Transcriptomics-based exploration of ubiquitination-related biomarkers and potential molecular mechanisms in laryngeal squamous cell carcinoma by Qiu Chen, Zhimin Wu, Yifei Ma

“…Abstract Background One of the most common and prevalent cancers is laryngeal squamous cell carcinoma (LSCC), which poses a great threat to the life and health of the patient. Nonetheless, it has been demonstrated that ubiquitination is crucial for the development and course of LSCC. …”
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Integrating bioinformatics and machine learning to uncover lncRNA LINC00269 as a key regulator in Parkinson's disease via pyroptosis pathways by LiLi Ma, Yue Liu, Yajing Ren, Na Mi, Jing Fang, Rui Bao, Xiuzhi Xu, Hongjia Zhang, Ying Tang

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Predicting cognitive decline in cognitively impaired patients with ischemic stroke with high risk of cerebral hemorrhage: a machine learning approach by Eun Namgung, Young Sun Kim, Sun U. Kwon, Dong-Wha Kang, Dong-Wha Kang

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Datafikasi Fandom K-Pop: Transformasi Identitas Pribadi dan Sosial di Era Digital by Ersa Putri Nabila, Innayah Pitopili, Muhammad Fakhri Rahmadi, Muhammad Haydar

“…Through in-depth interviews with 12 K-Pop fans who have been involved in idol voting activities, the study investigates how social media and algorithms are perceived to influence fan experiences, foster a sense of community solidarity, and contribute to reinterpreting their identities in digital spaces. …”
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Enhancing stroke prediction models: A mixing of data augmentation and transfer learning for small-scale dataset in machine learning by Imam Tahyudin, Ade Nurhopipah, Ades Tikaningsih, Puji Lestari, Yaya Suryana, Edi Winarko, Eko Winarto, Nazwan Haza, Hidetaka Nambo

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Deciphering the role of miR-71 in Echinococcus multilocularis early development in vitro. by Matías Gastón Pérez, Markus Spiliotis, Natalia Rego, Natalia Macchiaroli, Laura Kamenetzky, Nancy Holroyd, Marcela Alejandra Cucher, Klaus Brehm, Mara Cecilia Rosenzvit

“…Using genomic information and bioinformatic algorithms for miRNA binding prediction, we found a high number of potential miR-71 targets in E. multilocularis. …”
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A Soft Sensor Based Inference Engine for Water Quality Assessment and Prediction by Micheal A Ogundero, Theophilus A Fashanu, Foluso O Agunbiade, Kehinde Orolu, Ahmed A Yinusa, Usman A Daudu, Muhammed O H Amuda

“…Results show that machine learning algorithms including the Logistic Regression, Decision Trees, Random Forest, XGBoost, and Neural Networks schemes reliably predicted water potability in the absence of two missing instrumentation parameters namely: pH and DO. …”
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Machine learning-based prediction model for brain metastasis in patients with extensive-stage small cell lung cancer by Erha Munai, Siwei Zeng, Ze Yuan, Dingyi Yang, Yong Jiang, Qiang Wang, Yongzhong Wu, Yunyun Zhang, Dan Tao

“…Four different machine learning (ML) algorithms were used to create prediction models for BMs in ES-SCLC patients. …”
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Intelligent Wireless Power Scheduling for Lunar Multienergy Systems: Deep Reinforcement Learning for Real-Time Adaptive Beam Steering and Vehicle-to-Grid Energy Optimization by Thomas Tongxin Li, Shuangqi Li, Cynthia Xin Ding, Zhaoyao Bao, Mohannad Alhazmi

“…Future work will explore the integration of hybrid energy storage models, quantum-inspired optimization for real-time decision-making, and predictive beamforming algorithms to further enhance the reliability and efficiency of lunar energy networks.…”
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LCFC-Laptop: A Benchmark Dataset for Detecting Surface Defects in Consumer Electronics by Hua-Feng Dai, Jyun-Rong Wang, Quan Zhong, Dong Qin, Hao Liu, Fei Guo

“…However, the high level of automation and the short product life cycles in this industry make defect sample collection both difficult and inefficient. …”
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Using machine learning to predict the probability of incident 2-year depression in older adults with chronic diseases: a retrospective cohort study by Ying Zheng, Taotao Zhang, Shu Yang, Fuzhi Wang, Li Zhang, Yuwen Liu

“…Sex, self-rated health status, occupation, eyesight, memory and life satisfaction were identified as impactful predictors of depression. …”
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Nasolacrimal Duct Obstruction Secondary to Radioactive Iodine-131 Therapy for Differentiated Thyroid Cancer by A. A. Trukhin, V. D. Yartsev, M. S. Sheremeta, D. V. Yudakov, M. O. Korchagina, R. Kh. Salimkhanov, S. V. Grishkov

“…The latter may drastically deteriorate the quality of life of a patient after radionuclide therapy. In addition, the regulations of the Russian Federation require indicating the effective dose of radiation as a measure of damage (risk), but the presence of a deterministic effect in individual organs and tissues complicates monitoring and recording patient exposure doses. …”
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Digital remote monitoring of people with multiple sclerosis by Michelangelo Dini, Michelangelo Dini, Giancarlo Comi, Letizia Leocani, Letizia Leocani, Letizia Leocani

“…We focus on tools and techniques applied to data from wearable sensors, smartphones, and other connected devices, as well as AI-based methods for the analysis of big data.ResultsWearable sensors and machine learning algorithms show significant promise in monitoring motor symptoms, such as fall risk and gait disturbances. …”
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New Challenging Systemic Therapies for Juvenile Scleroderma: A Comprehensive Review by Chiara Sassetti, Claudia Borrelli, Martha Mazuy, Cristina Guerriero, Donato Rigante, Susanna Esposito

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Abnormal heart sound recognition using SVM and LSTM models in real-time mode by Moy’awiah A. Al-Shannaq, Areen Nasrawi, Abed Al-Raouf K. Bsoul, Ahmad A. Saifan

“…This can decrease the mortality rate for cardiovascular diseases and enhance the patient’s quality of life. This study aims to propose a real-time heart sound recognition system to classify both normal and abnormal phonocardiograms with the ability to define the abnormality type if existed. …”
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Comparing interpretable machine learning models for fall risk in middle-aged and older adults with and without pain by Shangmin Chen, Yongshan Gao, Lin Du, Mengzhen Min, Lei Xie, Liping Li, Xiaodong Chen, Zhigang Zhong

“…This study included 13,074 middle-aged and older adults from the China health and retirement longitudinal study (wave 2011–2015) to separately develop four-year fall risk prediction models for older adults with and without pain, using five machine learning algorithms with 145 input variables as candidate features. …”
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Machine learning for predicting all-cause mortality of metabolic dysfunction-associated fatty liver disease: a longitudinal study based on NHANES by Xueni Wang, Huihui Chen, Luqiao Wang, Wenguang Sun

“…While advanced machine learning algorithms are gaining recognition as effective tools for clinical prediction, their ability to predict all-cause mortality of MAFLD individuals remains uncertain. …”
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Monitoring and optimization of POCT devices in a multi-specialty hospital in Poland: usage trends, quality assurance, and clinical impact (2017–2024) by Agnieszka Woźniak-Kosek, Lucyna Drążek

“…Rapid access to blood laboratory test results is crucial for diagnosing and treating patients in life-threatening conditions. Oxygenation status and acid-base balance are determined on arterial blood gasometry and are vital components of modern treatment algorithms for critically ill patients, similar to capillary blood glucose level measurement. …”
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Associations between anthropometric indices and biological age acceleration in American adults: insights from NHANES 2009–2018 data by Xinyun Chen, Xia Chen, Fangyu Shi, Wenhui Yu, Chang Gao, Shenju Gou, Ping Fu

“…Biological aging acceleration was assessed using Phenotypic Age Acceleration (PhenoAgeAccel) and Klemera-Doubal Method Age Acceleration (KDM-AA) algorithms. Associations between anthropometric indices and biological aging acceleration were evaluated using weighted multiple linear and logistic regression models, adjusted for potential confounders. …”
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Machine Learning in Ambient Assisted Living for Enhanced Elderly Healthcare: A Systematic Literature Review by Aabid A. Mir, Ahmad S. Khalid, Shahrulniza Musa, Mohammad Faizal Ahmad Fauzi, Normy Norfiza Abdul Razak, Tong Boon Tang

“…As the global population ages, Ambient Assisted Living (AAL) systems have become essential in supporting the elderly to maintain independence and quality of life. Such systems integrate advanced technologies such as machine learning (ML), internet of things (IoT), and sensors to enhance safety and healthcare delivery. …”
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