Boosting porphyrin synthesis and ALA-mediated photoinactivation through near-infrared therapy.

Photodynamic inactivation (PDI) combines the use of photosensitizers with visible light to produce reactive oxygen species that effectively eliminate pathogens. To investigate the impact of near- infrared therapy (NIRT) on heme biosynthesis and permeability of the pro-photosensitizers 5-aminolevulinic acid (ALA) and Hexyl-ALA (H-ALA) through biofilms, we applied sub-lethal conditions for both NIRT and PDI to maintain intact bacterial viability. During NIRT, the temperature remained below 37°C, permitting rapid heating (ΔT = 11°C) without causing thermal damage.

Unveiling the interplay between soluble guanylate cyclase activation and redox signalling in stroke pathophysiology and treatment.

Soluble guanylate cyclase (sGC) stands as a pivotal regulatory element in intracellular signalling pathways, mediating the formation of cyclic guanosine monophosphate (cGMP) and impacting diverse physiological processes across tissues. Increased formation of reactive oxygen species (ROS) is widely recognized to modulate cGMP signalling. Indeed, oxidatively damaged, and therefore inactive sGC, contributes to poor vascular reactivity and more severe neurological damage upon stroke.

Implementation of Principal Component Analysis (PCA)/Singular Value Decomposition (SVD) and Neural Networks in Constructing a Reduced-Order Model for Virtual Sensing of Mechanical Stress.

This study presents the design and validation of a numerical method based on an AI-driven ROM framework for implementing stress virtual sensing. By leveraging Reduced-Order Models (ROMs), the research aims to develop a virtual stress transducer capable of the real-time monitoring of mechanical stresses in mechanical components previously analyzed with high-resolution FEM simulations under a wide range of multiple load scenarios. The ROM is constructed through neural networks trained on Finite Element Method (FEM) outputs from multiple scenarios, resulting in a simplified yet highly accurate model that can be easily implemented digitally.

Engineering biology and automation-Replicability as a design principle.

Applications in engineering biology increasingly share the need to run operations on very large numbers of biological samples. This is a direct consequence of the application of good engineering practices, the limited predictive power of current computational models and the desire to investigate very large design spaces in order to solve the hard, important problems the discipline promises to solve. Automation has been proposed as a key component for running large numbers of operations on biological samples.