Results 81 to 90 of about 131,766 (331)

In vitro properties of patient serum predict clinical outcome after high dose rate brachytherapy of hepatocellular carcinoma

open access: yesMolecular Oncology, EarlyView.
Following high dose rate brachytherapy (HDR‐BT) for hepatocellular carcinoma (HCC), patients were classified as responders and nonresponders. Post‐therapy serum induced increased BrdU incorporation and Cyclin E expression of Huh7 and HepG2 cells in nonresponders, but decreased levels in responders.
Lukas Salvermoser   +14 more
wiley   +1 more source

A synthetic benzoxazine dimer derivative targets c‐Myc to inhibit colorectal cancer progression

open access: yesMolecular Oncology, EarlyView.
Benzoxazine dimer derivatives bind to the bHLH‐LZ region of c‐Myc, disrupting c‐Myc/MAX complexes, which are evaluated from SAR analysis. This increases ubiquitination and reduces cellular c‐Myc. Impairing DNA repair mechanisms is shown through proteomic analysis.
Nicharat Sriratanasak   +8 more
wiley   +1 more source

Patient‐specific pharmacogenomics demonstrates xCT as predictive therapeutic target in colon cancer with possible implications in tumor connectivity

open access: yesMolecular Oncology, EarlyView.
This study integrates transcriptomic profiling of matched tumor and healthy tissues from 32 colorectal cancer patients with functional validation in patient‐derived organoids, revealing dysregulated metabolic programs driven by overexpressed xCT (SLC7A11) and SLC3A2, identifying an oncogenic cystine/glutamate transporter signature linked to ...
Marco Strecker   +16 more
wiley   +1 more source

Predictors of response and rational combinations for the novel MCL‐1 inhibitor MIK665 in acute myeloid leukemia

open access: yesMolecular Oncology, EarlyView.
This study characterizes the responses of primary acute myeloid leukemia (AML) patient samples to the MCL‐1 inhibitor MIK665. The results revealed that monocytic differentiation is associated with MIK665 sensitivity. Conversely, elevated ABCB1 expression is a potential biomarker of resistance to the treatment, which can be overcome by the combination ...
Joseph Saad   +17 more
wiley   +1 more source

DP-CCL: A Supervised Contrastive Learning Approach Using CodeBERT Model in Software Defect Prediction

open access: yesIEEE Access
Software Defect Prediction (SDP) reduces the overall cost of software development by identifying the code at a higher risk of defects at the initial phase of software development.
Sadia Sahar   +3 more
doaj   +1 more source

A Combined-Learning Based Framework for Improved Software Fault Prediction

open access: yesInternational Journal of Computational Intelligence Systems, 2017
Software Fault Prediction (SFP) is found to be vital to predict the fault-proneness of software modules, which allows software engineers to focus development activities on fault-prone modules, thereby prioritize and optimize tests, improve software ...
Chubato Wondaferaw Yohannese, Tianrui Li
doaj   +1 more source

Aggressive prostate cancer is associated with pericyte dysfunction

open access: yesMolecular Oncology, EarlyView.
Tumor‐produced TGF‐β drives pericyte dysfunction in prostate cancer. This dysfunction is characterized by downregulation of some canonical pericyte markers (i.e., DES, CSPG4, and ACTA2) while maintaining the expression of others (i.e., PDGFRB, NOTCH3, and RGS5).
Anabel Martinez‐Romero   +11 more
wiley   +1 more source

Semi-Supervised Deep Fuzzy C-Mean Clustering for Software Fault Prediction

open access: yesIEEE Access, 2018
Software fault prediction is a consequential research area in software quality promise. In this paper, we propose a semi-supervised deep fuzzy C-mean (DFCM) clustering for software fault prediction, which is the cumulation of semi-supervised DFCM ...
Ali Arshad   +3 more
doaj   +1 more source

A study of fault prediction and reliability assessment in the SEL environment [PDF]

open access: yes
An empirical study on estimation and prediction of faults, prediction of fault detection and correction effort, and reliability assessment in the Software Engineering Laboratory environment (SEL) is presented.
Basili, Victor R., Patnaik, Debabrata
core   +1 more source

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