Results 121 to 130 of about 603,662 (274)

Multiplex single‐cell profiling of putative cancer stem cell markers ALDH1, SOX9, SOX2, CD44, CD133 and CD15 in endometrial cancer

Molecular Oncology, EarlyView.
Cancer stem cells are associated with aggressive disease, but a deep characterization of such markers is lacking in endometrial cancer. This study uses imaging mass cytometry to explore putative cancer stem cell markers in endometrial tumors and corresponding organoid models.
Hilde E. Lien, Marta E. Hjelmeland, Hege F. Berg, Rose M. Gold, Kathrine Woie, Lars A. Akslen, Ingfrid S. Haldorsen, Camilla Krakstad   +7 more
wiley   +1 more source

Machine learning meets human-computer interaction - introduction to the special issue [PDF]

, 1997

openalex   +1 more source

Peripheral blood proteome biomarkers distinguish immunosuppressive features of cancer progression

Molecular Oncology, EarlyView.
Immune status significantly influences cancer progression. This study used plasma proteomics to analyze benign 67NR and malignant 4T1 breast tumor models at early and late tumor stages. Immune‐related proteins–osteopontin (Spp1), lactotransferrin (Ltf), calreticulin (Calr) and peroxiredoxin 2 (Prdx2)–were associated with systemic myeloid‐derived ...
Yeon Ji Park, Jae Won Oh, Hyewon Chung, Jung Won Kwon, Yi Rang Na, Kwang Pyo Kim, Seung Hyeok Seok   +6 more
wiley   +1 more source

Am I the odd one? Exploring (in)congruencies in the realism of avatars and virtual others in virtual reality

Frontiers in Virtual Reality
Virtual humans play a pivotal role in social virtual environments, shaping users’ VR experiences. The diversity in available options and users’ individual preferences can result in a heterogeneous mix of appearances among a group of virtual humans.
David Mal, Nina Döllinger, Erik Wolf, Erik Wolf, Stephan Wenninger, Mario Botsch, Carolin Wienrich, Marc Erich Latoschik   +7 more
doaj   +1 more source

Improving human computer interaction in a classroom environment using computer vision [PDF]

, 2000
Joshua Flachsbart, David Franklin, Kristian J. Hammond   +2 more
openalex   +1 more source

Stochastic variation in the FOXM1 transcription program mediates replication stress tolerance

Molecular Oncology, EarlyView.
Cellular heterogeneity is a major cause of drug resistance in cancer. Segeren et al. used single‐cell transcriptomics to investigate gene expression events that correlate with sensitivity to the DNA‐damaging drugs gemcitabine and prexasertib. They show that dampened expression of transcription factor FOXM1 and its target genes protected cells against ...
Hendrika A. Segeren, Kathryn A. Wierenga, Frank M. Riemers, Elsbeth A. van Liere, Bart Westendorp   +4 more
wiley   +1 more source

Introduction to This Special Issue on New Agendas for Human-Computer Interaction [PDF]

, 2000
Wendy A. Kellogg, Clayton Lewis, Peter G. Polson   +2 more
openalex   +1 more source

Targeting PRAME directly or via EZH2 inhibition overcomes retinoid resistance and represents a novel therapy for keratinocyte carcinoma

Molecular Oncology, EarlyView.
The study evaluated the function and therapeutic implications of PRAME in basal cell carcinoma (BCC) and squamous cell carcinoma (SCC). The findings demonstrate that PRAME impairs keratinocyte differentiation pathways. Furthermore, PRAME impairs anticancer response to retinoid compounds in BCC and SCC cells.
Brandon Ramchatesingh, Amelia Martinez Villarreal, Philippe Lefrançois, Jennifer Gantchev, Sriraam Sivachandran, Samy Abou Setah, Ivan V. Litvinov   +6 more
wiley   +1 more source

Cross-Domain Disentanglement: A Novel Approach to Financial Market Prediction

IEEE Access
Profit maximization and risk mitigation require good financial market predictions. Financial markets have a correlated nature, which means that there are some shared patterns between them; therefore, learning about one market might help understand the ...
Humam M. Abdulsahib, Foad Ghaderi
doaj   +1 more source

Classification of acute myeloid leukemia based on multi‐omics and prognosis prediction value

Molecular Oncology, EarlyView.
The Unsupervised AML Multi‐Omics Classification System (UAMOCS) integrates genomic, methylation, and transcriptomic data to categorize AML patients into three subtypes (UAMOCS1‐3). This classification reveals clinical relevance, highlighting immune and chromosomal characteristics, prognosis, and therapeutic vulnerabilities.
Yang Song, Zhe Wang, Guangji Zhang, Jiangxue Hou, Kaiqi Liu, Shuning Wei, Yan Li, Chunlin Zhou, Dong Lin, Min Wang, Hui Wei, Jianxiang Wang, Tao Cheng, Yingchang Mi   +13 more
wiley   +1 more source