Prediction of MYCN Gene Amplification in Pediatric Neuroblastomas: Development of a Deep Learning-Based Tool for Automatic Tumor Segmentation and Comparative Analysis of Computed Tomography-Based Radiomics Features Harmonization.

Objective: MYCN oncogene amplification is closely linked to high-grade neuroblastoma with poor prognosis. Accurate quantification is essential for risk assessment, which guides clinical decision making and disease management. This study proposes an end-to-end deep-learning framework for automatic tumor segmentation of pediatric neuroblastomas and radiomics features-based classification of MYCN gene amplification.

Development of a Smart Portable Hypoxic Chamber with Accurate Sensing, Control and Visualization of In Vitro Cell Culture for Replication of Cancer Microenvironment.

Clinical resistance towards treatment is a major concern in cancer therapy. This is due to in vitro studies lacking essential microenvironmental aspects. Tumor-hypoxia is an important pathophysiological phenomenon in numerous malignant tumors.

PIMesh: An automatic point cloud and unstructured mesh generation algorithm for meshless methods and finite element analysis-with applications in surgical simulations.

We propose a point cloud and mesh generation algorithm, particle injection mesh generator (PIMesh), that can be used to generate optimized high-quality point clouds and unstructured meshes for domains in any shape with minimum (or even no) user intervention. The domains can be scanned images in OBJ format in 2D and 3D or just a line drawing in 2D. Mesh grading can also be easily controlled.

Meiotic drive.

In this Quick Guide, Srinivasa and Zanders provide an overview of meiotic drivers and the diverse mechanisms these genetic elements use to bias their transmission to the next generation.