Multi-Level Biomarkers for Early Diagnosis of Ischaemic Stroke: A Systematic Review and Meta-Analysis

Near infrared-emitting carbon dots for the detection of glial fibrillary acidic protein (GFAP): a non-enzymatic approach for the early identification of stroke and glioblastoma

Immunoassay techniques are widely recognized for their sensitivity and selectivity in biomarker detection; however, their high cost, time-consuming protocols and limited stability often pose significant limitations. In this study, we address these challenges by developing an antibody-free fluorescent platform for the detection of glial fibrillary acidic protein (GFAP), a biomarker released from astrocytes, which plays a critical role in neurological diseases such as ischemic stroke and glioblastoma (GBM). Glutamic acid (GA), a neurotransmitter prevalent in the brain, was selected to quench a near-infrared (NIR) emitting carbon dot-based probe, exploiting the potential interaction between GA and GFAP. The probe demonstrated a turn-on response towards GFAP in the presence of various co-existing biomolecules and ions with a detection limit of 1.8 pg mL-1. A real sample assay conducted in human serum further validated the performance of the probe, achieving a recovery rate of 85% to 97%, underscoring the potential of the probe as a reliable and cost-effective tool for GFAP detection in clinical settings.

Cell-Free Nucleic Acids for Early Diagnosis of Acute Ischemic Stroke: A Systematic Review and Meta-Analysis

Rapid identification of acute ischemic stroke (AIS) is challenging in both pre-hospital and hospital settings. We aimed to identify the most promising cell-free nucleic acids (cfNAs) as diagnostic biomarkers for IS within 72 h from symptom onset. We searched PubMed, Web of Science, EMBASE, and Cochrane Library for published articles that evaluated blood cfNAs in the early diagnosis of AIS until 10 May 2023. The diagnostic performances of individual cfNAs were pooled by random-effects meta-analysis based on the fold change of biomarkers' level between AIS and non-AIS patients. Of 2955 records, 66 articles reporting 143 different cfNAs met the inclusion criteria. The median sample size was 110, and 21.4% of the studies performed validation. Among selected high-quality studies, miR-106b-5p, miR-124, miR-155, lncRNA H19, and cfDNA showed good diagnostic performance. Data from four studies on cfDNA involving 355 AIS patients and 97 controls were pooled in the meta-analysis, which showed a significant fold change between AIS and controls (pooled ratio 1.48, 95% confidence interval 1.23-1.79, p < 0.001). This review highlights that cfDNA, miR-106b-5p, miR-124, miR-155, and lncRNA H19 are the most promising biomarkers for AIS diagnosis, and further research is needed for verification.

Clinical and Epidemiological Characteristics of Patients with Functional Stroke Mimics: A Case-Control Study from Southern Portugal

BACKGROUND

Patients with functional neurological disorder presenting as stroke mimics or functional stroke mimics (FSMs) pose significant diagnostic challenges. In the acute phase, especially when patients are present within the therapeutic window for acute reperfusion treatments, a misdiagnosis of FSM can lead to unnecessary and costly interventions. Despite its clinical importance, the literature on the risk factors for FSM is limited. This study aims to compare the clinical and epidemiological characteristics of patients with FSM to those with confirmed acute ischemic stroke (AIS).

METHODS

This case-control study involved temporal matching between consecutive series of patients with FSM and controls with AIS from a single tertiary university hospital in southern Portugal.

RESULTS

A total of 188 patients were included: 64 cases (FSM) and 188 controls (AIS). The rate of stroke code activation and use of ambulance between was comparable between the two groups. The group of patients with FSM was younger (53.2 years vs. 69.5 years, p < 0.001) and had a higher proportion of females (52.4% vs. 47.6%, p = 0.001). There was no difference in terms of clinical severity at presentation. The proportion of specific signs, such as transcortical aphasia (3.1% vs. 20.9%, p = 0.014), gait abnormalities (15.6% vs. 33.9%, p = 0.004), and cranial nerve abnormalities (31.2% vs. 43.5%, p = 0.042), was lower in the FSM group compared to the AIS group. The proportion of patients on antithrombotic therapy (90.9% vs. 9.1%, p = 0.007) and antihypertensive drugs (78.5%, vs. 21.5%, p < 0.001) prior to the event was significantly higher in the AIS group. Likewise, the prevalence of cerebrovascular risk factors such as diabetes mellitus (14.3% vs. 85.7%, p = 0.005), arterial hypertension (23.8% vs. 76.2%, p = 0.001), and smoking (43.7% vs. 56.3%, p = 0.005) was lower in the FSM group compared to the AIS group. No statistically significant differences were observed in cholesterol levels or the prevalence of dyslipidemia between the two groups. Psychiatric comorbidities, including generalized anxiety disorder (71.4% vs. 28.6%, p = 0.05) and major depressive disorder (61.9% vs. 28.1%, p = 0.01), were more prevalent in the FSM group.

CONCLUSIONS

Patients with FSM display different clinical and epidemiological profiles, with a higher likelihood of being younger, female, having prior psychiatric conditions, and lacking traditional cerebrovascular risk factors.

Quantum dot-to-dye-based fluorescent ratiometric immunoassay for GFAP: a biomarker for ischaemic stroke and glioblastoma multiforme

Ischaemic stroke and glioma, as leading causes of mortality and long-term disability, pose critical challenges to healthcare systems, necessitating innovative approaches to enable early and cost-effective diagnosis for timely intervention. Glial fibrillary acidic protein (GFAP), an astrocyte-produced protein, is highly responsive to both ischaemic stroke and glioblastoma multiforme, with its levels correlating to the extent of brain damage. In this study, we present the development of an immunoassay probe for the ratiometric fluorescent detection of glial fibrillary acidic protein (GFAP), employing a monoclonal GFAP antibody-conjugated silicon quantum dots (Ab@SiQDs) and rhodamine B dye (RhB)-based immunoprobe. The developed probe exhibited a fluorescence emission shift from 580 nm to 530 nm in response to GFAP, demonstrating a linear detection range from 31.15 pg mL-1 to 243 pg mL-1, with a limit of detection of 0.7 pg mL-1. Additionally, the immunoprobe showed high selectivity for GFAP, effectively discriminating it from other potential interfering biomolecules and ions. The probe was also capable of detecting GFAP in spiked serum samples, achieving a recovery rate ranging from 83% to 111%. Notably, a cost-effective paper strip assay was developed, offering significant potential for the visual detection of GFAP under ultraviolet illumination.

Development, assessment and validation of a novel nomogram model for predicting stroke mimics in stroke center：A single-center observational study

BACKGROUND

Early recognition and prediction of stroke mimics (SM) can avoid inappropriate recanalization therapy and delay in the management of SM etiology. The purpose of this study is to screen the predictors for SM and develop a novel predictive nomogram model for predicting SM. Meanwhile, the diagnostic performance of the nomogram model was evaluated and validated. The diagnostic efficacy of the nomogram model was also compared with four other SM structured scales.

METHODS

The clinical data of eligible patients were retrospectively enrolled as training datasets from January 2020 to December 2021; and the clinical data of eligible patients were prospectively enrolled as validation datasets from February to December 2022 in stroke center, Shengjing hospital, respectively. Univariate analysis and Lasso regression were used to select the optimal predictors for the training set, and a nomogram model was constructed by multivariate logistics regression, predictive scoring based on nomogram model is performed for each subject suffering from suspected acute ischemic stroke. Area under the curve (AUC), Hosmer-Lemeshow goodness-of-fit test, Calibration curve, decision curve analysis (DCA), clinical impact curve (CIC) analysis and bootstrap sampling were performed to assess and validate the predictive performance and clinical utility of the nomogram model, and the DeLong test was used to compare the overall diagnostic performance of the nomogram model with the other four structured SM scales. The Delong test was also conducted to assess the external reliability of the SM nomogram model by comparing the predictive diagnostic performance of the validation set with the training set. Additionally, the Calibration curve was utilized to evaluate the diagnostic calibration capability of the SM nomogram model in the validation set.

RESULTS

703 eligible patients (68 with SM, accounting for 9.7 %) were assigned to the training set, while 301 patients (26 with SM, accounting for 8.6 %) were assigned to the validation set. A nomogram model was then developed using these six parameters (SBP, history of epilepsy, isolated dizziness, isolated sensory impairment, headache, and absence of speech impairment symptoms), a dynamic web-based version of the nomogram was subsequently created. Comparing with four other scales, the nomogram model showed the highest overall diagnostic performance (AUC = 0.929, 95%CI = 0.908-0.947). The Hosmer-Lemeshow goodness-of-fit test was conducted to assess the agreement between the predicted SM values from the model and the observed SM values. The results of the test indicated a favorable consistency (χ2 = 9.299, P = 0.3177) between the predicted and observed SM. The results obtained from the analysis of the Calibration curve, DCA curve, and CIC analysis suggested that the nomogram possesses a favorable predictive capacity and superior clinical usefulness. Furthermore, the external validation demonstrated that there is no significant difference in the overall predictive diagnostic performance between the validation set and training set (0.929 vs 0.910, P > 0.05), thereby confirming the favorable stability of the nomogram model.

CONCLUSION

Our study firstly proposed a nomogram prediction approach based on the clinical features of SM, which could effectively predict the occurrence of SM. The utilization of the nomogram in stroke center proves advantageous for the identification and evaluation of SM, thereby enhancing diagnostic decision-making and strategies employed for suspected acute stroke patients.

Biomarkers for Early Detection of Stroke: A Systematic Review

Stroke remains a leading cause of mortality and disability worldwide. Identifying reliable biomarkers for stroke diagnosis and risk prediction could significantly improve patient outcomes through earlier intervention and better risk management. The objective of this systematic review is to systematically review recent studies investigating biomarkers for stroke diagnosis and risk prediction and to synthesize the most promising findings. We conducted a systematic review of 10 studies published between 2008 and 2023 that examined various biomarkers in relation to stroke. Studies were evaluated for quality using a simplified version of the Mixed Methods Appraisal Tool. The reviewed studies investigated a diverse array of biomarkers, including lipids, inflammatory markers, hemodynamic markers, microRNAs, metabolites, and neurodegenerative markers. Key findings include the following: (1) non-traditional lipid markers such as triglycerides and non-high-density lipoprotein cholesterol may be more predictive of stroke risk than low-density lipoprotein; (2) inflammatory markers, particularly IL-6, showed strong associations with stroke risk; (3) hemodynamic markers like midregional proatrial natriuretic peptide (MRproANP) and N-terminal pro-B-type natriuretic peptide (NT-proBNP) demonstrated potential in distinguishing stroke subtypes; (4) specific microRNAs (miR-125a-5p, miR-125b-5p, miR-143-3p) were upregulated in acute ischemic stroke; (5) metabolomic studies identified novel markers such as tetradecanedioate and hexadecanedioate associated with cardioembolic stroke; (6) neurodegenerative markers (total-tau, neurofilament light chain) were linked to increased stroke risk. This review highlights the potential of various biomarkers in improving stroke diagnosis and risk prediction. While individual markers show promise, a multi-marker approach combined with clinical variables appears most likely to yield clinically useful tools. Further large-scale validation studies are needed before these biomarkers can be implemented in routine clinical practice.

Systemic-inflammatory indices and clinical outcomes in patients with anterior circulation acute ischemic stroke undergoing successful endovascular thrombectomy

Background

There is a lack of comprehensive profile assessment on complete blood count (CBC)-derived systemic-inflammatory indices, and their correlations with clinical outcome in patients with anterior circulation acute ischemic stroke (AIS) who achieved successful recanalization by endovascular thrombectomy (EVT).

Methods

Patients with anterior circulation AIS caused by large vessel occlusion (AIS-LVO) were retrospectively screened from December 2018 to December 2022. Systemic-inflammatory indices including ratios of neutrophil-to-lymphocyte (NLR), monocyte-to-lymphocyte (MLR), platelet-to-lymphocyte (PLR), and platelet-to-neutrophil (PNR), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), and aggregate inflammation systemic index (AISI) on admission and the first day post-EVT were calculated. Their correlations with symptomatic intracranial hemorrhage (sICH) and unfavorable 90-day functional outcome (modified Rankin Scale score of 3-6) were analyzed.

Results

A total of 482 patients [65 (IQR, 56-72) years; 33 % female] were enrolled, of which 231 (47.9 %) had unfavorable 90-day outcome and 50 (10.4 %) developed sICH. Day 1 neutrophil and monocyte counts, NLR, MLR, PLR, SII, SIRI, and AISI were increased, while lymphocyte and PNR were decreased compared to their admission levels. In multivariate analyses, neutrophil count, NLR, SII, and AISI on day 1 were independently associated with 90-day functional outcome. Moreover, day 1 neutrophil count, NLR, MLR, PLR, PNR, SII, and SIRI were independently linked to the occurrence of sICH. No admission variables were identified as independent risk factors for patient outcomes.

Conclusion

CBC-derived systemic-inflammatory indices measured on the first day after successful EVT are predictive of 90-day functional outcome and the sICH occurrence in patients with anterior circulation AIS-LVO.