Clinical Value of CD24 Expression in Retinoblastoma

Background. The expression of CD24 has been detected in a wide variety of human malignancies. Downregulation of CD24 inhibits proliferation and induces apoptosis in tumor cells, whereas its upregulation increases tumor growth and metastasis. However, no data on CD24 protein levels in retinoblastoma are available, and the mechanism of CD24 involvement in retinoblastoma progress has not been elucidated. The aim of this study was to explore the expression profile of CD24 in the retinoblastoma tumor samples and to correlate with clinicopathological parameters. Methods. Immunohistochemistry was performed for CD24 on the archival paraffin sections of retinoblastoma and correlated with clinicopathological features. Western blotting was performed to confirm immunoreactivity results. Results. CD24 immunoreactivity was observed in 72.0% (36/50) of the retinoblastoma specimens. Among the 35 low-risk tumors, CD24 was expressed in 62.9% (22/35) tumors and among the 15 high-risk tumors, CD24 was expressed in 93.3% (14/15) tumors. High-risk tumors showed significantly increased expression of CD24 compared to tumors with low-risk (P < 0.05). Conclusions. This is the first correlation between CD24 expression and histopathology in human retinoblastoma. Our study showed increased expression of CD24 in high risk tumors compared to low risk tumors. Further functional studies are required to explore the role of CD24 in retinoblastoma

Robust Stability Analysis and Synthesis for Switched Discrete-Time Systems with Time Delay

The problems of robust stability analysis and synthesis for a class of uncertain switched time-delay systems with polytopic type uncertainties are addressed. Based on the constructive use of an appropriate switched Lyapunov function, sufficient linear matrix inequalities (LMIs) conditions are investigated to make such systems a uniform quadratic stability with an L2-gain smaller than a given constant level. System synthesis is to design switched feedback schemes, whether based on state, output measurements, or by using dynamic output feedback, to guarantee that the corresponding closed-loop system satisfies the LMIs conditions. Two numerical examples are provided that demonstrate the efficiency of this approach

Conformal perturbations of dirac operators and general Kastler-Kalau-Walze type theorems for even dimensional manifolds with boundary

In this paper, we establish the proof of general Kastler-Kalau-Walze type theorems for conformal perturbations of dirac Operators on even dimensional compact manifolds with (respectively without) boundary.Comment: arXiv admin note: substantial text overlap with arXiv:2310.09775; text overlap with arXiv:2111.1503

propnet: Propagating 2D Annotation to 3D Segmentation for Gastric Tumors on CT Scans

**Background:** Accurate 3D CT scan segmentation of gastric tumors is pivotal for diagnosis and treatment. The challenges lie in the irregular shapes, blurred boundaries of tumors, and the inefficiency of existing methods. **Purpose:** We conducted a study to introduce a model, utilizing human-guided knowledge and unique modules, to address the challenges of 3D tumor segmentation. **Methods:** We developed the PropNet framework, propagating radiologists' knowledge from 2D annotations to the entire 3D space. This model consists of a proposing stage for coarse segmentation and a refining stage for improved segmentation, using two-way branches for enhanced performance and an up-down strategy for efficiency. **Results:** With 98 patient scans for training and 30 for validation, our method achieves a significant agreement with manual annotation (Dice of 0.803) and improves efficiency. The performance is comparable in different scenarios and with various radiologists' annotations (Dice between 0.785 and 0.803). Moreover, the model shows improved prognostic prediction performance (C-index of 0.620 vs. 0.576) on an independent validation set of 42 patients with advanced gastric cancer. **Conclusions:** Our model generates accurate tumor segmentation efficiently and stably, improving prognostic performance and reducing high-throughput image reading workload. This model can accelerate the quantitative analysis of gastric tumors and enhance downstream task performance