Results 181 to 190 of about 359,159 (253)

Diagnostic Accuracy of Size‐Based Preoperative CT Assessment for Predicting Lymph Node Metastasis in Colon Cancer: A Systematic Review and Meta‐Analysis

Annals of Gastroenterological Surgery, EarlyView.
Preoperative CT based on lymph node size shows moderate accuracy for detecting nodal metastasis in colon cancer. In this meta‐analysis of 29 studies (5,634 patients), pooled sensitivity and specificity were 0.69 and 0.66. Size‐based CT alone has limited value for clinical decision‐making.
Yuji Takayama, Tomoya Shimizu, Jun Watanabe, Shimpei Ogawa, Yukihide Kanemitsu   +4 more
wiley   +1 more source

Evaluation of the combined predictive value of multiple indicators based on diaphragmatic ultrasound using logistic regression and ROC curve in weaning from mechanical ventilation in pediatric patients. [PDF]

Front Pediatr
Ge H, Zhang A, Teng Y, Hu L.
europepmc   +1 more source

Can Machine Learning Reduce Unnecessary Surgeries? A Retrospective Analysis Using Threshold Optimization to Prevent Negative Appendectomies in Adults

Annals of Gastroenterological Surgery, EarlyView.
Threshold‐optimized machine learning models using routine clinical and laboratory data in 623 adults undergoing appendectomy. Logistic regression (AUC = 0.765) and random forest (AUC = 0.785) were the best‐performing models for appendicitis detection and complicated appendicitis prediction, respectively.
Ivan Males, Marko Kumric, Zvonimir Boban, Josip Vrdoljak, Dario Pecenkovic, Matej Ivanda, Marko Grahovac, Zenon Pogorelic, Josko Bozic   +8 more
wiley   +1 more source

Time‑dependent ROC curve analysis to determine the predictive capacity of seven clinical scales for mortality in patients with COVID‑19: Study of a hospital cohort with very high mortality. [PDF]

Biomed Rep
Mendoza-Hernandez MA, Hernandez-Fuentes GA, Sanchez-Ramirez CA, Rojas-Larios F, Guzman-Esquivel J, Rodriguez-Sanchez IP, Martinez-Fierro ML, Cardenas-Rojas MI, De-Leon-Zaragoza L, Trujillo-Hernandez B, Fuentes-Murguia M, Ochoa-Díaz-López H, Sánchez-Meza K, Delgado-Enciso I.   +13 more
europepmc   +1 more source

Lymphocyte‐C‐Reactive Protein Ratio as Promising New Marker for Predicting Surgical Site Infection in Children With Ulcerative Colitis

Annals of Gastroenterological Surgery, EarlyView.
ABSTRACT Background Surgical site infection (SSI) is a major complication after ileal pouch–anal anastomosis (IPAA) in pediatric ulcerative colitis (UC), significantly impairing quality of life. The lymphocyte‐to–C‐reactive protein ratio (LCR), a composite marker of systemic inflammation and immune/nutritional status, has emerged as a potential ...
Yuhki Koike, Koki Higashi, Yuki Sato, Yuka Nagano, Tadanobu Shimura, Takahito Kitajima, Kohei Matsushita, Yoshiki Okita, Yoshinaga Okugawa, Yuji Toiyama   +9 more
wiley   +1 more source

Macrophage Phenotype Detection Methodology on Textured Surfaces via Nuclear Morphology Using Machine Learning

Advanced Intelligent Discovery, EarlyView.
A novel machine learning approach classifies macrophage phenotypes with up to 98% accuracy using only nuclear morphology from DAPI‐stained images. Bypassing traditional surface markers, the method proves robust even on complex textured biomaterial surfaces. It offers a simpler, faster alternative for studying macrophage behavior in various experimental
Oleh Mezhenskyi, Kateryna Andriienkova, Tetyana Goldanova, Ezgi Antmen, Thibaut Soubrie, Nihal Engin Vrana   +5 more
wiley   +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

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source

Accelerating Primary Screening of USP8 Inhibitors from Drug Repurposing Databases with Tree‐Based Machine Learning

Advanced Intelligent Discovery, EarlyView.
This study introduces a tree‐based machine learning approach to accelerate USP8 inhibitor discovery. The best‐performing model identified 100 high‐confidence repurposable compounds, half already approved or in clinical trials, and uncovered novel scaffolds not previously studied. These findings offer a solid foundation for rapid experimental follow‐up,
Yik Kwong Ng, Sharon Ai Er Lam, Wei Chun Lim, Hannah Hui Min Toh, Shi Jun Ang   +4 more
wiley   +1 more source

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source