Machine learning-driven design of rare metal doped niobium alloys with enhanced strength and ductility by Zhenqiang Xiong, Zhaokun Song, Jianwei Li, Heran Wang, Xiaoxin Zhang, Bin Liang, Dong Wang

“…A comprehensive database of niobium alloys' properties was analyzed using feature engineering, and a high-accuracy prediction model, Gray Wolf Optimization-Extreme Learning Machine (GWO-ELM), was constructed, achieving R2 values of 0.95 and 0.88 for tensile strength and elongation, respectively. …”
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Deep learning-based framework for Mycobacterium tuberculosis bacterial growth detection for antimicrobial susceptibility testing by Hoang-Anh T. Vo, Sang Nguyen, Ai-Quynh T. Tran, Han Nguyen, Hai Bich Ho, Philip W. Fowler, Timothy M. Walker, Thuy Thi Nguyen

“…Using 4,018 plate images from the CRyPTIC (Comprehensive Resistance Prediction for Tuberculosis: An International Consortium) dataset to train models and refine the framework, TMAS achieved an essential agreement of 98.8%, significantly outperformed the 90% threshold established by the International Organization for Standardization (ISO). …”
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Microsatellite instability in endometrial cancer: the role of diagnostic methods and their clinical application. Expert consensus by A. S. Tyulyandina, E. A. Ulrikh, L. A. Kolomiets, S. E. Krasilnikov, A. G. Kedrova, A. A. Rumyantsev, G. A. Raskin, A. I. Nesterova, M. V. Volkonskiy, O. N. Churuksaeva, A. Yu. Goryainova, V. V. Zhavoronkova, V. N. Dmitriev, S. T. Nazranova, A. V. Shkradyuk, K. S. Volkova, A. I. Arutyunova, S. N. Kunitskaya, L. V. Stepura, T. G. Zolotoreva, E. B. Shakhnovich, E. V. Ponomareva, M. A. Strokova, A. S. Danilova, E. S. Martynova

“…This review details current concepts of MSI diagnostics and discusses its predictive value in patients with endometrial cancer. …”
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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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Integration of machine learning and experimental validation to identify the prognostic signature related to diverse programmed cell deaths in breast cancer by Longpeng Li, Longpeng Li, Jinfeng Zhao, Yaxin Wang, Zhibin Zhang, Wanquan Chen, Jirui Wang, Yue Cai

“…PCDRS, consisting of seven key genes, showed robust predictive ability over other signatures in different datasets. …”
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CECT-Based Radiomic Nomogram of Different Machine Learning Models for Differentiating Malignant and Benign Solid-Containing Renal Masses by Qian L, Fu B, He H, Liu S, Lu R

“…Among the various radiomic models, the LR model demonstrated superior predictive efficiency and robustness, yielding an AUC of 0.952 in the training cohort and 0.887 in the test cohort. …”
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Deciphering the heterogeneity of epithelial cells in pancreatic ductal adenocarcinoma: implications for metastasis and immune evasion by Jie Liu, Hui Li, Xiuyun Lin, Jiani Xiong, Guangfeng Wu, Lingyan Ding, Bin Lin

“…A prognostic model incorporating five key genes, including KLF6, was developed and demonstrated strong predictive accuracy for patient outcomes. Notably, KLF6 emerged as a critical prognostic marker associated with immune modulation, particularly through interactions with M2 macrophages. …”
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Dehydrotanshinone II A alleviates osteoarthritis via activating PPARγ to inhibit ferroptosis in chondrocytes by Wenli Guan, Fahu Yuan, Xin Wang

“…Bioinformatics analysis identified five key genes (CDKN1A, HMOX1, DPP4, GJA1, and PPARγ), among which PPARγ and HMOX1 exhibited strong predictive potential with AUC values > 0.8. Molecular docking analysis revealed that DHT IIA exhibited the lowest binding energy with PPARγ (-8.15 kcal/mol), indicating it as the most likely therapeutic target. …”
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Association Between Social Distancing Compliance and Public Place Crowding During the COVID-19 Pandemic: Cross-Sectional Observational Study Using Computer Vision to Analyze Survei... by Lasse Suonperä Liebst, Wim Bernasco, Peter Ejbye-Ernst, Nigel van Herwijnen, Thomas van der Veen, Dennis Koelma, Cees G M Snoek, Marie Rosenkrantz Lindegaard

“…We automatized the analysis of this footage using a computer vision algorithm designed for pedestrian detection and estimation of metric distances between individuals in the video still frames. …”
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Artificial intelligence (AI) in restorative dentistry: current trends and future prospects by Mariya Najeeb, Shahid Islam

“…Notable studies demonstrated AI’s ability to achieve high diagnostic accuracy, such as up to 95% accuracy in caries detection, and its capacity to improve treatment planning efficiency, thus reducing patient chair time. Predictive analytics for personalized treatments was another area where AI has shown substantial promise. …”
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The Social Construction of Categorical Data: Mixed Methods Approach to Assessing Data Features in Publicly Available Datasets by Theresa Willem, Alessandro Wollek, Theodor Cheslerean-Boghiu, Martha Kenney, Alena Buyx

“…ResultsOur quantitative study suggests scattered effects of including categorical data for machine learning model training across predictive classes. Our qualitative analysis gives insights into how the categorical data were collected and why they were published, explaining some of the quantitative effects that we observed. …”
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Projecting Forest Fire Probability in South Korea Under Climate Change, Population, and Forest Management Scenarios Using AI & Process-Based Hybrid Model (FLAM-Net) by Hyun-Woo Jo, Myoungsoo Won, Florian Kraxner, Seong Woo Jeon, Yowhan Son, Andrey Krasovskiy, Woo-Kyun Lee

“…Enhancements included improving backpropagation for optimization and introducing algorithms for national-specific fire ignition dynamics. …”
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A Clinically Interpretable Approach for Early Detection of Autism Using Machine Learning With Explainable AI by Oishi Jyoti, Hafsa Binte Kibria, Zareen Tasnim Pear, Md Nahiduzzaman, Md. Faysal Ahamed, Khandaker Reajul Islam, Jaya Kumar, Muhammad E. H. Chowdhury

“…Three different publicly available datasets have been used based on the age group to create the best predicting model for each case. After handling missing values, balancing the dataset, and analyzing the classifier’s performance, it is found that tree-based algorithms, particularly RF, perform better for all the datasets. …”
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Causes of embryo implantation failure: A systematic review and metaanalysis of procedures to increase embryo implantation potential by Francesco M. Bulletti, Romualdo Sciorio, Alessandro Conforti, Roberto De Luca, Carlo Bulletti, Antonio Palagiano, Marco Berrettini, Giulia Scaravelli, Roger A. Pierson

“…Subsequent studies ought to concentrate on modulating endometrial responses immunologically and developing algorithms to improve the precision of predicting implantation success; as well as the timing of endometrial receptivity and the occurrence of dormant embryo phenomena also warrants further investigation.…”
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Bioinformatics analysis of comorbid mechanisms between ischemic stroke and end stage renal disease by Shuhong Wang, Zhongda Li, Xiao Wang, Jiexue Zhou, Shandong Meng, Jinyang Zhuang, Yan Zhou, Qin Zhao, Chunli Zhu, Yusheng Zhang, Sheng Shen

“…Transcriptional regulatory networks were predicted using RcisTarget and miRcode. Key gene expressions were validated by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) in clinical samples. …”
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Enhancing ovarian cancer prognosis with an artificial intelligence-derived model: Multi-omics integration and therapeutic implications by You Wu, Kunyu Wang, Yan Song, Bin Li

“…Results: The AIDPI model demonstrated superior accuracy in predicting ovarian cancer prognosis compared to existing models. …”
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Social Media Suicide Watch by Katherine Prothro

“…Ethical Analysis The strongest argument in favor of apps like Samaritan’s Radar is their potential to predict individuals at risk for suicide and self-harm accurately. …”
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Deep Learning Based Large‐Area Contact Sensing for Safe Human–Robot Interaction Using Conformal Kirigami Structure‐Enabled Robotic E‐Skin by Rui Jiao, Zhengjun Wang, Ruoqin Wang, Qian Xu, Jiacheng Jiang, Boyang Zhang, Simin Yang, Yang Li, Yik Kin Cheung, Fan Shi, Hongyu Yu

“…Additionally, a convolutional neural network‐based deep learning algorithm is implemented to decode the features of guided wave signals and predict the contact location and energy intensity on the robot surface. …”
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The Forecasting Yield of Highland Barley and Wheat by Combining a Crop Model with Different Weather Fusion Methods in the Study of the Northeastern Tibetan Plateau by Peng Li, Liang He, Xuetong Wang, Mengfan Zhao, Fan Li, Ning Jin, Ning Yao, Chao Chen, Qi Tian, Bin Chen, Gang Zhao, Qiang Yu

“…The weather data fusion strategy for yield forecasts offered reliable prediction accuracy without the need for full-cycle weather observation.…”
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Capacity Estimation of Lithium-Ion Battery Systems in Fuel Cell Ships Based on Deep Learning Model by Xiangguo Yang, Jia Tang, Qijia Song, Yifan Liu, Lin Liu, Xingwei Zhou, Yuelin Chen, Telu Tang

“…To address the challenge of accurately estimating lithium-ion battery capacity under complex operating conditions, this study extracts universal health factors from battery data under varied charging and discharging scenarios and combines these with a deep learning model to enhance prediction accuracy. First, battery data from three complex conditions are analyzed, extracting partial charge and discharge data. …”
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