CD105
- Known as:
- CD105
- Catalog number:
- 11-298-C025
- Product Quantity:
- 0.025 mg
- Category:
- -
- Supplier:
- Exbio
- Gene target:
- CD105
Related genes to: CD105
- 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
Related articles to: CD105
- Quantitative assessment of myocardial deformation is increasingly important in clinical cardiology, yet conventional two-dimensional (2D) echocardiography and standard three-dimensional (3D) approaches remain limited by out-of-plane motion and incomplete characterization of transmural mechanics. To address these limitations, we introduce a physics-informed framework for 3D echocardiography that reconstructs the full finite strain tensor across the entire myocardial wall. As an initial methodological study, we demonstrate the framework and validate it against cardiac magnetic resonance in a small cohort. - Source: PubMed
Publication date: 2026/08/13
Pradhan Satya PrakashYavari ArashLindley Issac DBinesh NaderMatusov Yuri PGhafourian KambizPedrizzetti GianniKheradvar Arash - To develop and evaluate an automated workflow for the setup of single-vertebra finite element (FE) simulations from clinical CT data. Specifically, we quantified how automated endplate identification, vertebra-specific coordinate system definition, and load-application-point assignment influence the simulated fracture-load estimates. - Source: PubMed
Publication date: 2026/08/13
Strack DanielAmenini SaraSollmann NicoLerchl TanjaKirschke Jan SGastaldi DarioSubburaj Karupppasamy - Accurate delineation of epidermis and tumor boundaries is central to melanoma staging, yet pixel-level annotation on whole-slide images (WSIs) is labor-intensive and inconsistent across observers. Advancing this field requires standardized, publicly available benchmarks with expert-validated labels. We introduce Mel-DEPTHS, a new benchmark dataset for epidermis and tumor segmentation, designed to accelerate and standardize research for automated melanoma staging. Mel-DEPTHS comprises 50 anonymized melanoma WSIs (40x, 0.25[Formula: see text]m/pixel) with pixel-level masks for epidermis and tumor regions. Clinical variables such as invasion depth, ulceration, and pT stage are provided alongside fixed train/test partitions to ensure reproducibility. To mitigate annotation burden, we developed an Expert-Supervised Iterative Self-Training (ESIST) protocol: a pretrained model generates pseudo-labels, which dermatopathologists iteratively refine for retraining. We benchmarked six state-of-the-art segmentation models (UNet, UNet++, UNet3+, UPerNet, TransUNet, ConvUNeXt) using WSI-level precision, recall, IoU, and Dice. TransUNet achieved the best performance, closely followed by ConvUNeXt and UperNet. Three-fold cross-validation also confirmed consistent model rankings and label robustness. Mel-DEPTHS provides the fidelity and diversity necessary for clinically meaningful segmentation. It establishes a standardized benchmark and fosters reproducibility in computational pathology. - Source: PubMed
Publication date: 2026/08/13
Topuz YaseminGökcan M TahaMen A Mine ÖnenerkYıldız SerdarSertbudak İpekKaymaz SerhatGökbaşı ÖzgeUrgancı NilÇalık NurullahÜlgen Övgü AydınVarlı Songül - Digital pathology has enabled large-scale analysis of histological images. However, accurate detection of cellular nuclei remains challenging due to variability in morphology, staining, and especially image resolution. Existing object detection approaches often degrade when applied to low-resolution images, and current solutions typically address either multi-scale detection or image enhancement independently. In this work, we propose a hybrid framework that integrates super-resolution with a dual-branch detection strategy combining full-image and patch-based inference. This design leverages both global contextual information and localized high-detail analysis to improve detection robustness. The outputs of both branches are fused through a confidence-weighted mechanism followed by non-maximum suppression and clustering-based refinement. The proposed method was evaluated on the NuCLS dataset, demonstrating consistent improvements over baseline detection approaches. In particular, the combined workflow achieved up to a 20% increase in [email protected] at higher confidence thresholds, achieving competitive performance compared to state-of-the-art methods while maintaining a lightweight architecture. These results highlight the effectiveness of integrating super-resolution and multi-scale detection strategies for improving nuclei detection in histopathological images. - Source: PubMed
Publication date: 2026/08/13
Díaz-Gaxiola EduardoGarcía-Aguilar IvanYee-Rendón ArturoVega-López Ines FLópez-Rubio EzequielLuque-Baena Rafael Marcos - The purpose of this study is to report the effectiveness of meniscal allograft arthroplasty of the basal joint via patient-reported outcome measures, specifically highlighting subjective pain levels and reported return to function. - Source: PubMed
Publication date: 2026/08/13
Eng Emma MRakauskas TaylorVieira LucasKnopp Brandon WEsmaeili Ehsan