Gao, Weiying Kuang, Jiang Wang, Xiaohua Tan, Chao Li, Shipeng Li, Caifeng Li

“…A Bayesian network was constructed to integrate prediction algorithms of genetic mutations and clinical manifestations, exploring the complex relationship between genotype and phenotype through R (Version 4.4.1, R Core Development Team). …”
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Identifying key drivers of heatwaves: A novel spatio-temporal framework for extreme event detection by J. Pérez-Aracil, C. Peláez-Rodríguez, Ronan McAdam, Antonello Squintu, Cosmin M. Marina, Eugenio Lorente-Ramos, Niklas Luther, Verónica Torralba, Enrico Scoccimarro, Leone Cavicchia, Matteo Giuliani, Eduardo Zorita, Felicitas Hansen, David Barriopedro, Ricardo García-Herrera, Pedro A. Gutiérrez, Jürg Luterbacher, Elena Xoplaki, Andrea Castelletti, S. Salcedo-Sanz

“…A novel framework named Spatio-Temporal Cluster-Optimized Feature Selection (STCO-FS) is proposed to identify key immediate (short-term) HW drivers by combining clustering algorithms with an ensemble evolutionary algorithm. …”
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