Reduced response to regadenoson with increased weight: An artificial intelligence–based quantitative myocardial perfusion study by Emmanouil Androulakis, Georgios Georgiopoulos, Alessia Azzu, Elena Surkova, Adam Bakula, Panagiotis Papagkikas, Alexandros Briasoulis, Ranil De Silva, Peter Kellman, Dudley Pennell, Francisco Alpendurada

“…The aim of this study was to evaluate the effectiveness of regadenoson in patients with varying body weights using novel quantitative cardiovascular magnetic resonance (CMR) perfusion parameters in addition to standard clinical markers. …”
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Investigating potential biomarkers associated with antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis using Mendelian randomization and transcriptomic analysis by Yujia Wang, Zhimin Chen, Kaiqi Huang, Keng Ye, Shiwei He, Yanfang Xu, Hong Chen

“…Genes with a significant causal relationship were selected as candidate genes for further analysis. Machine learning algorithms, ROC curve analysis, and expression evaluation were employed to screen for biomarkers. …”
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Distribution of age at natural menopause, age at menarche, menstrual cycle length, height and BMI in BRCA1 and BRCA2 pathogenic variant carriers and non-carriers: results from EMBR... by Nasim Mavaddat, Debra Frost, Emily Zhao, Daniel R. Barnes, Munaza Ahmed, Julian Barwell, Angela F. Brady, Paul Brennan, Hector Conti, Jackie Cook, Harriet Copeland, Rosemarie Davidson, Alan Donaldson, Emma Douglas, David Gallagher, Rachel Hart, Louise Izatt, Zoe Kemp, Fiona Lalloo, Zosia Miedzybrodzka, Patrick J. Morrison, Jennie E. Murray, Alex Murray, Hannah Musgrave, Claire Searle, Lucy Side, Katie Snape, Vishakha Tripathi, Lisa Walker, Stephanie Archer, D. Gareth Evans, Marc Tischkowitz, Antonis C. Antoniou, Douglas F. Easton

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Hybrid closed-loop systems for managing blood glucose levels in type 1 diabetes: a systematic review and economic modelling by Asra Asgharzadeh, Mubarak Patel, Martin Connock, Sara Damery, Iman Ghosh, Mary Jordan, Karoline Freeman, Anna Brown, Rachel Court, Sharin Baldwin, Fatai Ogunlayi, Chris Stinton, Ewen Cummins, Lena Al-Khudairy

“…The system includes a combination of real-time continuous glucose monitoring from a continuous glucose monitoring device and a control algorithm to direct insulin delivery through an insulin pump. …”
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Comprehensive Analysis of the Expression, Prognosis and Function of TRAF Family Proteins 
in NSCLC by Yixuan WANG, Qiang CHEN, Yaguang FAN, Shuqi TU, Yang ZHANG, Xiuwen ZHANG, Hongli PAN, Xuexia ZHOU, Xuebing LI

“…TRAF family members differentially regulated multiple pathways, including NF-κB, immune response, cell adhesion and RNA splicing. …”
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Project quality, regulation quality by Elena Mussinelli

“…Instead, deductive design approaches seem to prevail today, due to the growing availability of algorithmic procedures that do not merely support the design process, but develop it in an almost automated manner through conditioning and prevailing indicators and parameters. …”
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Identification and quantification of transposable element transcripts using Long-Read RNA-seq in Drosophila germline tissues by Rebollo, Rita, Gerenton, Pierre, Cumunel, Eric, Mary, Arnaud, Sabot, François, Burlet, Nelly, Gillet, Benjamin, Hughes, Sandrine, S. Oliveira, Daniel, Goubert, Clément, Fablet, Marie, Vieira, Cristina, Lacroix, Vincent

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Can Different Cultivars of <i>Panicum maximum</i> Be Identified Using a VIS/NIR Sensor and Machine Learning? by Gelson dos Santos Difante, Gabriela Oliveira de Aquino Monteiro, Juliana Caroline Santos Santana, Néstor Eduardo Villamizar Frontado, Jéssica Gomes Rodrigues, Aryadne Rhoana Dias Chaves, Dthenifer Cordeiro Santana, Izabela Cristina de Oliveira, Luis Carlos Vinhas Ítavo, Fabio Henrique Rojo Baio, Gabriela Souza Oliveira, Carlos Antonio da Silva Junior, Vanessa Zirondi Longhini, Alexandre Menezes Dias, Paulo Eduardo Teodoro, Larissa Pereira Ribeiro Teodoro

“…These differences can be used in classification using machine learning (ML) algorithms to differentiate biodiversity within the same species. …”
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Topology aware multitask cascaded U-Net for cerebrovascular segmentation. by Pierre Rougé, Nicolas Passat, Odyssée Merveille

“…This loss requires computing the skeletons of both the manual annotation and the predicted segmentation in a differentiable way. Currently, differentiable skeletonization algorithms are either inaccurate or computationally demanding. …”
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Computed tomography enterography radiomics and machine learning for identification of Crohn’s disease by Qiao Shi, Yajing Hao, Huixian Liu, Xiaoling Liu, Weiqiang Yan, Jun Mao, Bihong T. Chen

“…A machine learning classification system was constructed by combining six selected radiomics features with eight classification algorithms. The models were trained using leave-one-out cross-validation and evaluated for accuracy. …”
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High resolution tissue and cell type identification via single cell transcriptomic profiling. by Muyi Liu, Suilan Zheng, Hongmin Li, Bruce Budowle, Le Wang, Zhaohuan Lou, Jianye Ge

“…By incorporating a crucial and unique reference cell quality differentiation phase of targeting only high confident cells as reference, scTissueID achieved better and consistent performance in determining cell and tissue types compared to 8 state-of-art single cell annotation pipelines and 6 widely adopted machine learning algorithms, as demonstrated through a large-scale and comprehensive comparison study using both forensic-relevant and Human Cell Atlas (HCA) data. …”
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Radiomics Analysis of Whole-Kidney Non-Contrast CT for Early Identification of Chronic Kidney Disease Stages 1–3 by Guirong Zhang, Pan Zhang, Yuwei Xia, Feng Shi, Yuelang Zhang, Dun Ding

“…Key features were selected through Relief, MRMR, and LASSO regression algorithms. A machine learning classifier was trained to differentiate CKD from healthy kidneys and compared with the radiologist assessments. …”
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OPTIMAL ESTIMATION OF RANDOM PROCESSES ON THE CRITERION OF MAXIMUM A POSTERIORI PROBABILITY by A. A. Lobaty, Y. F. Yacina, N. N. Arefiev

“…Unlike common approaches based on consideration as a criterion for optimization of the minimum mean square error of estimation, in this case, the optimization criterion is considered the maximum a posteriori probability density of the process being evaluated.The a priori probability density estimated Gaussian process originally considered a differentiable function that allows us to expand it in a Taylor series without use of intermediate transformations characteristic functions and harmonic decomposition. …”
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Classification of Non-Frozen and Frozen–Thawed Pork with Adaptive Support Vector Machine and Electronic Nose by Paul Christian E. Artista, Abraham M. Mendoza, Dionis A. Padilla

“…Its performance was evaluated with recall, F1 scores, and precision. To further enhance the model’s performance, future studies are mandated to integrate additional gas sensors, increase sample sizes, advance data preprocessing techniques, and explore different machine learning algorithms or ensemble methods.…”
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Machine learning-based diagnostic model of lymphatics-associated genes for new therapeutic target analysis in intervertebral disc degeneration by Maoqiang Lin, Maoqiang Lin, Shaolong Li, Yabin Wang, Yabin Wang, Guan Zheng, Guan Zheng, Fukang Hu, Fukang Hu, Qiang Zhang, Qiang Zhang, Pengjie Song, Haiyu Zhou, Haiyu Zhou

“…The receiver operating characteristic (ROC) curve, nomogram, and Decision Curve Analysis (DCA) were used to evaluate the model effect. In addition, we constructed a potential drug regulatory network and competitive endogenous RNA (ceRNA) network for key LAGs.ResultsA total of 15 differentially expressed LAGs were identified. …”
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Characteristic genes and immune landscape of interstitial cystitis. by Junhao Wang, Yan Zhou, Jie Hu, Jianpeng Han, Jianyong Feng, Kuo Guo, Wenbin Chen, Yanrui Yun, Yongzhang Li

“…<h4>Conclusion</h4>We screened the feature genes, CCL18, MMP10 and WIF1, among the differentially expressed genes (DEGs) by three different machine learning algorithms. …”
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Machine learning-driven multi-omics analysis identifies a prognostic gene signature associated with programmed cell death and metabolism in hepatocellular carcinoma by Xiang Li, Donghao Yin, Jiahao Geng, Yanyu Xu, Zijing Xu, Xuemeng Yang, Quanwei Li, Zimeng Shang, Zhiyun Yang, Zhong Xu, Jiabo Wang, Enxiang Zhang, Xinhua Song

“…Based on prognosis-related DEGs, patients and cells were stratified into high- and low-expression groups using corresponding computational algorithms. The intersecting DEGs from both datasets were analyzed using univariate Cox regression, and a prognostic risk score model was constructed through machine learning algorithms. …”
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Classification of <i>Verticillium dahliae</i> Vegetative Compatibility Groups (VCGs) with Machine Learning and Hyperspectral Imagery by Sudha GC Upadhaya, Chongyuan Zhang, Sindhuja Sankaran, Timothy Paulitz, David Wheeler

“…The study documented the spectral profiles of <i>V. dahliae</i>’s isolates and identified specific spectral features that can effectively differentiate among the VCGs. Multiple machine learning algorithms, including random forest and artificial neural networks (ANNs), were trained and evaluated on previously unseen isolates. …”
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Establishment of an MRI-based radiomics model for distinguishing between intramedullary spinal cord tumor and tumefactive demyelinating lesion by Zifeng Zhang, Ning Li, Yuhang Qian, Huilin Cheng

“…Abstract Objective Differentiating intramedullary spinal cord tumor (IMSCT) from spinal cord tumefactive demyelinating lesion (scTDL) remains challenging with standard diagnostic approaches. …”
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Comment on “Odontogenic Tumors: A Challenge for Clinical Diagnosis and an Opportunity for AI Innovation” by Hinpetch Daungsupawong, Viroj Wiwanitkit

“…Additionally, a more thorough exploration of the current limitations in diagnosing these tumors would have provided a more comprehensive understanding of the issue.Moving forward, future research should focus on developing AI algorithms that can accurately differentiate between different types of odontogenic tumors based on their unique characteristics. …”
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