MRI-based machine learning analysis of perivascular spaces and their link to sleep disturbances, dementia, and mental distress in young adults with long-time mobile phone use by Li Li, Yalan Wu, Jiaojiao Wu, Bin Li, Rui Hua, Feng Shi, Lizhou Chen, Yeke Wu

“…ObjectiveLong-term mobile phone use (LTMPU) has been linked to sleep disorders, mood disorders, and cognitive impairment, with MRI-detected enlarged perivascular spaces (EPVSs) as potential imaging markers. …”
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AI-powered advances in type II endometrial cancer: global trends and African contexts by Thulo Molefi, Thulo Molefi, Thulo Molefi, Lloyd Mabonga, Rodney Hull, Motshedisi Sebitloane, Zodwa Dlamini

“…With rising incidence and increasing concerns in Africa, type II ECs are often detected in advanced stages, exhibit aggressive progression, and resist conventional therapies. …”
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Sensor-Derived Measures of Motor and Cognitive Functions in People With Multiple Sclerosis Using Unsupervised Smartphone-Based Assessments: Proof-of-Concept Study by Matthew Scaramozza, Aurélie Ruet, Patrizia A Chiesa, Laïtissia Ahamada, Emmanuel Bartholomé, Loïc Carment, Julie Charre-Morin, Gautier Cosne, Léa Diouf, Christine C Guo, Adrien Juraver, Christoph M Kanzler, Angelos Karatsidis, Claudia Mazzà, Joaquin Penalver-Andres, Marta Ruiz, Aurore Saubusse, Gabrielle Simoneau, Alf Scotland, Zhaonan Sun, Minao Tang, Johan van Beek, Lauren Zajac, Shibeshih Belachew, Bruno Brochet, Nolan Campbell

“…The assessment provided high-quality recordings from cognitive, manual dexterity, and mobility tests, from which 47 SDMs, based on established literature, were extracted using previously developed and publicly available algorithms. These SDMs were first separately and then jointly screened for bias and normality by 2 expert assessors. …”
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Innovative Business Models Towards Sustainable Energy Development: Assessing Benefits, Risks, and Optimal Approaches of Blockchain Exploitation in the Energy Transition by Aikaterini Papapostolou, Ioanna Andreoulaki, Filippos Anagnostopoulos, Sokratis Divolis, Harris Niavis, Sokratis Vavilis, Vangelis Marinakis

“…Subsequently, specific mitigation strategies are proposed to address the detected risks. This research demonstrates that blockchain integration into these business models can significantly improve energy efficiency, reduce operational costs, enhance security, and support a more decentralised energy system, providing actionable insights for stakeholders to implement blockchain solutions effectively. …”
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Non-invasive urinary proteomic biomarkers for prognostic assessment in sepsis by Qingbo Zeng, Nianqing Zhang, Junjie Zeng, Lincui Zhong, Qingwei Lin, Longping He, Xiaomin Song, Jingchun Song

“…We used data-independent acquisition (DIA) proteomics to detect proteins in the urinary of death patients (n = 14) and survivals (n = 32). …”
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“Voice is the New Blood”: a discourse analysis of voice AI health-tech start-up websites by Alden Blatter, Hortense Gallois, Emily Evangelista, Yael Bensoussan, Bridge2AI-Voice Consortium, Jean-Christophe Bélisle-Pipon, Yael E. Bensoussan, Olivier Elemento, Jean-Christophe Bélisle-Pipon, David Dorr, Satrajit Ghosh, Alistair Johnson, Phillip Payne, Maria E. Powell, Anaïs Rameau, Vardit Ravitsky, Alexandros Sigaras

“…IntroductionVoice as a biomarker has emerged as a transformative field in health technology, providing non-invasive, accessible, and cost-effective methods for detecting, diagnosing, and monitoring various conditions. …”
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Radiomic Analysis and Liquid Biopsy in Preoperative CT of NSCLC: An Explorative Experience by Maria Paola Belfiore, Mario Sansone, Giovanni Ciani, Vittorio Patanè, Carlotta Genco, Roberta Grassi, Giovanni Savarese, Marco Montella, Riccardo Monti, Salvatore Cappabianca, Alfonso Reginelli

“…Radiomic features were extracted from CT images, and circulating tumor DNA (ctDNA) was sequenced to identify genetic mutations. Machine learning algorithms were employed to assess the association between radiomic features and gene mutations. …”
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Co-occurrence of sudden feeding behaviour deviations and welfare issue onsets in growing-finishing pigs by Jacinta D. Bus, Rudi M. de Mol, Laura E. Webb, Eddie A. M. Bokkers, Iris J. M. M. Boumans

“…Abstract Background Modern sensor technologies and algorithms have the potential to continuously monitor indicators of individual animal welfare, but in growing-finishing pigs the validity of such welfare monitoring remains low for unclear reasons. …”
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Testicular appendix (hydatid of Morgagni) torsion in adult urological management: evaluation of treatment outcomes by I. S. Shormanov, D. N. Shedrov, D. Y. Garova, A. I. Ryzhkov

“…Accordingly, there are no clear algorithms for the management of such patients.Objective. …”
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Machine learning-based brain magnetic resonance imaging radiomics for identifying rapid eye movement sleep behavior disorder in Parkinson’s disease patients by Yandong lian, Yibin Xu, Linlin Hu, Yuguo Wei, Zhaoge Wang

“…This study aims to develop and validate an magnetic resonance imaging (MRI) radiomics-based machine learning classifier to accurately detect RBD patients with Parkinson’s disease (PD). …”
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Computationally Optimized Hemagglutinin Proteins Adjuvanted with Infectimune^® Generate Broadly Protective Antibody Responses in Mice and Ferrets by James D. Allen, Xiaojian Zhang, Jessica M. Medina, Matthew H. Thomas, Amanda Lynch, Ron Nelson, Julia Aguirre, Ted M. Ross

“…When challenged with the A/Victoria/2570/2019 H1N1 influenza virus, the COBRA rHA vaccinated animals had minimal weight loss, and no detectable virus was present in their respiratory tissues on day 3 post-infection. …”
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Continuous heart rate measurements in patients with cardiac disease: Device comparison and development of a novel artefact removal procedure by Paulien Vermunicht, Katsiaryna Makayed, Christophe Buyck, Lieselotte Knaepen, Juan Sebastian Piedrahita Giraldo, Sebastiaan Naessens, Wendy Hens, Emeline Van Craenenbroeck, Kris Laukens, Lien Desteghe, Hein Heidbuchel

“…We developed and assessed an artefact removal procedure (ARP) using logistic regression machine learning models to detect unreliable PPG data. Results The ECG-based chest strap showed a strong correlation ( r  = 0.94) and clinically acceptable errors (mean absolute error, MAE = 3.4 bpm; mean absolute percentage error, MAPE = 4.9%). …”
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MEMS and IoT in HAR: Effective Monitoring for the Health of Older People by Luigi Bibbò, Giovanni Angiulli, Filippo Laganà, Danilo Pratticò, Francesco Cotroneo, Fabio La Foresta, Mario Versaci

“…The analysis methods employed include machine learning algorithms to identify movement patterns, statistical analysis to assess the frequency and quality of movements, and data visualization to track changes over time. …”
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Fuzzy logic-controlled three-phase dynamic voltage restorer for enhancing voltage stabilization and power quality by Abu Shufian, Sowrov Komar Shib, Durjoy Roy Dipto, Md Tanvir Rahman, Nasif Hannan, Shaikh Anowarul Fattah

“…Unlike conventional algorithms, which struggle under fluctuating grid conditions and complex disturbances, FLC dynamically adjusts the output of DVR based on real-time reference and load voltage measurements. …”
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The prognostic and therapeutic significance of polyunsaturated fatty acid‐derived oxylipins in ST‐segment elevation myocardial infarction by Zhiyong Du, Yingyuan Lu, Ying Ma, Yunxiao Yang, Wei Luo, Sheng Liu, Ming Zhang, Yong Wang, Lei Li, Chun Li, Wei Wang, Hai Gao

“…Herein, we used targeted metabolomics and machine learning algorithms to develop an oxylipin‐based risk model to accurately predict recurrent major adverse cardiovascular events (MACE) after STEMI in two independent prospective cohorts with 2 years of follow‐up. …”
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Fc-Binding Cyclopeptide Induces Allostery from Fc to Fab: Revealed Through in Silico Structural Analysis to Anti-Phenobarbital Antibody by Tao Zhou, Huiling Zhang, Xiaoting Yu, Kangliang Pan, Xiaojun Yao, Xing Shen, Hongtao Lei

“…The combination of molecular docking and multiple allosteric site prediction algorithms in these methods identified that the cyclopeptide binds to the interface of heavy chain region-1 (CH₁) in antibody Fab and heavy chain region-2 (CH₂) in antibody Fc. …”
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Unraveling the complex genetic landscape of OTOF-related hearing loss: a deep dive into cryptic variants and haplotype phasing by Pei-Hsuan Lin, Cheng-Yu Tsai, Yu-Ting Chiang, Chang-Han Ho, Yue-Sheng Lu, Jacob Shu-Jui Hsu, Yen-Fu Cheng, Shih-Feng Tsai, Chuan-Jen Hsu, Pei-Lung Chen, Chen-Chi Wu

“…The integration of advanced sequencing technologies, predictive algorithms, and functional validation assays will improve the accuracy of molecular diagnosis and inform personalized treatment strategies for individuals with DFNB9. …”
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Automatic recognition and differentiation of pulmonary contusion and bacterial pneumonia based on deep learning and radiomics by Tie Deng, Junbang Feng, Xingyan Le, Yuwei Xia, Feng Shi, Fei Yu, Yiqiang Zhan, Xinghua Liu, Chuanming Li

“…Four machine learning algorithms including Decision Trees, Logistic Regression, Random Forests and Support Vector Machines(SVM) were using to built the models. …”
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Bipolar disorders and creativity: the roles of ambition, effort-based decision-making, and exploration by Samson Tse, Sheri L. Johnson, Chong Ho Yu, Winnie Yuen, Iris Lo, Luke Clark, Luke Clark, Erin E. Michalak, Erin E. Michalak, Manon Ironside, Kiana Modavi

“…However, decision tree algorithms revealed multivariate contributors to creative accomplishments. …”
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Integrated metabolomic-lipidomic profiling reveals novel biomarkers and therapeutic targets for alcohol use disorder with cognitive impairment by Li Shi, Li Shi, Li Shi, Xiaofang Chen, Bojie Zhou, Bojie Zhou, Shanghao Yang, Shanghao Yang, Qinglin Ou, Qinglin Ou, Xuhui Zhou, Xuhui Zhou

“…Cognitive function was assessed using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Machine learning algorithms (Random Forest and LASSO regression) were employed for biomarker selection, and pathway analysis was performed using MetaboAnalyst 5.0.ResultsThe multi-omics platform detected 117 differentially expressed molecules (11 metabolites and 106 lipids) with high diagnostic accuracy (mean AUC=0.92 ± 0.03). …”
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