There was a trend toward an increase in the injured unsupplemented group, relative to sham which was reversed by supplement A but not by supplement B. CD68 staining in injured animals was concentrated in the perivascular domain. The consistency between trends across different measures of neuroinflammation showing benefits of high-dose O3FA supplementation following TBI suggests that the observed effects are real. These findings are preliminary, but they justify further study to determine the functional benefits associated with improvements in histological outcomes and understand associated dose-response curves. Calumenin (CALU) has been reported to be associated with invasiveness and metastasis in some malignancies. However, in glioma, the role of CALU remains unclear. Clinical and transcriptome data of 998 glioma patients, including 301 from CGGA and 697 from TCGA dataset, were included. R language was used to perform statistical analyses. CALU expression was significantly upregulated in more malignant gliomas, including higher grade, IDH wildtype, mesenchymal, and classical subtype. Gene Ontology analysis revealed that CALU-correlated genes were mainly enriched in cell/biological adhesion, response to wounding, and extracellular matrix/structure organization, all of which were strongly correlated with the epithelial-mesenchymal transition (EMT) phenotype. GSEA further validated the profound involvement of CALU in EMT. Subsequent GSVA suggested that CALU was particularly correlated with three EMT signaling pathways, including TGFβ, PI3K/AKT, and hypoxia pathway. Furthermore, CALU played synergistically with EMT key markers, including -cadherin, vimentin, snail, slug, and TWIST1. Survival and Cox regression analysis showed that higher CALU predicted worse survival, and the prognostic value was independent of WHO grade and age. CALU was correlated with more malignant phenotypes in glioma. HSP inhibitor clinical trial Moreover, CALU seemed to serve as a pro-EMT molecular target and could contribute to predict prognosis independently in glioma.CALU was correlated with more malignant phenotypes in glioma. Moreover, CALU seemed to serve as a pro-EMT molecular target and could contribute to predict prognosis independently in glioma.Organoids are in vitro miniaturized and simplified model systems of organs that have gained enormous interest for modelling tissue development and disease, and for personalized medicine, drug screening and cell therapy. Despite considerable success in culturing physiologically relevant organoids, challenges remain to achieve real-life applications. In particular, the high variability of self-organizing growth and restricted experimental and analytical access hamper the translatability of organoid systems. In this Review, we argue that many limitations of traditional organoid culture can be addressed by engineering approaches at all levels of organoid systems. We investigate cell surface and genetic engineering approaches, and discuss stem cell niche engineering based on the design of matrices that allow spatiotemporal control of organoid growth and shape-guided morphogenesis. We examine how microfluidic approaches and lessons learnt from organs-on-a-chip enable the integration of mechano-physiological parameters and increase accessibility of organoids to improve functional readouts. Applying engineering principles to organoids increases reproducibility and provides experimental control, which will, ultimately, be required to enable clinical translation. The scope and productivity of artificial intelligence applications in health science and medicine, particularly in medical imaging, are rapidly progressing, with relatively recent developments in big data and deep learning and increasingly powerful computer algorithms. Accordingly, there are a number of opportunities and challenges for the radiological community. To provide review on the challenges and barriers experienced in diagnostic radiology on the basis of the key clinical applications of machine learning techniques. Studies published in 2010-2019 were selected that report on the efficacy of machine learning models. A single contingency table was selected for each study to report the highest accuracy of radiology professionals and machine learning algorithms, and a meta-analysis of studies was conducted based on contingency tables. The specificity for all the deep learning models ranged from 39% to 100%, whereas sensitivity ranged from 85% to 100%. The pooled sensitivity and specificity were 89% and 85% for the deep learning algorithms for detecting abnormalities compared to 75% and 91% for radiology experts, respectively. The pooled specificity and sensitivity for comparison between radiology professionals and deep learning algorithms were 91% and 81% for deep learning models and 85% and 73% for radiology professionals (p < 0.000), respectively. The pooled sensitivity detection was 82% for health-care professionals and 83% for deep learning algorithms (p < 0.005). Radiomic information extracted through machine learning programs form images that may not be discernible through visual examination, thus may improve the prognostic and diagnostic value of data sets.Radiomic information extracted through machine learning programs form images that may not be discernible through visual examination, thus may improve the prognostic and diagnostic value of data sets. The narrowing of the carotid arteries with plaque formation represents a major risk factor for ischemic stroke and cognitive impairments. Carotid angioplasty and stenting is a standard clinical treatment to reduce stroke risk. The cognitive effect of carotid angioplasty and stenting remains largely unknown. This study aims to provide direct evidence of possible effects of carotid angioplasty and stenting on cognition, using task-phase functional magnetic resonance imaging. This study received harmonized institutional ethics board approval (Grant number REB ID =H18-02495/FHREB 2018-058). Two patients had MRI scans pre-carotid angioplasty and stenting and two-month post-carotid angioplasty and stenting. Case 1 had severe (>95%) flow-limiting stenosis in the right carotid artery. Case 2 had 70% non-flow limiting stenosis in the left carotid artery. At each scan, patients completed two functional magnetic resonance imaging sessions while performing a working memory task. Accuracy, reaction time, and brain activation were analyzed for each patient for possible pre-post carotid angioplasty and stenting changes.