Results 161 to 170 of about 9,519 (266)

MiR‐146a‐5p Suppresses M1 Polarization of Tumor‐Associated Macrophages via IRAK1/NF‐κB Signaling in Colorectal Polyps

Annals of Gastroenterological Surgery, EarlyView.
MiR‐146a‐5p is downregulated in colorectal polyps, leading to IRAK1 upregulation and NF‐κB activation, which promotes M1 macrophage polarization and inflammation. Overexpression of miR‐146a‐5p suppresses IRAK1/NF‐κB signaling, reduces M1 polarization, and alleviates inflammatory responses.
Peng Song, Lianhui Wu, Huabin Qiu, Lei Luo, Jianzhao Qiu, Shuping Xiao, Jincai He   +6 more
wiley   +1 more source

Using Machine Learning to Improve Control for Confounding in the Dynamic Weighted Ordinary Least Squares Estimator of Optimal Adaptive Treatment Strategies. [PDF]

Biom J
Trenou KC, Mésidor M, Eslami A, Nabi H, Diorio C, Talbot D.   +5 more
europepmc   +1 more source

Artificial Intelligence‐Driven Insights into Electrospinning: Machine Learning Models to Predict Cotton‐Wool‐Like Structure of Electrospun Fibers

Advanced Intelligent Discovery, EarlyView.
Electrospinning allows the fabrication of fibrous 3D cotton‐wool‐like scaffolds for tissue engineering. Optimizing this process traditionally relies on trial‐and‐error approaches, and artificial intelligence (AI)‐based tools can support it, with the prediction of fiber properties. This work uses machine learning to classify and predict the structure of
Paolo D’Elia, Giovanna Pappalardo, Aldo R. Boccaccini, Liliana Liverani   +3 more
wiley   +1 more source

Classical and Bayesian Inference for the Two-Parameter Rayleigh Distribution with Random Censored Data. [PDF]

Entropy (Basel)
Zhang L, Gui W, Zhao Z, Liu M.
europepmc   +1 more source

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

Testing and Quantifying Site-Level Variability in Diagnostic Sensitivity of an Anchor Variable. [PDF]

Stat Med
Baek S, Ma Y, Garcia TP.
europepmc   +1 more source

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Advanced Intelligent Discovery, EarlyView.
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
wiley   +1 more source

Do Meta-Analyses of Total Hip Arthroplasty Produce Reliable Results? A Systematic Review and Meta-Epidemiological Study of Statistical Methods. [PDF]

Orthop Surg
Ramadanov N, Voss M, Diallo RM, Lettner J, Hakam HT, Prill R, Becker R, Hable R.   +7 more
europepmc   +1 more source

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

Advanced Intelligent Discovery, EarlyView.
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley   +1 more source

A Bayesian ARMA Probability Density Estimator. [PDF]

Entropy (Basel)
Hart JD.
europepmc   +1 more source