Abstract Ischemic stroke is a neurological disorder caused by vascular stenosis or occlusion, accounting for approximately 87% of strokes. Clinically, the most effective therapy for ischemic stroke is vascular recanalization, which aims to rescue neurons undergoing ischemic insults. Although reperfusion therapy is the most effective treatment for ischemic stroke, it still has limited benefits for many patients, and ischemia-reperfusion (I/R) injury is a widely recognized cause of poor prognosis. Here, we aim to investigate the mechanism of protein phosphatase Mg 2+ /Mn 2+ dependent 1 K (PPM1K) mediates metabolic disorder of branched-chain amino acids (BCAA) by promoting fatty acid oxidation led to ferroptosis after cerebral I/R injury. We established the I/R model in mice and used BT2, a highly specific BCAA dehydrogenase (BCKD) kinase inhibitor to promote BCAA metabolism. It was further verified by lentivirus knocking down PPM1K in neurons. We found that BCAA levels were elevated after I/R injury due to dysfunctional oxidative degradation caused by phosphorylated BCKD E1α subunit (BCKDHA). Additionally, the level of phosphorylated BCKDHA was determined by decreased PPM1K in neurons. We next demonstrated that BCAA could induce oxidative stress, lipid peroxidation, and ferroptosis in primary cultured cortical neurons in vitro. Our results further showed that BT2 could reduce neuronal ferroptosis by enhancing BCAA oxidation through inhibition of BCKDHA phosphorylation. We further found that defective BCAA catabolism could induce neuronal ferroptosis by PPM1K knockdown. Furthermore, BT2 was found to alleviate neurological behavior disorders after I/R injury in mice, and the effect was similar to ferroptosis inhibitor ferrostatin-1. Our findings reveal a novel role of BCAA in neuronal ferroptosis after cerebral ischemia and provide a new potential target for the treatment of ischemic stroke. 

PPM1K mediates metabolic disorder of branched-chain amino acid and regulates cerebral ischemia-reperfusion injury by activating ferroptosis in neurons

The inheritance of Apolipoprotein E4 (APOEε4) brings the highest genetic risk of Alzheimer’s disease (AD), arguably the highest genetic risk in human pathology. Since the discovery of the association, APOE protein isoforms have been at the center of tens of thousands of studies and reports. While, without a doubt, our knowledge about the normal physiological function of APOE isoforms in the brain has increased tremendously, the questions of how the inheritance of the APOEε4 allele translates into a risk of AD, and the risk is materialized, remain unanswered. Moreover, the knowledge about the risk associated with APOEε4 has not helped design a meaningful preventative or therapeutic strategy. Animal models with targeted replacement of Apoe have been generated and, thanks to the recent NIH/NIA/Alzheimer’s disease Association initiative, are now freely available to AD researchers. While helpful in many aspects, none of the available models recapitulates normal physiological transcriptional regulation of the human APOE gene cluster. Changes in epigenetic regulation of APOE alleles in animal models in response to external insults have rarely been if ever, addressed. However, these animal models provide a useful tool to handle questions and investigate protein–protein interactions with proteins expressed by other recently discovered genes and gene variants considered genetic risk factors of AD, like Triggering Receptor expressed on Myeloid cells 2 (TREM2). In this review, we discuss genetic and epigenetic regulatory mechanisms controlling and influencing APOE expression and focus on interactions of APOE and TREM2 in the context of microglia and astrocytes’ role in AD-like pathology in animal models.

/pdf/apoee4-and-risk-of-alzheimers-disease-time-to-move-forward-3bo7dhfb.pdf

APOEε4 and risk of Alzheimer’s disease – time to move forward

Cerebrovascular diseases are known to cause significant morbidity and mortality to the general population. In patients with cerebrovascular disease, prompt clinical evaluation and radiographic interpretation are both essential in optimizing clinical management and in triaging patients for critical and potentially life-saving neurosurgical interventions. With recent advancements in the domains of artificial intelligence (AI) and machine learning (ML), many AI and ML algorithms have been developed to further optimize the diagnosis and subsequent management of cerebrovascular disease. Despite such advances, further studies are needed to substantively evaluate both the diagnostic accuracy and feasibility of these techniques for their application in clinical practice. This review aims to analyze the current use of AI and MI algorithms in the diagnosis of, and clinical decision making for cerebrovascular disease, and to discuss both the feasibility and future applications of utilizing such algorithms.We review the use of AI and ML algorithms to assist clinicians in the diagnosis and management of ischemic stroke, hemorrhagic stroke, intracranial aneurysms, and arteriovenous malformations (AVMs). After identifying the most widely used algorithms, we provide a detailed analysis of the accuracy and effectiveness of these algorithms in practice.The incorporation of AI and ML algorithms for cerebrovascular patients has demonstrated improvements in time to detection of intracranial pathologies such as intracerebral hemorrhage (ICH) and infarcts. For ischemic and hemorrhagic strokes, commercial AI software platforms such as RapidAI and Viz.AI have bene implemented into routine clinical practice at many stroke centers to expedite the detection of infarcts and ICH, respectively. Such algorithms and neural networks have also been analyzed for use in prognostication for such cerebrovascular pathologies. These include predicting outcomes for ischemic stroke patients, hematoma expansion, risk of aneurysm rupture, bleeding of AVMs, and in predicting outcomes following interventions such as risk of occlusion for various endovascular devices. Preliminary analyses have yielded promising sensitivities when AI and ML are used in concert with imaging modalities and a multidisciplinary team of health care providers.The implementation of AI and ML algorithms to supplement clinical practice has conferred a high degree of accuracy, efficiency, and expedited detection in the clinical and radiographic evaluation and management of ischemic and hemorrhagic strokes, AVMs, and aneurysms. Such algorithms have been explored for further purposes of prognostication for these conditions, with promising preliminary results. Further studies should evaluate the longitudinal implementation of such techniques into hospital networks and residency programs to supplement clinical practice, and the extent to which these techniques improve patient care and clinical outcomes in the long-term.

Role of artificial intelligence and machine learning in the diagnosis of cerebrovascular disease

Early identification of acute stroke lowers the fatality rate since clinicians can quickly decide on a quick decision of therapy. Brain computed tomography (CT) was one of the imaging techniques that were testified to be of utmost value in the evaluation of acute stroke, apart from unenhanced CT for emergency circumstances. The non-contrast image based treatment causes improper treatment of the patient. To overcome these issues, a novel hybrid image enhancement technique with an optimal feature selection based acute stroke approach was proposed. An advanced machine learning (ML) model was utilised to detect the stroke from the enhanced CT image. Hybrid preprocessing approaches were used to improve the contrast of collected CT images. Then, the enhanced images were segmented by using a modified active contour model, which was further proceeded for the feature extraction approach. Here, three various feature extraction approaches, local binary pattern, Gabor filter and discrete wavelet transform, were utilised to extract the features from the segmented image. Through the utilisation of a novel algorithm, the optimal features from the extracted images were selected. These optimal features were given to the ML model to predict the acute stroke appropriately. In order to reduce complexity and enhance the performance of a classifier, optimisation based regularisation parameters were chosen. The proposed XGBoost acute stroke detection approach provides 0.97 accuracy, 0.94 sensitivity and 0.015 false positive rate. Further, the proposed model outcome was contrasted with present models like support vector machine (SVM), artificial neural network (ANN), and naive Bayes (NB). The advanced XGBoost model provides a better contrast enhancement from the raw CT image as well as predicts acute stroke effectively. 

Enhanced non-contrast computed tomography images for early acute stroke detection using machine learning approach

Acute ischemic stroke (AIS) is the most prevalent type of stroke, and due to its high incidence, disability rate, and mortality rate, it imposes a significant burden on the health care system. Amino acids constitute one of the most crucial metabolic products within the human body, and alterations in their metabolic pathways have been identified in the microenvironment of AIS, thereby influencing the pathogenesis, severity, and prognosis of AIS. The amino acid metabolism characteristics in AIS are complex. On one hand, the dynamic progression of AIS continuously reshapes the amino acid metabolism pattern. Conversely, changes in the amino acid metabolism pattern also exert a double-edged effect on AIS. This interaction is bidirectional, dynamic, heterogeneous, and dose-specific. Therefore, the distinctive metabolic reprogramming features surrounding amino acids during the AIS process are systematically summarized in this paper, aiming to provide potential investigative strategies for the early diagnosis, treatment approaches, and prognostic enhancement of AIS. 

The role of amino acid metabolism alterations in acute ischemic stroke: From mechanism to application

The time window from stroke onset is critical for the treatment decision. However, in unknown onset stroke, it is often difficult to determine the exact onset time because of the lack of assessment methods, which can result in controversial and random treatment decisions. Previous studies have shown that serum biomarkers, in addition to imaging assessment, are useful for determining the stroke onset time. However, as yet there are no specific biomarkers or corresponding methodologies that are accurate and effective for determining the onset time of unknown onset stroke. Herein, we describe our novel advanced metabolites-based machine learning method (termed extreme gradient boost [XGBoost]) combined with recursive feature elimination, which accurately screened five metabolites from 1124 metabolites detected in serum. These metabolites were capable of both detecting acute ischemic stroke and backtracking the acute ischemic stroke onset time. To further investigate the pathological mechanisms of acute ischemic stroke, we also examined characteristic metabolites in different brain regions, and found two metabolites that could distinguish the core infarct area from the ischemic penumbra. Although this study is based on animal experiments, our machine learning framework and selected metabolites may provide a basis for clinical stroke evaluation and treatment. 

Detection of acute ischemic stroke and backtracking stroke onset time via machine learning analysis of metabolomics.

There is emerging evidence that antineoplastic agents and the cytotoxic effects on tumor tissues attenuate the benefits of chemotherapy due to tumor microenvironment changes. Nevertheless, the actual relationship between chemotherapy and recurrent tumors in which the genotypes differ from the original tumor after chemotherapy is unclear.Bone marrow transplantation, flow cytometer, immune inhibition and immunofluorescence will be utilized to investigate the effect of antineoplastic agents on bone-marrow-derived cells (BMDCs) release and recruitment, and to explore the pathways and mechanisms of antineoplastic agents in promoting tumor growth.Tumor growth and angiogenesis were significantly enhanced in the mouse model after treatment with antineoplastic agents such as cyclophosphamide, 5-fluorouracil, or cisplatin, along with large increases in proangiogenic vascular endothelial growth factor receptor-2 (VEGFR2+), β3+, CD11b+Gr-1+, and VEGFR2+β3+ BMDCs in circulating blood. BMDC recruitment and VEGFR2 and β3 mRNA transcription in tumor tissues were also enhanced by antineoplastic agents. Antineoplastic-agent-treated BMDCs markedly augmented tumor and endothelial cell proliferation, and β3 mRNA transcription in endothelial cells (ECs).The results suggested that antineoplastic-agent treatment augmented the tumor microenvironment by mobilizing proangiogenic BMDCs, enhancing BMDC recruitment and angiogenesis, and increasing BMDC-mediated tumor and EC functions. These results led to tumor growth and angiogenesis aggravation. It is paramount to elucidate the potential mechanism by which the cellular and molecular effects triggered by the antineoplastic agents attenuate the effects of cancer therapy, and thereafter to explore possible methods for improving tumor treatment efficacy. 

Antineoplastic agents in chemotherapy facilitating tumor growth and angiogenesis in the interval administrations.

Background The apolipoprotein E (ApoE) genetic variation may be involved in the development of Carotid Atherosclerosis (CAS) disease. So far, few data are available on the role of ApoE isoforms in CAS. The association between this ApoE genotype and CAS remains controversial. The aim of this study was to investigate ApoE gene polymorphism in relation to CAS and the relationships between ApoE gene polymorphism and plasma lipid levels in the ShaanXi Han populations. Patients and methods The study group enrolled 399 CAS participants and 399 non-CAS controls. ApoE gene polymorphisms were determined by Polymerase chain reaction and hybridization. Results The ε3/ε4 genotype and ε4 allele in patients with CAS were significantly higher than control participants. In stratified analyses by age and sex, the elevated risk conferred by ɛ4 allele was evident in adults under 60 years old, but not in adults over 60 years old, females and males. ε4 carriers had significantly elevated ApoB and ApoB/ApoA and decreased ApoE levels than ε2 carriers in CAS patients. After adjusting for confounding factors, hypertension, ApoA-I, low density lipoprotein cholesterol (LDL-C), triglycerides (TG) and ε4 allele were significant independent risk factor for CAS. ApoE-ε4 allele was associated with a nearly 1.5-fold increased risk of CAS. Conclusion This study provides convincing evidence that ε4 allele, hypertension, ApoA-I, LDL-C and TG levels are independent risk factor for CAS in the ShaanXi Han populations. ApoE polymorphism was associated with CAS and this association was partly mediated through blood lipids. Also, the clinical use of genomic data may become useful in optimizing individual preventative and therapeutic strategies.

https://www.dovepress.com/getfile.php?fileID=81638

Effect of Apolipoprotein E ε4 Allele on the Progression of Carotid Atherosclerosis Through Apolipoprotein Levels

Objective Genetic variation in the methylenetetrahydrofolate reductase (MTHFR) gene may contribute to the development of cerebral infarction (CI); however, results have been inconsistent across studies with different populations, including studies of the Chinese population. The aim of this study was to analyze the effect of MTHFR gene polymorphism on serum lipid and homocysteine levels among patients with CI in the Northwest Chinese Han population. Patients and methods A total of 521 CI patients and 524 non-CI controls were enrolled in the study. Polymerase chain reaction and hybridization were utilized to identify MTHFR gene polymorphisms. Multivariate logistic regression analysis was used to assess the associations of MTHFR gene polymorphism with risk of CI. Results Frequencies of the TT genotype and the T allele were markedly higher among CI patients than among controls. After stratifying our data by sex and age, we determined that these differences in frequency of the TT genotype and the T allele were statistically significant among participants of two different age brackets and among men, but not among women (i.e., there were no statistically significant differences between female patients and female controls). CI patients and control participants with the CT or TT genotype had significantly higher homocysteine (Hcy) levels than those with the CC genotype. Among CI patients, CT/TT carriers showed significantly lower high-density lipoprotein cholesterol (HDL-C) and apolipoprotein A-I (ApoA-I) levels as compared with CC carriers, but there was no significant difference for control participants. Multivariable logistic regression analysis showed that drinking; smoking; diabetes mellitus; levels of Hcy, direct bilirubin (DB), indirect bilirubin (IB), ApoA-I, and total protein (TP); and TT genotype were significant independent risk factors for CI. Conclusions The results suggested that the TT genotype of the MTHFR C677T gene polymorphism, which is associated with hyperhomocysteinemia (HHcy), might be of great clinical significance in the identification of new biomarkers for CI and in the development of individualized preventive and therapeutic strategies.

Associations of MTHFR gene polymorphism with lipid metabolism and risk of cerebral infarction in the Northwest Han Chinese population

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Detection of acute ischemic stroke and backtracking stroke onset time via machine learning analysis of metabolomics.

Antineoplastic agents in chemotherapy facilitating tumor growth and angiogenesis in the interval administrations.

Effect of Apolipoprotein E ε4 Allele on the Progression of Carotid Atherosclerosis Through Apolipoprotein Levels

Associations of MTHFR gene polymorphism with lipid metabolism and risk of cerebral infarction in the Northwest Han Chinese population