Results 181 to 190 of about 2,089,555 (322)

Comparison Between Upfront Surgery and Preoperative Chemotherapy for CY1P0 Gastric Cancer: A Japanese Sub‐Analysis of CONVO‐GC‐1

open access: yesAnnals of Gastroenterological Surgery, EarlyView.
ABSTRACT Background Radical gastrectomy, followed by adjuvant chemotherapy has been a common practice in Japan for peritoneal lavage cytology‐positive (CY1) but peritoneal dissemination‐negative (P0) stage IV gastric cancer. This study aimed to clarify the differences in treatment outcomes between upfront surgery and preoperative chemotherapy, followed
Kenichiro Furukawa   +12 more
wiley   +1 more source

Sequential Monte Carlo with likelihood tempering and parallel implementation for uncertainty quantification

open access: yesAIChE Journal, EarlyView.
Abstract Bayesian estimation enables uncertainty quantification, but analytical implementation is often intractable. As an approximate approach, the Markov Chain Monte Carlo (MCMC) method is widely used, though it entails a high computational cost due to frequent evaluations of the likelihood function.
Tatsuki Maruchi   +2 more
wiley   +1 more source

Microbial diversity and water quality changes in mangrove sediments in Quanzhou Bay. [PDF]

open access: yesFront Microbiol
Zhang W   +7 more
europepmc   +1 more source

Phase I clinical and pharmacokinetic study of the Novel Raf kinase and vascular endothelial growth factor receptor inhibitor BAY 43-9006 in patients with advanced refractory solid tumors.

open access: yesJournal of Clinical Oncology, 2005
D. Strumberg   +14 more
semanticscholar   +1 more source

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

open access: yesAdvanced 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   +3 more
wiley   +1 more source

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