Results 261 to 270 of about 18,390,026 (306)

Dual PI3K/AKT and CDK4/6 inhibition reveals selective sensitivity in an SHH medulloblastoma stem cell model

Molecular Oncology, EarlyView.
Targeted therapy was evaluated in SHH medulloblastoma using neuroepithelial stem cell (NES) and tumor‐derived NES‐like (tNES) models in 2D monolayers and 3D spheroids. PI3K, AKT, and CDK4/6 inhibitors had minimal effects in NES but markedly reduced viability and growth and induced apoptosis in tNES cells, revealing distinct therapeutic vulnerabilities.
Monika Lukoseviciute, Madeleine Birgersson, Paolo Ceriani, Margareta Wilhelm, Ourania N Kostopoulou   +4 more
wiley   +1 more source

Identifying inflammatory phenotypes associated with lung involvement in systemic sclerosis: k-means clustering approach. [PDF]

Front Immunol
Cano-García L, García-Studer A, Manrique-Arija S, Ortiz-Marquez F, Redondo-Rodríguez R, Borregón-Garrido P, Mena-Vázquez N, Fernández-Nebro A.   +7 more
europepmc   +1 more source

Establishment of a humanized patient‐derived xenograft mouse model of high‐grade serous ovarian cancer for preclinical evaluation of combination immunotherapy

Molecular Oncology, EarlyView.
We have established a humanized orthotopic patient‐derived xenograft (Hu‐oPDX) mouse model of high‐grade serous ovarian cancer (HGSOC) that recapitulates human tumor–immune interactions. Using combined anti‐PD‐L1/anti‐CD73 immunotherapy, we demonstrate the model's improved biological relevance and enhanced translational value for preclinical ...
Luka Tandaric, Line Bjørge, Martine Rott Lode, Cecilie Fredvik Torkildsen, Pia Aehnlich, Rammah Elnour, Daniela Elena Costea, Lars Andreas Akslen, Liv Cecilie Vestrheim Thomsen, Emmet McCormack, Katrin Kleinmanns   +10 more
wiley   +1 more source

Prediabetes risk classification algorithm via carotid bodies and K-means clustering technique. [PDF]

PeerJ Comput Sci
Pinheiro RF, Guarino MP, Lages M, Fonseca-Pinto R.   +3 more
europepmc   +1 more source

Assessment of intratumor heterogeneity in non-small cell lung cancer by unsupervised K-means clustering of radiomics features based on multiphase computed tomography images. [PDF]

Quant Imaging Med Surg
Wang R, Wang L, Feng H, Jing Y, Wang X, Fang J, Du J, E L.   +7 more
europepmc   +1 more source

Profiling Patients with Chronic Ulcers Using K-Means Clustering and Analysis of the Impact on the Consumption of Medical Resources: Retrospective Study on Hospitalized Patients in Romania. [PDF]

J Clin Med
Taroi Yassin Cataniciu M, Gligorea I, Fleacă R, Vecerzan Novac L, Prihoi A, Domnariu CD.   +5 more
europepmc   +1 more source

Unveiling Health Security Patterns in the European Union through a Hybrid Entropy-CoCoSo and K-Means Clustering Framework. [PDF]

F1000Res
Nasser AA, Al-Samawi YA, Alghawli ASA, Essayed AAK.   +3 more
europepmc   +1 more source

Investigating the Quality of Mobile Apps for Drug-Drug Interaction Management Using the Mobile App Rating Scale and K-Means Clustering: Systematic Search of App Stores.

JMIR Mhealth Uhealth
Bhattacharya A, Florez-Arango JF.
europepmc   +1 more source

Correction to 'Using the K-Means Node Clustering Method and ROC Curve Analysis to Define Cut-Off Scores for the Caregiving System Scale'. [PDF]

Int J Psychol
europepmc   +1 more source

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An Effective and Efficient Algorithm for K-Means Clustering With New Formulation

IEEE Transactions on Knowledge and Data Engineering, 2023
K-means is one of the most simple and popular clustering algorithms, which implemented as a standard clustering method in most of machine learning researches.
F. Nie, Ziheng Li, Rong Wang, Xuelong Li
semanticscholar   +1 more source