Accurate and affordable multi-cancer early detection and localization via plasma cfDNA multi-omic profiling by Pin Cui, Weihuang He, Mingji Feng, Hanming Lai

“…Findings: In this study, we performed both WGBS and WGS on 17 healthy individuals, 26 HCC patients, 32 lung cancer patients, and 15 colorectal patients to prove the feasibility of inferring cfDNA methylation patterns using cfDNA fragmentation profile. …”
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Machine learning approach for water quality predictions based on multispectral satellite imageries by Vicky Anand, Bakimchandra Oinam, Silke Wieprecht

“…Spectral bands of each satellite were used as independent parameter to generate the algorithms for pH, Dissolved Oxygen (DO), Total Suspended Solids (TSS) and Total Dissolved Solids (TDS). …”
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Mapping the use of artificial intelligence in medical education: a scoping review by Erwin Hernando Hernández Rincón, Daniel Jimenez, Lizeth Alexandra Chavarro Aguilar, Juan Miguel Pérez Flórez, Álvaro Enrique Romero Tapia, Claudia Liliana Jaimes Peñuela

“…Abstract Introduction The integration of artificial intelligence (AI) in healthcare has transformed clinical practices and medical education, with technologies like diagnostic algorithms and clinical decision support increasingly incorporated into curricula. …”
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Identification of M1 macrophage infiltration-related genes for immunotherapy in Her2-positive breast cancer based on bioinformatics analysis and machine learning by Sizhang Wang, Xiaoyan Wang, Jing Xia, Qiang Mu

“…Then, four overlapping M1 macrophage infiltration-related genes (M1 MIRGs), namely CCDC69, PPP1R16B, IL21R, and FOXP3, were obtained using five machine-learning algorithms. Subsequently, nomogram models were constructed to predict the incidence of Her2-positive breast cancer patients. …”
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MetaCGRP is a high-precision meta-model for large-scale identification of CGRP inhibitors using multi-view information by Nalini Schaduangrat, Phisit Khemawoot, Apisada Jiso, Phasit Charoenkwan, Watshara Shoombuatong

“…In brief, we initially employed different molecular representation methods coupled with popular ML algorithms to construct a pool of baseline models. Then, all baseline models were optimized and used to generate multi-view features. …”
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EXPRESSION OF MATRIX METALLOPROTEINASES IN LACRIMAL FLUID AND GENE POLYMORPHISM OF COMPLEMENT FACTOR H (CFH) IN PATIENTS WITH PRIMARY OPEN-ANGLE GLAUCOMA by V. A. Sokolov, V. G. Likhvantseva, O. N. Levanova, A. A. Nikiforov, V. A. Vygodin

“…The levels of MMP-2 and MMP-9 were determined by sandwich ELISA for the both  eyes. Venous blood was used for genetic studies. …”
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A Review and Comparative Analysis of Solar Tracking Systems by Reza Sadeghi, Mattia Parenti, Samuele Memme, Marco Fossa, Stefano Morchio

“…In particular, dual-axis systems outperform others in high-latitude and equatorial regions due to their ability to follow both azimuth and elevation angles throughout the year. …”
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Enhancing seizure detection with hybrid XGBoost and recurrent neural networks by Santushti Santosh Betgeri, Madhu Shukla, Dinesh Kumar, Surbhi B. Khan, Muhammad Attique Khan, Nora A. Alkhaldi

“…This study investigates machine learning and deep learning algorithms for seizure prediction, comparing their effectiveness on a large EEG dataset of epileptic patients. …”
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Social Media and Men’s Health: Separating Science from Speculation in Andrology by Michael George, Vaibhav Modgil, Ian Pearce, Theodora Stasinou

“…To leverage the opportunity that social media offers, urologists are required to modernise their approach to communication in order to ensure that accurate health information is both accessible and engaging.…”
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Quantum-enabled topological optimization of distributed energy storage for resilient black-start operations by Yinchi Shao, Yu Gong, Xiaoyu Wan, Xianmiao Huang, Shanna Luo, Yuntao Cao, Tao Zhang

“…By integrating quantum annealing algorithms, the framework efficiently addresses the combinatorial complexity of large-scale ESS placement and dispatch, outperforming traditional heuristic and classical optimization techniques in both computational speed and solution quality. …”
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Shedding light on the DICER1 mutational spectrum of uncertain significance in malignant neoplasms by D. S. Bug, I. S. Moiseev, Yu. B. Porozov, Yu. B. Porozov, N. V. Petukhova

“…The latest contemporary methods of variant effect prediction utilize machine learning algorithms on bulk data, yielding suboptimal correlation with biological data. …”
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Machine Learning Modeling of Foam Concrete Performance: Predicting Mechanical Strength and Thermal Conductivity from Material Compositions by Leifa Li, Wangwen Sun, Askar Ayti, Wangping Chen, Zhuangzhuang Liu, Lauren Y. Gómez-Zamorano

“…The predictive model used in this study provides reliable technical support for the optimization and engineering application of foam concrete, enhancing both prediction accuracy and practical efficiency.…”
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The future of spatial epidemiology in the AI era: enhancing machine learning approaches with explicit spatial structure by Nima Kianfar, Benn Sartorius, Colleen L. Lau, Robert Bergquist, Behzad Kiani

“…Research in spatial epidemiology relies on both conventional approaches and Machine- Learning (ML) algorithms to explore geographic patterns of diseases and identify influential factors (Pfeiffer & Stevens, 2015). …”
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Fed-DPSDG-WGAN: Differentially Private Synthetic Data Generation for Loan Default Prediction via Federated Wasserstein GAN by Padmaja Ramachandra, Santhi Vaithiyanathan

“…However, creating robust deep-learning algorithms that classify loan defaulters requires abundant data, potentially compromising individuals’ privacy. …”
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Transfer learning for securing electric vehicle charging infrastructure from cyber-physical attacks by Ahmad Almadhor, Shtwai Alsubai, Imen Bouazzi, Vincent Karovic, Monika Davidekova, Abdullah Al Hejaili, Gabriel Avelino Sampedro

“…It is common for these systems to be constructed using conventional machine learning algorithms. So many common signs of attacks are ignored. …”
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Curvilinear relationship between life's crucial 9 and metabolic syndrome in U.S. adults: a cross-sectional study by Bo Wang, Chunqi Jiang, Pingping Yu, Zhen Nie, Ning Wang, Xin Zhang, Xin Zhang

“…To pinpoint inflection points, we integrated recursive partitioning algorithms with a two-stage linear regression model. …”
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Establishment of an alternative splicing prognostic risk model and identification of FN1 as a potential biomarker in glioblastoma multiforme by Xi Liu, Jinming Song, Zhiming Zhou, Yuting He, Shaochun Wu, Jin Yang, Zhonglu Ren

“…These abnormal ASEs may be regulated by tumour-related splicing factors according to the PPI network. Furthermore, both mRNA and protein levels of FN1 were highly expressed in GBM compared to LGG, correlating with poor prognosis in GBM. …”
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Critical structural and functional roles for the N-terminal insertion sequence in surfactant protein B analogs. by Frans J Walther, Alan J Waring, Jose M Hernandez-Juviel, Larry M Gordon, Zhengdong Wang, Chun-Ling Jung, Piotr Ruchala, Andrew P Clark, Wesley M Smith, Shantanu Sharma, Robert H Notter

“…Similar to native SP-B, both Mini-B and Super Mini-B exhibit in vitro activity with spread films showing near-zero minimum surface tension during cycling using captive bubble surfactometry. …”
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Interpretable machine learning models for predicting in-hospital mortality in patients with chronic critical illness and heart failure: A multicenter study by Min He, Yongqi Lin, Siyu Ren, Pengzhan Li, Guoqing Liu, Liangbo Hu, Xueshuang Bei, Lingyan Lei, Yue Wang, Qianghong Zhang, Xiaocong Zeng

“…Only patients diagnosed with both CCI and HF were included. The MIMIC datasets served as the derivation cohort, while the eICU-CRD dataset was used for external validation. …”
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M3S-GRPred: a novel ensemble learning approach for the interpretable prediction of glucocorticoid receptor antagonists using a multi-step stacking strategy by Nalini Schaduangrat, Hathaichanok Chuntakaruk, Thanyada Rungrotmongkol, Pakpoom Mookdarsanit, Watshara Shoombuatong

“…Compared to traditional ML classifiers, M3S-GRPred attained superior performance in terms of both the training and independent test datasets. …”
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