Brain Hemorrhage Research Articles

Abstract TMP25: Common Molecular Biomarkers in Different Types of Acute Brain Injuries Predicting Outcome

Background: Acute brain injuries, including aneurysmal subarachnoid hemorrhage (aSAH), ischemic stroke (IS), and traumatic brain injury (TBI), entail complex recovery processes involving brain plasticity and synaptic regeneration mechanisms. Exploring lipidomic profiles, microRNA expression, and metabolomic changes can yield insights into recovery mechanisms, unveil conserved molecular associations, and identify potential biomarkers. Hypotheses: We hypothesized that regardless of injury type, variations in lipidomic and metabolomic profiles, along with differentially expressed microRNAs, share common characteristics. These changes likely mirror essential biological mechanisms and may correlate with outcomes. Methods: We studied a prospective cohort of patients with IS, aSAH, and TBI (N=71). Serum samples were collected once, followed by comprehensive profiling: lipidomics (Lipidyzer™ Platform), metabolomics (Orbitrap Profiling), and microRNA sequencing (DESeq2). Results: Univariate analysis unveiled candidate molecules predictive of outcomes. Multivariate combinatory linear discriminant analysis (LDA) demonstrated significant prognostic potential for favorable outcomes. The LDA equation combining eight biomarkers was: -0.239[LPC17:0] - 1.125[TAG51:2:FA15:0] + [TAG51:2:FA18:1] + [D-glucuronic Acid] + 0.416[hsa-miR-146b-3p] + 0.082[hsa-miR-485-3p] + 0.433[hsa-miR-5010-5p] + 0.727[hsa-miR-485-5p], effectively predicting favorable outcomes. The AUC was 95.8%, 95% CI (0.88 - 1.00), p-value 0.023, indicating strong discriminatory power of the combinatory biomarker (OR 8.7, 95% CI 1.35-56.9). Conclusion: Findings reveal a shared pattern of lipidomic, metabolomic, and microRNA changes across distinct acute brain injuries. This warrants further investigation and validation, offering prospects for biomarker development, mechanistic validation, and therapeutic interventions in brain recovery. These shared features underscore the importance of interdisciplinary research for targeted, personalized interventions in diverse acute brain injuries.

Abstract WP146: Critical Care After Thrombolytic Therapy in Acute Stroke: Who Really Needs the ICU?

Introduction: Intravenous thrombolytic therapy remains the cornerstone of management in acute ischemic stroke (AIS) patients. Given the potential adverse effects of thrombolysis, patients are admitted to an intensive care unit (ICU) for close monitoring following administration. Alternative post-thrombolytic pathways may provide for safe cost-effective care in certain populations. We aimed to determine the proportion of patients treated with thrombolytics who required ICU care for reasons other than frequent neurologic monitoring and define their characteristics. Methods: We retrospectively reviewed patients ≥ 18 years of age who received TNK/ tPA for AIS at our community-based stroke center between May 2020 and August 2022. Patients were classified as requiring ICU care if they required intubation within 24 hours of admission or neurosurgical intervention, had symptomatic hemorrhagic conversion or brain compression, required a continuous infusion for hemodynamic management, or were in status epilepticus. Univariate and multivariate statistical analysis was performed. The study protocol was deemed exempt by our institutional IRB. Results: 262 patients met inclusion criteria. 54 (20.6%) required ICU level of care. Characteristics of those requiring ICU level of care are described in Table 1. Multivariate analysis showed that patients on antiplatelet or anticoagulant therapies prior to arrival (AOR: 2.205, p= 0.021) or who presented with higher initial NIH stroke scale (AOR: 1.108, p < 0.001) had significantly higher likelihood of requiring ICU level of care. Conclusions: In our cohort, approximately 21% of patients required critical care. Anticoagulant or antiplatelet therapy prior to admission and greater NIH stroke scale on arrival were associated with an increased likelihood of requiring ICU level of care. Further prospective studies are indicated to assess the efficacy of alternative settings for post-thrombolytic care in selected AIS patients.

Abstract WP296: Human Vascular Smooth Muscle Cells Derived Small Extracellular Vesicles Reduce the Neurovascular Damage After Stroke in Diabetic Mice

Introduction: Stroke patients with type-2 diabetes exhibit exacerbated cerebrovascular dysfunction and increased neurological deficits compared to non-diabetic stroke patients. Vascular smooth muscle cells (VSMCs) play an important role in mediating neurovascular function, however, how VSMCs communicate with cerebral endothelial cells (CECs) has not been studied. In this study, we tested the hypothesis that small extracellular vesicles (sEVs) derived from healthy human VSMCs (VSMC-sEVs) reduce diabetes- and ischemia-neurovascular damage. Methods and Results: VSMC-sEVs were characterized. In vitro CEC permeability assay showed that treatment of hypoxia-challenged human brain microvascular endothelial cells (BMECs) or primary CECs isolated from type-2 diabetic mice (n=6/group) with VSMC-sEVs significantly reduced and augmented cell permeability and trans-endothelial electrical resistance, respectively, compared to non VSMC-sEVs control. Inhibition of sEV endocytosis in BMECs with clathrin (CPZ) or caveolin (Nystatin) inhibitors abrogated the effect of VSMC-sEVs on CEC proliferation and migration. We then examined the effect of VSMC-sEVs on diabetic ischemia in which diabetic mice, with diabetes induced by a high fat diet and streptozotocin, were subjected to permanent MCAo. Middle-age diabetic male mice (10-12m old) were treated with intranasal administration of VSMC-sEVs (1x10 9 particles per injection) or saline (n=12/group) at 24h after cerebral ischemia. We found that VSMC-sEVs treatment significantly increased regional cerebral blood flow and FITC-dextran perfused functional microvessels (17.55% vs 13.32% control) as well as decreased brain hemorrhage and BBB leakage (1.51% vs 1.76% of albumin, p<0.05) in the ischemic border zone 28 days after MCAo compared with the saline treatment mice. Moreover, compared to the saline treatment, VSMC-sEVs significantly improved neurological function after stroke as measured by modified neurological severity score, and foot-fault and adhesive removal tests, respectively. Conclusion: This study suggests that VSMC-sEVs have a therapeutic effect to improve neurological outcome after diabetic stroke, likely by reducing diabetes and ischemia impaired neurovascular function.

Abstract WP160: Mechanisms of Brain Hemorrhage in Chronic Kidney Disease

Introduction: Chronic kidney disease (CKD) is a risk factor for intracerebral hemorrhage (ICH) and for worse outcomes following ICH. In this study, we examined the relationship between ICH and CKD, as well as intracranial arterial calcification (IAC) as an intermediate biomarker. We hypothesized that CKD is an independent risk factor for IAC burden, and that high IAC burden is an independent risk factor for ICH. Methods: A retrospective cohort of spontaneous ICH and non-ICH patients with non-contrast head CT imaging was identified. For each patient, eGFR (estimated glomerular filtration rate) along with age, sex, race, ethnicity, hypertension (HTN), diabetes (DM), heart disease, and hyperlipidemia were abstracted. A convolutional neural network was developed to automate quantification of IAC burden within the anterior and posterior circulation, and all results were visually verified for accuracy by an expert neuroradiologist. The relationship between eGFR and IAC was assessed with Pearson correlation and one-way ANOVA. Univariate analysis of ICH risk factors was evaluated using t-test and Chi-square, while multivariate models were analyzed using logistic regression. Results: Among the 717 patients in this study, 98 were ICH patients. In all patients, mean IAC burden increased significantly with CKD stage (p < 0.01). Univariate analysis for IAC risk factors yielded statistically significant associations for eGFR (p < 0.01) as well as age (p < 0.01), HTN (p = 0.01), and African-American race (p = 0.02). In multivariate analysis, eGFR remained a significant independent predictor of IAC (p < 0.01) along with HTN (p < 0.01), DM (p < 0.01), and race (p < 0.01). Furthermore, IAC burden was significantly higher in ICH patients (15.4 mL vs. 8.7 mL, p = 0.02). In multivariate analysis, IAC remained a significant independent predictor of ICH (p < 0.01) along with HTN (p < 0.01), DM (p = 0.03), sex (p < 0.01), and race (p < 0.01). Conclusion: This is the first demonstration that eGFR predicts strictly quantified IAC, which then predicts ICH. These findings offer novel insights into determinants of IAC and mechanisms underlying the CKD-ICH relationship.

Abstract WMP74: Inhibition of T Cell Receptor Activation Improves Neurological Outcomes After Intracerebral Hemorrhage

Purpose: We previously reported that activated T cells accumulate in the perihematomal regions after Intracerebral hemorrhage (ICH) and aggravate hemorrhagic brain injury. However, brain-infiltrating T cells precise activation mechanisms and resulting pathological impacts remain largely unexplored. This study aims to address these knowledge gaps. Methodology: ICH was induced via standard collagenase injection in male C57BL/6J mice (10 weeks old). Activation pathways of T cells were analyzed through single-cell RNA data (GSE230414). We assessed T cell receptor (TCR) activation, pro-inflammatory cytokines expression, and immune cell infiltration via flow cytometry and immunostaining at 1- and 3-days post-ICH. Neurobehavioral evaluations were conducted at 1-, 3-, 7-, and 14-days post-ICH. TCR activation was pharmacologically inhibited using the TCR-specific inhibitor AX-024, administered (10 mg/kg, i.p.) one hour after ICH. Results: Single-cell transcriptomic analysis revealed an upregulation of the TCR pathway in activated T cells following ICH in mice. Flow cytometry and immunostaining validated TCR activation markers (p-CD3, p-Zap70) in ICH mouse models. These findings suggest that TCR activation likely plays a central role in the activation of brain-infiltrating T cells. In the subsequent experiment, we explored the pathological implications of TCR activation on T cell activation, leukocyte brain infiltration, and neurological outcomes post-ICH. We found that administration of AX-024 substantially inhibited TCR activation (CD69 expression) and expression of pro-inflammatory cytokines (IL-17, IL-1β) in brain-infiltrating T cells at 1- and 3-days post-ICH. Notably, AX-024 administration also significantly decreased the infiltration of other leukocytes and remarkably enhanced long-term sensorimotor and cognitive function up to 14 days after ICH. Discussion: This study underscores TCR activation as a pivotal mechanism driving activation of brain-infiltrating T cells following ICH. Inhibiting TCR activation through AX-024 proves beneficial for improving neurological outcomes after ICH. However, further investigations are warranted to comprehensively elucidate the intricate underlying pathological mechanisms.

Reprimo (RPRM) mediates neuronal ferroptosis via CREB-Nrf2/SCD1 pathways in radiation-induced brain injury

Neuronal ferroptosis has been found to contribute to degenerative brain disorders and traumatic and hemorrhagic brain injury, but whether radiation-induced brain injury (RIBI), a critical deleterious effect of cranial radiation therapy for primary and metastatic brain tumors, involves neuronal ferroptosis remains unclear. We have recently discovered that deletion of reprimo (RPRM), a tumor suppressor gene, ameliorates RIBI, in which its protective effect on neurons is one of the underlying mechanisms. In this study, we found that whole brain irradiation (WBI) induced ferroptosis in mouse brain, manifesting as alterations in mitochondrial morphology, iron accumulation, lipid peroxidation and a dramatic reduction in glutathione peroxidase 4 (GPX4) level. Moreover, the hippocampal ferroptosis induced by ionizing irradiation (IR) mainly happened in neurons. Intriguingly, RPRM deletion protected the brain and primary neurons against IR-induced ferroptosis. Mechanistically, RPRM deletion prevented iron accumulation by reversing the significant increase in the expression of iron storage protein ferritin heavy chain (Fth), ferritin light chain (Ftl) and iron importer transferrin receptor 1 (Tfr1), as well as enhancing the expression of iron exporter ferroportin (Fpn) after IR. RPRM deletion also inhibited lipid peroxidation by abolishing the reduction of GPX4 and stearoyl coenzyme A desaturase-1 (SCD1) induced by IR. Importantly, RPRM deletion restored or even increased the expression of nuclear factor, erythroid 2 like 2 (Nrf2) in irradiated neurons. On top of that, compromised cyclic AMP response element (CRE)-binding protein (CREB) signaling was found to be responsible for the down-regulation of Nrf2 and SCD1 after irradiation, specifically, RPRM bound to CREB and promoted its degradation after IR, leading to a reduction of CREB protein level, which in turn down-regulated Nrf2 and SCD1. Thus, RPRM deletion recovered Nrf2 and SCD1 through its impact on CREB. Taken together, neuronal ferroptosis is involved in RIBI, RPRM deletion prevents IR-induced neuronal ferroptosis through restoring CREB-Nrf2/SCD1 pathways.

Two-Stage Anatomic Myocutaneous Flap Dissection in Cranioplasty: Technical Nuances and Single-Surgeon Experience.

Temporalis muscle management remains one of the most challenging aspects of cranioplasty, which accounts for considerable rates of dissection-related complications. Since 2019, the senior author has developed and consistently used a methodical, two-stage anatomic dissection technique to separate the scalp and temporalis muscle from the underlying brain. This technique is believed to facilitate dissection and minimize the risk of brain injury, while optimizing cosmetic outcomes. All patients who underwent cranioplasty between January 2019 and February 2023 were identified from a prospectively maintained database. Charts were retrospectively reviewed. Demographic, clinical, and procedural data were extracted and analyzed. Twenty-nine patients, 20 men and 9 women with a median age of 37 years (range 17-72), were identified. Indications for craniectomy were traumatic brain injury in 18 (62.1%), hemorrhagic stroke in five (17.2%), ischemic stroke in four (13.8%), and aneurysmal subarachnoid hemorrhage in two (6.9%). Median precranioplasty modified Rankin Scale and Glasgow Coma Scale scores were 5 (range in series: 0-5) and 14 (range in series: 3-15), respectively. The median time to cranioplasty was 131 days (32-1717). Cranioplasty was technically successful in all patients, with a median operative time of 106 minutes (62-182). There were no intraoperative complications. Postoperative complications occurred in three patients (10.3%): hemorrhagic brain contusion (n = 1), meningitis (n = 1), and seizure (n = 1). Of those, one patient (3.4%) died 2 weeks after surgery from suspected pulmonary embolism. After a median follow-up of 4 months (1-44), all 28 survivors have either remained clinically stable or exhibited neurological improvement. Cosmetic results were good or excellent in 27 (96.4%) and fair in one (3.6%). Two-stage anatomic dissection of the scalp and temporalis muscle during cranioplasty can maximize surgical efficiency and result in excellent outcomes. Cranioplasty should be considered a low-risk, low-complexity neurosurgical procedure. Safe and efficient management of the temporalis muscle is key.

Risk factors in developing amyloid related imaging abnormalities (ARIA) and clinical implications.

Alzheimer's disease (AD) affects over 6 million people over the age of 65. The advent of new anti-amyloid monoclonal antibodies as treatment for early Alzheimer's disease these immunotherapeutics may slow disease progression but also pose significant risks. Amyloid related imaging abnormalities (ARIA) identified on MRI following administration of these new monoclonal antibodies can cause both brain edema (ARIA-E) and hemorrhage (ARIA-H). While most ARIA is asymptomatic, some patients can develop headache, confusion, nausea, dizziness, seizures and in rare cases death. By analyzing lecanemab, aducanumab, gantenerumab, donanemab, and bapineuzumab clinical trials; risk factors for developing ARIA can be identified to mitigate some of the ARIA risk. Risk factors for developing ARIA-E are a positive Apoε4 carrier status and prior multiple cerebral microhemorrhages. Risk factors for ARIA-H are age, antithrombotic use, and history of prior strokes. With lecanemab, ARIA-E and ARIA-H were seen at lower rates 12 and 17%, respectively, compared to aducanumab (ARIA-E 35% and ARIA-H 19%) in treated patients. ARIA risk factors have impacted inclusion and exclusion criteria, determining who can receive lecanemab. In some clinics, almost 90% of Alzheimer's patients are excluded from receiving these new anti-amyloid therapeutics. This review aims to discuss risk factors of ARIA and highlight important areas for further research. With more anti-amyloid monoclonal antibodies approved by the Food and Drug Administration, considering patient risk factors for developing ARIA is important to identify to minimize patient's risk while receiving these new therapies.

Analysis of Wide Modified Rankin Score Dataset using Markov Chain Monte Carlo Simulation

Brain hemorrhage and strokes are serious medical conditions that can have devastating effects on a person's overall well-being and are influenced by several factors. We often encounter such scenarios specially in medical field where a single variable is associated with several other features. Visualizing such datasets with a higher number of features poses a challenge due to their complexity. Additionally, the presence of a strong correlation structure among the features makes it hard to determine the impactful variables with the usual statistical procedure. The present paper deals with analysing real life wide Modified Rankin Score dataset within a Bayesian framework using a logistic regression model by employing Markov chain Monte Carlo simulation. Latterly, multiple covariates in the model are subject to testing against zero in order to simplify the model by utilizing a model comparison tool based on Bayes Information Criterion.

Brain Plasticity Modulator p75 Neurotrophin Receptor in Human Urine after Different Acute Brain Injuries-A Prospective Cohort Study.

Acute brain injuries (ABIs) pose a substantial global burden, demanding effective prognostic indicators for outcomes. This study explores the potential of urinary p75 neurotrophin receptor (p75NTR) concentration as a prognostic biomarker, particularly in relation to unfavorable outcomes. The study involved 46 ABI patients, comprising sub-cohorts of aneurysmal subarachnoid hemorrhage, ischemic stroke, and traumatic brain injury. Furthermore, we had four healthy controls. Samples were systematically collected from patients treated at the University Hospital of Turku between 2017 and 2019, at early (1.50 ± 0.70 days) and late (9.17 ± 3.40 days) post-admission time points. Urinary p75NTR levels, measured by ELISA and normalized to creatinine, were compared against patients' outcomes using the modified Rankin Scale (mRS). Early urine samples showed no significant p75NTR concentration difference between favorable and unfavorable mRS groups. In contrast, late samples exhibited a statistically significant increase in p75NTR concentrations in the unfavorable group (p = 0.033), demonstrating good prognostic accuracy (AUC = 70.9%, 95% CI = 53-89%, p = 0.03). Assessment of p75NTR concentration changes over time revealed no significant variation in the favorable group (p = 0.992) but a significant increase in the unfavorable group (p = 0.009). Moreover, p75NTR concentration was significantly higher in ABI patients (mean ± SD 40.49 ± 28.83-65.85 ± 35.04 ng/mg) compared to healthy controls (mean ± SD 0.54 ± 0.44 ng/mg), irrespective of sampling time or outcome (p < 0.0001). In conclusion, late urinary p75NTR concentrations emerged as a potential prognostic biomarker for ABIs, showing increased levels associated with unfavorable outcomes regardless of the specific type of brain injury. While early samples exhibited no significant differences, the observed late increases emphasize the time-dependent nature of this potential biomarker. Further validation in larger patient cohorts is crucial, highlighting the need for additional research to establish p75NTR as a reliable prognostic biomarker across various ABIs. Additionally, its potential role as a diagnostic biomarker warrants exploration.

Enhancing Intracranial Hemorrhage Diagnosis through Deep Learning Models

Timely diagnosis is crucial for the successful treatment of a serious medical condition like brain hemorrhage. Deep learning algorithms have shown great promise in applications for medical image analysis, like the identification of brain hemorrhages. The goal of this study is to assess how well various deep learning algorithms can detect brain hemorrhages. Using a suitable dataset, the study evaluates the computational efficiency, accuracy, sensitivity, and specificity of the selected algorithms. The results demonstrate the potential of deep learning models to assist physicians in identifying this potentially fatal condition and demonstrate how well they can identify brain hemorrhages. The study’s findings improve automated brain hemorrhage detection technology, improving patient outcomes and the efficiency of healthcare delivery. EfficientNetB3 typically achieves higher accuracy due to its increased model complexity. Despite its increased complexity, EfficientNetB3 is still more parameter-efficient and computationally efficient than many alternative architectures. EfficientNetB3’s strong performance and feature extraction capabilities make it a good choice for transfer learning tasks. Out of all the models implemented in this paper, the proposed model with EfficientNetB3 gave the best accuracy for training as well as validation i.e. 99.95% and 93.29% respectively followed by EfficientNetB2, ResNet, SEResNext and ResNext.

Neurorazvojni ishod preterminske novorođenčadi

Preterm birth is the leading cause of neonate mortality and the second cause of death for children under 5 years of age in the world. The neonates born with very low birth weight (&lt;1500 g) and extremely low birth weight (&lt;1000 g), as well as neonates born very preterm and extremely preterm are at high risk for neurodevelopmental disabilities. Three common and frequent forms of brain injury in preterm neonates are intraventricular hemorrhage, white and gray brain matter injury. Motor developmental delay is early identified in premature neonates, especially during the first three years of life. The major motor deficit is cerebral palsy, but in school age, minor motor dysfunctions are diagnosed and named developmental coordination disorder. The second domain in which developmental delays are identified is cognitive development. Children born prematurely have lower intelligence quotient and a high risk of speech and language disabilities. The low gestational age and low birth weight are the risk factors for emotional disorders, attention deficit/hyperactivity disorders and autism spectrum disorders. The frequency of visual impairment and hearing loss decreased with enlarging gestational age at birth. Intracranial hemorrhage and neonatal seizures are identified as prominent risk factors for later-onset epilepsy. A key strategy to reduce neurodevelopmental disability is the prevention of preterm delivery. Transport of pregnant women with a risk of preterm delivery and delivery in tertiary centers, prenatal administration of glucocorticoids and magnesium sulfate decreases the risk of preterm brain injury and improves neurodevelopmental outcomes. Also, the program "Neonatal Individualized Developmental Care and Assessment Program" and the application of the "skin-to-skin contact" method (Kangaroo Mother Care) have favorable effects on the neurodevelopmental outcome in premature neonates. Future research should make new recommendations for neuroprotection of the preterm neonates.

Vitamin D deficiency in German hospital patients with aneurysmal subarachnoid hemorrhage and traumatic brain injury

Vitamin D deficiency in German hospital patients with aneurysmal subarachnoid hemorrhage and traumatic brain injury

Exploring Deep Learning and Machine Learning Approaches for Brain Hemorrhage Detection

Exploring Deep Learning and Machine Learning Approaches for Brain Hemorrhage Detection

Дефицит VII фактора свертывания крови (гипопроконвертинемия): клиническое наблюдение

Factor VII (proconvertin) is a pivotal initial step in the activation of the extrinsic pathway of blood coagulation. Factor VII deficiency is a rare coagulopathy that can manifest in a wide range of clinical symptoms, from asymptomatic deficiency to severe hemorrhagic syndrome, which can often lead to death due to hemorrhages, including brain hemorrhages. The article provides a brief overview of the epidemiology of the disease, its clinical manifestations, diagnostic approaches, and treatment options. The authors present a case of relatively late diagnosis of hypoproconvertinemia, which manifested with severe bleeding in the neonatal period. In the first year of life, the child experienced recurrent hemorrhagic syndrome, which was managed by administering menadione sodium bisulfite trihydrate solution. Examination did not reveal deficiency of factor VIII or IX. The diagnosis was established at the age of 3 years, and the child received recombinant factor VII. However, on- demand therapy and the lack of long-term preventive administration of the drug did not provide a sufficient hemostatic effect, as evidenced by repeated episodes of hemarthrosis and severe gastrointestinal bleeding at the age of 15 years These complications necessitated a switch to continuous administration of recombinant factor VII. KEYWORDS: hypoproconvertinemia, hemophilia, children, bleeding, recombinant FVII. FOR CITATION: Dmitriev A.V., Fedina N.V., Yudakova M.S., Dronova S.N., Slabachkov K.O., Zaplatnikov A.L. Deficiency of factor VII (hypoproconvertinemia): case report. Russian Journal of Woman and Child Health. 2024;7(2):184–188 (in Russ.). DOI: 10.32364/2618- 8430-2024-7-2-15.

Microwave Antenna-Assisted Machine Learning: A Paradigm Shift in Non-Invasive Brain Hemorrhage Detection

Microwave Antenna-Assisted Machine Learning: A Paradigm Shift in Non-Invasive Brain Hemorrhage Detection

Brain haemorrhage classification from CT scan images using fine-tuned transfer learning deep features

Brain haemorrhage classification from CT scan images using fine-tuned transfer learning deep features

A Computational Deep Fuzzy Network-Based Neuroimaging Analysis for Brain Hemorrhage Classification.

The boundaries and regions between individual classes in biomedical image classification are hazy and overlapping. These overlapping features make predicting the correct classification result for biomedical imaging data a difficult diagnostic task. Thus, in precise classification, it is frequently necessary to obtain all necessary information before making a decision. This paper presents a novel deep-layered design architecture based on Neuro-Fuzzy-Rough intuition to predict hemorrhages using fractured bone images and head CT scans. To deal with data uncertainty, the proposed architecture design employs a parallel pipeline with rough-fuzzy layers. In this case, the rough-fuzzy function functions as a membership function, incorporating the ability to process rough-fuzzy uncertainty information. It not only improves the deep model's overall learning process, but it also reduces feature dimensions. The proposed architecture design improves the model's learning and self-adaptation capabilities. In experiments, the proposed model performed well, with training and testing accuracies of 96.77% and 94.52%, respectively, in detecting hemorrhages using fractured head images. The comparative analysis shows that the model outperforms existing models by an average of 2.6 ±0.90% on various performance metrics.

Should we be afraid of radiotherapy for hemorrhagic brain metastases? A narrative review.

Brain metastases (BM) are the most common intracranial malignancies. They are responsible for death as well as impairment of quality of life and cognitive function. In some cases, BMs can cause intracranial hemorrhage, which is not only responsible for the acute onset of either a new focal neurological deficit or worsening of a preexisting focal deficit but also poses a new challenge in treatment planning and clinical management. The aim of this study was to evaluate the available treatment modalities and their efficacy in hemorrhagic brain metastases (HBMs) with special attention to radiotherapy. In this review, we searched PubMed, BMJ, NCBI, Springer, BMC Cancer, Cochrane, and Google Scholar for articles containing data on the diagnosis and treatment of patients with HBMs, excluding the pediatric population. Treatment strategies consist of neurosurgery, whole brain radiotherapy, and stereotactic techniques (fractionated stereotactic radiosurgery (fSRS)/stereotactic radiosurgery (SRS)). Although the optimal treatment strategy for HBMs has not been established, we found no convincing evidence that radiotherapy, especially fSRS/SRS, is contraindicated in HBMs. We concluded that fSRS/SRS is a promising option for patients with HBM, particularly when surgical intervention poses risks.

A Smart Machine Learning Model for Detection of Brain Hemorrhage Using IoT

Abstract: For the most part, the development of Web of Things empowered applications propelled the world during the most recent couple of years, giving cutting edge and novel-based answers for various issues. This developmental field is fundamentally lead by remote sensor organization, radio recurrence ID, and brilliant versatile advances. The Internet of Things, or IoT, plays a crucial role in smart medical devices and wearables that can collect a wide range of longitudinal patient-generated health data while also providing options for preliminary diagnosis. As far as endeavors made for aiding the patients utilizing IoT-based arrangements, specialists exploit capacities of the AI calculations to give proficient arrangements in drain conclusion. To decrease the demise rates and propose precise treatment, this paper presents a savvy IoT-based application utilizing AI calculations for the human cerebrum drain finding. In view of the modernized tomography check pictures for intracranial dataset, the help vector machine and feedforward brain network have been applied for the grouping purposes. Generally, characterization consequences of 80.67% and 86.7% are determined for the help vector machine and feedforward brain organization, separately. It is finished up from the resultant examination that the feedforward brain network beats in ordering intracranial pictures. The classification tool's output provides information about the kind of brain hemorrhage that, in the end, helps validate the expert's diagnosis. It is used as a learning tool for trainee radiologists to reduce system errors.