Quantiferon Monitor Testing Sheds Light on Immune System Disparities between Multiple Sclerosis Patients and Healthy Individuals.

The aim of this study was to conduct QuantiFERON Monitor (QFM) testing in patients with multiple sclerosis (MS), which is used to monitor the state of the immune system through the non-specific stimulation of leukocytes followed by determining the level of interferon-gamma (IFN-γ) released from activated cells. Additionally, we tested the level of selected cytokines (IFN-α, IFN-γ, IL-1α, IL-1ß, IL-1ra, IL-2, IL-3, IL-4, IL-6, IL-7, IL-10, IL-15, IL-33, VEGF) from stimulated blood samples to further understand the immune response. This study builds upon a previously published study, utilizing activated serum samples that were initially used for IFN-γ determination. However, our current focus shifts from IFN-γ to exploring other cytokines that could provide further insights into the immune response. A screening was conducted using Luminex technology, which yielded promising results. These results were then further elaborated upon using ELISA to provide a more detailed understanding of the cytokine profiles involved. This study, conducted from August 2019 to June 2023, included 280 participants: 98 RRMS patients treated with fingolimod (fMS), 96 untreated patients with progressive MS (pMS), and 86 healthy controls (HC). Our results include Violin plots showing elevated IL-1α in pMS and fMS. Statistical analysis indicated significant differences in the interleukin levels between groups, with IL-1ra and age as key predictors in differentiating HC from pMS and IL-1ra, IL-1α, age, and EDSS in distinguishing pMS from fMS. These findings suggest cytokines' potential as biomarkers in MS progression and treatment response.

Intravenous Thrombolysis in Posterior versus Anterior Circulation Stroke: Clinical Outcome Differs Only in Patients with Large Vessel Occlusion.

The safety and efficacy of intravenous thrombolysis (IVT) are well established in anterior circulation stroke (ACS) but are much less clear for posterior circulation stroke (PCS). The aim of this study was to evaluate the occurrence of parenchymal hematoma (PH) and 3-month clinical outcomes after IVT in PCS and ACS. In an observational, cohort multicenter study, we analyzed data from ischemic stroke patients treated with IVT prospectively collected in the SITS (Safe Implementation of Treatments in Stroke) registry in the Czech Republic between 2004 and 2018. Out of 10,211 patients, 1166 (11.4%) had PCS, and 9045 (88.6%) ACS. PH was less frequent in PCS versus ACS patients: 3.6 vs. 5.9%, odds ratio (OR) = 0.594 in the whole set, 4.4 vs. 7.8%, OR = 0.543 in those with large vessel occlusion (LVO), and 2.2 vs. 4.7%, OR = 0.463 in those without LVO. At 3 months, PCS patients compared with ACS patients achieved more frequently excellent clinical outcomes (modified Rankin scale [mRS] 0-1: 55.5 vs. 47.6%, OR = 1.371 in the whole set and 49.2 vs. 37.6%, OR = 1.307 in those with LVO), good clinical outcomes (mRS 0-2: 69.9 vs. 62.8%, OR = 1.377 in the whole set and 64.5 vs. 50.5%, OR = 1.279 in those with LVO), and had lower mortality (12.4 vs. 16.6%, OR = 0.716 in the whole set and 18.4 vs. 25.5%, OR = 0.723 in those with LVO) (p < 0.05 in all cases). In PCS versus ACS patients, an extensive analysis showed a lower risk of PH both in patients with and without LVO, more frequent excellent and good clinical outcomes, and lower mortality 3 months after IVT in patients with LVO.

Assessment of Functional Capacity of Immune System in Patients with Multiple Sclerosis using QuantiFERON Monitor.

Background: The QuantiFERON®-Monitor (QFM) is an assay that measures interferon-γ production and was developed to provide an objective marker of complex immune response. In this study, we evaluated the use of the QFM test in patients with two forms of multiple sclerosis (MS), relapsing-remitting form treated with fingolimod (fMS) and secondarily progressive form not treated pharmacologically (pMS), and in healthy controls (HC). We hypothesized that IFN-γ levels would be lower in those subjects who are relatively more immunosuppressed and higher in those with normal or activated immune function. Methods: This single-center observational study was conducted from November 2020 to October 2021 and compared results in three groups of patients: 86 healthy controls, 96 patients with pMS, and 78 fMS. Combination of lyophilized stimulants was added to 1 ml heparinized whole blood within 8 hr of collection. Plasmatic IFN-γ was measured using the ELISA kit for the QFM and data were obtained in IU/ml. Results: The results showed that controls had nearly 2-fold higher levels of IFN-γ (QFM score) in median (q25, q75) 228.00 (112.20, 358.67) than the MS patient groups: pMS 144.80 (31.23, 302.00); fMS 130.50 (39.95, 217.07) which is statistically significant difference P-value: HC vs. pMS = 0.0071; HC vs. fMS = 0.0468. This result was also confirmed by a validation analysis to exclude impact of variable factors, such as disease duration and Expanded Disability Status Scale scores. Conclusions: Results showed that controls had higher levels of IFN-γ production than the MS patient groups and suggest that MS patients included in this study have a lower ability of immune system activation than HC. Results confirm that fingolimod is able to suppress production of IFN-γ. The fact that the QFM score of MS patients is significantly lower than that of HC may indicate a dysfunctional state of the immune system in baseline conditions.

The Effect of Face Masks on Physiological Data and the Classification of Rehabilitation Walking.

Gait analysis and the assessment of rehabilitation exercises are important processes that occur during fitness level monitoring and the treatment of neurological disorders. This paper presents the possibility of using oximetric, heart rate (HR), accelerometric, and global navigation satellite systems (GNSSs) to analyse signals recorded during uphill and downhill walking without and with a face mask to find its influence on physiological functions during selected walking patterns. The experimental dataset includes 86 signal segments acquired under different conditions. The proposed methodology is based on signal analysis in both the time and frequency domains. The results indicate that face mask use has a minimal effect on blood oxygen concentration and heart rate, with the average mean changes of these parameters being less than 2%. The support vector machine, a Bayesian method, the k -nearest neighbour method, and a two-layer neural network showed very good separation abilities and successfully classified different walking patterns only in the case when the effect of face mask wearing was not included in the classification process. Our methodology suggests that artificial intelligence and machine learning tools are efficient methods for the assessment of motion patterns in different motion conditions and that face masks have a negligible effect for short-duration experiments.

Acute Recanalization of Large Vessel Occlusion in the Anterior Circulation Stroke: Is Mechanical Thrombectomy Alone Better in Patients over 80 Years of Age? Findings from a Retrospective Observational Study.

Real-world data report worse 3-month clinical outcomes in elderly patients with acute ischemic stroke (AIS) treated with mechanical thrombectomy (MT). The aim was to identify factors influencing clinical outcome in elderly patients with anterior circulation AIS treated with MT (±intravenous thrombolysis (IVT)). In a retrospective, monocentric study, analysis of prospectively collected data of 138 patients (≥80 years) was performed. IVT was an independent negative predictor (OR 0.356; 95% CI: 0.134-0.942) and female sex an independent positive predictor (OR 4.179, 95% CI: 1.300-13.438) of 3-month good clinical outcome (modified Rankin scale 0-2). Female sex was also an independent negative predictor of 3-month mortality (OR 0.244, 95% CI: 0.100-0.599). Other independent negative predictors of 3-month good clinical outcome were older age, lower pre-stroke self-sufficiency, more severe neurological deficit and longer procedural intervals. Mortality was also independently predicted by longer procedural interval and by the occurrence of symptomatic intracerebral hemorrhage (p < 0.05 in all cases). Our results demonstrated, that in patients aged ≥80 years with anterior circulation AIS undergoing MT (±IVT), IVT reduced the chance of 3-month good clinical outcome and female sex was associated with a greater likelihood of 3-month good clinical outcome and lower probability of 3-month mortality.

Novelty detection-based approach for Alzheimer's disease and mild cognitive impairment diagnosis from EEG.

Alzheimer's disease is diagnosed via means of daily activity assessment. The EEG recording evaluation is a supporting tool that can assist the practitioner to recognize the illness, especially in the early stages. This paper presents a new approach for detecting Alzheimer's disease and potentially mild cognitive impairment according to the measured EEG records. The proposed method evaluates the amount of novelty in the EEG signal as a feature for EEG record classification. The novelty is measured from the parameters of EEG signal adaptive filtration. A linear neuron with gradient descent adaptation was used as the filter in predictive settings. The extracted feature (novelty measure) is later classified to obtain Alzheimer's disease diagnosis. The proposed approach was cross-validated on a dataset containing EEG records of 59 patients suffering from Alzheimer's disease; seven patients with mild cognitive impairment (MCI) and 102 controls. The results of cross-validation yield 90.73% specificity and 89.51% sensitivity. The proposed method of feature extraction from EEG is completely new and can be used with any classifier for the diagnosis of Alzheimer's disease from EEG records.

Classification of Ataxic Gait.

Gait disorders accompany a number of neurological and musculoskeletal disorders that significantly reduce the quality of life. Motion sensors enable high-quality modelling of gait stereotypes. However, they produce large volumes of data, the evaluation of which is a challenge. In this publication, we compare different data reduction methods and classification of reduced data for use in clinical practice. The best accuracy achieved between a group of healthy individuals and patients with ataxic gait extracted from the records of 43 participants (23 ataxic, 20 healthy), forming 418 segments of straight gait pattern, is 98% by random forest classifier preprocessed by t-distributed stochastic neighbour embedding.

Deep Learning for Accelerometric Data Assessment and Ataxic Gait Monitoring.

Ataxic gait monitoring and assessment of neurological disorders belong to important multidisciplinary areas that are supported by digital signal processing methods and machine learning tools. This paper presents the possibility of using accelerometric data to optimise deep learning convolutional neural network systems to distinguish between ataxic and normal gait. The experimental dataset includes 860 signal segments of 16 ataxic patients and 19 individuals from the control set with the mean age of 38.6 and 39.6 years, respectively. The proposed methodology is based upon the analysis of frequency components of accelerometric signals simultaneously recorded at specific body positions with a sampling frequency of 60 Hz. The deep learning system uses all of the frequency components in a range of 〈0,30 〉 Hz. Our classification results are compared with those obtained by standard methods, which include the support vector machine, Bayesian methods, and the two-layer neural network with features estimated as the relative power in selected frequency bands. Our results show that the appropriate selection of sensor positions can increase the accuracy from 81.2% for the foot position to 91.7% for the spine position. Combining the input data and the deep learning methodology with five layers increased the accuracy to 95.8%. Our methodology suggests that artificial intelligence methods and deep learning are efficient methods in the assessment of motion disorders and they have a wide range of further applications.

Factors influencing therapy choice and clinical outcome in cerebral venous sinus thrombosis.

We aimed was to assess the factors influencing therapy choice and clinical outcome after 3-4 months in patients with cerebral venous sinus thrombosis (CVST). In a retrospective, bi-centric study, the set consisted of 82 consecutive CVST patients (61 females; mean age 33.5 ± 15.7 years). Following data were collected: baseline characteristics, presence of gender-specific risk factors (GSRF), location and extent of venous sinus impairment, clinical presentation, type of treatment, recanalization, presence of parenchymal lesions, and clinical outcome after 3-4 months (assessed using the modified Rankin Scale [mRS], with excellent outcome defined as mRS 0-1). Multivariate logistic regression analysis was used for statistical evaluation. After 3-4 months, complete recovery was achieved in 41 (50%) and excellent clinical outcome in 67 (81.7%) patients. Female sex (OR 0.11; p = 0.0189) and presence of focal neurologic deficit (OR 0.16; p = 0.0165) were identified as significant independent negative predictors and, the presence of GSRF (OR 15.63; p = 0.0011) as significant independent positive predictor of excellent clinical outcome. In conclusion, in our CVST patients, the presence of GSRF was associated with excellent clinical outcome, while the female sex itself was associated with poorer clinical outcome.

Simple capacitor-switch model of excitatory and inhibitory neuron with all parts biologically explained allows input fire pattern dependent chaotic oscillations.

Due to known information processing capabilities of the brain, neurons are modeled at many different levels. Circuit theory is also often used to describe the function of neurons, especially in complex multi-compartment models, but when used for simple models, there is no subsequent biological justification of used parts. We propose a new single-compartment model of excitatory and inhibitory neuron, the capacitor-switch model of excitatory and inhibitory neuron, as an extension of the existing integrate-and-fire model, preserving the signal properties of more complex multi-compartment models. The correspondence to existing structures in the neuronal cell is then discussed for each part of the model. We demonstrate that a few such inter-connected model units are capable of acting as a chaotic oscillator dependent on fire patterns of the input signal providing a complex deterministic and specific response through the output signal. The well-known necessary conditions for constructing a chaotic oscillator are met for our presented model. The capacitor-switch model provides a biologically-plausible concept of chaotic oscillator based on neuronal cells.

Recognition of Patient Groups with Sleep Related Disorders using Bio-signal Processing and Deep Learning.

Accurately diagnosing sleep disorders is essential for clinical assessments and treatments. Polysomnography (PSG) has long been used for detection of various sleep disorders. In this research, electrocardiography (ECG) and electromayography (EMG) have been used for recognition of breathing and movement-related sleep disorders. Bio-signal processing has been performed by extracting EMG features exploiting entropy and statistical moments, in addition to developing an iterative pulse peak detection algorithm using synchrosqueezed wavelet transform (SSWT) for reliable extraction of heart rate and breathing-related features from ECG. A deep learning framework has been designed to incorporate EMG and ECG features. The framework has been used to classify four groups: healthy subjects, patients with obstructive sleep apnea (OSA), patients with restless leg syndrome (RLS) and patients with both OSA and RLS. The proposed deep learning framework produced a mean accuracy of 72% and weighted F1 score of 0.57 across subjects for our formulated four-class problem.

Sleep Apnea Detection with Polysomnography and Depth Sensors.

This paper is devoted to proving two goals, to show that various depth sensors can be used to record breathing rate with the same accuracy as contact sensors used in polysomnography (PSG), in addition to proving that breathing signals from depth sensors have the same sensitivity to breathing changes as in PSG records. The breathing signal from depth sensors can be used for classification of sleep [d=R2]apneaapnoa events with the same success rate as with PSG data. The recent development of computational technologies has led to a big leap in the usability of range imaging sensors. New depth sensors are smaller, have a higher sampling rate, with better resolution, and have bigger precision. They are widely used for computer vision in robotics, but they can be used as non-contact and non-invasive systems for monitoring breathing and its features. The breathing rate can be easily represented as the frequency of a recorded signal. All tested depth sensors (MS Kinect v2, RealSense SR300, R200, D415 and D435) are capable of recording depth data with enough precision in depth sensing and sampling frequency in time (20-35 frames per second (FPS)) to capture breathing rate. The spectral analysis shows a breathing rate between 0.2 Hz and 0.33 Hz, which corresponds to the breathing rate of an adult person during sleep. To test the quality of breathing signal processed by the proposed workflow, a neural network classifier (simple competitive NN) was trained on a set of 57 whole night polysomnographic records with a classification of sleep [d=R2]apneaapnoas by a sleep specialist. The resulting classifier can mark all [d=R2]apneaapnoa events with 100% accuracy when compared to the classification of a sleep specialist, which is useful to estimate the number of events per hour. [d=R2]When compared to the classification of polysomnographic breathing signal segments by a sleep specialistand, which is used for calculating length of the event, the classifier has an [d=R1] F 1 score of 92.2%Accuracy of 96.8% (sensitivity 89.1% and specificity 98.8%). The classifier also proves successful when tested on breathing signals from MS Kinect v2 and RealSense R200 with simulated sleep [d=R2]apneaapnoa events. The whole process can be fully automatic after implementation of automatic chest area segmentation of depth data.

Motion Assessment for Accelerometric and Heart Rate Cycling Data Analysis.

Motion analysis is an important topic in the monitoring of physical activities and recognition of neurological disorders. The present paper is devoted to motion assessment using accelerometers inside mobile phones located at selected body positions and the records of changes in the heart rate during cycling, under different body loads. Acquired data include 1293 signal segments recorded by the mobile phone and the Garmin device for uphill and downhill cycling. The proposed method is based upon digital processing of the heart rate and the mean power in different frequency bands of accelerometric data. The classification of the resulting features was performed by the support vector machine, Bayesian methods, k-nearest neighbor method, and neural networks. The proposed criterion is then used to find the best positions for the sensors with the highest discrimination abilities. The results suggest the sensors be positioned on the spine for the classification of uphill and downhill cycling, yielding an accuracy of 96.5% and a cross-validation error of 0.04 evaluated by a two-layer neural network system for features based on the mean power in the frequency bands 〈 3 , 8 〉 and 〈 8 , 15 〉 Hz. This paper shows the possibility of increasing this accuracy to 98.3% by the use of more features and the influence of appropriate sensor positioning for motion monitoring and classification.

The Concentration of Memantine in the Cerebrospinal Fluid of Alzheimer's Disease Patients and Its Consequence to Oxidative Stress Biomarkers.

Memantine is a noncompetitive N-methyl-d-aspartate (NMDA) receptor antagonist utilized as a palliative cure for Alzheimer's disease. This is the second study examining the memantine concentrations in cerebrospinal fluid. The previously published study enrolled six patients, and three of them were theoretically in a steady state. In our study, we enrolled 22 patients who regularly used a standard therapeutic dose of memantine (20 mg/day, oral administration) before the sample collection. Patients were divided into four groups, according to the time of plasma and cerebrospinal fluid collection: 6, 12, 18, and 24 h after memantine administration. The cerebrospinal fluid samples were also assessed for selected oxidative stress parameters (malondialdehyde, 3-nitrotyrosine, glutathione, non-protein thiols, and non-protein disulfides). The plasma/cerebrospinal fluid (CSF) ratio for all time intervals were within the range of 45.89% (6 h) to 55.60% (18 h), which corresponds with previously published findings in most patients. The other aim of our study was to deduce whether the achieved "real" memantine concentration in the central compartment was sufficient to block NMDA receptors. The IC50 value of memantine as an NMDA antagonist is in micromolar range; the lowest limit is 112 ng/ml (GluN2C), and this value was achieved only in three cases. The memantine cerebrospinal fluid concentration did not reach one quarter of the IC50 value in five cases (one patient was excluded for noncompliance); therefore, the potency of memantine as a therapeutic effect in patients may be questionable. However, it appears that memantine therapy positively affected the levels of some oxidative stress parameters, especially non-protein thiols and 3-nitrotyrosine.

CD4+/CD45RO+: A Potential Biomarker of the Clinical Response to Glatiramer Acetate.

Background: Glatiramer acetate (GA) is an effective treatment for the earliest stages of multiple sclerosis (MS)-clinically isolated syndrome (CIS) or clinically definite MS (CDMS). Objective: This study aims to determine the differences in the lymphocyte population (at baseline and the course of five years) between confirmed sustained progression (CSP) and non-CSP groups and to identify potential biomarkers among these parameters that can predict a positive response to the treatment. Methods: Twelve male and 60 female patients were included in the study. Peripheral blood samples were collected before and five years after treatment with GA. The authors compared lymphocyte parameters between the CSP and non-CSP groups by statistical analyses. Univariate and penalized logistic regression models were fitted to identify the best lymphocyte parameters at baseline and their combination for potential biomarkers. Subsequently, the ROC analysis was used to identify cut-offs for selected parameters. Results: The parameter CD4+/CD45RO+ was identified as the best single potential biomarker, demonstrating the ability to identify patients with CSP. Moreover, a combination of four lymphocyte parameters at baseline, relative lymphocyte counts, CD3+/CD69+, CD4+/CD45RO+, and CD4+/CD45RA+ab, was identified as a potential composite biomarker. This combination explains 23% of the variability in CSP, which is better than the best univariate parameter when compared to CD4+/CD45RO+ at baseline. Conclusions: The results suggest that other biomarkers can help monitor the conditions of patients and predict a favourable outcome.

The Complex Behaviour of a Simple Neural Oscillator Model in the Human Cortex.

The brain is a complex organ responsible for memory storage and reasoning; however, the mechanisms underlying these processes remain unknown. This paper forms a contribution to a lot of theoretical studies devoted to regular or chaotic oscillations of interconnected neurons assuming that the smallest information unit in the brain is not a neuron but, instead, a coupling of inhibitory and excitatory neurons forming a simple oscillator. Several coefficients of variation for peak intervals and correlation coefficients for peak interval histograms are evaluated and the sensitivity of such oscillator units is tested to changes in initial membrane potentials, interconnection signal delays, and changes in synaptic weights based on known histologically verified neuron couplings. Results present only a low dependence of oscillation patterns to changes in initial membrane potentials or interconnection signal delays in comparison to a strong sensitivity to changes in synaptic weights showing the stability and robustness of encoded oscillating patterns to signal outages or remoteness of interconnected neurons. Presented simulations prove that the selected neuronal couplings are able to produce a variety of different behavioural patterns, with periodicity ranging from milliseconds to thousands of milliseconds between the spikes. Many detected different intrinsic frequencies then support the idea of possibly large informational capacity of such memory units.

Monitoring of Lymphocyte Populations During Treatment with Interferon-ß-1b to Predict Multiple Sclerosis Disability Progression.

The authors aim to understand how lymphocyte populations could predict the course of multiple sclerosis (MS) in people treated with interferon-ß (IFN-ß). Twenty-five male patients and 72 female patients were analyzed in the study. Peripheral blood samples were taken before and 5 years after the treatment with IFN-ß. Lymphocyte subsets were analyzed by flow cytometry. The authors compared lymphocyte parameters between confirmed sustained progression (CSP) and non-CSP groups by using Welch's one-way analysis of means or a chi-square test of independence. A penalized (lasso) logistic regression model was fitted to identify the combination of lymphocyte parameters for potential biomarkers. The combination of lymphocyte counts, relative CD3+/CD25+ cells, absolute CD8 T cells, absolute CD8+/CD38+ cells, absolute CD38+ cells, and relative CD5+/CD19+ cells was identified as potential biomarker for the IFN-ß treatment to monitor MS development in relation to CSP. The results suggest that other biomarkers aid in patient observation, predict a favorable outcome, and assist in the decision-making process for the early therapy escalation.

Lymphocyte populations and their change during five-year glatiramer acetate treatment.

BACKGROUND: The goal of this study was to determine the characteristics that are affected in patients treated with glatiramer acetate (GA). METHODS: A total of 113 patients were included in this study. Patients were treated with glatiramer acetate (subcutaneous injection, 20 mg, each day). Peripheral blood samples were obtained just prior to treatment as well as 5 years after GA treatment. All the calculations were performed with the statistical system R (r-project.org). RESULTS: After 5 years of treatment, a significant decrease was found in the absolute and relative CD3+/CD69+ counts, the absolute and relative CD69 counts, the relative CD8+/CD38+ count and the relative CD38 count. A significant increase was found in the absolute and relative CD5+/CD45RA+ counts and the absolute CD5+/CD45RO+ count after 5 years of treatment. CONCLUSION: This study presents some parameters that were affected by long-term GA treatment.

Machine Learning in Rehabilitation Assessment for Thermal and Heart Rate Data Processing.

Multimodal signal analysis based on sophisticated noninvasive sensors, efficient communication systems, and machine learning, have a rapidly increasing range of different applications. The present paper is devoted to pattern recognition and the analysis of physiological data acquired by heart rate and thermal camera sensors during rehabilitation. A total number of 56 experimental data sets, each 40 min long, of the heart rate and breathing temperature recorded on an exercise bike have been processed to determine the fitness level and possible medical disorders. The proposed general methodology combines machine learning methods for the detection of the changing temperature ranges of the thermal camera and adaptive image processing methods to evaluate the frequency of breathing. To determine the individual temperature values, a neural network model with the sigmoidal and the probabilistic transfer function in the first and the second layers are applied. Appropriate statistical methods are then used to find the correspondence between the exercise activity and selected physiological functions. The evaluated mean delay of 21 s of the heart rate drop related to the change of the activity level corresponds to results obtained in real cycling conditions. Further results include the average value of the change of the breathing temperature (167 s) and breathing frequency (49 s).

Fibrin Clot Architecture in Acute Ischemic Stroke Treated With Mechanical Thrombectomy With Stent-Retrievers　- Cohort Study.

BACKGROUND: The composition of intra-arterial clots might influence the efficacy of mechanical thrombectomy (MT) in ischemic stroke (IS) due to the acute occlusions within large cerebral arteries. The aims were to assess the factors associated with blood clot structure and the impact of thromboembolus structure on MT using stent-retrievers in patients with acute large artery IS in the anterior circulation.Methods and Results:In an observational cohort study, we studied the components of intra-arterial clots retrieved from large cerebral arteries in 80 patients with acute IS treated with MT with or without i.v. thrombolysis (IVT). Histology of the clots was carried out without knowledge of the clinical findings, including the treatment methods. The components of the clots, their age, origin and semi-quantitative graded changes in the architecture of the fibrin components (e.g., "thinning") were compared via neuro-interventional, clinical and laboratory data. The most prominent changes in the architecture of the fibrin components in the thromboemboli were associated with IVT (applied in 44 patients; OR, 3.50; 95% CI: 1.21-10.10, P=0.02) and platelet count (OR, 2.94; 95% CI: 1.06-8.12, P=0.04). CONCLUSIONS: In patients with large artery IS treated with the MT using stent-retrievers, bridging therapy with IVT preceding MT and higher platelet count were associated with significant changes of the histological structure of blood clots.