Results 121 to 130 of about 3,627,751 (263)

Single‐Cell RNA Editing Identifies T Cell ADAR1 as a Key Regulator of Immune Exhaustion and Anti‐PD‐1 Resistance in Colorectal Cancer

Advanced Science, EarlyView.
Single‐cell RNA editing analysis identifies ADAR1 as a regulator of dysfunctional T cell states in colorectal cancer. Elevated ADAR1 activity promotes T cell exhaustion and impairs antitumor immunity partly through TGF‐β‐SMAD signaling, contributing to anti‐PD‐1 resistance and highlighting T cell ADAR1 as a potential therapeutic target and biomarker ...
Da Kang, Song‐Zuo Xie, Yong‐Zhou Luo, Xin‐Pei Deng, Xi‐Rong Tan, Ze‐Geng Chen, Ling‐Xing Zeng, Gong Chen, Pei‐Rong Ding, Zhi‐Zhong Pan, Rong‐Xin Zhang   +10 more
wiley   +1 more source

The categorisation of the Short Warwick Edinburgh Mental Wellbeing Scale scores: an exploration from an epidemiological perspective

Frontiers in Psychiatry
BackgroundThere has been a notable increase in the utilisation of SWEMWBS as a measure of mental well-being globally. To enhance its interpretability for both healthcare professionals and laypeople, categorising SWEMWBS scores is considered beneficial ...
Lawrence T. Lam, Lawrence T. Lam, Lawrence T. Lam, Mary K. Lam   +3 more
doaj   +1 more source

ROC (receiver operating characteristics) curve.

, 2014
ROC (receiver operating characteristics) curve.
Isabelle Claudet (667993), Eric Bui (667991), Hélène Grandjean (667995), Bertrand Olliac (667989), Charlotte Allenou (667992), Alain Brunet (386631), Jean-Philippe Raynaud (667996), Philippe Birmes (667990), Jérôme Sales de Gauzy (667994)   +8 more
core   +1 more source

Brain‐Computer Interface Training Fosters Perceptual Skills to Detect Errors

Advanced Science, EarlyView.
Accurate perception of visuomotor errors underpins motor precision and learning, yet conventional behavioral training fails to improve sensitivity to subtle errors. Real‐time EEG‐based brain‐computer interface feedback targeting the error positivity component enhances perceptual learning of small errors.
Deland H. Liu, Fumiaki Iwane, Minsu Zhang, Leonardo G. Cohen, José del R. Millán   +4 more
wiley   +1 more source

Condition‐Associated Pattern Extraction and Recovery From Multi‐Condition Single‐Cell RNA‐seq Data With CAPER

Advanced Science, EarlyView.
Decoupling biological signals from unwanted variation in multi‑condition single‑cell RNA sequencing data remains challenging. CAPER disentangles condition‑associated biological effects from sample heterogeneity through matrix factorization, producing interpretable latent factors and a batch‑corrected expression matrix.
Ye Li, Jin Ning, An Wang, Minxi Shi, Yuanze Chen, Guoliang Liu, Shiquan Sun   +6 more
wiley   +1 more source

Risk factors for necrotizing enterocolitis in preterm infants: a 1:1 matched case-control study

Frontiers in Pediatrics
ImpactThis study demonstrates that neonatal sepsis, blood transfusion, combined antibiotic use, asphyxia, and maternal gestational diabetes (GDM) are independent risk factors for NEC.
Lie Huang, Jianhui Wang, Xiaomei Fu, Meile Cheng, Xi Xu   +4 more
doaj   +1 more source

Receiver operating characteristic (ROC) analysis.

, 2018
Receiver operating characteristic (ROC) analysis.
James Fink (5395061), Kathleen Schmainda (5395094), Zheng Zhang (88857), Benjamin Ellingson (5395088), Lucy Hanna (5395076), Lale Kostakoglu (5395079), Eva-Maria Ratai (353640), Erin Greco (5395067), Daniel Barboriak (5395091), Daniel Kim (491713), Gregory Sorensen (5395070), David Mankoff (4182589), Elizabeth R. Gerstner (5395073), Mark Muzi (5395085), Ovidiu C. Andronesi (2440099), Akiva Mintz (4876543), Todd Richards (5395082), Melissa Prah (5395064)   +17 more
core   +1 more source

An Integrative Strategy Delineates Modular Metabolic Remodeling and Potential Therapeutic Targets Across Metabolic Diseases

Advanced Science, EarlyView.
An integrative single‐cell atlas across multiple metabolic diseases reveals coordinated metabolic modules and disease‐shared versus disease‐specific pathway activities. By systematically comparing scoring strategies, a robust RankAve framework is established. Coupled with network analysis and drug‐target prediction, this resource uncovers cross‐disease
Kuan Yang, Congxue Hu, Hang Zhao, Xiao Peng, Yiming Yan, Jiasi Wang, Wenqi Jiang, Depeng Mu, Yan Cao, Xia Li, Yunpeng Zhang   +10 more
wiley   +1 more source

Machine Learning-Based Classification and Statistical Analysis of Liver Cancer: A Comprehensive Study of Model Performance and Clinical Significance

Applied Medical Informatics
Background: The liver is an internal organ located in the upper right section of the abdomen, just beneath the diaphragm, and near the stomach. It performs numerous functions that are essential for metabolism, digestion, detoxification, and nutrient ...
Pratyush Kumar MAHARANA, Tapan Kumar BEHERA, Pradeep Kumar NAIK   +2 more
doaj  

Ovarian Cancer Diagnosis and Chemoresistance Prediction Model Based on cfRNA Molecular Signature

Advanced Science, EarlyView.
A deep learning model analyzes cfRNA profiles extracted from the blood of OVCA patients. This innovative approach distinguishes OVCA from healthy controls with high accuracy. Crucially, it reliably predicts patient response to chemotherapy (sensitive versus resistant subgroups).
Qinhao Guo, Yangyang Zhang, Yongcheng Jin, Siwei Deng, Yongqi Chen, Zheng Feng, Hao Wen, Liu Wang, Yilin Li, Fanghong Ou, Yong Shen, Haiming Li, Tianyao Zhou, Xingzhu Ju, Xiaohua Wu   +14 more
wiley   +1 more source