Effects of salvianolic acid on cerebral perfusion in patients after acute stroke: A single-center randomized controlled trial

Targeting post-stroke neuroinflammation with Salvianolic acid A: molecular mechanisms and preclinical evidence

Salvianolic acid A (SalA), a bioactive compound extracted from Salvia miltiorrhiza, has garnered considerable interest for its potential in ameliorating the post-stroke neuroinflammation. This review delineates the possible molecular underpinnings of anti-inflammatory and neuroprotective roles of SalA, offering a comprehensive analysis of its therapeutic efficacy in preclinical studies of ischemic stroke. We explore the intricate interplay between post-stroke neuroinflammation and the modulatory effects of SalA on pro-inflammatory cytokines, inflammatory signaling pathways, the peripheral immune cell infiltration through blood-brain barrier disruption, and endothelial cell function. The pharmacokinetic profiles of SalA in the context of stroke, characterized by enhanced cerebral penetration post-ischemia, makes it particularly suitable as a therapeutic agent. Preliminary clinical findings have demonstrated that salvianolic acids (SA) has a positive impact on cerebral perfusion and neurological deficits in stroke patients, warranting further investigation. This review emphasizes SalA as a potential anti-inflammatory agent for the advancement of innovative therapeutic approaches in the treatment of ischemic stroke.

Potential implications of polyphenolic compounds in neurodegenerative diseases

Neurodegenerative diseases are common chronic diseases related to progressive damage to the nervous system. Current neurodegenerative diseases present difficulties and despite extensive research efforts to develop new disease-modifying therapies, there is still no effective treatment for halting the neurodegenerative process. Polyphenols are biologically active organic compounds abundantly found in various plants. It has been reported that plant-derived dietary polyphenols may improve some disease states and promote health. Emerging pieces of evidence indicate that polyphenols are associated with neurodegenerative diseases. This review aims to overview the potential neuroprotective roles of polyphenols in most common neurodegenerative disorders, including Alzheimer's disease, Parkinson's disease, epilepsy, and ischemic stroke.

Predictive value of perfusion weighted imaging for early new lesions after stroke patients receive endovascular treatment

Background

Previous studies have focused on early new lesion-associated factors, but the differences in the perfusion status between the at-risk hypoperfusion areas with new lesions and the other hypoperfusion areas in stroke patients before thrombectomy is not clear. We investigated the value of perfusion-weighted imaging (PWI) in predicting early new lesions in patients after stroke.

Methods

Fifty-five acute stroke patients who underwent diffusion-weighted imaging (DWI) and PWI before and after thrombectomy within 24 h were eligible. The PWI parameters of the core infarct areas (high signal tissue on the DWI), the at-risk hypoperfusion areas (hypoperfusion area with new lesions at follow-up PWI) and the other hypoperfusion areas of patients with new lesions were collected. Statistical analysis was performed to predict new lesions after stroke. The differences in the PWI parameters of the core infarct areas, the at-risk hypoperfusion areas and the other hypoperfusion areas were compared. Receiver operating characteristic (ROC) curve analysis was used to assess the predictive value of the PWI parameters (P<0.05) for the occurrence of new lesions in patients with acute stroke after thrombectomy.

Results

Fifty-five stroke patients were analyzed, including forty patients (72.73%) with new lesions and fifteen patients (27.27%) without new lesions. Acute stroke patients with new lesions had a longer mean transit time (MTT) and time to peak (TTP) in the at-risk hypoperfusion areas (11.95±3.29; 38.30±11.39) than in the other hypoperfusion areas (8.68±2.08; 29.76±6.86), both of which were significantly different (P<0.0001; P<0.0001, respectively). The ROC analysis showed that the sensitivity and specificity of MTT for predicting the occurrence of new lesions after stroke were 70.00% and 87.50%, respectively; the sensitivity and specificity of TTP were 70.00% and 80.00%, respectively.

Conclusions

MTT and TTP may be useful in predicting early new lesions in acute stroke patients after thrombectomy.

Outcome prediction for patients with anterior circulation acute ischemic stroke following endovascular treatment: A single-center study

Previous studies have identified various factors associated with the outcomes of acute ischemic stroke (AIS) but considered only 1 or 2 predictive factors. The present study aimed to use outcome-related factors derived from biochemical, imaging and clinical data to establish a logistic regression model that can predict the outcome of patients with AIS following endovascular treatment (EVT). The data of 118 patients with anterior circulation AIS (ACAIS) who underwent EVT between October 2014 and August 2018 were retrospectively analyzed. The patients were divided into 2 groups based on the modified Rankin Scale score at three months after surgery, where 0–2 points were considered to indicate a favorable outcome and 3–6 points were considered a poor outcome. Non-conditional logistic stepwise regression was used to identify independent variables that were significantly associated with patient outcome, which were subsequently used to establish a predictive statistical model, receiver operating characteristic (ROC) curve was used to show the performance of statistical model and analyze the specific association between each factor and outcome. Among the 118 patients, 47 (39.83%) exhibited a good and 71 (60.17%) exhibited a poor outcome. Multivariate analysis revealed that the predictive model was statistically significant (χ²=78.92; P<0.001), and that the predictive accuracy of the model was 83.1%, which was higher compared with that obtained using only a single factor. ROC curve analysis shows the area under curve of the statistical model was 0.823, the analysis of diagnostic threshold for prognostic factors indicated that age, diffusion-weighted imaging lesion volume, glucose on admission, National Institutes of Health Stroke Scale score on admission and hypersensitive C-reactive protein were valuable predictive factors for the outcome of EVT (P<0.05). In conclusion, a predictive model based on non-conditional logistic stepwise regression analysis was able to predict the outcome of EVT for patients with ACAIS.