Pathway-based analyses of gene expression profiles at low doses of ionizing radiation.

Radiation exposure poses a significant threat to human health. Emerging research indicates that even low-dose radiation once believed to be safe, may have harmful effects. This perception has spurred a growing interest in investigating the potential risks associated with low-dose radiation exposure across various scenarios. To comprehensively explore the health consequences of low-dose radiation, our study employs a robust statistical framework that examines whether specific groups of genes, belonging to known pathways, exhibit coordinated expression patterns that align with the radiation levels. Notably, our findings reveal the existence of intricate yet consistent signatures that reflect the molecular response to radiation exposure, distinguishing between low-dose and high-dose radiation. Moreover, we leverage a pathway-constrained variational autoencoder to capture the nonlinear interactions within gene expression data. By comparing these two analytical approaches, our study aims to gain valuable insights into the impact of low-dose radiation on gene expression patterns, identify pathways that are differentially affected, and harness the potential of machine learning to uncover hidden activity within biological networks. This comparative analysis contributes to a deeper understanding of the molecular consequences of low-dose radiation exposure.

Development of a small-scale rotary lobe-pump cell culture model for examining cell damage in large-scale N-1 seed perfusion process.

Perfusion technology has been identified as a process improvement capable of eliminating some of the constraints in cell culture and allows for high cell densities and viabilities. However, when implementing this N-1 seed perfusion platform in large-scale manufacturing, unexpected cell damage was observed as early as Day 1. Given that the shear rate within recirculation hollow fibers was normalized and aligned correctly across bench, pilot, and manufacture scale, the primary mitigation was placed on the rotary lobe pump. Lowering the pump rate in manufacture scale successfully alleviated the cell damage. To understand the source of cell damage within the pump, a small-scale rotary lobe-pump robustness model was developed. Testing different pump flow rates and back pressures, it was concluded that high back pressure can cause cell damage. The back pressure within the system can cause back flow and high shear within small clearances inside the pump, which lead to the primary cell damage observed at a large scale. This shear level can be significantly higher than the shear in the hollow fiber. This pump robustness model can be utilized to aid the perfusion skid design, including pump operation efficiency and cell shear sensitivity. Methods to reduce the back pressure and cell shearing were determined to better predict manufacturing performance in the future.