Machine learning models reveal the critical role of nighttime systolic blood pressure in predicting functional outcome for acute ischemic stroke after endovascular thrombectomy.

Background: Blood pressure (BP) is a key factor for the clinical outcomes of acute ischemic stroke (AIS) receiving endovascular thrombectomy (EVT). However, the effect of the circadian pattern of BP on functional outcome is unclear. Methods: This multicenter, retrospective, observational study was conducted from 2016 to 2023 at three hospitals in China (ChiCTR2300077202). A total of 407 patients who underwent endovascular thrombectomy (EVT) and continuous 24-h BP monitoring were included. Two hundred forty-one cases from Beijing Hospital were allocated to the development group, while 166 cases from Peking University Shenzhen Hospital and Hainan General Hospital were used for external validation. Postoperative systolic BP (SBP) included daytime SBP, nighttime SBP, and 24-h average SBP. Least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), Boruta were used to screen for potential features associated with functional dependence defined as 3-month modified Rankin scale (mRS) score ≥ 3. Nine algorithms were applied for model construction and evaluated using area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Results: Three hundred twenty-eight of 407 (80.6%) patients achieved successful recanalization and 182 patients (44.7%) were functional independent. NIHSS at onset, modified cerebral infarction thrombolysis grade, atrial fibrillation, coronary atherosclerotic heart disease, hypertension were identified as prognostic factors by the intersection of three algorithms to construct the baseline model. Compared to daytime SBP and 24-h SBP models, the AUC of baseline + nighttime SBP showed the highest AUC in all algorithms. The XGboost model performed the best among all the algorithms. ROC results showed an AUC of 0.841 in the development set and an AUC of 0.752 in the validation set for the baseline plus nighttime SBP model, with a brier score of 0.198. Conclusion: This study firstly explored the association between circadian BP patterns with functional outcome for AIS. Nighttime SBP may provide more clinical information regarding the prognosis of patients with AIS after EVT.

LC-MS/MS based metabolomic analysis of serum from patients with cerebrovascular stenosis.

Cerebrovascular stenosis (CVS) is the main cause of ischemic stroke, which greatly threatens human life. Hence, it's important to perform early screenings for CVS. Metabolomics is an emerging omics approach that has great advantages in disease screening and diagnosis. Therefore, we aim to elucidate the correlation between CVS and metabolomics, which can aid in conducting CVS screening at an early stage. Patients with CVS in Beijing Hospital were included in the study. A total of 36 participants, including 18 patients diagnosed with CVS and 18 healthy individuals, were recruited at Beijing Hospital between May 2022 and October 2021. The serum samples were analyzed for liquid chromatography-tandem mass spectrometry (LC-MS/MS). Then, multivariate statistical methods, including principal component analysis (PCA), partial least squares-discriminant analysis (PLS-DA) and orthogonal partial least squares-discriminant analysis (OPLS-DA) were performed. Differential metabolites were obtained and demonstrated by volcano plot and heatmap. The study recruited 36 participants, including 18 patients with CVS and 18 healthy participants. A total of 150 metabolites were identified. Multivariate statistical analysis revealed significant differences between patients and healthy participants. Furthermore, 30 serum metabolites levels differed significantly between two groups. Differential metabolites were enriched in phenylalanine, tyrosine, and tryptophan biosynthesis; primary bile acid biosynthesis, and other pathways. This study identified differential metabolites in patients with CVS and elucidated the relevant metabolic pathways. Thus, these findings aid in the study of the pathogenesis of CVS and its early diagnosis. DATA AVAILABILITY STATEMENT: The datasets generated for this study are available on request to the corresponding author.

Non-Contrasted CT Radiomics for SAH Prognosis Prediction.

Subarachnoid hemorrhage (SAH) denotes a serious type of hemorrhagic stroke that often leads to a poor prognosis and poses a significant socioeconomic burden. Timely assessment of the prognosis of SAH patients is of paramount clinical importance for medical decision making. Currently, clinical prognosis evaluation heavily relies on patients' clinical information, which suffers from limited accuracy. Non-contrast computed tomography (NCCT) is the primary diagnostic tool for SAH. Radiomics, an emerging technology, involves extracting quantitative radiomics features from medical images to serve as diagnostic markers. However, there is a scarcity of studies exploring the prognostic prediction of SAH using NCCT radiomics features. The objective of this study is to utilize machine learning (ML) algorithms that leverage NCCT radiomics features for the prognostic prediction of SAH. Retrospectively, we collected NCCT and clinical data of SAH patients treated at Beijing Hospital between May 2012 and November 2022. The modified Rankin Scale (mRS) was utilized to assess the prognosis of patients with SAH at the 3-month mark after the SAH event. Based on follow-up data, patients were classified into two groups: good outcome (mRS ≤ 2) and poor outcome (mRS > 2) groups. The region of interest in NCCT images was delineated using 3D Slicer software, and radiomic features were extracted. The most stable and significant radiomic features were identified using the intraclass correlation coefficient, t-test, and least absolute shrinkage and selection operator (LASSO) regression. The data were randomly divided into training and testing cohorts in a 7:3 ratio. Various ML algorithms were utilized to construct predictive models, encompassing logistic regression (LR), support vector machine (SVM), random forest (RF), light gradient boosting machine (LGBM), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and multi-layer perceptron (MLP). Seven prediction models based on radiomic features related to the outcome of SAH patients were constructed using the training cohort. Internal validation was performed using five-fold cross-validation in the entire training cohort. The receiver operating characteristic curve, accuracy, precision, recall, and f-1 score evaluation metrics were employed to assess the performance of the classifier in the overall dataset. Furthermore, decision curve analysis was conducted to evaluate model effectiveness. The study included 105 SAH patients. A comprehensive set of 1316 radiomics characteristics were initially derived, from which 13 distinct features were chosen for the construction of the ML model. Significant differences in age were observed between patients with good and poor outcomes. Among the seven constructed models, model_SVM exhibited optimal outcomes during a five-fold cross-validation assessment, with an average area under the curve (AUC) of 0.98 (standard deviation: 0.01) and 0.88 (standard deviation: 0.08) on the training and testing cohorts, respectively. In the overall dataset, model_SVM achieved an accuracy, precision, recall, f-1 score, and AUC of 0.88, 0.84, 0.87, 0.84, and 0.82, respectively, in the testing cohort. Radiomics features associated with the outcome of SAH patients were successfully obtained, and seven ML models were constructed. Model_SVM exhibited the best predictive performance. The radiomics model has the potential to provide guidance for SAH prognosis prediction and treatment guidance.

Computed tomography angiography-based radiomics model for predicting carotid atherosclerotic plaque vulnerability.

Vulnerable carotid atherosclerotic plaque (CAP) significantly contributes to ischemic stroke. Neovascularization within plaques is an emerging biomarker linked to plaque vulnerability that can be detected using contrast-enhanced ultrasound (CEUS). Computed tomography angiography (CTA) is a common method used in clinical cerebrovascular assessments that can be employed to evaluate the vulnerability of CAPs. Radiomics is a technique that automatically extracts radiomic features from images. This study aimed to identify radiomic features associated with the neovascularization of CAP and construct a prediction model for CAP vulnerability based on radiomic features. CTA data and clinical data of patients with CAPs who underwent CTA and CEUS between January 2018 and December 2021 in Beijing Hospital were retrospectively collected. The data were divided into a training cohort and a testing cohort using a 7:3 split. According to the examination of CEUS, CAPs were dichotomized into vulnerable and stable groups. 3D Slicer software was used to delineate the region of interest in CTA images, and the Pyradiomics package was used to extract radiomic features in Python. Machine learning algorithms containing logistic regression (LR), support vector machine (SVM), random forest (RF), light gradient boosting machine (LGBM), adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and multi-layer perception (MLP) were used to construct the models. The confusion matrix, receiver operating characteristic (ROC) curve, accuracy, precision, recall, and f-1 score were used to evaluate the performance of the models. A total of 74 patients with 110 CAPs were included. In all, 1,316 radiomic features were extracted, and 10 radiomic features were selected for machine-learning model construction. After evaluating several models on the testing cohorts, it was discovered that model_RF outperformed the others, achieving an AUC value of 0.93 (95% CI: 0.88-0.99). The accuracy, precision, recall, and f-1 score of model_RF in the testing cohort were 0.85, 0.87, 0.85, and 0.85, respectively. Radiomic features associated with the neovascularization of CAP were obtained. Our study highlights the potential of radiomics-based models for improving the accuracy and efficiency of diagnosing vulnerable CAP. In particular, the model_RF, utilizing radiomic features extracted from CTA, provides a noninvasive and efficient method for accurately predicting the vulnerability status of CAP. This model shows great potential for offering clinical guidance for early detection and improving patient outcomes.

Metabolism-Associated DNA Methylation Signature Stratifies Lower-Grade Glioma Patients and Predicts Response to Immunotherapy.

Metabolism and DNA methylation (DNAm) are closely linked. The value of the metabolism-DNAm interplay in stratifying glioma patients has not been explored. In the present study, we aimed to stratify lower-grade glioma (LGG) patients based on the DNAm associated with metabolic reprogramming. Four data sets of LGGs from three databases (TCGA/CGGA/GEO) were used in this study. By screening the Kendall's correlation of DNAm with 87 metabolic processes from KEGG, we identified 391 CpGs with a strong correlation with metabolism. Based on these metabolism-associated CpGs, we performed consensus clustering and identified three distinct subgroups of LGGs. These three subgroups were characterized by distinct molecular features and clinical outcomes. We also constructed a subgroup-related, quantifiable CpG signature with strong prognostic power to stratify LGGs. It also serves as a potential biomarker to predict the response to immunotherapy. Overall, our findings provide new perspectives for the stratification of LGGs and for understanding the mechanisms driving malignancy.

Immune-Related lncRNA Pairs Clinical Prognosis Model Construction for Hepatocellular Carcinoma.

BACKGROUND: Long non-coding RNA (lncRNA) plays an essential regulatory role in the occurrence and development of hepatocellular carcinoma (HCC). This paper aims to establish an immune-related lncRNA (irlncRNA) pairs model independent of expression level for risk assessment and prognosis prediction of HCC. METHODS: Transcriptome data and corresponding clinical data were downloaded from TCGA. HCC patients were randomly divided into training group and test group. Univariate Cox regression analysis, LASSO regression analysis, and stepwise multiple Cox regression analysis were used to establish a prognostic model. The prediction ability of the model was verified by ROC curves. Next, the patients were divided into low-risk and high-risk groups. We compared the differences between the two groups in survival rate, clinicopathological characteristics, tumor immune cell infiltration status, chemotherapeutic drug sensitivity and immunosuppressive molecules. RESULTS: A prognosis prediction model was established based on 7 irlncRNA pairs, namely irlncRNA pairs (IRLP). ROC curves of the training group and test group showed that the IRLP model had high sensitivity and specificity for survival prediction. Kaplan-Meier analysis showed that the survival rate of the high-risk group was significantly lower than that of the low-risk group. Immune cell infiltration analysis showed that the high-risk group was significantly correlated with various immune cell infiltration. Finally, there were statistically significant differences in chemosensitivity and molecular marker expression between the two groups. CONCLUSION: The prognosis prediction model established by irlncRNA pairs has a certain guiding significance for the prognosis prediction of HCC. It may provide valuable clinical applications in antitumor immunotherapy.

Integrated Transcriptional Profiling Analysis and Immune-Related Risk Model Construction for Intracranial Aneurysm Rupture.

Intracranial aneurysms (IAs) may cause lethal subarachnoid hemorrhage upon rupture, but the molecular mechanisms are poorly understood. The aims of this study were to analyze the transcriptional profiles to explore the functions and regulatory networks of differentially expressed genes (DEGs) in IA rupture by bioinformatics methods and to identify the underlying mechanisms. In this study, 1,471 DEGs were obtained, of which 619 were upregulated and 852 were downregulated. Gene enrichment analysis showed that the DEGs were mainly enriched in the inflammatory response, immune response, neutrophil chemotaxis, and macrophage differentiation. Related pathways include the regulation of actin cytoskeleton, leukocyte transendothelial migration, nuclear factor κB signaling pathway, Toll-like receptor signaling pathway, tumor necrosis factor signaling pathway, and chemokine signaling pathway. The enrichment analysis of 20 hub genes, subnetworks, and significant enrichment modules of weighted gene coexpression network analysis showed that the inflammatory response and immune response had a causal relationship with the rupture of unruptured IAs (UIAs). Next, the CIBERSORT method was used to analyze immune cell infiltration into ruptured IAs (RIAs) and UIAs. Macrophage infiltration into RIAs increased significantly compared with that into UIAs. The result of principal component analysis revealed that there was a difference between RIAs and UIAs in immune cell infiltration. A 4-gene immune-related risk model for IA rupture (IRMIR), containing CXCR4, CXCL3, CX3CL1, and CXCL16, was established using the glmnet package in R software. The receiver operating characteristic value revealed that the model represented an excellent clinical situation for potential application. Enzyme-linked immunosorbent assay was performed and showed that the concentrations of CXCR4 and CXCL3 in serum from RIA patients were significantly higher than those in serum from UIA patients. Finally, a competing endogenous RNA network was constructed to provide a potential explanation for the mechanism of immune cell infiltration into IAs. Our findings highlighted the importance of immune cell infiltration into RIAs, providing a direction for further research.

Prevalence and trigger factors of functional gastrointestinal disorders among male civil pilots in China.

Functional gastrointestinal disorders (FGIDs) are common among the aircrew due to their arduous working environment. This study investigated the prevalence of FGIDs in Chinese male pilots and assessed the effects of trigger factors on the FGIDs. A cross-sectional study including 212 male pilots was performed in a Chinese large civil airline company. FGIDs were diagnosed according to the Rome IV diagnostic criteria. The psychological performance, dietary pattern, sleep situation, and physical activity of the respondents were assessed. Logistic regression analysis and structural equation modeling were used to explore the association between these trigger factors and FGIDs. FGIDs were observed in 83 (39.22%) respondents, of which 31 (37.35%) had overlap syndromes. Age, flight level, flight time, high-salt food pattern, anxiety, and sleep performance were found to be associated with FGIDs (all P < 0.05). Stepwise logistic regression analysis revealed that the flight level (OR 0.59, 95% CI 0.31-0.080), high-salt food pattern (OR 2.31, 95% CI 1.28-4.16), and sleep performance (OR 2.39, 95% CI 1.11-5.14) were the influencing factors associated with FGIDs. Structural equation modeling confirmed the correlations between FGIDs and the occupational, dietary, and psychological factors with a reasonable fit. The preventive strategies were necessitated according to occupational and psychological characteristics.

Case Report: Identification of Multiple TERT and FGFR2 Gene Fusions in a Pineal Region Glioblastoma Case.

As an oncogenic somatic variant, telomerase reverse transcriptase promoter (TERTp) mutations are frequently observed in adult glioblastoma (GBM). Alternatively, we report the first case of glioblastoma with TERT amplification accompanied by multiple TERT and FGFR2 gene fusions instead of TERTp mutation. A 55-year-old woman presented with dizziness, headache, and diplopia for three weeks. Magnetic resonance imaging (MRI) demonstrated a heterogeneously enhancing lobulated mass centered in the pineal region. Partial tumor resection and ventriculoperitoneal shunt were achieved, and the residual tumor was then treated with standard radiation. The tumor was diagnosed as GBM, IDH-wild type, WHO grade IV, and the Ki67 proliferation index was high (30-40%). Intriguingly, TERT amplification without TERTp mutation was identified via next generation sequencing (NGS). Further analysis revealed multiple TERT (TERT-NUBPL, MARCH6-TERT, and CJD4-TERT) and FGFR2 (CXCL17-FGFR2, SIPA1L3-FGFR2, FGFR2-SIPA1L3, and FGFR2-CEACAM1) gene fusions. After the surgery, the patient's condition deteriorated rapidly due to the malignant nature of the tumor and she died with an overall survival of 3 months. Our report provides the molecular clue for a novel telomerase activation and maintenance mechanism in GBM.

Extracellular vesicle-mediated transfer of miR-21-5p from mesenchymal stromal cells to neurons alleviates early brain injury to improve cognitive function via the PTEN/Akt pathway after subarachnoid hemorrhage.

Patients with subarachnoid hemorrhage (SAH) often suffer from cognitive function impairments even when they have received proper treatment, such as the clipping or coiling of aneurysms, and this causes problems with returning to work and burdens the family. Increasing attention has been paid to mesenchymal stem cell (MSC)-derived extracellular vesicle (MSC-EV) as promising therapeutic vesicles for stroke management. In this study, we explored the potential role of MSC-EV in a rat model of SAH. We observed that MSC-EV ameliorated early brain injury (EBI) after SAH by reducing the apoptosis of neurons and that SAH induced an increase in the expression level of miR-21 in the prefrontal cortex and hippocampus. In addition, using miRNA profiling and CSF sequencing data from the exRNA Atlas, we demonstrated that EV-derived miR-21 protected neurons from apoptosis and alleviated SAH-induced cognitive dysfunction. The neuroprotective role of MSC-EV was abrogated by miR-21 knockdown or the administration of MK2206, a PTEN/Akt inhibitor. Overall, our results suggest that MSC-EV promotes neuronal survival and alleviates EBI after SAH through transferring miR-21 to recipient neurons.

[Detection and analysis of serum level of anti-Candida enolase IgG antibody in patients with autoimmune diseases].

Objective To detect IgG antibody against Candida enolase in the sera of patients with autoimmune diseases. Methods Using purified recombinant Candida enolase as the coating antigen, an ELISA was established for enolase IgG antibody detection and the reactive conditions were optimized. The enolase IgG antibody in the sera from patients with autoimmune diseases and healthy controls were detected by ELISA. The specificity of the positive sera was confirmed by Western blotting. Results The study collected 70 serum samples from the patients with autoimmune diseases and 44 from the healthy individuals. ELISA showed anti-Candida enolase IgG antibody in 19 cases of the autoimmune disease group and and 3 cases of the healthy control group, the positive rates of which were 27.14% (19/70) and 6.82% (3/44), respectively. In the autoimmune disease group, the positive rate of anti-Candida enolase IgG antibody in the systemic lupus erythematosus patients was 45.8% (11/24), significant higher than that in the rheumatoid arthritis patients (11.8%, 2/17). Western blotting validated the specificity of the positive sera. Conclusion The positive rate of anti-Candida enolase IgG antibody in patients with autoimmune disease is high, which would be an interference factor in the application of IgG antibody detection for the diagnosis of invasive candidiasis.