Repurposing the mucolytic agent ambroxol for treatment of sub-acute and chronic ischaemic stroke.

Ambroxol is a well-known mucolytic expectorant, which has gained much attention in amyotrophic lateral sclerosis, Parkinson's and Gaucher's disease. A specific focus has been placed on ambroxol's glucocerebrosidase-stimulating activity, on grounds that the point mutation of the gba1 gene, which codes for this enzyme, is a risk factor for developing Parkinson's disease. However, ambroxol has been attributed other characteristics, such as the potent inhibition of sodium channels, modification of calcium homeostasis, anti-inflammatory effects and modifications of oxygen radical scavengers. We hypothesized that ambroxol could have a direct impact on neuronal rescue if administered directly after ischaemic stroke induction. We longitudinally evaluated 53 rats using magnetic resonance imaging to examine stroke volume, oedema, white matter integrity, resting state functional MRI and behaviour for 1 month after ischemic stroke onset. For closer mechanistic insights, we evaluated tissue metabolomics of different brain regions in a subgroup of animals using ex vivo nuclear magnetic resonance spectroscopy. Ambroxol-treated animals presented reduced stroke volumes, reduced cytotoxic oedema, reduced white matter degeneration, reduced necrosis, improved behavioural outcomes and complex changes in functional brain connectivity. Nuclear magnetic resonance spectroscopy tissue metabolomic data at 24 h post-stroke proposes several metabolites that are capable of minimizing post-ischaemic damage and that presented prominent shifts during ambroxol treatment in comparison to controls. Taking everything together, we propose that ambroxol catalyzes recovery in energy metabolism, cellular homeostasis, membrane repair mechanisms and redox balance. One week of ambroxol administration following stroke onset reduced ischaemic stroke severity and improved functional outcome in the subacute phase followed by reduced necrosis in the chronic stroke phase.

Multiparametric Longitudinal Profiling of RCAS-tva-Induced PDGFB-Driven Experimental Glioma.

Glioblastomas are incurable primary brain tumors harboring a heterogeneous landscape of genetic and metabolic alterations. Longitudinal imaging by MRI and [18F]FET-PET measurements enable us to visualize the features of evolving tumors in a dynamic manner. Yet, close-meshed longitudinal imaging time points for characterizing temporal and spatial metabolic alterations during tumor evolution in patients is not feasible because patients usually present with already established tumors. The replication-competent avian sarcoma-leukosis virus (RCAS)/tumor virus receptor-A (tva) system is a powerful preclinical glioma model offering a high grade of spatial and temporal control of somatic gene delivery in vivo. Consequently, here, we aimed at using MRI and [18F]FET-PET to identify typical neuroimaging characteristics of the platelet-derived growth factor B (PDGFB)-driven glioma model using the RCAS-tva system. Our study showed that this preclinical glioma model displays MRI and [18F]FET-PET features that highly resemble the corresponding established human disease, emphasizing the high translational relevance of this experimental model. Furthermore, our investigations unravel exponential growth dynamics and a model-specific tumor microenvironment, as assessed by histology and immunochemistry. Taken together, our study provides further insights into this preclinical model and advocates for the imaging-stratified design of preclinical therapeutic interventions.

Machine learning identifies stroke features between species.

Identification and localization of ischemic stroke (IS) lesions is routinely performed to confirm diagnosis, assess stroke severity, predict disability and plan rehabilitation strategies using magnetic resonance imaging (MRI). In basic research, stroke lesion segmentation is necessary to study complex peri-infarction tissue changes. Moreover, final stroke volume is a critical outcome evaluated in clinical and preclinical experiments to determine therapy or intervention success. Manual segmentations are performed but they require a specialized skill set, are prone to inter-observer variation, are not entirely objective and are often not supported by histology. The task is even more challenging when dealing with large multi-center datasets, multiple experimenters or large animal cohorts. On the other hand, current automatized segmentation approaches often lack histological validation, are not entirely user independent, are often based on single parameters, or in the case of complex machine learning methods, require vast training datasets and are prone to a lack of model interpretation. Methods: We induced IS using the middle cerebral artery occlusion model on two rat cohorts. We acquired apparent diffusion coefficient (ADC) and T2-weighted (T2W) images at 24 h and 1-week after IS induction. Subsets of the animals at 24 h and 1-week post IS were evaluated using histology and immunohistochemistry. Using a Gaussian mixture model, we segmented voxel-wise interactions between ADC and T2W parameters at 24 h using one of the rat cohorts. We then used these segmentation results to train a random forest classifier, which we applied to the second rat cohort. The algorithms' stroke segmentations were compared to manual stroke delineations, T2W and ADC thresholding methods and the final stroke segmentation at 1-week. Volume correlations to histology were also performed for every segmentation method. Metrics of success were calculated with respect to the final stroke volume. Finally, the trained random forest classifier was tested on a human dataset with a similar temporal stroke on-set. Manual segmentations, ADC and T2W thresholds were again used to evaluate and perform comparisons with the proposed algorithms' output. Results: In preclinical rat data our framework significantly outperformed commonly applied automatized thresholding approaches and segmented stroke regions similarly to manual delineation. The framework predicted the localization of final stroke regions in 1-week post-stroke MRI with a median Dice similarity coefficient of 0.86, Matthew's correlation coefficient of 0.80 and false positive rate of 0.04. The predicted stroke volumes also strongly correlated with final histological stroke regions (Pearson correlation = 0.88, P < 0.0001). Lastly, the stroke region characteristics identified by our framework in rats also identified stroke lesions in human brains, largely outperforming thresholding approaches in stroke volume prediction (P<0.01). Conclusion: Our findings reveal that the segmentation produced by our proposed framework using 24 h MRI rat data strongly correlated with the final stroke volume, denoting a predictive effect. In addition, we show for the first time that the stroke imaging features can be directly translated between species, allowing identification of acute stroke in humans using the model trained on animal data. This discovery reduces the gap between the clinical and preclinical fields, unveiling a novel approach to directly co-analyze clinical and preclinical data. Such methods can provide further biological insights into human stroke and highlight the differences between species in order to help improve the experimental setups and animal models of the disease.