Cerebrospinal fluid may mediate CNS ischemic injury

BACKGROUND: The central nervous system (CNS) is extremely vulnerable to ischemic injury. The details underlying this susceptibility are not completely understood. Since the CNS is surrounded by cerebrospinal fluid (CSF) that contains a low concentration of plasma protein, we examined the effect of changing the CSF in the evolution of CNS injury during ischemic insult. METHODS: Lumbar spinal cord ischemia was induced in rabbits by cross-clamping the descending abdominal aorta for 1 h, 2 h or 3 h followed by 7 d of reperfusion. Prior to ischemia, rabbits were subjected to the following procedures; 1) CSF depletion, 2) CSF replenishment at 0 mmHg intracranial pressure (ICP), and 3) replacement of CSF with 8% albumin- or 1% gelatin-modified artificial CSF, respectively. Motor function of the hind limbs and histopathological changes of the spinal cord were scored. Post-ischemic microcirculation of the spinal cord was visualized by fluorescein isothiocyanate (FITC) albumin. RESULTS: The severity of histopathological damage paralleled the neurological deficit scores. Paraplegia and associated histopathological changes were accompanied by a clear post-ischemic deficit in blood perfusion. Spinal cord ischemia for 1 h resulted in permanent paraplegia in the control group. Depletion of the CSF significantly prevented paraplegia. CSF replenishment with the ICP reduced to 0 mmHg, did not prevent paraplegia. Replacement of CSF with albumin- or gelatin-modified artificial CSF prevented paraplegia in rabbits even when the ICP was maintained at 10–15 mmHg. CONCLUSION: We conclude that the presence of normal CSF may contribute to the vulnerability of the spinal cord to ischemic injury. Depletion of the CSF or replacement of the CSF with an albumin- or gelatin-modified artificial CSF can be neuroprotective

Orbit image analysis machine learning software can be used for the histological quantification of acute ischemic stroke blood clots

Our aim was to assess the utility of a novel machine learning software (Orbit Image Analysis) in the histological quantification of acute ischemic stroke (AIS) clots. We analyzed 50 AIS blood clots retrieved using mechanical thrombectomy procedures. Following HandE staining, quantification of clot components was performed by two different methods: a pathologist using a reference standard method (Adobe Photoshop CC) and an experienced researcher using Orbit Image Analysis. Following quantification, the clots were categorized into 3 types: RBC dominant (\u3e/=60% RBCs), Mixed and Fibrin dominant ( \u3e /=60% Fibrin). Correlations between clot composition and Hounsfield Units density on Computed Tomography (CT) were assessed. There was a significant correlation between the components of clots as quantified by the Orbit Image Analysis algorithm and the reference standard approach (rho = 0.944**, p \u3c 0.001, n = 150). A significant relationship was found between clot composition (RBC-Rich, Mixed, Fibrin-Rich) and the presence of a Hyperdense artery sign using the algorithmic method (X2(2) = 6.712, p = 0.035*) but not using the reference standard method (X2(2) = 3.924, p = 0.141). Orbit Image Analysis machine learning software can be used for the histological quantification of AIS clots, reproducibly generating composition analyses similar to current reference standard methods

Generative deep learning as a tool for inverse design of high entropy refractory alloys

Generative deep learning is powering a wave of new innovations in materials design. This article discusses the basic operating principles of these methods and their advantages over rational design through the lens of a case study on refractory high-entropy alloys for ultra-high-temperature applications. We present our computational infrastructure and workflow for the inverse design of new alloys powered by these methods. Our preliminary results show that generative models can learn complex relationships to generate novelty on demand, making them a valuable tool for materials informatics

Platelet-rich clots as identified by Martius Scarlet Blue staining are isodense on NCCT

Background Current studies on clot characterization in acute ischemic stroke focus on fibrin and red blood cell composition. Few studies have examined platelet composition in acute ischemic stroke clots. We characterize clot composition using the Martius Scarlet Blue stain and assess associations between platelet density and CT density. Materials and method Histopathological analysis of the clots collected as part of the multi-institutional STRIP registry was performed using Martius Scarlet Blue stain and the composition of the clots was quantified using Orbit Image Analysis (www.orbit.bio) machine learning software. Prior to endovascular treatment, each patient underwent non-contrast CT (NCCT) and the CT density of each clot was measured. Correlations between clot components and clinical information were assessed using the χ2 test. Results Eighty-five patients were included in the study. The mean platelet density of the clots was 15.7% (2.5–72.5%). There was a significant correlation between platelet-rich clots and the absence of hyperdensity on NCCT, (ρ=0.321, p=0.003*, n=85). Similarly, there was a significant inverse correlation between the percentage of platelets and the mean Hounsfield Units on NCCT (ρ=−0.243, p=0.025*, n=85). Conclusion Martius Scarlet Blue stain can identify patients who have platelet-rich clots. Platelet-rich clots are isodense on NCCT.This work was supported by the National Institutes of Health grant number (R01 NS105853) and the European Regional Development Fund and Science Foundation Ireland grant number (13/RC/2073).peer-reviewe