Results 231 to 240 of about 5,876,056 (368)

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

Genetic Determinants of Colonic Diverticulosis-A Systematic Review. [PDF]

Genes (Basel)
Nehring P, Przybyłkowski A.
europepmc   +1 more source

The organization of trilobites, deduced from their living affinities : with a systematic review of the species hitherto described / by Hermann Burmeister ; edited from the German, by Professor Bell and Professor E. Forbes.

Hermann Burmeister, Thomas Bell, Edward Forbes   +2 more
openalex   +2 more sources

Refining the indications for carotid endarterectomy in patients with symptomatic carotid stenosis: A systematic review

, 1999
Claudio S. Cinà, Catherine M. Clase, Brian R. Haynes   +2 more
openalex   +1 more source

CircCCNB1 inhibits vasculogenic mimicry by sequestering NF90 to promote miR‐15b‐5p and miR‐7‐1‐3p processing in nasopharyngeal carcinoma

Molecular Oncology, EarlyView.
CircCCNB1 expression is down‐regulated in nasopharyngeal carcinoma (NPC); thus, less NF90 protein is bound to circCCNB1 and more binds to pri‐miRNAs, blocking their (pri‐miRNAs) binding to DGCR8 and inhibiting the processing and generation of miR‐15b‐5p/miR‐7‐1‐3p. Furthermore, decreased miR‐15b‐5p/miR‐7‐1‐3p promotes the expression of the target genes
Chunmei Fan, Fenghua Tan, Jie Wu, Zhaoyang Zeng, Wenjia Guo, He Huang, Wei Xiong   +6 more
wiley   +1 more source

A Systematic Review of Cost-Effectiveness Research of Stroke Evaluation and Treatment [PDF]

, 1999
Robert G. Holloway, Curtis Benesch, Catherine R. Rahilly, Caryn E. Courtright   +3 more
openalex   +1 more source

How to do a systematic review [PDF]

, 2018
Alex Pollock, Banks JL, Barnett-Page E, Barnett-Page E, Bastian H, Berge E, Bobath B, Boers M, Boger EJ, Booth A, Booth A, Borah R, Brazzelli M, Cahill LS, Carr JH, Chalmers I, Chalmers I, Craig P, Davies PM, Deeks JJ, Dixon-Woods M, Eivind Berge, Fain JA, Feigin VL, Ganann R, Garner P, Glasziou P, Gotzsche PC, Gough D, Gough D, Gough D, Green BN, Greenwood N, Grimshaw J, Guyatt GH, Guyatt GH, Harden A, Higgins JPT, Hoffmann TC, Hoffmann TC, Joanna Briggs Institute, Kastner M, Katrak P, Kirkham JJ, Kreis J, Langhorne P, Lees R, Levac D, Moher D, Moher D, Moher D, Morley RF, Pendlebury ST, Peoples H, PLoS Medicine Editors, Pollock A, Pollock A, Pollock A, Pollock A, Rathbone J, Riva JJ, Rowat A, Sackett DL, Santisteban L, Saunders DH, Silagy CA, Snilstveit B, Stroke Unit Trialists Collaboration, Stroup DF, Thomas J, Tong A, Tyson SF, Tyson SF, Vickrey BG, Waddington H, Wardlaw JM, Weir MC, Whiting PF, Wildridge V, Williamson PR, Wong G, Yaffe J   +81 more
core   +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

Screening for ovarian cancer: a systematic review.

, 1998
Bell, Petticrew, Luengo, Sheldon
openalex   +2 more sources

Adverse prognosis gene expression patterns in metastatic castration‐resistant prostate cancer

Molecular Oncology, EarlyView.
We aggregated a cohort of 1012 mCRPC tissue samples from 769 patients and investigated the association of gene expression‐based pathways with clinical outcomes. Loss of AR signaling, high proliferation, and a glycolytic phenotype were independently prognostic for poor outcomes, and an adverse transcriptional feature score incorporating these pathways ...
Marina N. Sharifi, Eric Feng, Nicholas R. Rydzewski, Amy K. Taylor, Jamie M. Sperger, Yue Shi, Kyle T. Helzer, Matthew L. Bootsma, Viridiana Carreno, Alex H. Chang, Luke A. Nunamaker, Grace C. Blitzer, Tianfu Andy Shang, Aishwarya Subramanian, Anders Bjartell, Andreas Josefsson, Pernilla Wikström, Emily Feng, Manish Kohli, Rendong Yang, Scott M. Dehm, Eric J. Small, Rahul Aggarwal, David A. Quigley, Joshua M. Lang, Shuang G. Zhao, Martin Sjöström   +26 more
wiley   +1 more source