Translational Block in Stroke: A Constructive and “Out-of-the-Box” Reappraisal

Why can we still not translate preclinical research to clinical treatments for acute strokes? Despite > 1000 successful preclinical studies, drugs, and concepts for acute stroke, only two have reached clinical translation. This is the translational block. Yet, we continue to routinely model strokes using almost the same concepts we have used for over 30 years. Methodological improvements and criteria from the last decade have shed some light but have not solved the problem. In this conceptual analysis, we review the current status and reappraise it by thinking “out-of-the-box” and over the edges. As such, we query why other scientific fields have also faced the same translational failures, to find common denominators. In parallel, we query how migraine, multiple sclerosis, and hypothermia in hypoxic encephalopathy have achieved significant translation successes. Should we view ischemic stroke as a “chronic, relapsing, vascular” disease, then secondary prevention strategies are also a successful translation. Finally, based on the lessons learned, we propose how stroke should be modeled, and how preclinical and clinical scientists, editors, grant reviewers, and industry should reconsider their routine way of conducting research. Translational success for stroke treatments may eventually require a bold change with solutions that are outside of the box. © Copyright © 2021 Lourbopoulos, Mourouzis, Xinaris, Zerva, Filippakis, Pavlopoulos and Pantos

Machine learning based analysis of stroke lesions on mouse tissue sections

An unbiased, automated and reliable method for analysis of brain lesions in tissue after ischemic stroke is missing. Manual infarct volumetry or by threshold-based semi-automated approaches is laborious, and biased to human error or biased by many false -positive and -negative data, respectively. Thereby, we developed a novel machine learning, atlas-based method for fully automated stroke analysis in mouse brain slices stained with 2% Triphenyltetrazolium-chloride (2% TTC), named “StrokeAnalyst”, which runs on a user-friendly graphical interface. StrokeAnalyst registers subject images on a common spatial domain (a novel mouse TTC- brain atlas of 80 average mathematical images), calculates pixel-based, tissue-intensity statistics (z-scores), applies outlier-detection and machine learning (Random-Forest) models to increase accuracy of lesion detection, and produces volumetry data and detailed neuroanatomical information per lesion. We validated StrokeAnalyst in two separate experimental sets using the filament stroke model. StrokeAnalyst detects stroke lesions in a rater-independent and reproducible way, correctly detects hemispheric volumes even in presence of post-stroke edema and significantly minimizes false-positive errors compared to threshold-based approaches (false-positive rate 1.2–2.3%, p < 0.05). It can process scanner-acquired, and even smartphone-captured or pdf-retrieved images. Overall, StrokeAnalyst surpasses all previous TTC-volumetry approaches and increases quality, reproducibility and reliability of stroke detection in relevant preclinical models. © The Author(s) 2022

Gene variants of adhesion molecules act as modifiers of disease severity in MS

Objective: To assess the potential effect of variants in genes encoding molecules that are implicated in leukocyte trafficking into the CNS on the clinical phenotype of multiple sclerosis (MS). Methods: A total of 389 Greek MS cases and 336 controls were recruited in 3 MS centers from Cyprus and Greece. We genotyped 147 tagging single nucleotide polymorphisms (SNPs) in 9 genes encoding for P-selectin (SELP), integrins (ITGA4, ITGB1, and ITGB7), adhesion molecules (ICAM1, VCAM1, and MADCAM1), fibronectin 1 (FN1), and osteopontin (SPP1) involved in lymphocyte adhesion and trafficking into the CNS. Clinical end points of the study were age at MS onset and MS severity as measured by the Multiple Sclerosis Severity Score. Permutation testing was applied to all analyses. Results: SNPs rs6721763 of the ITGA4 and rs6532040 of the SPP1 were found to significantly influence disease severity (permutation p values: 3.00e-06 and 0.009884, respectively). SNP rs1250249 of the FN1 had a dose-dependent effect on age at disease onset (permutation p value: 0.0002). Conclusions: This study provides evidence implicating variants encoding adhesion molecules, responsible for lymphocyte adhesion and trafficking within the CNS, as modifiers of MS disease severity. These genetic biomarkers, which can be available at the time of diagnosis, may be used to assess the biological aggressiveness of the disease and thus guide decisions on treatment

Losartan Increases No Production from the Bovine Aortic Wall That is Stimulated by Angiotensin II

In these studies we investigated if losartan, an AT 1 - receptor blocker has any beneficial effect on NO production from the bovine aortic preparations in vitro while under stimulation from angiotensin II. Experiments were performed on intact specimens of bovine thoracic aorta, incubated in Dulbeco's MOD medium in a metabolic shaker for 24 hours under 95 % O 2 and 5 % CO 2 at a temperature of 37°C. We found that angiotensin II 1nM −10 μM does not exert any statistically significant action on NO production. On the contrary, angiotensin II 10nM increases the production of NO by 58.14 % (from 12.16 + 2.9 μm/l to 19.23 + 4.2 μm/l in the presence of losartan 1nM (P<0.05). Nitric oxide levels depend on both rate production and rate catabolism or chemical inactivation. Such an equilibrium is vital for the normal function of many systems including the cardiovascular one. The above results demonstrate that the blockade of AT 1 -receptors favors the biosynthesis of NO and indicate the protective role of losartan on the vascular wall