deepFPlearn +: enhancing toxicity prediction across the chemical universe using graph neural networks.

Summary: Sophisticated approaches for the in silico prediction of toxicity are required to support the risk assessment of chemicals. The number of chemicals on the global chemical market and the speed of chemical innovation stand in massive contrast to the capacity for regularizing chemical use. We recently proved our ready-to-use application deepFPlearn as a suitable approach for this task.

Error quantification in multi-parameter mapping facilitates robust estimation and enhanced group level sensitivity.

Multi-Parameter Mapping (MPM) is a comprehensive quantitative neuroimaging protocol that enables estimation of four physical parameters (longitudinal and effective transverse relaxation rates R and R, proton density PD, and magnetization transfer saturation MT) that are sensitive to microstructural tissue properties such as iron and myelin content. Their capability to reveal microstructural brain differences, however, is tightly bound to controlling random noise and artefacts (e.g.

AI for predicting chemical-effect associations at the chemical universe level-deepFPlearn.

Many chemicals are present in our environment, and all living species are exposed to them. However, numerous chemicals pose risks, such as developing severe diseases, if they occur at the wrong time in the wrong place. For the majority of the chemicals, these risks are not known.

Transforming Growth Factor Beta 3-Loaded Decellularized Equine Tendon Matrix for Orthopedic Tissue Engineering.

Transforming growth factor beta 3 (TGFβ3) promotes tenogenic differentiation and may enhance tendon regeneration in vivo. This study aimed to apply TGFβ3 absorbed in decellularized equine superficial digital flexor tendon scaffolds, and to investigate the bioactivity of scaffold-associated TGFβ3 in an in vitro model. TGFβ3 could effectively be loaded onto tendon scaffolds so that at least 88% of the applied TGFβ3 were not detected in the rinsing fluid of the TGFβ3-loaded scaffolds.

Growth Factor-Mediated Tenogenic Induction of Multipotent Mesenchymal Stromal Cells Is Altered by the Microenvironment of Tendon Matrix.

Age-related degenerative changes in tendon tissue represent a common cause for acute tendon pathologies. Although the regenerative potential of multipotent mesenchymal stromal cells (MSC) was reported to restore functionality in injured tendon tissue, cellular mechanisms of action remain partly unclear. Potential tenogenic differentiation of applied MSC is affected by various intrinsic and extrinsic factors.