Translational Stroke Research最新文献

Laboratory ratios such as the neutrophil-to-lymphocyte, platelet-to-lymphocyte, and stress hyperglycemia ratios have been widely studied as prognostic markers in stroke. Despite hundreds of reports and multiple meta-analyses, these indices have shown modest effect sizes and have not influenced clinical guidelines or trial design. This commentary argues that such ratios serve as surrogates of systemic physiology rather than actionable prognostic tools, highlighting the gap between statistical association and clinical translation.

Intracerebral hemorrhage (ICH) along with aggravating factors, such as edema, can raise intracranial pressure (ICP) to pathological levels. Diversion of some cerebrospinal fluid (CSF) and venous blood out of the cranium can limit ICP rises while maintaining cerebral perfusion pressure. Brain tissue itself is widely considered immutable in volume but prone to distortion (e.g., midline shift). However, distal brain regions shrink acutely following ICH in rodents. Tissue contraction arises from cell shrinkage and increased packing density. This "tissue compliance" is hypothesized to be an additional mechanism to limit ICP rises. Here, we examined whether and by how much parenchyma volume reduction occurs in ICH patients. We conducted a retrospective analysis on computed tomography (CT) scans of 96 ICH patients (average age 63.8 years old, 55% male) with an average hematoma volume of 32.4 and 35.3 mL at the first and second scan (separated by ~ 23 h), respectively. Hematoma growth (any absolute increase) occurred in 44% of patients, with a minimal but significant growth of the hematoma of 2.9 mL on average across all patients (p = 0.028). As hypothesized, the contralateral hemisphere volume was significantly reduced by 12.7 mL (p < 0.0001) between scans. This was unrelated to midline shift (R² = 0.012, p = 0.21), which averaged 2.3 mm. These findings suggest that distal parenchymal shrinkage may be a major compliance mechanism after ICH; the implications for ICP and brain function merit further study.

Intracerebral hemorrhage (ICH) is characterized by the rupture of blood vessels, allowing components from peripheral circulation to infiltrate the brain and impair central immune functions. This study employs non-targeted metabolomics to compare cerebrospinal fluid (CSF) metabolites between acute-phase and recovery-phase of ICH, aiming to identify metabolites associated with ICH central inflammation. CSF and plasma samples were collected from a retrospective observational cohort of participants with ICH (n = 38). Additionally, we obtained CSF samples from patients who underwent lower limb surgery due to accidental injuries, serving as healthy controls (n = 12). Non-targeted metabolomics analysis was performed, and inflammatory factors in the CSF were measured. The association between these metabolites and inflammation in the CSF was validated using a collagenase-induced ICH mouse model and microglial cultures in vitro. Our results demonstrate that the levels of certain metabolites in the cerebrospinal fluid of ICH patients changed significantly from the acute phase to the recovery phase (P < 0.05, VIP > 1). Furthermore, the concentration of inflammatory factors in the acute-phase CSF was significantly higher compared to both the recovery phase of ICH and healthy control levels. Correlation analyses of inflammatory factors and the patients' CSF metabolites revealed several metabolites associated with central inflammation. Notably, kynurenic acid (Kyna) exhibited a positive correlation with central inflammation and a negative correlation with the Glasgow Coma Scale (GCS). In the collagenase-induced ICH mouse model, elevated levels of Kyna were also associated with increased inflammation in the CSF. Additionally, in vitro studies demonstrated that Kyna regulates inflammatory cytokines by activating microglia. Our study highlights a significant relationship between metabolites in the CSF of ICH patients and central inflammation. Specifically, Kyna promotes inflammation by activating microglia, suggesting its potential as a promising target for therapeutic intervention in ICH central inflammation. Registration: 2023-KY-155-02.

Microcirculatory dysfunction is an important pathophysiology mechanism of early brain injury after aneurysmal subarachnoid hemorrhage (aSAH), which contributes to poor outcomes. The study was performed in Beijing Tiantan Hospital from October 2020 to July 2023. Patients with aSAH who underwent computed tomographic perfusion (CTP) within 24 h after ictus were enrolled prospectively. The peak time of arterial inflow (PTA), peak time of venous outflow (PTV), total venous outflow time (TVT), and difference value of arteriovenous peak time (DV) were collected from the time-density curve of CTP. Primary outcome was 3-month unfavorable functional outcome (modified Rankin Scale score of 4-6). Secondary outcomes included 3-month all-cause death and delayed cerebral ischemia. Multivariable logistic regression analysis and restricted cubic splines were performed to explore the relationship between cerebral hemodynamic parameters and outcomes. We also assessed the prognostic performance of incorporating hemodynamic parameters into previous nomogram models for 3-month poor clinical outcomes. A total of 612 patients were enrolled, among whom the mean age was 56.9 ± 12.3 years old and 391 (63.9%) were female. On multivariable analysis, prolonged TVT could significantly predict 3-month poor functional outcome (adjusted OR 1.074, 95%CI 1.013-1.139), while prolonged PTA was an independent predictor of 3-month all-cause death (adjusted OR 1.293, 95%CI 1.099-1.521). The addition of TVT or PTA to previous nomogram models led to improvements in C-statistics, net reclassification (NRI), and integrated discrimination improvement (IDI). Our study underscores the vital role of arterial inflow and venous outflow in sustaining microcirculation during the acute phase after aSAH, thereby offering new directions for future investigations into therapeutic targets.

Perihematomal edema (PHE) significantly aggravates secondary brain injury in patients with intracerebral hemorrhage (ICH), yet its detailed mechanisms remain elusive. Neutrophil extracellular traps (NETs) are known to exacerbate neurological deficits and worsen outcomes after stroke. This study explores the potential role of NETs in the pathogenesis of brain edema following ICH. The rat ICH model was created, immunofluorescence and Western blot were used to examine neutrophil accumulation, NET markers citrullinated histone H3 (CitH3) and myeloperoxidase (MPO), tight junction proteins (ZO-1 and Occludin), Aquaporin-4 (AQP4), matrix metalloproteinase-9 (MMP-9), and ERK phosphorylation (p-ERK) in brain tissues surrounding the hematoma. TUNEL staining and behavioral tests were employed to evaluate neuronal apoptosis and neurological dysfunction, while blood-brain barrier (BBB) permeability and brain edema were also measured by Evans blue and brain water content. Furthermore, the molecular mechanisms related to NETs-induced PHE were investigated using NETs, ERK, MMP-9 and AQP4 regulators, respectively. Ly6G⁺ neutrophils surrounding the hematoma developed NETs within 3 days post-ICH. NETs decreased tight junction proteins, destroyed BBB integrity, promoted brain edema, increased neuronal apoptosis, and exacerbated neurological deficits. Conversely, inhibition of NETs mitigated PHE, reduced neuronal apoptosis, and improved neurological functions. Mechanistically, NET-induced PHE was originated from impairment of BBB tight junction via ERK/MMP9 pathway, coupled with ERK-mediated AQP4 downregulation in perihematomal regions. These findings elucidated the effects of NETs on PHE, which offered promising insights for targeting NETs to relieve brain edema and secondary brain injury post-ICH.

Ischemic stroke (IS) commonly results in long-term disability, largely due to alterations in neuronal networks. In repeatable rodent IS model under naturalistic conditions, the difficulty of capturing single-cell neuronal activities and how this solves a long-standing challenge is still remained. Here, we combined a photothrombotic stroke model with head-mounted miniaturized two-photon microscopy (mTPM) to achieve longitudinal, real-time imaging of GABAergic neurons in the contralesional primary motor cortex (M1) in freely moving mice. We observed pronounced reductions in calcium dynamics in GABAergic neurons. These calcium dynamics emerged as early as day 3 post-stroke and persisted through day 19, despite no detectable gross motor deficits. Our findings highlight subtle cortical dysfunction persists despite normal gross motor function, underscoring the need for finer behavioral tests. This approach offered a powerful tool to bridge the gap between cellular-level dysfunction and macroscopic behaviors after focal ischemic stroke.

To investigate the association between rs402007 polymorphism in the ADAMTS-1 gene and carotid atherosclerotic plaque vulnerability, as well as the lipid-lowering efficacy of atorvastatin in cerebral infarction patients. Clinical data from 684 cerebral infarction patients admitted to The First Hospital of Hebei Medical University (2016-2019) were analyzed. Patients were stratified into stable plaque (n = 338) and vulnerable plaque (n = 346) groups based on carotid ultrasound. General information, biochemical markers, rs402007 (G/C) genotypes (dominant model), and allele frequencies were compared. Polymorphism genotyping was performed using TaqMan SNP assays (Applied Biosystems) on an ABI 7500 Fast Real-Time PCR system. Logistic regression evaluated plaque vulnerability risk factors and gene-risk factor interactions. Atorvastatin's lipid-lowering efficacy was compared across genotypes. Diabetes prevalence, LDL-C, TC, HCY, and FIB levels differed significantly between groups (P < 0.05). Genotypic distribution analysis revealed a higher frequency of the GG genotype in the stable plaque group (29.59% vs. 21.68%, χ² = 5.618, P = 0.018). Diabetes, LDL-C, HCY, and FIB were independent risk factors for plaque vulnerability (P < 0.05). A significant interaction between rs402007 polymorphism and LDL-C was observed (P < 0.05). Atorvastatin efficacy rates were 82.29% (GG), 84.27% (GC), and 89.27% (CC), with significant post-treatment lipid improvements in all genotypes (P < 0.05). The CC genotype exhibited superior efficacy compared to GG (P < 0.05). The rs402007 polymorphism influences carotid plaque vulnerability and modulates atorvastatin efficacy, underscoring its potential role in genotype-guided therapeutic strategies.

Electroencephalogram (EEG) during pinprick stimulation has the potential to unveil neural mechanisms underlying sensorimotor impairments post-stroke. A proof-of-concept study explored event-related peak pinprick amplitude and oscillatory responses in healthy controls and in people with acute and subuacute motor and sensorimotor stroke, their relationship, and to what extent EEG somatosensory responses can predict sensorimotor impairment. In this study, 26 individuals participated, 10 people with an acute and early subacute sensorimotor stroke, 6 people with an acute and early subacute motor stroke, and 10 age-matched controls. Pinpricks were applied to the dorsa of the impaired hand to collect somatosensory evoked potentials. Time(-frequency) analyses of somatosensory evoked potential (SEP) data at electrodes C3 and C4 explored peak pinprick amplitude and oscillatory responses across the three groups. Also, in stroke, (sensori-)motor impairments were assessed with the Fugl Meyer Assessment Upper Extremity (FMA) and Erasmus modified Nottingham Sensory Assessment (EmNSA) at baseline and 7 to 14 days later. Mixed model analyses were used to address objectives. It was demonstrated that increased beta desynchronization magnitude correlated with milder motor impairments (R²_adjusted = 0.213), whereas increased beta resynchronization and delta power were associated to milder somatosensory impairment (R²_adjusted = 0.550). At the second session, larger peak-to-peak SEP amplitude and beta band resynchronization at baseline were related to greater improvements in EMNSA and FMA scores, respectively, in the sensorimotor stroke group. These findings highlight the potential of EEG combined with somatosensory stimuli to differentiate between sensorimotor and motor impairments in stroke, offering preliminary insights into both diagnostic and prognostic aspects of upper limb recovery.

The rupture of vulnerable plaques is the principal cause of luminal thrombosis in acute ischemic stroke. The identification of plaque features that indicate risk for disruption may predict cerebrovascular events. Here, we aimed to build a high-risk intracranial plaque model that differentiates symptomatic from asymptomatic plaques using radiomic features based on high-resolution magnetic resonance imaging (HRMRI). One hundred and seventy-two patients with 188 intracranial atherosclerotic plaques (100 symptomatic and 88 asymptomatic) with available HRMRI data were recruited. Clinical characteristics and conventional plaque features on HRMRI were measured, including high signal on T1-weighted images (HST1), the degree of stenosis, normalized wall index, remodeling index, and enhancement ratio (ER). Univariate and multivariate analyses were performed to build a traditional model to differentiate between symptomatic and asymptomatic plaques. Radiomic features were extracted from pre-contrast and post-contrast HRMRI. A radiomic model based on HRMRI was constructed using random forests, ridge, least absolute shrinkage and selection operator, and deep learning (DL). A MIX model was constructed based on the radiomic model and the traditional model. Gender, HST1, and ER were associated with symptomatic plaques and were included in the traditional model, which had an area under the curve (AUC) of 0.697 in the training set and 0.704 in the test set. The radiomic model achieved an AUC of 0.982 in the training set and 0.867 in the test dataset for identifying symptomatic plaques. In the training set, the MIX model showed an AUC of 0.977. In the test set, the MIX model exhibited an improved AUC of 0.895, which outperformed the traditional model (p = 0.032). Radiomic analysis based on DL and machine learning can accurately identify high-risk intracranial plaques.