Results 241 to 250 of about 727,903 (338)

Rising Pancreatic Cancer Incidence in Young Japanese Women and Increasing Distal Pancreatectomy in Older Adults: A Nationwide Claims‐Based Analysis From 2016 to 2023

Annals of Gastroenterological Surgery, EarlyView.
This nationwide claims‐based study analyzed recent trends in pancreatic cancer incidence (2016–2021) and surgery (2016–2023) in Japan. The study revealed a rising incidence of pancreatic cancer, notably among young women, and an increasing use of distal pancreatectomy among older adults.
Masamitsu Kido, Ryo Morimura, Yusuke Yamamoto, Tomohiro Arita, Shingo Nakashima, Katsutoshi Shoda, Taisuke Imamura, Hidemasa Kubo, Kengo Yoshii, Iichiroh Onishi, Atsushi Shiozaki   +10 more
wiley   +1 more source

Evaluating AI decision tools in Ecuador's courts: efficiency, consistency, and uncertainty in legal judgments. [PDF]

Front Artif Intell
Rodríguez-Salcedo E, Martínez-Bonilla C, Pérez-Mayorga B, Salame-Ortiz M, Armas-Freire P, Espín-Miniguano A, Pino-Loza E.   +6 more
europepmc   +1 more source

Multifactor Risk Stratification for Post‐Transplant Alcohol Relapse Using Abstinence, Psychosocial, and Socioeconomic Factors

Annals of Gastroenterological Surgery, EarlyView.
Alcohol relapse after liver transplantation is difficult to predict using abstinence duration alone. We developed a multifactor model integrating abstinence duration, psychosocial risk (SIPAT), and socioeconomic context (AUC 0.70). This approach may support individualized risk assessment and tailored follow‐up intensity; external validation is needed ...
Ayato Obana, Garren Montgomery, Miho Akabane, Matthew Hamilton, Navdeep Singh, Musab Alebrahim, Austin Schenk, Sylvester Black, Kenneth Washburn, Khalid Mumtaz   +9 more
wiley   +1 more source

Understanding the acceptance of medical marijuana among Malaysian adults: a cross-sectional online survey. [PDF]

BMJ Open
Rahman AB, Naserrudin NA, Seman Z, Zin ZM, Dapari R, Hassan MR, Rashid AA, Dahaban MUM, Jahaya NH, Balamurugan H, Krishnan M.   +10 more
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

Unlicensed medical practice as a public health risk: Implications for Korean Medicine. [PDF]

Integr Med Res
Kim YS.
europepmc   +1 more source

Bachner, Thomas: Creditor Protection in Private Companies. Anglo-German Perspectives for a European Legal Discourse

, 2012
Alexander Schall
openalex   +1 more source

Large Language Model‐Based Chatbots in Higher Education

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
The use of large language models (LLMs) in higher education can facilitate personalized learning experiences, advance asynchronized learning, and support instructors, students, and researchers across diverse fields. The development of regulations and guidelines that address ethical and legal issues is essential to ensure safe and responsible adaptation
Defne Yigci, Merve Eryilmaz, Ail K. Yetisen, Savas Tasoglu, Aydogan Ozcan   +4 more
wiley   +1 more source

The Sci-Hub Ban in India: When Copyright Collides with Clinical Reality. [PDF]

Indian J Crit Care Med
Ghatak T, Mani UA.
europepmc   +1 more source

Predicting Materials Thermodynamics Enabled by Large Language Model‐Driven Dataset Building and Machine Learning

Advanced Intelligent Systems, EarlyView.
Illustration of text data mining of rare earth mineral thermodynamic parameters with the large language model‐powered LMExt. A dataset is built with mined thermodynamic properties. Subsequently, a machine learning model is trained to predict formation enthalpy from the dataset.
Juejing Liu, Haydn Anderson, Noah I. Waxman, Vsevolod Kovalev, Byron Fisher, Elizabeth Li, Xiaofeng Guo   +6 more
wiley   +1 more source