Results 271 to 280 of about 26,575,973 (337)

High LRIG1 expression predicts lymph node metastasis in patients with uterine cervical cancer

FEBS Open Bio, EarlyView.
Lymph node status is crucial in determining treatment for women with early‐stage cervical cancer. We demonstrate that high LRIG1 protein expression in primary tumors can predict lymph node metastases. Our findings support further investigation of LRIG1 as a biomarker to improve staging accuracy and guide treatment decisions in cervical cancer patients.
Pernilla Israelsson, Lisa Lif, Husam Oda, Alexandra Lorenzzi de Melo, David Lindquist, Håkan Hedman   +5 more
wiley   +1 more source

Predicting macular hole surgery outcomes: Integrating preoperative OCT features with supervised machine learning statistical models. [PDF]

Indian J Ophthalmol
Venkatesh R, Gandhi P, Choudhary A, Sehgal G, Godani K, Darade S, Kathare R, Hande P, Prabhu V, Chhablani J.   +9 more
europepmc   +1 more source

Teaching Python with team‐based learning: using cloud‐based notebooks for interactive coding education

FEBS Open Bio, EarlyView.
This study presents a novel approach to teaching Python and bioinformatics using team‐based learning and cloud‐hosted notebooks. By integrating interactive coding into biomedical education, the method improves accessibility, student engagement, and confidence—especially for those without a computing background.
Nuno S. Osório, Leonardo D. Garma
wiley   +1 more source

An examination of daily CO₂ emissions prediction through a comparative analysis of machine learning, deep learning, and statistical models. [PDF]

Environ Sci Pollut Res Int
Ajala AA, Adeoye OL, Salami OM, Jimoh AY.   +3 more
europepmc   +1 more source

Downregulation of sST2, a decoy receptor for interleukin‐33, enhances subcutaneous tumor growth in murine pancreatic cancer cells

FEBS Open Bio, EarlyView.
Subcutaneous implantation of murine Panc02 pancreatic cancer cells depleted of sST2, a soluble decoy receptor for the proinflammatory interleukin‐33 (IL‐33), leads to a decreased number of GLUT4‐positive cancer‐associated adipocytes, reduced levels of the anti‐inflammatory molecule adiponectin, increased phosphorylation of IκBα, elevated Cxcl3 ...
Miho Akimoto, Nobuko Koshikawa, Takao Morinaga, Mimi Tamamori‐Adachi, Atsushi Takatori, Keizo Takenaga   +5 more
wiley   +1 more source

Can machine learning models improve the prediction of surgical site infection in abdominal surgery than traditional statistical models? [PDF]

J Int Med Res
Piebpien P, Tansawet A, Pattanaprateep O, Pattanateepapon A, Wilasrusmee C, Mckay GJ, Attia J, Thakkinstian A.   +7 more
europepmc   +1 more source

Cis‐unsaturated sphingolipids support growth of sphingoid base‐deficient yeast but impair plasma membrane integrity

FEBS Open Bio, EarlyView.
Sphingoid base structures, the sphingolipid backbones, vary among species. We established yeast cells in which the native sphingoid base was replaced with plant‐type bases containing cis or trans double bonds. This is, to our knowledge, the first eukaryotic model mostly composed of sphingolipids containing cis‐unsaturated sphingoid base, providing a ...
Takashi Higuchi, Saki Sugihara, Yohei Ishibashi, Kono Yushi, Hazuki Yamauchi, Motohiro Tani   +5 more
wiley   +1 more source

Identifying risk clusters for African swine fever in Korea by developing statistical models. [PDF]

Front Vet Sci
Ko KT, Oh J, Son C, Choi Y, Lee H.
europepmc   +1 more source

Statins induce monocytic differentiation in acute myeloid leukemia cells through the KLF4/DPYSL2A axis

FEBS Open Bio, EarlyView.
Statins, identified via the Comparative Toxicogenomics Database, promote monocytic differentiation and apoptosis in non‐APL AML cells by upregulating DPYSL2A through a KLF4‐dependent pathway. Mevalonate supplementation reversed these effects, indicating involvement of the mevalonate pathway.
Mina Noura, Kota Shoji, Michidai Nobe, Moe Ishikawa, Miu Tanaka, Akiko Okayama, Souichi Adachi, Hidemasa Matsuo   +7 more
wiley   +1 more source

Malignancy risk stratification for pulmonary nodules: comparing a deep learning approach to multiparametric statistical models in different disease groups. [PDF]

Eur Radiol
Piskorski L, Debic M, von Stackelberg O, Schlamp K, Welzel L, Weinheimer O, Peters AA, Wielpütz MO, Frauenfelder T, Kauczor HU, Heußel CP, Kroschke J.   +11 more
europepmc   +1 more source