Genetics of Atrial Fibrillation and Possible Implications for Ischemic Stroke

Atrial fibrillation is the most common cardiac arrhythmia mainly caused by valvular, ischemic, hypertensive, and myopathic heart disease. Atrial fibrillation can occur in families suggesting a genetic background especially in younger subjects. Additionally recent studies have identified common genetic variants to be associated with atrial fibrillation in the general population. This cardiac arrhythmia has important public health implications because of its main complications: congestive heart failure and ischemic stroke. Since atrial fibrillation can result in ischemic stroke, one might assume that genetic determinants of this cardiac arrhythmia are also implicated in cerebrovascular disease. Ischemic stroke is a multifactorial, complex disease where multiple environmental and genetic factors interact. Whether genetic variants associated with a risk factor for ischemic stroke also increase the risk of a particular vascular endpoint still needs to be confirmed in many cases. Here we review the current knowledge on the genetic background of atrial fibrillation and the consequences for cerebrovascular disease

Final infarct prediction in acute ischemic stroke

This article focuses on the control center of each human body: the brain. We will point out the pivotal role of the cerebral vasculature and how its complex mechanisms may vary between subjects. We then emphasize a specific acute pathological state, i.e., acute ischemic stroke, and show how medical imaging and its analysis can be used to define the treatment. We show how the core-penumbra concept is used in practice using mismatch criteria and how machine learning can be used to make predictions of the final infarct, either via deconvolution or convolutional neural networks.Comment: 17 pages, 5 figures, part of PhD thesis KU Leuven 2022 "Understanding Final Infarct Prediction in Acute Ischemic Stroke Using Convolutional Neural Networks

Dice Semimetric Losses: Optimizing the Dice Score with Soft Labels

The soft Dice loss (SDL) has taken a pivotal role in many automated segmentation pipelines in the medical imaging community. Over the last years, some reasons behind its superior functioning have been uncovered and further optimizations have been explored. However, there is currently no implementation that supports its direct use in settings with soft labels. Hence, a synergy between the use of SDL and research leveraging the use of soft labels, also in the context of model calibration, is still missing. In this work, we introduce Dice semimetric losses (DMLs), which (i) are by design identical to SDL in a standard setting with hard labels, but (ii) can be used in settings with soft labels. Our experiments on the public QUBIQ, LiTS and KiTS benchmarks confirm the potential synergy of DMLs with soft labels (e.g. averaging, label smoothing, and knowledge distillation) over hard labels (e.g. majority voting and random selection). As a result, we obtain superior Dice scores and model calibration, which supports the wider adoption of DMLs in practice. Code is available at \href{https://github.com/zifuwanggg/JDTLosses}{https://github.com/zifuwanggg/JDTLosses}.Comment: Submitted to MICCAI2023. Code is available at https://github.com/zifuwanggg/JDTLosse

Identification and characterization of nanobodies targeting the EphA4 receptor

The ephrin receptor A4 (EphA4) is one of the receptors in the ephrin system that plays a pivotal role in a variety of cell-cell interactions, mostly studied during development. In addition, EphA4 has been found to play a role in cancer biology as well as in the pathogenesis of several neurological disorders such as stroke, spinal cord injury, multiple sclerosis, amyotrophic lateral sclerosis (ALS), and Alzheimer's disease. Pharmacological blocking of EphA4 has been suggested to be a therapeutic strategy for these disorders. Therefore, the aim of our study was to generate potent and selective Nanobodies against the ligand-binding domain of the human EphA4 receptor. Weidentified two Nanobodies, Nb 39 and Nb 53, that bind EphA4 with affinities in the nanomolar range. These Nanobodies were most selective for EphA4, with residual binding to EphA7 only. Using Alphascreen technology, we found that both Nanobodies displaced all known EphA4-binding ephrins from the receptor. Furthermore, Nb39 andNb53 inhibited ephrin-induced phosphorylationoftheEphA4proteininacell-basedassay. Finally, in a cortical neuron primary culture, both Nanobodies were able to inhibit endogenous EphA4-mediated growth-cone collapse induced by ephrin-B3. Our results demonstrate the potential of Nanobodies to target the ligand-binding domain of EphA4. These Nanobodiesmaydeservefurtherevaluationaspotentialtherapeutics in disorders in which EphA4-mediated signaling plays a role

Stroke with unknown time of symptom onset: baseline clinical and magnetic resonance imaging data of the first thousand patients in WAKE-UP (efficacy and safety of mri-based thrombolysis in wake-up stroke: a randomized, doubleblind, placebo-controlled trial)

Background and Purpose—We describe clinical and magnetic resonance imaging (MRI) characteristics of stroke patients with unknown time of symptom onset potentially eligible for thrombolysis from a large prospective cohort. Methods—We analyzed baseline data from WAKE-UP (Efficacy and Safety of MRI-Based Thrombolysis in Wake-Up Stroke: A Randomized, Doubleblind, Placebo-Controlled Trial), an investigator-initiated, randomized, placebo-controlled trial of MRI-based thrombolysis in stroke patients with unknown time of symptom onset. MRI judgment included assessment of the mismatch between visibility of the acute ischemic lesion on diffusion-weighted imaging and fluid-attenuated inversion recovery. Results—Of 1005 patients included, diffusion-weighted imaging and fluid-attenuated inversion recovery mismatch was present in 479 patients (48.0%). Patients with daytime-unwitnessed stroke (n=138, 13.7%) had a shorter delay between symptom recognition and hospital arrival (1.5 versus 1.8 hours; P=0.002), a higher National Institutes of Stroke Scale score on admission (8 versus 6; P<0.001), and more often aphasia (72.5% versus 34.0%; P<0.001) when compared with stroke patients waking up from nighttime sleep. Frequency of diffusion-weighted imaging and fluid-attenuated inversion recovery mismatch was comparable between both groups (43.7% versus 48.7%; P=0.30). Conclusions—Almost half of the patients with unknown time of symptom onset stroke otherwise eligible for thrombolysis had MRI findings making them likely to be within a time window for safe and effective thrombolysis. Patients with daytime onset unwitnessed stroke differ from wake-up stroke patients with regards to clinical characteristics but are comparable in terms of MRI characteristics of lesion age. Clinical Trial Registration—URL: http://www.clinicaltrials.gov. Unique identifier: NCT01525290. URL: https://www.clinicaltrialsregister.eu. Unique identifier: 2011-005906-32

Clinical characteristics of unknown symptom onset stroke patients with and without diffusion-weighted imaging and fluid-attenuated inversion recovery mismatch

Background: Diffusion-weighted imaging (DWI) and fluid-attenuated inversion recovery (FLAIR) mismatch was suggested to identify stroke patients with unknown time of symptom onset likely to be within the time window for thrombolysis. Aims: We aimed to study clinical characteristics associated with DWI-FLAIR mismatch in patients with unknown onset stroke. Methods: We analyzed baseline MRI and clinical data from patients with acute ischemic stroke proven by DWI from WAKE-UP, an investigator-initiated, randomized, placebo-controlled trial of MRI-based thrombolysis in stroke patients with unknown time of symptom onset. Clinical characteristics were compared between patients with and without DWI-FLAIR mismatch. Results: Of 699 patients included, 418 (59.8%) presented with DWI-FLAIR mismatch. A shorter delay between last seen well and symptom recognition (p = 0.0063), a shorter delay between symptom recognition and arrival at hospital (p = 0.0025), and history of atrial fibrillation (p = 0.19) were predictors of DWI-FLAIR mismatch in multivariate analysis. All other characteristics were comparable between groups. Conclusions: There are only minor differences in measured clinical characteristics between unknown symptom onset stroke patients with and without DWI-FLAIR mismatch. DWI-FLAIR mismatch as an indicator of stroke onset within 4.5 h shows no relevant association with commonly collected clinical characteristics of stroke patients

Effect of informed consent on patient characteristics in a stroke thrombolysis trial

Objective: To determine whether the manner of consent, i.e., informed consent by patients themselves or informed consent by proxy, affects clinical characteristics of samples of acute stroke patients enrolled in clinical trials. Methods: We analyzed the manner of obtaining informed consent in the first 1,005 patients from WAKE-UP, an investigator-initiated, randomized, placebo-controlled trial of MRI-based thrombolysis in stroke patients with unknown time of symptom onset running in 6 European countries. Patients providing informed consent by themselves were compared with patients enrolled by proxy consent. Baseline clinical measures were compared between groups. Results: In 359 (35.7%) patients, informed consent was by proxy. Patients with proxy consent were older (median 71 vs 66 years, p < 0.0001) and had a higher frequency of arterial hypertension (58.2% vs 43.4%, p < 0.0001). They showed higher scores on the NIH Stroke Scale (median 11 vs 5, p < 0.0001) and more frequently aphasia (73.7% vs 20.0%, p < 0.0001). The rate of proxy consent varied among countries (p < 0.0001), ranging from 77.1% in Spain to 1.2% in Denmark. Conclusions: Patients recruited by proxy consent were older, had more severe strokes, and had higher prevalence of aphasia than those with capacity to give personal consent. Variations in the manner of consent across countries may influence trial results. Clinicaltrials.gov and Clinicaltrialsregister.eu identifiers: NCT01525290 (clinicaltrials.gov); 2011-005906-32 (clinicaltrialsregister.eu)

Prediction of final infarct volume from native CT perfusion and treatment parameters using deep learning

CT Perfusion (CTP) imaging has gained importance in the diagnosis of acute stroke. Conventional perfusion analysis performs a deconvolution of the measurements and thresholds the perfusion parameters to determine the tissue status. We pursue a data-driven and deconvolution-free approach, where a deep neural network learns to predict the final infarct volume directly from the native CTP images and metadata such as the time parameters and treatment. This would allow clinicians to simulate various treatments and gain insight into predicted tissue status over time. We demonstrate on a multicenter dataset that our approach is able to predict the final infarct and effectively uses the metadata. An ablation study shows that using the native CTP measurements instead of the deconvolved measurements improves the prediction.Comment: Accepted for publication in Medical Image Analysi