Results 91 to 100 of about 123 (123)

Clinical Significance of PD‐L1 and HLA Expression in Esophageal Squamous Cell Carcinoma in Response to Immunotherapy

open access: yesAnnals of Gastroenterological Surgery, EarlyView.
This study evaluated immune‐related biomarkers in esophageal squamous cell carcinoma treated with immune checkpoint inhibitors after postoperative recurrence. PD‐L1 expression in tumor‐associated macrophages and tumor cells, together with HLA class I downregulation and HLA‐DR expression, were associated with favorable responses to immunotherapy.
Kosuke Kanemitsu   +9 more
wiley   +1 more source

Sex‐Based Body Composition Changes in Patients With Rectal Cancer Undergoing Preoperative Chemoradiotherapy: A Prospective Observational Study

open access: yesAnnals of Gastroenterological Surgery, EarlyView.
This prospective study observed that preoperative chemoradiotherapy for rectal cancer was associated with reductions in body fat and grip strength, which appeared to be more pronounced in female patients. These findings suggest that sex‐sensitive nutritional and exercise support may be beneficial to help maintain muscle mass and physical function ...
Shinya Abe   +9 more
wiley   +1 more source

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

open access: yesAnnals 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   +9 more
wiley   +1 more source

The Challenge of Handling Structured Missingness in Integrated Data Sources

open access: yesAdvanced Intelligent Discovery, EarlyView.
As data integration becomes ever more prevalent, a new research question that emerges is how to handle missing values that will inevitably arise in these large‐scale integrated databases? This missingness can be described as structured missingness, encompassing scenarios involving multivariate missingness mechanisms and deterministic, nonrandom ...
James Jackson   +6 more
wiley   +1 more source

Toward Knowledge‐Guided AI for Inverse Design in Manufacturing: A Perspective on Domain, Physics, and Human–AI Synergy

open access: yesAdvanced Intelligent Discovery, EarlyView.
This perspective highlights how knowledge‐guided artificial intelligence can address key challenges in manufacturing inverse design, including high‐dimensional search spaces, limited data, and process constraints. It focused on three complementary pillars—expert‐guided problem definition, physics‐informed machine learning, and large language model ...
Hugon Lee   +3 more
wiley   +1 more source

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

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

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

open access: yesAdvanced 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   +4 more
wiley   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

open access: yesAdvanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source

Interpretable Machine Learning for Bandgap Prediction and Descriptor‐Guided Design Rules of Phosphates

open access: yesAdvanced Intelligent Discovery, EarlyView.
An explainable CatBoost model was trained to predict the bandgaps of 474 phosphate crystals based on composition and density descriptors. SHAP analysis identified two key variables—d‐electron‐count dispersion and atomic‐density dispersion—as the primary drivers of the model's predictions.
Wenhu Wang   +3 more
wiley   +1 more source

Calibration‐Free Electromyography Motor Intent Decoding Using Large‐Scale Supervised Pretraining

open access: yesAdvanced Intelligent Systems, EarlyView.
Calibration‐free electromyography motor intent decoding is enabled through large‐scale supervised pretraining across heterogeneous datasets. A Spatially Aware Feature‐learning Transformer processes variable channel counts and electrode geometries, allowing transfer across users and recording setups. On a held‐out benchmark, fine‐tuned cross‐user models
Alexander E. Olsson   +3 more
wiley   +1 more source

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