Recanalization therapy is the most effective treatment for eligible patients with acute ischemic stroke (AIS). Gut microbiota are involved in the pathological mechanisms and outcomes of AIS. However, the association of gut microbiota features with adverse recanalization therapy outcomes remains unclear. Herein, we investigated gut microbiota features associated with neurological deficits in patients with AIS after recanalization therapy and whether they predict the patients’ functional outcomes. We collected fecal samples from 51 patients with AIS who received recanalization therapy and performed 16S rRNA gene sequencing (V3–V4). We compared the gut microbiota diversity and community composition between mild to moderate and severe disability groups. Next, the characteristic gut microbiota was compared between groups, and we noted that the characteristic gut microbiota in patients with mild to moderate disability included Bilophila, Butyricimonas, Oscillospiraceae_UCG-003, and Megamonas. Moreover, the relative abundance of Bacteroides fragilis, Fusobacterium sp., and Parabacteroides gordonii was high in patients with severe disability. The characteristic gut microbiota was correlated with neurological deficits, and areas under the receiver operating characteristic curves confirmed that the characteristic microbiota predicted adverse recanalization therapy outcomes. In conclusion, gut microbiota characteristics are correlated with recanalization therapy outcomes in patients with AIS. Gut microbiota may thus be a promising biomarker associated with early neurological deficits and predict recanalization therapy outcomes.

Bidirectional communication of the microbiota–gut–brain axis is crucial in stroke. Recanalization therapy, namely intravenous thrombolysis (IVT) and endovascular thrombectomy (EVT), are recommended for eligible patients with acute ischemic stroke (AIS). It remains unclear whether gut microbiota metabolites, namely trimethylamine N-oxide (TMAO) and short-chain fatty acids (SCFAs), can predict the prognosis after recanalization therapy. This prospective study recruited patients with AIS receiving IVT, EVT, or both. The National Institutes of Health Stroke Scale (NIHSS) and modified Rankin scale (mRS) scores were used to assess the severity and functional outcomes of AIS, respectively. A functional outcome of mild-to-moderate disability was defined as a mRS score of 0–3 at discharge. Plasma TMAO and SCFA levels were measured through liquid chromatography with triple-quadrupole mass spectrometry. Fifty-six adults undergoing recanalization therapy for AIS were enrolled. Results showed that TMAO levels were not associated with stroke severity and functional outcomes, while isovalerate levels (one of the SCFAs) were negatively correlated with NIHSS scores at admission and discharge. In addition, high isovalerate levels were independently associated with a decreased likelihood of severe disability. The study concluded that an elevated plasma isovalerate level was correlated with mild stroke severity and disability after recanalization therapy for AIS.

This study proposes a method to improve fractional-order particle swarm optimizer to overcome the shortcomings of traditional swarm algorithms, such as low search accuracy in a high-dimensional space, falling into local minimums, and nonrobust results. In natural phenomena, our controllable fractional-order particle swarm optimizer can explore search spaces in detail to obtain high resolutions. Moreover, the proposed algorithm is memorable, i.e., position updates focus on the particle position of previous and last generations, rendering it conservative when updating the position, and obtained results are robust. For verifying the algorithm’s effectiveness, 11 test functions compare the average value, overall best value, and standard deviation of the controllable fractional-order particle swarm optimizer and controllable particle swarm optimizer; experimental results show that the stability of the former is better than the latter. Furthermore, the solution position found by the controllable fractional-order particle swarm optimizer is more reliable. Therefore, the improved method proposed herein is effective. Moreover, this research describes how a heart disease prediction application uses the optimizer we proposed to optimize XGBoost hyperparameters with custom target values. The final verification of the obtained prediction model is effective and reliable, which shows the controllability of our proposed fractional-order particle swarm optimizer.