Small heat shock proteins are induced during multiple sclerosis lesion development in white but not grey matter

INTRODUCTION: The important protective role of small heat-shock proteins (HSPs) in regulating cellular survival and migration, counteracting protein aggregation, preventing apoptosis, and regulating inflammation in the central nervous system is now well-recognized. Yet, their role in the neuroinflammatory disorder multiple sclerosis (MS) is largely undocumented. With the exception of alpha B-crystallin (HSPB5), little is known about the roles of small HSPs in disease. RESULTS: Here, we examined the expression of four small HSPs during lesion development in MS, focussing on their cellular distribution, and regional differences between white matter (WM) and grey matter (GM). It is well known that MS lesions in these areas differ markedly in their pathology, with substantially more intense blood-brain barrier damage, leukocyte infiltration and microglial activation typifying WM but not GM lesions. We analysed transcript levels and protein distribution profiles for HSPB1, HSPB6, HSPB8 and HSPB11 in MS lesions at different stages, comparing them with normal-appearing brain tissue from MS patients and non-neurological controls. During active stages of demyelination in WM, and especially the centre of chronic active MS lesions, we found significantly increased expression of HSPB1, HSPB6 and HSPB8, but not HSPB11. When induced, small HSPs were exclusively found in astrocytes but not in oligodendrocytes, microglia or neurons. Surprisingly, while the numbers of astrocytes displaying high expression of small HSPs were markedly increased in actively demyelinating lesions in WM, no such induction was observed in GM lesions. This difference was particularly obvious in leukocortical lesions covering both WM and GM areas. CONCLUSIONS: Since induction of small HSPs in astrocytes is apparently a secondary response to damage, their differential expression between WM and GM likely reflects differences in mediators that accompany demyelination in either WM or GM during MS. Our findings also suggest that during MS, cortical structures fail to benefit from the protective actions of small HSPs. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s40478-015-0267-2) contains supplementary material, which is available to authorized users

Axonal abnormalities in vanishing white matter

ObjectiveWe aimed to study the occurrence and development of axonal pathology and the influence of astrocytes in vanishing white matter. MethodsAxons and myelin were analyzed using electron microscopy and immunohistochemistry on Eif2b4 and Eif2b5 single- and double-mutant mice and patient brain tissue. In addition, astrocyte-forebrain co-culture studies were performed. ResultsIn the corpus callosum of Eif2b5-mutant mice, myelin sheath thickness, axonal diameter, and G-ratio developed normally up to 4 months. At 7 months, however, axons had become thinner, while in control mice axonal diameters had increased further. Myelin sheath thickness remained close to normal, resulting in an abnormally low G-ratio in Eif2b5-mutant mice. In more severely affected Eif2b4-Eif2b5 double-mutants, similar abnormalities were already present at 4 months, while in milder affected Eif2b4 mutants, few abnormalities were observed at 7 months. Additionally, from 2 months onward an increased percentage of thin, unmyelinated axons and increased axonal density were present in Eif2b5-mutant mice. Co-cultures showed that Eif2b5 mutant astrocytes induced increased axonal density, also in control forebrain tissue, and that control astrocytes induced normal axonal density, also in mutant forebrain tissue. In vanishing white matter patient brains, axons and myelin sheaths were thinner than normal in moderately and severely affected white matter. In mutant mice and patients, signs of axonal transport defects and cytoskeletal abnormalities were minimal. InterpretationIn vanishing white matter, axons are initially normal and atrophy later. Astrocytes are central in this process. If therapy becomes available, axonal pathology may be prevented with early intervention

Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

Long-term experience with radiotherapy for the treatment of non-melanoma skin cancer

Contains fulltext : 217577.pdf (Publisher’s version ) (Closed access

Data analysis of electronic nose technology in lung cancer: Generating prediction models by means of Aethena

INTRODUCTION: Only 15% of lung cancer cases present with potentially curable disease. Therefore, there is much interest in a fast, non-invasive tool to detect lung cancer earlier. Exhaled breath analysis using electronic nose technology measures volatile organic compounds (VOCs) in exhaled breath that are associated with lung cancer. METHODS: The diagnostic accuracy of the Aeonose™ is currently being studied in a multi-centre, prospective study in 210 subjects suspected for lung cancer, where approximately half will have a confirmed diagnosis and the other half will have a rejected diagnosis of lung cancer. We will also include 100-150 healthy control subjects. The eNose Company (provider of the Aeonose™) uses a software program, called Aethena, comprising pre-processing, data compression and neural networks to handle big data analyses. Each individual exhaled breath measurement comprises a data matrix with thousands of conductivity values. This is followed by data compression using a Tucker3-like algorithm, resulting in a vector. Subsequently, model selection takes place after entering vectors with different presets in an artificial neural network to train and evaluate the results. Next, a 'judge model' is formed, which is a combination of models for optimizing performance. Finally, two types of cross-validation, being 'leave-10%-out' cross-validation and 'bagging', are used when recalculating the judge models. These judge models are subsequently used to classify new, blind measurements. DISCUSSION: Data analysis in eNose technology is principally based on generating prediction models that need to be validated internally and externally for eventual use in clinical practice. This paper describes the analysis of big data, captured by eNose technology in lung cancer. This is done by means of generating prediction models with Aethena, a data analysis program specifically developed for analysing VOC data

Combining exhaled-breath analysis data with clinical parameters to improve the diagnosis of lung cancer

Introduction: Lung cancer remains a leading cause of cancer mortality. Exhaled-breath analysis of volatile organic compounds (VOC’s), reflecting pathological processes, might detect lung cancer at an early stage, possibly leading to improved outcomes. Combining breath patterns with clinical parameters may improve the accuracy to diagnose lung cancer. Methods: In a multi-center study 144 subjects diagnosed with non-small cell lung cancer (NSCLC) and 146 healthy subjects breathed into the Aeonose™ (The eNose Company, Zutphen, Netherlands). The diagnostic accuracy, presented as Area under the Curve (AUC) of the Aeonose™ sec was compared with the diagnostic accuracy when combined with clinical parameters in a multivariate logistic regression analysis. Results: Confirmed NSCLC patients (67.1 (9.0) years; 57.6% male) were compared with controls without NSCLC (62.1 (7.1) years; 40.4% male). The AUC of the absolute Aeonose™ value obtained by a trained neural network was 0.76 (95% CI: 0.71-0.82). Adding age, number of pack years, and presence of COPD to this absolute value of the Aeonose™ from the neural network resulted in an improved performance with an AUC of 0.86 (95% CI: 0.81-0.90). By choosing an appropriate threshold value in the ROC-diagram of the multivariate model, we observed a sensitivity of 95.7%, a specificity of 59.7%, and a positive and negative predictive value of 69.5% and 92.5%, respectively. Conclusion: Adding readily available clinical information to the absolute obtained value of exhaled-breath analysis with the Aeonose™ improves the diagnostic accuracy to detect the presence or absence of lung cancer