A Machine Learning Approach to Investigate the Uncertainty of Tissue-Level Injury Metrics for Cerebral Contusion

Controlled cortical impact (CCI) on porcine brain is often utilized to investigate the pathophysiology and functional outcome of focal traumatic brain injury (TBI), such as cerebral contusion (CC). Using a finite element (FE) model of the porcine brain, the localized brain strain and strain rate resulting from CCI can be computed and compared to the experimentally assessed cortical lesion. This way, tissue-level injury metrics and corresponding thresholds specific for CC can be established. However, the variability and uncertainty associated with the CCI experimental parameters contribute to the uncertainty of the provoked cortical lesion and, in turn, of the predicted injury metrics. Uncertainty quantification via probabilistic methods (Monte Carlo simulation, MCS) requires a large number of FE simulations, which results in a time-consuming process. Following the recent success of machine learning (ML) in TBI biomechanical modeling, we developed an artificial neural network as surrogate of the FE porcine brain model to predict the brain strain and the strain rate in a computationally efficient way. We assessed the effect of several experimental and modeling parameters on four FE-derived CC injury metrics (maximum principal strain, maximum principal strain rate, product of maximum principal strain and strain rate, and maximum shear strain). Next, we compared the in silico brain mechanical response with cortical damage data from in vivo CCI experiments on pig brains to evaluate the predictive performance of the CC injury metrics. Our ML surrogate was capable of rapidly predicting the outcome of the FE porcine brain undergoing CCI. The now computationally efficient MCS showed that depth and velocity of indentation were the most influential parameters for the strain and the strain rate-based injury metrics, respectively. The sensitivity analysis and comparison with the cortical damage experimental data indicate a better performance of maximum principal strain and maximum shear strain as tissue-level injury metrics for CC. These results provide guidelines to optimize the design of CCI tests and bring new insights to the understanding of the mechanical response of brain tissue to focal traumatic brain injury. Our findings also highlight the potential of using ML for computationally efficient TBI biomechanics investigations.

Efflux at the Blood-Brain Barrier Reduces the Cerebral Exposure to Ochratoxin A, Ochratoxin α, Citrinin and Dihydrocitrinone

Recent studies have implied that environmental toxins, such as mycotoxins, are risk factors for neurodegenerative diseases. To act directly as neurotoxins, mycotoxins need to penetrate or affect the integrity of the blood-brain barrier, which protects the mammalian brain from potentially harmful substances. As common food and feed contaminants of fungal origin, the interest in the potential neurotoxicity of ochratoxin A, citrinin and their metabolites has recently increased. Primary porcine brain capillary endothelial cells were used to investigate cytotoxic or barrier-weakening effects of ochratoxin A, ochratoxin α, citrinin and dihydrocitrinone. The transfer and transport properties of the mycotoxins across the barrier formed by porcine brain capillary endothelial cell monolayers were analysed using HPLC-MS/MS. High levels of Ochratoxin A caused cytotoxic and barrier-weakening effects, whereas ochratoxin α, citrinin and dihydrocitrinone showed no adverse effects up to 10 µM. Likely due to efflux transporter proteins, the transfer to the brain compartment was much slower than expected from their high lipophilicity. Due to their slow transfer across the blood-brain barrier, cerebral exposure of ochratoxin A, ochratoxin α, citrinin and dihydrocitrinone is low and neurotoxicity is likely to play a subordinate role in their toxicity at common physiological concentrations.

Brain organoid formation on decellularized porcine brain ECM hydrogels

Human brain tissue models such as cerebral organoids are essential tools for developmental and biomedical research. Current methods to generate cerebral organoids often utilize Matrigel as an external scaffold to provide structure and biologically relevant signals. Matrigel however is a nonspecific hydrogel of mouse tumor origin and does not represent the complexity of the brain protein environment. In this study, we investigated the application of a decellularized adult porcine brain extracellular matrix (B-ECM) which could be processed into a hydrogel (B-ECM hydrogel) to be used as a scaffold for human embryonic stem cell (hESC)-derived brain organoids. We decellularized pig brains with a novel detergent- and enzyme-based method and analyzed the biomaterial properties, including protein composition and content, DNA content, mechanical characteristics, surface structure, and antigen presence. Then, we compared the growth of human brain organoid models with the B-ECM hydrogel or Matrigel controls in vitro. We found that the native brain source material was successfully decellularized with little remaining DNA content, while Mass Spectrometry (MS) showed the loss of several brain-specific proteins, while mainly different collagen types remained in the B-ECM. Rheological results revealed stable hydrogel formation, starting from B-ECM hydrogel concentrations of 5 mg/mL. hESCs cultured in B-ECM hydrogels showed gene expression and differentiation outcomes similar to those grown in Matrigel. These results indicate that B-ECM hydrogels can be used as an alternative scaffold for human cerebral organoid formation, and may be further optimized for improved organoid growth by further improving protein retention other than collagen after decellularization.

The effects of electric fields on the mechanical properties and microstructure of ex-vivo porcine brain tissues

As a popular tool for regulating the physiological conditions of the brain and treating brain diseases, electrotherapy has become increasingly mature in clinical applications. However, the mechanical properties and microstructure...

A porcine brain-wide RNA editing landscape

Adenosine-to-inosine (A-to-I) RNA editing, catalyzed by ADAR enzymes, is an essential post-transcriptional modification. Although hundreds of thousands of RNA editing sites have been reported in mammals, brain-wide analysis of the RNA editing in the mammalian brain remains rare. Here, a genome-wide RNA editing investigation is performed in 119 samples, representing 30 anatomically defined subregions in the pig brain. We identify a total of 682,037 A-to-I RNA editing sites of which 97% are not identified before. Within the pig brain, cerebellum and olfactory bulb are regions with most edited transcripts. The editing level of sites residing in protein-coding regions are similar across brain regions, whereas region-distinct editing is observed in repetitive sequences. Highly edited conserved recoding events in pig and human brain are found in neurotransmitter receptors, demonstrating the evolutionary importance of RNA editing in neurotransmission functions. The porcine brain-wide RNA landscape provides a rich resource to better understand the evolutionally importance of post-transcriptional RNA editing.

In situ impedance measurements on postmortem porcine brain

The paper presents an experimental study where the distinctness of grey and white matter of an in situ postmortem porcine brain by impedance measurements is investigated. Experimental conditions that would allow to conduct the same experiment on in vivo human brain tissue are replicated.