Carotid artery transplantation of brain endothelial cells enhances neuroprotection and neurorepair in ischaemic stroke rats.

Brain microvascular endothelial cells (BMECs), an important component of the neurovascular unit, can promote angiogenesis and synaptic formation in ischaemic mice after brain parenchyma transplantation. Since the therapeutic efficacy of cell-based therapies depends on the extent of transplanted cell residence in the target tissue and cell migration ability, the delivery route has become a hot research topic. In this study, we investigated the effects of carotid artery transplantation of BMECs on neuronal injury, neurorepair, and neurological dysfunction in rats after cerebral ischaemic attack. Purified passage 1 endothelial cells (P1-BMECs) were prepared from mouse brain tissue. Adult rats were subjected to transient middle cerebral artery occlusion (MCAO) for 30 min. Then, the rats were treated with 5 × 105 P1-BMECs through carotid artery infusion or tail vein injection. We observed that carotid artery transplantation of BMECs produced more potent neuroprotective effects than caudal injection in MCAO rats, including reducing infarct size and alleviating neurological deficits in behavioural tests. Carotid artery-transplanted BMECs displayed a wider distribution in the ischaemic rat brain. Immunostaining for endothelial progenitor cells and the mature endothelial cell markers CD34 and RECA-1 showed that carotid artery transplantation of BMECs significantly increased angiogenesis. Carotid artery transplantation of BMECs significantly increased the number of surviving neurons, decreased the cerebral infarction volume, and alleviated neurological deficits. In addition, we found that carotid artery transplantation of BMECs significantly enhanced ischaemia-induced hippocampal neurogenesis, as measured by doublecortin (DCX) and Ki67 double staining within 2 weeks after ischaemic injury. We conclude that carotid artery transplantation of BMECs can promote cerebral angiogenesis, neurogenesis, and neurological function recovery in adult rats after ischaemic stroke. Our results suggest that carotid injection of BMECs may be a promising new approach for treating acute brain injuries.

Sensitivity and Specificity of Qualitative Visual Field Tests for Screening Visual Hemifield Deficits in Right-Brain-Damaged Stroke Patients.

A timely detection of visual hemifield deficits (VHFDs; hemianopias or quadrantanopias) is critical for both the diagnosis and treatment of stroke patients. The present study determined the sensitivity and specificity of four qualitative visual field tests, including face description, confrontation tests (finger wiggle), and kinetic boundary perimetry, to screen large and dense VHFDs in right-brain-damaged (RBD) stroke patients. Previously, the accuracy of qualitative visual field tests was examined in unselected samples of patients with heterogeneous aetiology, in which stroke patients represented a very small fraction. Building upon existing tests, we introduced some procedural ameliorations (incl. a novel procedure for kinetic boundary perimetry) and provided a scoresheet to facilitate the grading. The qualitative visual field tests' outcome of 67 consecutive RBD stroke patients was compared with the standard automated perimetry (SAP; i.e., reference standard) outcome to calculate sensitivity and specificity, as well as positive and negative predictive values (PPV and NPV), both for each individual test and their combinations. The face description test scored the lowest sensitivity and NPV, while the kinetic boundary perimetry scored the highest. No test returned false positives. Combining the monocular static finger wiggle test (by quadrants) and the kinetic boundary perimetry returned the highest sensitivity and specificity, in line with previous studies, but with higher accuracy (100% sensitivity and specificity). These findings indicate that the combination of these two tests is a valid approach with RBD stroke patients, prompting referral for a formal visual field examination, and representing a quick, easy-to-perform, and inexpensive tool for improving their care and prognosis.

An Update of Kaempferol Protection against Brain Damage Induced by Ischemia-Reperfusion and by 3-Nitropropionic Acid.

Kaempferol, a flavonoid present in many food products, has chemical and cellular antioxidant properties that are beneficial for protection against the oxidative stress caused by reactive oxygen and nitrogen species. Kaempferol administration to model experimental animals can provide extensive protection against brain damage of the striatum and proximal cortical areas induced by transient brain cerebral ischemic stroke and by 3-nitropropionic acid. This article is an updated review of the molecular and cellular mechanisms of protection by kaempferol administration against brain damage induced by these insults, integrated with an overview of the contributions of the work performed in our laboratories during the past years. Kaempferol administration at doses that prevent neurological dysfunctions inhibit the critical molecular events that underlie the initial and delayed brain damage induced by ischemic stroke and by 3-nitropropionic acid. It is highlighted that the protection afforded by kaempferol against the initial mitochondrial dysfunction can largely account for its protection against the reported delayed spreading of brain damage, which can develop from many hours to several days. This allows us to conclude that kaempferol administration can be beneficial not only in preventive treatments, but also in post-insult therapeutic treatments.

NMR based Serum metabolomics revealed metabolic signatures associated with oxidative stress and mitochondrial damage in brain stroke.

Brain stroke (BS, also known as a cerebrovascular accident), represents a serious global health crisis. It has been a leading cause of permanent disability and unfortunately, frequent fatalities due to lack of timely medical intervention. While progress has been made in prevention and management, the complexities and consequences of stroke continue to pose significant challenges, especially, its impact on patient's quality of life and independence. During stroke, there is a substantial decrease in oxygen supply to the brain leading to alteration of cellular metabolic pathways, including those involved in mitochondrial-damage, leading to mitochondrial-dysfunction. The present proof-of-the-concept metabolomics study has been performed to gain insights into the metabolic pathways altered following a brain stroke and discover new potential targets for timely interventions to mitigate the effects of cellular and mitochondrial damage in BS. The serum metabolic profiles of 108 BS-patients were measured using 800 MHz NMR spectroscopy and compared with 60 age and sex matched normal control (NC) subjects. Compared to NC, the serum levels of glutamate, TCA-cycle intermediates (such as citrate, succinate, etc.), and membrane metabolites (betaine, choline, etc.) were found to be decreased BS patients, whereas those of methionine, mannose, mannitol, phenylalanine, urea, creatine and organic acids (such as 3-hydroxybutyrate and acetone) were found to be elevated in BS patients. These metabolic changes hinted towards hypoxia mediated mitochondrial dysfunction in BS-patients. Further, the area under receiver operating characteristic curve (ROC) values for five metabolic features (methionine, mannitol, phenylalanine, mannose and urea) found to be more than 0.9 suggesting their high sensitivity and specificity for differentiating BS from NC subjects.

Diffusion-Weighted MRI Monitoring of Embolic Brain Stroke for COVID-19 Patients.

Background: Coronavirus disease (COVID-19) pandemic around the world has some adverse effects on the human body, and there is limited data about the impacts of this pandemic disease on embolic brain stroke. Materials and Methods: Fifty-two COVID-19 patients with embolic brain stroke were included in this study. The COVID-19 patients were diagnosed according to their clinical findings. The patients underwent diffusion-weighted imaging (DWI) and the apparent diffusion coefficient (ADC) values of different points of their brain were calculated using MRIcro software. Results: The embolic strokes were mostly diagnosed in the medial temporal lobe for both COVID-19 and others. In addition, a combination of COVID-19 with other inflammations and infections was not diagnosed in the studied patients. The mean ADC values of the central region were significantly lower than other regions of the brain stroke for the COVID-19 and other patients. Moreover, the maximum and minimum ADC values of the central region for COVID-19 and other patients were significantly different compared to the other regions. Whereas, the mean and minimum ADC values of the brain's normal regions were not significantly different in the edge regions for both groups, while in the COVID-19 and other patients the maximum ADC value of the edge regions was considerably lower compared to the normal regions. Conclusion: The embolic stroke of COVID-19 patients is likely to occur in the medial temporal lobe of the brain. Moreover, the ADC and relative ADC (rADC) values of embolic brain stroke in COVID-19 patients are not significantly different compared to others.

Investigation of NO Role in Neural Tissue in Brain and Spinal Cord Injury.

Nitric oxide (NO) production in injured and intact brain regions was compared by EPR spectroscopy in a model of brain and spinal cord injury in Wistar rats. The precentral gyrus of the brain was injured, followed by the spinal cord at the level of the first lumbar vertebra. Seven days after brain injury, a reduction in NO content of 84% in injured brain regions and 66% in intact brain regions was found. The difference in NO production in injured and uninjured brain regions persisted 7 days after injury. The copper content in the brain remained unchanged one week after modeling of brain and spinal cord injury. The data obtained in the experiments help to explain the problems in the therapy of patients with combined brain injury.

End to end vision transformer architecture for brain stroke assessment based on multi-slice classification and localization using computed tomography.

BACKGROUND: Brain stroke is a leading cause of disability and death worldwide, and early diagnosis and treatment are critical to improving patient outcomes. Current stroke diagnosis methods are subjective and prone to errors, as radiologists rely on manual selection of the most important CT slice. This highlights the need for more accurate and reliable automated brain stroke diagnosis and localization methods to improve patient outcomes. PURPOSE: In this study, we aimed to enhance the vision transformer architecture for the multi-slice classification of CT scans of each patient into three categories, including Normal, Infarction, Hemorrhage, and patient-wise stroke localization, based on end-to-end vision transformer architecture. This framework can provide an automated, objective, and consistent approach to stroke diagnosis and localization, enabling personalized treatment plans based on the location and extent of the stroke. METHODS: We modified the Vision Transformer (ViT) in combination with neural network layers for the multi-slice classification of brain CT scans of each patient into normal, infarction, and hemorrhage classes. For stroke localization, we used the ViT architecture and convolutional neural network layers to detect stroke and localize it by bounding boxes for infarction and hemorrhage regions in a patient-wise manner based on multi slices. RESULTS: Our proposed framework achieved an overall accuracy of 87.51% in classifying brain CT scan slices and showed high precision in localizing the stroke patient-wise. Our results demonstrate the potential of our method for accurate and reliable stroke diagnosis and localization. CONCLUSION: Our study enhanced ViT architecture for automated stroke diagnosis and localization using brain CT scans, which could have significant implications for stroke management and treatment. The use of deep learning algorithms can provide a more objective and consistent approach to stroke diagnosis and potentially enable personalized treatment plans based on the location and extent of the stroke. Further studies are needed to validate our method on larger and more diverse datasets and to explore its clinical utility in real-world settings.

Capsaicin, The Vanilloid Receptor TRPV1 Agonist in Neuroprotection: Mechanisms Involved and Significance.

Hot peppers, also called chilli, chilli pepper, or paprika of the plant genus Capsicum (family Solanaceae), are one of the most used vegetables and spices worldwide. Capsaicin (8-methyl N-vanillyl-6-noneamide) is the main pungent principle of hot green and red peppers. By acting on the capsaicin receptor or transient receptor potential cation channel vanilloid subfamily member 1 (TRPV1), capsaicin selectively stimulates and in high doses defunctionalizes capsaicin-sensitive chemonociceptors with C and Aδ afferent fibers. This channel, which is involved in a wide range of neuronal processes, is expressed in peripheral and central branches of capsaicin-sensitive nociceptive neurons, sensory ganglia, the spinal cord, and different brain regions in neuronal cell bodies, dendrites, astrocytes, and pericytes. Several experimental and clinical studies provided evidence that capsaicin protected against ischaemic or excitotoxic cerebral neuronal injury and may lower the risk of cerebral stroke. By preventing neuronal death, memory impairment and inhibiting the amyloidogenic process, capsaicin may also be beneficial in neurodegenerative disorders such as Parkinson's or Alzheimer's diseases. Capsaicin given in systemic inflammation/sepsis exerted beneficial antioxidant and anti-inflammatory effects while defunctionalization of capsaicin-sensitive vagal afferents has been demonstrated to increase brain oxidative stress. Capsaicin may act in the periphery via the vagal sensory fibers expressing TRPV1 receptors to reduce immune oxidative and inflammatory signalling to the brain. Capsaicin given in small doses has also been reported to inhibit the experimentally-induced epileptic seizures. The aim of this review is to provide a concise account on the most recent findings related to this topic. We attempted to delineate such mechanisms by which capsaicin exerts its neuronal protective effects. We also aimed to provide the reader with the current knowledge on the mechanism of action of capsaicin on sensory receptors.

On the Role of Training Data for SVM-Based Microwave Brain Stroke Detection and Classification.

The aim of this work was to test microwave brain stroke detection and classification using support vector machines (SVMs). We tested how the nature and variability of training data and system parameters impact the achieved classification accuracy. Using experimentally verified numerical models, a large database of synthetic training and test data was created. The models consist of an antenna array surrounding reconfigurable geometrically and dielectrically realistic human head phantoms with virtually inserted strokes of arbitrary size, and different dielectric parameters in different positions. The generated synthetic data sets were used to test four different hypotheses, regarding the appropriate parameters of the training dataset, the appropriate frequency range and the number of frequency points, as well as the level of subject variability to reach the highest SVM classification accuracy. The results indicate that the SVM algorithm is able to detect the presence of the stroke and classify it (i.e., ischemic or hemorrhagic) even when trained with single-frequency data. Moreover, it is shown that data of subjects with smaller strokes appear to be the most suitable for training accurate SVM predictors with high generalization capabilities. Finally, the datasets created for this study are made available to the community for testing and developing their own algorithms.

A search for acrolein scavengers among food components.

Brain stroke is a major cause of being bedridden for elderly people, and preventing stroke is important for maintaining quality of life (QOL). Acrolein is a highly reactive aldehyde and causes tissue damage during stroke. Decreasing acrolein toxicity ameliorates tissue injury during brain stroke. In this study, we tried to identify food components which decrease acrolein toxicity. We found that 2-furanmethanethiol, cysteine methyl and ethyl esters, alliin, lysine and taurine decreased acrolein toxicity. These compounds neutralized acrolein by direct interaction. However, the interaction between acrolein and taurine was not so strong. Approximately 30 mM taurine was necessary to interact with 10 µM acrolein, and 2 g/kg taurine was necessary to decrease the size of mouse brain infarction. Taurine also slightly increased polyamine contents, which are involved in decrease in the acrolein toxicity. Mitochondrial potential damage by acrolein was also protected by taurine. Our results indicate that daily intake of foods containing 2-furanmethanethiol, cysteine methyl and ethyl esters, alliin, lysine and taurine may prevent severe injury in brain stroke and improve the quality of life for elderly people.

Bilateral antererior circulation stroke: A rare but threatening consequence of pituitary apoplexy. Case report and systematic literature review.

BACKGROUND: Brain stroke is a rare, life-threatening condition associated with pituitary apoplexy (PA), resulting from direct arterial occlusion due to mechanical compression secondary to the sudden enlargement of the pituitary adenoma, or to vessel vasospasm, induced by tumor hemorrhage. CASE REPORT: We report the case of a 64-year-old woman with PA complicated by bilateral anterior circulation stroke due to critical stenosis of both anterior cerebral arteries (ACA). Despite the quick surgical decompression and consequent blood flow restoration, the neurological conditions of the patient did not improve and she died 18 days later. Ten other cases of anterior circulation stroke due to PA were retrieved in a systematic review of literature. Clinical and neuroradiological features of these patients and treatment outcome were assessed to suggest the most proper management. CONCLUSION: The onset of neurological symptoms suggestive for brain stroke in patients with PA requires performing an emergency Magnetic Resonance Imaging (MRI), including Diffusion-weighted and angiographic MR-sequences. The role of surgery in these cases is debated, however, transsphenoidal adenomectomy would permit us to decompress the ACA and restore blood flow in their territories. Although the prognosis of PA-induced anterior circulation stroke is generally poor, a timely diagnosis and treatment would be paramount for improving patient outcome.

A Deep Learning Approach for Detecting Stroke from Brain CT Images Using OzNet.

A brain stroke is a life-threatening medical disorder caused by the inadequate blood supply to the brain. After the stroke, the damaged area of the brain will not operate normally. As a result, early detection is crucial for more effective therapy. Computed tomography (CT) images supply a rapid diagnosis of brain stroke. However, while doctors are analyzing each brain CT image, time is running fast. This circumstance may lead to result in a delay in treatment and making errors. Therefore, we targeted the utilization of an efficient artificial intelligence algorithm in stroke detection. In this paper, we designed hybrid algorithms that include a new convolution neural networks (CNN) architecture called OzNet and various machine learning algorithms for binary classification of real brain stroke CT images. When we classified the dataset with OzNet, we acquired successful performance. However, for this target, we combined it with a minimum Redundancy Maximum Relevance (mRMR) method and Decision Tree (DT), k-Nearest Neighbors (kNN), Linear Discriminant Analysis (LDA), Naïve Bayes (NB), and Support Vector Machines (SVM). In addition, 4096 significant features were obtained from the fully connected layer of OzNet, and we reduced the dimension of features from 4096 to 250 using the mRMR method. Finally, we utilized these machine learning algorithms to classify important features. As a result, OzNet-mRMR-NB was an excellent hybrid algorithm and achieved an accuracy of 98.42% and AUC of 0.99 to detect stroke from brain CT images.

The multi-level classification network (MCN) with modified residual U-Net for ischemic stroke lesions segmentation from ATLAS.

Ischemic and hemorrhagic strokes are two major types of internal brain injury. 3D brain MRI is suggested by neurologists to examine the brain. Manual examination of brain MRI is very sensitive and time-consuming task. However, automatic diagnosis can assist doctors in this regard. Anatomical Tracings of Lesions After Stroke (ATLAS) is publicly available dataset for research experiments. One of the major issues in medical imaging is class imbalance. Similarly, pixel representation of stroke lesion is less than 1% in ATLAS. Second major challenge in this dataset is inter-class similarity. A multi-level classification network (MCN) is proposed for segmentation of ischemic stroke lesions. MCN consists of three cascaded discrete networks. The first network designed to reduce the slice level class imbalance, where a classifier model is trained to extract the slices of stroke lesions from a whole brain MRI volume. The interclass similarity cause to produce false positives in segmented output. Therefore, all extracted stroke slices were divided into overlapping patches (64 × 64) and carried to the second network. The task associated with second network is to classify the patches comprises of stroke lesion. The third network is a 2D modified residual U-Net that segments out the stroke lesions from the patches extracted by the second network. MCN achieved 0.754 mean dice score on test dataset which is higher than the other state-of-the-art methods on the same dataset.

Application of Machine Learning Techniques for Characterization of Ischemic Stroke with MRI Images: A Review.

Magnetic resonance imaging (MRI) is a standard tool for the diagnosis of stroke, but its manual interpretation by experts is arduous and time-consuming. Thus, there is a need for computer-aided-diagnosis (CAD) models for the automatic segmentation and classification of stroke on brain MRI. The heterogeneity of stroke pathogenesis, morphology, image acquisition modalities, sequences, and intralesional tissue signal intensity, as well as lesion-to-normal tissue contrast, pose significant challenges to the development of such systems. Machine learning (ML) is increasingly being used in predictive neuroimaging diagnosis and prognostication. This paper reviews image processing and machine learning techniques that have been applied to detect ischemic stroke on brain MRI, including details on image acquisition, pre-processing, techniques to segment, extraction of features, and classification into stroke types. The main objective of this work is to find the state-of-art machine learning techniques used to predict the ischemic stroke and their application in clinical set-up. The article selection is performed according to PRISMA guideline. The state-of-the-art on automated MRI stroke diagnosis, with a focus on machine learning, is discussed, along with its advantages and limitations. We found that the various machine learning models discussed in this article are able to detect the infarcts with an acceptable accuracy of 70-90%. However, no one has highlighted the time complexity to predict the stroke in the model developed, which is an important factor. The work concludes with proposals for future recommendations for building efficient and robust deep learning (DL) models for quantitative brain MRI analysis. In recent work, with the application of DL approaches, using large datasets to train the models has improved the detection accuracy and reduced computational complexity. We suggest that the design of a decision support system based on artificial intelligence (AI) and clinical data presenting symptoms is essential to support clinicians to accelerate diagnosis and timeous therapy in the emergency management of stroke.

Brain stroke classification and segmentation using encoder-decoder based deep convolutional neural networks.

Accurate diagnosis of brain stroke, classification and segmentation of the stroke are extremely important for physicians to focus on specific points of the brain and apply the right treatment to patients. Encoder-decoder deep learning-based methods have been effectively integrated into many artificial intelligence applications. On the other hand, such networks have many disadvantages due to sampling methods, learning methodologies, and efficient operations. In this study, U-Net, one of the encoder-decoder deep learning-based convolutional neural networks (CNNs), has been developed and proposed for the classification and segmentation of brain stroke. A convolutional deep network architecture is proposed with an optimized dimensional U-Net (D-UNet) by blocking and adaptively sequencing the convolution layers and then optimizing the number of activation functions and hyperparameters. The proposed method examines the computed tomography (CT) images from the dataset used to determine whether there is a brain stroke. It can determine if a stroke is caused by ischemia or hemorrhage once it has occurred. Additionally, the proposed method can precisely reveal the region overlaid by the radiologist and segment the existing stroke. The proposed method is compared with other existing CNN-type architectures by performing various experiments on the same real dataset via Python scripts. The results show that the proposed model performs well, with accuracy rates for stroke classification of 98.9% and ischemia and hemorrhage classification of 98.5%, respectively. Moreover, the segmentation of brain strokes using the proposed model yielded an intersection over union (IoU) rate of 95.2%.

Evaluation and Treatment of Depression in Stroke Patients: A Systematic Review.

Those who received early diagnosis and treatment for poststroke depression had lower mortality rates, cognitive impairments, improved long-term disability, a higher quality of life, and lower rates of suicidal thoughts than those who did not. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 standards were used to conduct this systematic review. Until May 1, 2022, a systematic search was conducted utilizing ScienceDirect, Cochrane, PubMed, Google Scholar, and PubMed central databases, which have been used during the previous 10 years. Randomized controlled trials (RCTs), observational studies, systematic reviews, review articles, case reports, clinical studies, and meta-analyses were included in the research, which covered post-stroke depression patients and how to identify and treat them. There were 545 possibly related titles found in the database search. Finally, each publication was given a quality rating, and 10 studies with a score of higher than 70% were allowed into the review. Because of their brevity and ease of use, they employed the Patient Health Questionnaire-9 (PHQ-9) and PHQ-2 screening instruments in stroke patients. According to pooled studies, the risk of acquiring post-stroke depression (PSD) was lower in participants undergoing pharmacological therapy with selective serotonin reuptake inhibitors (SSRIs), especially after a year. Identifying further features of the PSD process, we believe, is the most pressing need for future study since it might lead to a more precise treatment strategy.

Using convolutional neural network to analyze brain MRI images for predicting functional outcomes of stroke.

Nowadays, the physicians usually predict functional outcomes of stroke based on clinical experiences and big data, so we wish to develop a model to accurately identify imaging features for predicting functional outcomes of stroke patients. Using magnetic resonance imaging of ischemic and hemorrhagic stroke patients, we developed and trained a VGG-16 convolutional neural network (CNN) to predict functional outcomes after 28-day hospitalization. A total of 44 individuals (24 men and 20 women) were recruited from Taoyuan General Hospital and China Medical University Hsinchu Hospital to enroll in the study. Based on "modified Rankin Scale (mRS)" and "National Institutes of Health Stroke Scale (NIHSS)" assessments, men, women, and mixed men and women were trained separately to evaluate the differences of the results, and we have shown that VGG-16 demonstrated high accuracy in predicting the functional outcomes of stroke patients. The new deep-learning approach has provided an automated decision support system for personalized recommendations and treatments, assisting the physicians to predict functional outcomes of stroke patients in clinical practice.

Activation of BDNF- and VEGF-mediated Neuroprotection by Treadmill Exercise Training in Experimental Stroke.

Early treatment of ischemic stroke is one of the most effective ways to reduce brains' cell death and promote functional recovery. This study was designed to examine the effect of aerobic exercise on post ischemia/reperfusion injury on concentration and expression of brain-derived neurotrophic factor (BDNF) and vascular endothelial growth factor (VEGF) after inducing a neuronal loss in CA1 region of hippocampus in Male Wistar rats. Three experimental groups including sham(S), ischemia/reperfusion-control (IRC) and ischemia/reperfusion exercise (IRE) were used for this purpose. The rats in the IRE group received a bilateral carotid artery occlusion treatment. They ran for 45 minutes on a treadmill five days per week for eight consecutive weeks. Cresyl violet (Nissl), Hematoxylin (H & E) and Eosin staining procedure were used to determine the extent of damage. A ladder rung walking task was used to assess the functional impairments and recovery after the ischemic lesion. ELISA and immunohistochemistry method were employed to measure BDNF and VEGF protein expressions. The result showed that the brain ischemia/reperfusion condition increased the cell death in hippocampal CA1 neurons and impaired motor performance on the ladder rung task whereas the aerobic exercise program significantly decreased the brain cell's death and improved motor skill performance. It was concluded that ischemic brain lesion decreased the BDNF and VEGF expression. It seems that the aerobic exercise following the ischemia/reperfusion potentially promotes neuroprotective mechanisms and neuronal repair and survival mediated partly by BDNF and other pathways.

A novel use of time separation technique to improve flat detector CT perfusion imaging in stroke patients.

BACKGROUND: CT perfusion imaging (CTP) is used in the diagnostic workup of acute ischemic stroke (AIS). CTP may be performed within the angio suite using flat detector CT (FDCT) to help reduce patient management time. PURPOSE: In order to significantly improve FDCT perfusion (FDCTP) imaging, data-processing algorithms need to be able to compensate for the higher levels of noise, slow rotation speed, and a lower frame rate of current FDCT devices. METHODS: We performed a realistic simulation of FDCTP acquisition based on CTP data from seven subjects. We used the time separation technique (TST) as a model-based approach for FDCTP data processing. We propose a novel dimension reduction in which we approximate the time attenuation curves by a linear combination of trigonometric functions. Our goal was to show that the TST can be used even without prior assumptions on the shape of the attenuation profiles. RESULTS: We first demonstrated that a trigonometric basis is suitable for dimension reduction of perfusion data. Using simulated FDCTP data, we have shown that a trigonometric basis in the TST provided better results than the classical straightforward processing even with additional noise. Average correlation coefficients of perfusion maps were improved for cerebral blood flow (CBF), cerebral blood volume, mean transit time (MTT) maps. In a moderate noise scenario, the average Pearson's coefficient for the CBF map was improved using the TST from 0.76 to 0.81. For the MTT map, it was improved from 0.37 to 0.45. Furthermore, we achieved a total processing time from the reconstruction of FDCTP data to the generation of perfusion maps of under 5 min. CONCLUSIONS: In our study cohort, perfusion maps created from FDCTP data using the TST with a trigonometric basis showed equivalent perfusion deficits to classic CT perfusion maps. It follows, that this novel FDCTP technique has potential to provide fast and accurate FDCTP imaging for AIS patients.

Alterações na cinemática da marcha hemiparética: um estudo comparativo / Changes in the kinematics of hemiparetic gait: a comparative study

O objetivo principal deste estudo foi analisar as alterações da marcha de adultos com hemiparesia após acidente vascular encefálico (AVE) e comparar com sujeitos saudáveis. A amostra foi composta por 14 participantes do grupo AVE e 14 participantes pareados do grupo-controle (CON). Foi realizada uma análise tridimensional da marcha mediante um sistema de cinemetria. Os parâmetros analisados foram a velocidade, o comprimento da passada, a largura da passada, a cadência e o tempo da passada, sendo utilizado o teste t independente para as comparações entre os grupos e considerando p < 0,05 como critério de decisão. Os participantes do grupo AVE apresentaram valores médios significantemente inferiores em todos os parâmetros analisados. Além disso, os pacientes do grupo AVE também tiveram valores muito inferiores quando comparados aos de outros estudos com pacientes pós-AVE, possivelmente devido ao curto período entre o AVE (média de 14,14 meses) e a avaliação da marcha.

The main objective of this study was to analyze the gait alterations of adults with hemiparesis after cerebrovascular accident (CVA) and compare it with healthy subjects. The sample consisted of 14 participants from the stroke group and 14 matched participants from the control group (CON). A three-dimensional gait analysis was performed using a kinemetry system. The parameters analyzed were velocity, stride length, stride width, cadence, and stride time, using the independent t test for comparisons between groups and considering p < 0.05 as a decision criterion. Participants in the stroke group had significantly lower mean values in all analyzed parameters. In addition, patients in the CVA group also had much lower values when compared to other studies with post-CVA patients, possibly due to the short period between the CVA (mean of 14.14 months) and the gait assessment.