Results 221 to 230 of about 802,617 (317)

T-lymphoblastic leukemia/lymphoma: a comprehensive review of pathology, molecular features, and differential diagnosis. [PDF]

J Pathol Transl Med
Danielson D, Lagerstrom IT, Rogers M, Simon K, Aguilera NS, Auerbach A.   +5 more
europepmc   +1 more source

Targeting Cannabinoid 1 Receptor in Macrophages Alleviates Psoriatic Skin Inflammation Through the AUF1–NLRP3 Axis

Advanced Science, EarlyView.
Macrophage CB1R emerges as a key driver of psoriatic inflammation by suppressing AUF1‐mediated post‐transcriptional control of NLRP3. CB1R inhibition restores AUF1‐dependent NLRP3 mRNA decay, restrains inflammasome activation, and alleviates psoriasis‐like inflammation.
Hongseo Choi, Sukyoung Han, Jinho Kim, Ji Hye Han, Eunsu Kim, Yeonjeong Heo, Minju Lee, Jooyoung Lee, Heerim You, Soyoun Lee, Su‐Hee Han, Ilrok Yoon, Sung Anjeon, Hyerim Kang, Seungyeon Ryu, Hyun‐Ji Park, Eunha Kim, Chul Hwan Bang, Ji Hyun Lee, Josephine M. Egan, Jung‐Woong Kim, Eun Kyung Lee, Wook Kim   +22 more
wiley   +1 more source

Allogeneic Hematopoietic Stem Cell Transplantation Achieves Durable Remission in Blast-Phase Chronic Myeloid Leukemia Presenting as Early T-Cell Precursor Acute Lymphoblastic Leukemia. [PDF]

EJHaem
Tsushima T, Konishi Y, Yoda N, Wada K, Okamoto K, Matsuo K, Isogai S, Shimoji S, Utsu Y, Masuda SI, Aotsuka N.   +10 more
europepmc   +1 more source

A Robust Deep Temporal Causal Discovery Platform for Single‐Cell Gene Regulatory Network Reconstruction

Advanced Intelligent Discovery, EarlyView.
scTIGER2.0 is a deep‐learning framework that infers gene regulatory networks from single‐cell RNA sequencing data. By integrating correlation, pseudotime ordering, deep learning and bootstrap‐based significance testing, it reduces false positives and reveals directional gene interactions.
Nishi Gupta, Madison Dautle, Shaoqiang Zhang, Yong Chen   +3 more
wiley   +1 more source

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

Advanced Intelligent Discovery, EarlyView.
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley   +1 more source

Clinically Informed Intelligent Classification of Ovarian Cancer Cells by Label‐Free Holographic Imaging Flow Cytometry

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
Quantitative phase maps of single cells recorded in flow cytometry modality feed a hierarchical architecture of machine learning models for the label‐free identification of subtypes of ovarian cancer. The employment of a priori clinical information improves the classification performance, thus emulating the clinical application of liquid biopsy during ...
Daniele Pirone, Beatrice Cavina, Daniele Gaetano Sirico, Martina Mugnano, Vittorio Bianco, Lisa Miccio, Anna Myriam Perrone, Anna Maria Porcelli, Giuseppe Gasparre, Ivana Kurelac, Pasquale Memmolo, Pietro Ferraro   +11 more
wiley   +1 more source

MetaPaCS: A Novel Meta‐Learning Framework for Pancreatic Cancer Subtype Identification

Advanced Intelligent Systems, EarlyView.
We have developed MetaPaCS, an ensemble meta‐learning framework for reliable prediction of pancreatic cancer subtypes with high accuracy. MetaPaCS is capable of capturing subtype‐specific distinct gene expression patterns and drug‐disease associations for pancreatic cancer.
Nick B. Peterson, Mengtao Sun, Xinchao Wu, Jieqiong Wang, Shibiao Wan   +4 more
wiley   +1 more source

The Inv(16) Oncogene CBFB::MYH11 is Required for the Survival of Leukemia Cells in the Blood and Spleen, but not the Bone Marrow. [PDF]

Oncogene
Panda S, Wang Y, Dogiparthi VR, Becker M, Dhir A, Lam C, Rivas C, Alemu L, Garrett L, Xiao P, Swenson SA, Hewitt KJ, Hyde RK.   +12 more
europepmc   +1 more source