Cytotoxic edema associated with hemorrhage predicts poor outcome after traumatic brain injury

Magnetic resonance imaging (MRI) is rarely used in the acute evaluation of traumatic brain injury (TBI), but may identify findings of clinical importance not detected by computed tomography (CT). We aimed to characterize the association of cytotoxic edema and hemorrhage, including traumatic microbleeds, on MRI obtained within hours of acute head trauma and investigated the relationship to clinical outcomes. Patients prospectively enrolled in the Traumatic Head Injury Neuroimaging Classification study (NCT01132937) with evidence of diffusion-related findings or hemorrhage on neuroimaging were included. Blinded interpretation of MRI for diffusion-weighted imaging and hemorrhage was conducted, with subsequent quantification of apparent diffusion coefficient (ADC) values. Of 161 who met criteria, 82 patients had conspicuous hyperintense lesions on diffusion-weighted imaging (DWI) with corresponding regions of hypointense ADC in proximity to hemorrhage. Median time from injury to MRI was 21 (10-30) hours. Median ADC values per patient grouped by time from injury to MRI were lowest within 24 hours after injury. ADC values associated with hemorrhagic lesions are lowest early after injury, with an increase in diffusion during the subacute period, suggesting transformation from cytotoxic to vasogenic edema during the subacute post-injury period. Of 118 patients with outcome data, 60 had Glasgow Outcome Scale Extended <6 at 30/90 days post-injury. Cytotoxic edema on MRI (OR 2.91 [1.32-6.37], P=0.008) and TBI severity (OR 2.51 [1.32-4.74], P=0.005) were independent predictors of outcome. These findings suggest that in TBI patients with findings of hemorrhage on CT, patients with DWI/ADC lesions on MRI are more likely to do worse

Coronavirus HKU1 Infection in the United States

Virus is associated with respiratory tract disease in children <5 years of age

Circulating CD133+CD34+ progenitor cells inversely correlate with soluble ICAM-1 in early ischemic stroke patients

<p>Abstract</p> <p>Background and Purpose</p> <p>Both endothelial progenitor cells (EPC) and markers of neuroinflammation are candidate biomarkers for stroke severity and outcome prediction. A relationship between EPC and neuroinflammatory markers in early stroke is not fully elucidated. The objectives were to investigate correlations between EPC and neuroinflammation markers (adhesion molecules ICAM-1, VCAM-1, E-selectin, tumor necrosis factor (TNF)-α, interleukin (IL)-6, endothelin (ET)-1, markers of tissue injury (matrix metalloproteinases (MMP)-9 and tissue inhibitor of matrix metalloproteinases (TIMP)-1) in early stroke patients.</p> <p>Methods</p> <p>We prospectively recruited symptomatic patients with ischemic cerebrovascular disease. We assessed stroke severity by using of acute (diffusion-weighted imaging (DWI) and final lesion volumes (fluid attenuated inversion recovery (FLAIR). We measured serum soluble ICAM-1, VCAM-1, E-selectin, MMP-9, TIMP-1 and plasma TNF-α, IL-6, ET-1 by ELISA, and quantified EPC in mononuclear fraction of peripheral blood on days 1 and 3 in 17 patients (mean(SD) age 62(14), with admission National Institutes of Health Stroke Scale (NIHSS) 10(8)) selected from 175 patients with imaging confirmed ischemic stroke. Non-parametric statistics, univariate and multivariate analysis were used.</p> <p>Results</p> <p>Only ICAM-1 inversely correlated with EPC subset CD133+CD34+ on day 1 (Spearman r = -0.6, p < 0.01) and on day 3 (r = -0.967, p < 0.001). This correlation remained significant after adjustment for age and NIHSS (beta -0.992, p < 0.004), for glucose and systolic blood pressure (beta -0.86, p < 0.005), and for white blood cells and hematocrit (beta -1.057, p < 0.0001) on day 3. MMP-9 (r = 0.509, p < 0.04) and MMP-9/TIMP-1 (r = 0.59, p < 0.013) on day 1 correlated with acute lesion volume. Both IL-6 (r = 0.624, p < 0.01) and MMP-9/TIMP-1 (r = 0.56, p < 0.02) correlated with admission NIHSS.</p> <p>Conclusion</p> <p>Our study showed that high ICAM-1 is associated with low CD133+CD34+subset of EPC. Biomarkers of neuroinflammation may predict tissue injury and stroke severity in early ischemia.</p

Why small-quantity lipid-based nutrient supplements should be integrated into comprehensive strategies to prevent child undernutrition in nutritionally vulnerable populations : response to Gupta et al.’s commentary

We write in response to the commentary by Gupta et al. (2023) on small-quantity lipid-based nutrient supplements (SQ-LNS) for infants and young children 6 to 24 months of age, which was prompted by the recent brief guidance note from UNICEF (2023) explaining when, why and how SQ-LNS are being prioritized as part of their package of preventive actions to combat early childhood malnutrition. The UNICEF document was disseminated shortly after publication of a correspondence in Nature Food (Aguayo et al. 2023), authored by nutrition leaders from several organizations, that summarized the evidence on the benefits of SQ-LNS and called for this intervention to be scaled up and integrated into programs for populations in which child undernutrition is prevalent and dietary quality is very poor. We agree with Gupta et al. that child malnutrition is the result of many factors and there is no single “quick fix” or “magic bullet”. In fact, the above-cited documents state clearly and frequently that provision of SQ-LNS is not a stand-alone intervention and must be integrated into comprehensive strategies to improve infant and young child feeding (IYCF), including the promotion of dietary diversity, as well as other actions needed to prevent malnutrition. SQ-LNS are intended for vulnerable populations who lack access to an affordable, nutritionally adequate complementary feeding diet and have high rates of stunting, wasting and mortality. In such populations, we agree with Gupta et al. that IYCF messages alone are not enough. This is precisely why SQ-LNS were originally developed

Deep learning-based detection and segmentation of diffusion abnormalities in acute ischemic stroke

Background: Accessible tools to efficiently detect and segment diffusion abnormalities in acute strokes are highly anticipated by the clinical and research communities. Methods: We developed a tool with deep learning networks trained and tested on a large dataset of 2,348 clinical diffusion weighted MRIs of patients with acute and sub-acute ischemic strokes, and further tested for generalization on 280 MRIs of an external dataset (STIR). Results: Our proposed model outperforms generic networks and DeepMedic, particularly in small lesions, with lower false positive rate, balanced precision and sensitivity, and robustness to data perturbs (e.g., artefacts, low resolution, technical heterogeneity). The agreement with human delineation rivals the inter-evaluator agreement; the automated lesion quantification of volume and contrast has virtually total agreement with human quantification. Conclusion: Our tool is fast, public, accessible to non-experts, with minimal computational requirements, to detect and segment lesions via a single command line. Therefore, it fulfills the conditions to perform large scale, reliable and reproducible clinical and translational research

Acute Stroke Imaging Research Roadmap II

No abstract available

Multi-parametric predictive model for end infarct volume in stroke patients

We investigated the intra- and inter-rater reliability of ischemic lesion volumes measurements assessed by different MRI sequences at various times from onset. The reliability of these measurements are essential for validating their usage in stroke outcome prediction models. Ischemic lesion volumes were measured for infra-rater reliability using diffusion weighted (DWI), mean transit time (MTT) perfusion and Fluid Attenuated Inversion Recovery (FLAIR) MRI at chronic time points. There was good concordance of the mean sample volumes of the two infra-rater reads (deviations were \u3c4% and 2 cc globally, \u3c2% and 2 cc for DWI, \u3c6% and 7 cc for MTT and \u3c2% and 1 cc for FLAIR). There was also good concordance of the inter-rater reads (\u3c5% and 2 cc globally). Repeat measurements of stroke lesion volumes show excellent intra- and inter-rater correlations and concordance for DWI, MTT and FLAIR at acute through chronic time points. ^ We also investigated two methods of measuring MRI perfusion-diffusion mismatch to determine whether reliability is improved by direct measurement on a single, blended map. Image software was used for measurement of lesion volumes from diffusion-weighted (DWI) and mean transit time (MTT) calculated from perfusion weighted (PWI) images on 64 acute stroke patients. For the first method the DWI and MTT lesions were measured separately. For the second method the mismatch volume was measured directly on the blended images created from the registered DWI and MTT images. Test-retest agreement was 100% and 97% for the separate and blended methods using mismatch cutoffs of \u3e20% versus \u3c20%. There were no significant differences in the mismatch statistics between the methods. Mismatch volumes by a single reader can provide highly reliable and consistent results even when separately measuring DWI and MTT lesions. Propagation of measurement error was not demonstrated and the methods were statistically comparable. ^ Combined clinical and imaging prediction models were created to best describe the outcome of a stroke patient in a meaningful way. For comparison to these combined prediction models, we investigated imaging based prediction models based on pixel or voxel classification techniques using multiple MRI sequences. These predictive maps can be interpreted as the potential for disease growth given the conditions immediately after the patient has suffered a stroke. We measured the amount of abnormal tissue in these predictive maps to compare to the volume measurement results from the two combined models.

UCP2 as a Cancer Target through Energy Metabolism and Oxidative Stress Control

Despite numerous therapies, cancer remains one of the leading causes of death worldwide due to the lack of markers for early detection and response to treatment in many patients. Technological advances in tumor screening and renewed interest in energy metabolism have allowed us to identify new cellular players in order to develop personalized treatments. Among the metabolic actors, the mitochondrial transporter uncoupling protein 2 (UCP2), whose expression is increased in many cancers, has been identified as an interesting target in tumor metabolic reprogramming. Over the past decade, a better understanding of its biochemical and physiological functions has established a role for UCP2 in (1) protecting cells from oxidative stress, (2) regulating tumor progression through changes in glycolytic, oxidative and calcium metabolism, and (3) increasing antitumor immunity in the tumor microenvironment to limit cancer development. With these pleiotropic roles, UCP2 can be considered as a potential tumor biomarker that may be interesting to target positively or negatively, depending on the type, metabolic status and stage of tumors, in combination with conventional chemotherapy or immunotherapy to control tumor development and increase response to treatment. This review provides an overview of the latest published science linking mitochondrial UCP2 activity to the tumor context

Targeting Metabolism to Control Immune Responses in Cancer and Improve Checkpoint Blockade Immunotherapy

International audienceOver the past decade, advances in cancer immunotherapy through PD1–PDL1 and CTLA4 immune checkpoint blockade have revolutionized the management of cancer treatment. However, these treatments are inefficient for many cancers, and unfortunately, few patients respond to these treatments. Indeed, altered metabolic pathways in the tumor play a pivotal role in tumor growth and immune response. Thus, the immunosuppressive tumor microenvironment (TME) reprograms the behavior of immune cells by altering their cellular machinery and nutrient availability to limit antitumor functions. Today, thanks to a better understanding of cancer metabolism, immunometabolism and immune checkpoint evasion, the development of new therapeutic approaches targeting the energy metabolism of cancer or immune cells greatly improve the efficacy of immunotherapy in different cancer models. Herein, we highlight the changes in metabolic pathways that regulate the differentiation of pro- and antitumor immune cells and how TME-induced metabolic stress impedes their antitumor activity. Finally, we propose some drug strategies to target these pathways in the context of cancer immunotherapy