CD105 protein (Mouse)
- Known as:
- CD105 protein (Mouse)
- Catalog number:
- 30r-acd105
- Product Quantity:
- USD
- Category:
- -
- Supplier:
- Fitzgerald industries international
- Gene target:
- CD105 protein (Mouse)
Related genes to: CD105 protein (Mouse)
- Gene:
- ENG NIH gene
- Name:
- endoglin
- Previous symbol:
- ORW1, ORW
- Synonyms:
- END, HHT1, CD105
- Chromosome:
- 9q34.11
- Locus Type:
- gene with protein product
- Date approved:
- 1993-03-03
- Date modifiied:
- 2019-04-23
Related products to: CD105 protein (Mouse)
Related articles to: CD105 protein (Mouse)
- Radiation-induced xerostomia remains a common and debilitating side effect in head-and-neck cancer radiotherapy, despite advances in volumetric modulated arc therapy (VMAT). Traditional dose-volume histogram (DVH) metrics capture only part of the variation in toxicity, motivating the use of multimodal imaging biomarkers such as dosiomics and radiomics to characterize dose distribution and tissue response better. In this pilot study, we present an integrated framework combining DVH metrics, 3D dosiomics features, baseline planning CT (pCT) radiomics, and novel longitudinal delta-radiomics derived from daily cone-beam CT-based synthetic CT (sCT) images to predict post-treatment xerostomia severity. In a cohort of ten high-risk oropharyngeal cancer patients treated with VMAT at the Cleveland Clinic, wrapper-based feature selection yielded a compact set of 15 predictors (5 DVH, 3 dosiomics, 4 pCT radiomics, 3 Δ-sCT radiomics). Using cross-validation, four classifiers, including support-vector machine (SVM), regularized logistic regression (GLMnet), Naïve Bayes, and k-nearest neighbors, achieved consistently strong performance for discriminating grade I vs. grade II xerostomia, with AUC of 0.97-1.00, accuracy of 0.90-0.93, uniformly high sensitivity (1.00), specificity of 0.75-0.83, and F1 scores of 0.923-0.945. SVM and GLMnet showed the best overall balance of discrimination and robustness. These results demonstrate the potential of integrating dosiomics with multiphase radiomics, particularly time-resolved delta-radiomics, for individualized xerostomia risk prediction. - Source: PubMed
Publication date: 2026/04/02
Chen PengYang KailinSafari MojtabaPeng JunboGuo BingqiQi PengDurmaz ArdaMiller JacobKoyfman ShlomoQiu Richard L JYang XiaofengScott Jacob G - Prostate cancer is one of the most common malignancies in men, and accurate classification of lesions into clinically significant or insignificant categories is essential for patient management. Multiparametric MRI, including T2-weighted (T2W) and apparent diffusion coefficient (ADC) imaging, enables extraction of quantitative radiomic features that can be exploited by machine learning for improved diagnosis. While classical machine-learning models such as support vector machines (SVM), random forests (RF), and extreme gradient boosting (XGBoost) have shown strong performance in radiomics-based classification, quantum machine learning offers a new paradigm that leverages quantum feature spaces, potentially uncovering complex patterns inaccessible to classical kernels. In this study, we systematically compared three classical classifiers (SVM-RBF, RF, and XGBoost) with three quantum support vector machine (QSVM) variants: amplitude encoding, angle encoding, and angle encoding with a projected quantum kernel, for classifying 299 prostate lesions from the PROSTATEx Challenge dataset. Radiomics features were extracted from T2W and ADC images. A nested stratified cross-validation pipeline was employed, with feature selection performed in each outer fold and hyperparameters optimized via grid search. QSVM-amplitude encoding achieved the highest mean AUC (0.799 ± 0.082), outperforming SVM-RBF (0.608 ± 0.244) and matching or exceeding RF (0.728 ± 0.083) and XGBoost (0.720 ± 0.065), while offering higher sensitivity at comparable specificity. These findings demonstrate that qubit-efficient QSVMs can deliver competitive or superior performance in small-sample, low-dimensional clinical imaging settings, highlighting their potential for prostate cancer lesion classification. - Source: PubMed
Publication date: 2026/04/02
Chen PengSafari MojtabaBarker-Clarke RowanYang XiaofengScott Jacob G - Medial knee osteoarthritis (KOA) is a prevalent degenerative joint disease causing pain and functional impairment. Off-loading knee braces reduce pain but may decrease muscle activity, leading to weakness. Integrating local muscle vibration (LMV) into off-loading braces may enhance muscle activation and clinical outcomes. - Source: PubMed
Publication date: 2026/04/20
Zangi MahsaBahramizadeh MahmoodKhosravi MobinaFarahmand FarzamArazpour MokhtarBarati Koorosh - - Source: PubMed
Publication date: 2026/04/27
Guo LeZhang PihongZhang MinghuaLiang PengfeiZhou Situo - Visualising magnetic nanoparticle (MNP) clusters is important for inductive moderate hyperthermia (IMH) as their formation influences mechanical force transduction and heat generation in malignant tumours. An applied inhomogeneous stationary magnetic field (ISMF) can direct the arrangement of MNP clusters and the force exerted on cancer cells. Herein, we analysed MNP cluster formation and changes in mechanical properties using digital breast tomosynthesis (DBT) and ultrasound shear wave elastography (SWE) for a breast phantom containing MCF-7 breast cancer cells under the influence of IMH with ISMF. - Source: PubMed
Publication date: 2026/04/27
Orel Valerii BTovstolytkin Alexandr IOrel Valerii EMamilov Serhii OOstapenko Oleksandra SDasyukevich Olga YoRykhalskyi Oleksandr YuLyalkin Sergii ADunaievskyi Vadim INazarchuk Svitlana SKotovskyi Vitaliy YoGarmanchuk Lyudmyla VGalkin Olexander Yu