Results 81 to 90 of about 4,840,174 (316)

Cell‐cycle‐specific lesion evolution rather than inhibition of double‐strand‐break repair underpins cisplatin radiosensitization

Molecular Oncology, EarlyView.
We analyze cisplatin–DNA adducts (CDAs) and double‐strand breaks (DSBs) in a cell‐cycle‐dependent manner. We find that CDAs form similarly across all cell cycle phases. DSBs arise only in S‐phase. CDAs might not directly impair DSB repair, but S‐phase DSB lesions evolve in the presence of CDAs and disrupt repair in G2, also causing radiosensitization ...
Ye Qiu, Xixi Lin, Emil Mladenov, Veronika Mladenova, Jürgen Thomale, Ali Sak, Yao Wang, Yunxuan Deng, Eleni Gkika, Martin Stuschke, George Iliakis   +10 more
wiley   +1 more source

Cancer diagnosis using deep learning: A bibliographic review [PDF]

, 2019
In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the ...
Ayub, Afsheen, Elahi, Hassan, Frezza, Fabrizio, Munir, Khushboo, Rizzi, Antonello   +4 more
core   +1 more source

NKCC1: A key regulator of glioblastoma progression

Molecular Oncology, EarlyView.
Glioblastoma (GBM) progression is driven by disrupted chloride cotransporter homeostasis. NKCC1 is highly expressed in stem‐like, astrocytic, and progenitor cells, correlating with earlier recurrence, while overall survival remains unaffected. NKCC1 serves as a prognostic marker and potential therapeutic target, linking chloride transporter imbalance ...
Anja Thomsen, Diana Freitag, Madlen Haase, Christian Senft, Falko Schwarz, Silke Keiner   +5 more
wiley   +1 more source

Reducing Domain Shift For Mitosis Detection Using Preprocessing Homogenizers [PDF]

, 2021
The detection of mitotic figures in histological tumor images plays a vital role in the decision-making of the appropriate therapy. However, tissue preparation and image acquisition methods degrade the performances of the deep learning-based approaches for mitotic figures detection.
Sahar Almahfouz Nasser, Nikhil Cherian Kurian, Amit Sethi   +2 more
openaire   +1 more source

Cell viscoelasticity is linked to fluctuations in cell biomass distributions. [PDF]

, 2020
The viscoelastic properties of mammalian cells can vary with biological state, such as during the epithelial-to-mesenchymal (EMT) transition in cancer, and therefore may serve as a useful physical biomarker.
Nguyen, Thang L, Patananan, Alexander N, Polanco, Edward R, Teitell, Michael A, Zangle, Thomas A   +4 more
core  

BMI‐1 modulation and trafficking during M phase in diffuse intrinsic pontine glioma

FEBS Open Bio, EarlyView.
The schematic illustrates BMI‐1 phosphorylation during M phase, which triggers its translocation from the nucleus to the cytoplasm. In cycling cells, BMI‐1 functions within the PRC1 complex to mediate H2A K119 monoubiquitination. Following PTC596‐induced M phase arrest, phosphorylated BMI‐1 dissociates from PRC1 and is exported to the cytoplasm via its
Banlanjo Umaru, Deepak Kumar Mishra, Shiva Senthil Kumar, Hao‐Han Pang, Brendan Devine, Komal Khan, Rachid Drissi   +6 more
wiley   +1 more source

ReCasNet: Improving consistency within the two-stage mitosis detection framework

Artificial Intelligence in Medicine, 2023
Mitotic count (MC) is an important histological parameter for cancer diagnosis and grading, but the manual process for obtaining MC from whole-slide histopathological images is very time-consuming and prone to error. Therefore, deep learning models have been proposed to facilitate this process.
Chawan Piansaddhayanaon, Sakun Santisukwongchote, Shanop Shuangshoti, Qingyi Tao, Sira Sriswasdi, Ekapol Chuangsuwanich   +5 more
openaire   +3 more sources

MILD-Net: Minimal Information Loss Dilated Network for Gland Instance Segmentation in Colon Histology Images [PDF]

, 2019
The analysis of glandular morphology within colon histopathology images is an important step in determining the grade of colon cancer. Despite the importance of this task, manual segmentation is laborious, time-consuming and can suffer from subjectivity ...
Chen, Hao, Dou, Qi, Gamper, Jevgenij, Graham, Simon, Heng, Pheng-Ann, Rajpoot, Nasir, Snead, David, Tsang, Yee Wah   +7 more
core   +2 more sources

Anchorage‐independent and faster growth in clonal population from UV‐irradiated NER‐deficient cells

FEBS Open Bio, EarlyView.
UV‐irradiated cells expressing a DDB2 mutant protein unable to interact with PCNA (DDB2PCNA‐) form clones able to grow without anchorage. Different experimental approaches reveal heterogeneity in cell cycle regulation and drug response within these clones, emphasizing the crucial role of the DDB2‐PCNA interaction in preventing cellular transformation ...
Paola Perucca, Anna Tricarico, Martina Furfaro, Priyam Ghosh, Ennio Prosperi, Lucia Anna Stivala, Ornella Cazzalini   +6 more
wiley   +1 more source

Applicability of mitotic figure counting by deep learning: a development and pan‐cancer validation study

FEBS Open Bio, EarlyView.
In this study, we developed a deep learning method for mitotic figure counting in H&E‐stained whole‐slide images and evaluated its prognostic impact in 13 external validation cohorts from seven different cancer types. Patients with more mitotic figures per mm2 had significantly worse patient outcome in all the studied cancer types except colorectal ...
Joakim Kalsnes, Maria X. Isaksen, Frida Julbø, Manohar Pradhan, Andreas Kleppe, Sepp De Raedt, Ole‐Johan Skrede, Turid Torheim, John Arne Nesheim, Hans Martin Mohn, Hanne A. Askautrud, Karolina Cyll, Wanja Kildal, Emma Rewcastle, Melinda Lillesand, Vebjørn Kvikstad, Emiel Janssen, Robert Jones, Odd Terje Brustugun, Bjørn Brennhovd, Erik Skaaheim Haug, Lill‐Tove Rasmussen Busund, Elin Richardsen, Sigve Andersen, Tom Dønnem, Kristina Lindemann, Gunnar Kristensen, Neil A. Shepherd, Marco Novelli, Knut Liestøl, David Kerr, Håvard E. Danielsen, Tarjei S. Hveem   +32 more
wiley   +1 more source