Acute Endovascular Treatment of Patients With lschemic Stroke From Intracranial Large Vessel Occlusion and Extracranial Carotid Dissection

Introduction: Carotid artery dissection (CAD) and atherosclerotic carotid artery occlusion (ACAO) are major causes of a tandem occlusion in patients with intracranial large vessel occlusion (LVO). Presence of tandem occlusions may hamper intracranial access and potentially increases the risk of procedural complications of endovascular treatment (EVT). Our aim was to assess neurological, functional and technical outcome and complications of EVT for intracranial LVO in patients with CAD in comparison to patients with ACAO and to patients without CAD or ACAO. Methods: We analyzed data of the MR CLEAN trial intervention arm and MR CLEAN Registry, acquired in 16 Dutch EVT-centers. Primary outcome was the change in stroke severity by comparing the National Institute of Health Stroke Scale (NIHSS) score at 24-48 h after treatment vs. baseline. Secondary outcomes included reperfusion rate and symptomatic intracranial hemorrhage (sICH). We compared outcomes and complications between patients with CAD vs. patients with ACAO and patients without CAD or ACAO. Results: In total, we identified 74 (4.7%) patients with CAD, 92 (5.9%) patients with ACAO and 1398 (89.4%) patients without CAD or ACAO. Neurological improvement at short-term after EVT in patients with CAD was significantly better compared to ACAO (raw mean -5 vs. mean -1 NIHSS point; p = 0.03) and did not differ compared to patients without CAD or ACAO (-4 NIHSS points; p = 0.62). Rates of successful reperfusion in patients with CAD (47%) was comparable to patients with ACAO (47%; p = 1.00), but was less often achieved compared to patients without CAD or ACAO (58%; p = 0.08). Occurrence of sICH did not differ significantly between CAD patients (5%) and ACAO (11%; p = 0.33) or without CAD/ACAO (6%; p = 1.00). Conclusion: EVT in patients with intracranial LVO due to CAD results in neurological improvement comparable to patients without tandem occlusions. Therefore, carotid artery dissection by itself should not be a contraindication for endovascular treatment in stroke patients with intracranial large vessel occlusion. Although more challenging endovascular procedures are to be suspected in both patients with CAD or ACAO, accurate distinction between CAD and ACAO might influence clinical decision making as better clinical outcome can be expected in patients with CAD

Early-Life Nutritional Factors and Mucosal Immunity in the Development of Autoimmune Diabetes

Type 1 diabetes (T1D) is an immune-mediated disease with a strong genetic basis but might be influenced by non-genetic factors such as microbiome development that “programs” the immune system during early life as well. Factors influencing pathogenesis, including a leaky intestinal mucosal barrier, an aberrant gut microbiota composition, and altered immune responsiveness, offer potential targets for prevention and/or treatment of T1D through nutritional or pharmacologic means. In this review, nutritional approaches during early life in order to protect against T1D development have been discussed. The critical role of tolerogenic dendritic cells in central and peripheral tolerance has been emphasized. In addition, since the gut microbiota affects the development of T1D through short-chain fatty acid (SCFA)-dependent mechanisms, we hypothesize that nutritional intervention boosting SCFA production may be used as a novel prevention strategy. Current retrospective evidence has suggested that exclusive and prolonged breastfeeding might play a protective role against the development of T1D. The beneficial properties of human milk are possibly attributed to its bioactive components such as unique immune-modulatory components human milk oligosaccharides and metabolites derived thereof, including SCFAs. These components might play a key role in healthy immune development and creating a fit and resilient immune system in early and later life

Automated CT-derived skeletal muscle mass determination in lower hind limbs of mice using a 3D U-Net deep learning network

The loss of skeletal muscle mass is recognized as a complication of several chronic diseases and is associated with increased mortality and a decreased quality of life. Relevant and reliable animal models in which muscle wasting can be monitored noninvasively over time are instrumental to investigate and develop new therapies. In this work, we developed a fully automatic deep learning algorithm for segmentation of micro cone beam computed tomography images of the lower limb muscle complex in mice and subsequent muscle mass calculation. A deep learning algorithm was trained on manually segmented data from 32 mice. Muscle wet mass measurements were obtained from 47 mice and served as a data set for model validation and reverse model validation. The automatic algorithm performance was ~150 times faster than manual segmentation. Reverse validation of the algorithm showed high quantitative metrics (i.e., a Dice similarity coefficient of 0.93, a Hausdorff distance of 0.4 mm, and a center of mass displacement of 0.1 mm), substantiating the robustness and accuracy of the model. A high correlation (R(2) = 0.92) was obtained between the computed tomography-derived muscle mass measurements and the muscle wet masses. Longitudinal follow-up revealed time-dependent changes in muscle mass that separated control from lung tumor-bearing mice, which was confirmed as cachexia. In conclusion, this deep learning model for automated assessment of the lower limb muscle complex provides highly accurate noninvasive longitudinal evaluation of skeletal muscle mass. Furthermore, it facilitates the workflow and increases the amount of data derived from mouse studies while reducing the animal numbers.NEW & NOTEWORTHY This deep learning application enables highly accurate noninvasive longitudinal evaluation of skeletal muscle mass changes in mice with minimal requirement for operator involvement in the data analysis. It provides a unique opportunity to increase and analyze the amount of data derived from animal studies automatically while reducing animal numbers and analytical workload

Deep Learning Based Automated Orthotopic Lung Tumor Segmentation in Whole-Body Mouse CT-Scans

SIMPLE SUMMARY: The development of more translatable orthotopic mouse models is essential in order to study lung cancer more realistically. However, a major challenge in these orthotopic mouse models is the monitoring of tumor take, tumor growth and the detection of therapeutic effects. Therefore, the aim of this study was to train and validate a deep learning algorithm for fully automatic lung tumor quantification in whole-body mouse µCBCT scans. This deep learning application enables highly accurate longitudinal evaluation of tumor volume changes in mice with minimal operator involvement in the data analysis. In addition to longitudinal quantification of tumor development, the algorithm can also be deployed to optimize the randomization and 3R animal welfare aspects of the experimental design in preclinical studies. ABSTRACT: Lung cancer is the leading cause of cancer related deaths worldwide. The development of orthotopic mouse models of lung cancer, which recapitulates the disease more realistically compared to the widely used subcutaneous tumor models, is expected to critically aid the development of novel therapies to battle lung cancer or related comorbidities such as cachexia. However, follow-up of tumor take, tumor growth and detection of therapeutic effects is difficult, time consuming and requires a vast number of animals in orthotopic models. Here, we describe a solution for the fully automatic segmentation and quantification of orthotopic lung tumor volume and mass in whole-body mouse computed tomography (CT) scans. The goal is to drastically enhance the efficiency of the research process by replacing time-consuming manual procedures with fast, automated ones. A deep learning algorithm was trained on 60 unique manually delineated lung tumors and evaluated by four-fold cross validation. Quantitative performance metrics demonstrated high accuracy and robustness of the deep learning algorithm for automated tumor volume analyses (mean dice similarity coefficient of 0.80), and superior processing time (69 times faster) compared to manual segmentation. Moreover, manual delineations of the tumor volume by three independent annotators was sensitive to bias in human interpretation while the algorithm was less vulnerable to bias. In addition, we showed that besides longitudinal quantification of tumor development, the deep learning algorithm can also be used in parallel with the previously published method for muscle mass quantification and to optimize the experimental design reducing the number of animals needed in preclinical studies. In conclusion, we implemented a method for fast and highly accurate tumor quantification with minimal operator involvement in data analysis. This deep learning algorithm provides a helpful tool for the noninvasive detection and analysis of tumor take, tumor growth and therapeutic effects in mouse orthotopic lung cancer models

The effect of dietary components on inflammatory lung diseases – a literature review

Anti-inflammatory treatment in chronic inflammatory lung diseases usually involves glucocorticosteroids. With patients suffering from serious side effects or becoming resistant, specific nutrients, that are suggested to positively influence disease progression, can be considered as new treatment options. The dietary inflammatory index is used to calculate effects of dietary components on inflammation and lung function to identify most potent dietary components, based on 162 articles. The positive effects of n-3 PUFAs and vitamin E on lung function can at least partially be explained by their anti-inflammatory effect. Many other dietary components showed only small or no effects on inflammation and/or lung function, although the number of weighted studies was often too small for a reliable assessment. Optimal beneficial dietary elements might reduce the required amounts of anti-inflammatory treatments, thereby decreasing both side effects and development of resistance as to improve quality of life of patients suffering from chronic inflammatory lung diseases

Workflow Intervals of Endovascular Acute Stroke Therapy During On- Versus Off-Hours The MR CLEAN Registry

Background and Purpose-Endovascular treatment (EVT) of patients with acute ischemic stroke because of large vessel occlusion involves complicated logistics, which may cause a delay in treatment initiation during off-hours. This might lead to a worse functional outcome. We compared workflow intervals between endovascular treatment-treated patients presenting during off- and on-hours.Methods-We retrospectively analyzed data from the MR CLEAN Registry, a prospective, multicenter, observational study in the Netherlands and included patients with an anterior circulation large vessel occlusion who presented between March 2014 and June 2016. Off-hours were defined as presentation on Monday to Friday between 17:00 and 08:00 hours, weekends (Friday 17:00 to Monday 8:00) and national holidays. Primary end point was first door to groin time. Secondary end points were functional outcome at 90 days (modified Rankin Scale) and workflow time intervals. We stratified for transfer status, adjusted for prognostic factors, and used linear and ordinal regression models.Results-We included 1488 patients of which 936 (62.9%) presented during off-hours. Median first door to groin time was 140 minutes (95% CI, 110-182) during off-hours and 121 minutes (95% CI, 85-157) during on-hours. Adjusted first door to groin time was 14.6 minutes (95% CI, 9.3-20.0) longer during off-hours. Door to needle times for intravenous therapy were slightly longer (3.5 minutes, 95% CI, 0.7-6.3) during off-hours. Groin puncture to reperfusion times did not differ between groups. For transferred patients, the delay within the intervention center was 5.0 minutes (95% CI, 0.5-9.6) longer. There was no significant difference in functional outcome between patients presenting during off- and on-hours (adjusted odds ratio, 0.92; 95% CI, 0.74-1.14). Reperfusion rates and complication rates were similar.Conclusions-Presentation during off-hours is associated with a slight delay in start of endovascular treatment in patients with acute ischemic stroke. This treatment delay did not translate into worse functional outcome or increased complication rates.</p

Impact of the lockdown on acute stroke treatments during the first surge of the COVID-19 outbreak in the Netherlands

Introduction: We investigated the impact of the Corona Virus Disease 2019 (COVID-19) pandemic and the resulting lockdown on reperfusion treatments and door-to-treatment times during the first surge in Dutch comprehensive stroke centers. Furthermore, we studied the association between COVID-19-status and treatment times. Methods: We included all patients receiving reperfusion treatment in 17 Dutch stroke centers from May 11th, 2017, until May 11th, 2020. We collected baseline characteristics, National Institutes of Health Stroke Scale (NIHSS) at admission, onset-to-door time (ODT), door-to-needle time (DNT), door-to-groin time (DGT) and COVID-19-status at admission. Parameters during the lockdown (March 15th, 2020 until May 11th, 2020) were compared with those in the same period in 2019, and between groups stratified by COVID-19-status. We used nationwide data and extrapolated our findings to the increasing trend of EVT numbers since May 2017. Results: A decline of 14% was seen in reperfusion treatments during lockdown, with a decline in both IVT and EVT delivery. DGT increased by 12 min (50 to 62 min, p-value of < 0.001). Furthermore, median NIHSS-scores were higher in COVID-19 - suspected or positive patients (7 to 11, p-value of 0.004), door-to-treatment times did not differ significantly when stratified for COVID-19-status. Conclusions: During the first surge of the COVID-19 pandemic, a decline in acute reperfusion treatments and a delay in DGT was seen, which indicates a target for attention. It also appeared that COVID-19-positive or -suspected patients had more severe neurologic symptoms, whereas their EVT-workflow was not affected