Post-stroke anxiety (PSA) has caused wide public concern in recent years, and the study on risk factors analysis and prediction is still an open issue. With the deepening of the research, machine learning has been widely applied to various scenarios and make great achievements increasingly, which brings new approaches to this field. In this paper, 395 patients with acute ischemic stroke are collected and evaluated by anxiety scales (i.e., HADS-A, HAMA, and SAS), hence the patients are divided into anxiety group and non-anxiety group. Afterward, the results of demographic data and general laboratory examination between the two groups are compared to identify the risk factors with statistical differences accordingly. Then the factors with statistical differences are incorporated into a multivariate logistic regression to obtain risk factors and protective factors of PSA. Statistical analysis shows great differences in gender, age, serious stroke, hypertension, diabetes mellitus, drinking, and HDL-C level between PSA group and non-anxiety group with HADS-A and HAMA evaluation. Meanwhile, as evaluated by SAS scale, gender, serious stroke, hypertension, diabetes mellitus, drinking, and HDL-C level differ in the PSA group and the non-anxiety group. Multivariate logistic regression analysis of HADS-A, HAMA, and SAS scales suggest that hypertension, diabetes mellitus, drinking, high NIHSS score, and low serum HDL-C level are related to PSA. In other words, gender, age, disability, hypertension, diabetes mellitus, HDL-C, and drinking are closely related to anxiety during the acute stage of ischemic stroke. Hypertension, diabetes mellitus, drinking, and disability increased the risk of PSA, and higher serum HDL-C level decreased the risk of PSA. Several machine learning methods are employed to predict PSA according to HADS-A, HAMA, and SAS scores, respectively. The experimental results indicate that random forest outperforms the competitive methods in PSA prediction, which contributes to early intervention for clinical treatment.

The blood–brain barrier (BBB) has a vital role in maintaining the homeostasis of the central nervous system (CNS). Changes in the structure and function of BBB can accelerate Alzheimer’s disease (AD) development. β-Amyloid (Aβ) deposition is the major pathological event of AD. We elucidated the function and possible molecular mechanisms of the effect of pseudogene ACTBP2 on the permeability of BBB in Aβ1–42 microenvironment. BBB model treated with Aβ1–42 for 48 h were used to simulate Aβ-mediated BBB dysfunction in AD. We proved that pseudogene ACTBP2, RNA-binding protein KHDRBS2, and transcription factor HEY2 are highly expressed in ECs that were obtained in a BBB model in vitro in Aβ1–42 microenvironment. In Aβ1–42-incubated ECs, ACTBP2 recruits methyltransferases KMT2D and WDR5, binds to KHDRBS2 promoter, and promotes KHDRBS2 transcription. The interaction of KHDRBS2 with the 3′UTR of HEY2 mRNA increases the stability of HEY2 and promotes its expression. HEY2 increases BBB permeability in Aβ1–42 microenvironment by transcriptionally inhibiting the expression of ZO-1, occludin, and claudin-5. We confirmed that knocking down of Khdrbs2 or Hey2 increased the expression levels of ZO-1, occludin, and claudin-5 in APP/PS1 mice brain microvessels. ACTBP2/KHDRBS2/HEY2 axis has a crucial role in the regulation of BBB permeability in Aβ1–42 microenvironment, which may provide a novel target for the therapy of AD.

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