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The role of a low-profile visualized intraluminal support stent (LVIS) and Enterprise in the treatment of unruptured intracranial aneurysms is well established. Although previous studies have investigated one single type of stent for the treatment of ruptured intracranial aneurysms (RIA), the safety and efficacy between the two types of stents has not been fully explored. Herein we conducted a study to compare the outcomes of the two stents for treatment of RIA. This is a prospective registry database of aneurysmal subarachnoid hemorrhage (aSAH) patients admitted to a single institution between 2018 and 2021. We collected patient baseline information, secondary complications, follow-up angiographic data, long-term prognostic outcomes, and conducted propensity score matching (PSM) analysis with 1:1 ratio and a multivariable logistic regression to compare the outcomes of the two types of stents. A total of 231 patients with RIAs were included in this study, with 108 treated using the LVIS device and 123 treated using the Enterprise device. Before PSM analysis, only the incidence of poor prognosis after 12 months was higher in the Enterprise group comparing to the LVIS group (20% vs. 10%, P = 0.049). After PSM analysis, there was a higher occurrence of delayed cerebral ischemia (DCI) in the Enterprise group compared to the LVIS group (odds ratio [OR] 3.95, 95% confidence interval [CI] [1.20-13.01], P = 0.024). However, no significant difference in prognosis was observed after PSM adjustment. Furthermore, subgroup analysis revealed that patients with female (P = 0.019), hypertension (P = 0.048), and anterior circulation aneurysms (P = 0.019) receiving the Enterprise device had a higher risk of DCI. The overall efficacy of LVIS and Enterprise in the treatment of RIA is comparable, while the incidence of DCI in the LVIS group is lower than that in the Enterprise group after PSM analysis. Registration number: NCT05738083 ( https://clinicaltrials.gov/ ).

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BACKGROUND: Drug resistance is one of the major reasons of the poor prognosis and recurs frequently in glioma. Ferroptosis is considered to be a new therapeutic strategy for glioma. METHODS: Microsomal glutathione S-transferase 1 (MGST1) expression in glioma samples was ensured through GAPIA database, qRT-PCR, western blotting assay and immunohistochemistry. The interaction between zinc finger protein 384 (ZNF384) and MGST1 promoter was analyzed through UCSC and JASPAR databases and further verified by ChIP and luciferase reporter assay. Cell viability and IC50 value of temozolomide (TMZ) was measured by CCK-8 assay. The production of MDA, GSH and ROS and the level of Fe2+ were determined using the corresponding kit. RESULTS: MGST1 expression was increased in clinical glioma tissues and glioma cells. MGST1 expression was increased but ferroptosis was suppressed in TMZ-resistant cells when contrasted to parent cells. MGST1 silencing downregulated IC50 value of TMZ and cell viability but facilitated ferroptosis in TMZ-resistant cells and parent glioma cells. Moreover, our data indicated that ZNF384 interacted with MGST1 promoter and facilitated MGST1 expression. ZNF384 was also increased expression in TMZ-resistant cells, and showed a positive correlation with MGST1 expression in clinical level. ZNF384 decreasing enhanced the sensitivity of resistant cells to TMZ, while the effect of ZNF384 could be reversed by overexpression of MGST1. CONCLUSION: MGST1 transcription is regulated by transcription factor ZNF384 in TMZ-resistant cells. ZNF384 confers the resistance of glioma cells to TMZ through inhibition of ferroptosis by positively regulating MGST1 expression. The current study may provide some new understand to the mechanism of TMZ resistance in glioma.

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BACKGROUND: Previous evidences has highlighted the pivotal role of NOD-like receptor family pyrin domain-containing 3 (NLRP3)-mediated inflammasomes and pyroptosis activation in driving tumor malignancy and shaping the tumor microenvironment. Herein, we aimed to elucidate the impact of high-mobility group box 3 (HMGB3) released in glioma-derived exosomes on macrophage infiltration in gliomas, NLRP3 inflammasome activation and polarization. METHODS: Transcripts and protein levels of HMGB3, and cytokines associated with macrophage phenotypes and pyroptosis were assessed in glioma tissues and cell lines (U251, LN229, T98G, A172) using qRT-PCR and/or Western blot analysis. Exosomes secreted from LN229 and NHA cells were isolated via differential ultracentrifugation and characterized by transmission electron microscopy (TEM), nanoparticle tracking analysis (NTA), and analysis of exosome-related markers. PKH67 staining was employed to examine exosomes uptake by THP-1 differentiated macrophages. Flow cytometry was utilized to assess macrophage pyroptotic rates and polarization-related markers. RESULTS: HMGB3 expression was elevated in glioma tissues, serum samples and tumor cell lines. Kaplan-Meier curves revealed a positive correlation between higher HMGB3 expression and poor overall survival and recurrence-free survival. Moreover, glioma tissues with increased HMGB3 expression exhibited significant upregulation of M2 macrophages markers (CD68, CD206, Arg1) and NLRP3 inflammasome components (NLRP3, IL-1ß, ASC), suggesting that HMGB3 was closely associated with macrophage infiltration and NLRP3 inflammasome activation. Notably, HMGB3 was found to be enriched in glioma cell- secreted exosomes and could be internalized by macrophages. Knockdown of HMGB3 in glioma cell exosomes could restrain M2 macrophage polarization, NLRP3 inflammasome activation and pyroptosis. CONCLUSION: These findings suggested that glioma cells secreted exosomal HMGB3 could facilitate macrophage M2 polarization, pyroptosis and inflammatory infiltration, indicating HMGB3 might be a poor prognosis factor for glioma.

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OBJECTIVE: Circulating tumor cell (CTC) detection is a promising noninvasive technique that can be used to diagnose cancer, monitor progression, and predict prognosis. In this study, the authors aimed to investigate the clinical utility of CTCs in the management of diffuse glioma. METHODS: Sixty-three patients with newly diagnosed diffuse glioma were included in this multicenter clinical cohort. The authors used a platform based on isolation by size of epithelial tumor cells (ISET) to detect and analyze CTCs and circulating tumor microemboli (CTMs) in the peripheral blood of patients both before and after surgery. Least absolute shrinkage and selector operation (LASSO) and Cox regression analyses were used to verify whether CTCs and CTMs are independent prognostic factors for diffuse glioma. RESULTS: CTC levels were closely related to the degree of malignancy, WHO grade, and pathological subtypes. Receiver operating characteristic curve analysis revealed that a high CTC level was a predictor for glioblastoma. The results also showed that CTMs originate from the parental tumor rather than from the circulation and are an independent prognostic factor for diffuse glioma. The postoperative CTC level is related to the peripheral immune system and patient survival. Cox regression analysis showed that postoperative CTC levels and CTM status are independent prognostic factors for diffuse glioma, and CTC- and CTM-based survival models had high accuracy in internal validation. CONCLUSIONS: The authors revealed a correlation between CTCs and clinical characteristics and demonstrated that CTCs and CTMs are independent predictors for the diagnosis and prognosis of diffuse glioma. Their CTC- and CTM-based survival models can enable clinicians to evaluate patients' response to surgery as well as their outcomes.

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OBJECTIVE: Accurate prognostic prediction in patients with high-grade aneruysmal subarachnoid hemorrhage (aSAH) is essential for personalized treatment. In this study, we developed an interpretable prognostic machine learning model for high-grade aSAH patients using SHapley Additive exPlanations (SHAP). METHODS: A prospective registry cohort of high-grade aSAH patients was collected in one single-center hospital. The endpoint in our study is a 12-month follow-up outcome. The dataset was divided into training and validation sets in a 7:3 ratio. Machine learning algorithms, including Logistic regression model (LR), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), were employed to develop a prognostic prediction model for high-grade aSAH. The optimal model was selected for SHAP analysis. RESULTS: Among the 421 patients, 204 (48.5%) exhibited poor prognosis. The RF model demonstrated superior performance compared to LR (AUC = 0.850, 95% CI: 0.783-0.918), SVM (AUC = 0.862, 95% CI: 0.799-0.926), and XGBoost (AUC = 0.850, 95% CI: 0.783-0.917) with an AUC of 0.867 (95% CI: 0.806-0 .929). Primary prognostic features identified through SHAP analysis included higher World Federation of Neurosurgical Societies (WFNS) grade, higher modified Fisher score (mFS) and advanced age, were found to be associated with 12-month unfavorable outcome, while the treatment of coiling embolization for aSAH drove the prediction towards favorable prognosis. Additionally, the SHAP force plot visualized individual prognosis predictions. CONCLUSIONS: This study demonstrated the potential of machine learning techniques in prognostic prediction for high-grade aSAH patients. The features identified through SHAP analysis enhance model interpretability and provide guidance for clinical decision-making.

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BACKGROUND: Deep learning (DL)-assisted detection and segmentation of intracranial hemorrhage stroke in noncontrast computed tomography (NCCT) scans are well-established, but evidence on this topic is lacking. MATERIALS AND METHODS: PubMed and Embase databases were searched from their inception to November 2023 to identify related studies. The primary outcomes included sensitivity, specificity, and the Dice Similarity Coefficient (DSC); while the secondary outcomes were positive predictive value (PPV), negative predictive value (NPV), precision, area under the receiver operating characteristic curve (AUROC), processing time, and volume of bleeding. Random-effect model and bivariate model were used to pooled independent effect size and diagnostic meta-analysis data, respectively. RESULTS: A total of 36 original studies were included in this meta-analysis. Pooled results indicated that DL technologies have a comparable performance in intracranial hemorrhage detection and segmentation with high values of sensitivity (0.89, 95% CI: 0.88-0.90), specificity (0.91, 95% CI: 0.89-0.93), AUROC (0.94, 95% CI: 0.93-0.95), PPV (0.92, 95% CI: 0.91-0.93), NPV (0.94, 95% CI: 0.91-0.96), precision (0.83, 95% CI: 0.77-0.90), DSC (0.84, 95% CI: 0.82-0.87). There is no significant difference between manual labeling and DL technologies in hemorrhage quantification (MD 0.08, 95% CI: -5.45-5.60, P =0.98), but the latter takes less process time than manual labeling (WMD 2.26, 95% CI: 1.96-2.56, P =0.001). CONCLUSION: This systematic review has identified a range of DL algorithms that the performance was comparable to experienced clinicians in hemorrhage lesions identification, segmentation, and quantification but with greater efficiency and reduced cost. It is highly emphasized that multicenter randomized controlled clinical trials will be needed to validate the performance of these tools in the future, paving the way for fast and efficient decision-making during clinical procedure in patients with acute hemorrhagic stroke.

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OBJECTIVE: The relationships between immediate bleeding severity, postoperative complications, and long-term functional outcomes in patients with aneurysmal subarachnoid hemorrhage (aSAH) remain uncertain. Here, the authors apply their recently developed automated deep learning technique to quantify total bleeding volume (TBV) in patients with aSAH and investigate associations between quantitative TBV and secondary complications, adverse long-term functional outcomes, and death. METHODS: Electronic health record data were extracted for adult patients admitted to a single institution within 72 hours of aSAH onset between 2018 and 2021. An automatic deep learning model was used to fully segment and quantify TBV on admission noncontrast head CT images. Patients were subgrouped by TBV quartile, and multivariable logistic regression, restricted cubic splines, and subgroup analysis were used to explore the relationships between TBV and each clinical outcome. RESULTS: A total of 819 patients were included in the study. Sixty-six (8.1%) patients developed hydrocephalus, while 43 (5.3%) experienced rebleeding, 141 (17.2%) had delayed cerebral ischemia, 88 (10.7%) died in the 12 months after discharge, and 208 (25.7%) had a modified Rankin Scale score ≥ 3 12 months after discharge. On multivariable analysis, patients in the highest TBV quartile (> 37.94 ml) had an increased risk of hydrocephalus (adjusted OR [aOR] 4.38, 95% CI 1.61-11.87; p = 0.004), rebleeding (aOR 3.26, 95% CI 1.03-10.33; p = 0.045), death (aOR 6.92, 95% CI 1.89-25.37; p = 0.004), and 12-month disability (aOR 3.30, 95% CI 1.62-6.72; p = 0.001) compared with the lowest TBV quantile (< 8.34 ml). The risks of hydrocephalus (nonlinear, p = 0.025), rebleeding, death, and disability (linear, p > 0.05) were positively associated with TBV by restricted cubic splines. In subgroup analysis, TBV had a stronger effect on 12-month outcome in female than male patients (p for interaction = 0.0499) and on rebleeding prevalence in patients with endovascular coiling than those with surgical clipping (p for interaction = 0.008). CONCLUSIONS: Elevated TBV is associated with a greater risk of hydrocephalus, rebleeding, death, and poor prognosis.

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Glioblastoma multiforme (GBM) is a significantly malignant and lethal brain tumor with an average survival time of less than 12 months. Several researches had shown that Claudin-3 (CLDN3) is overexpressed in various cancers and might be important in their growth and spread. In this study, we used qRT-PCR, western blotting, immunohistochemistry, and immunofluorescence staining assays to investigate the expression levels of various proteins. To explore the proliferation abilities of GBM cells, we conducted the CCK-8 and EdU-DNA formation assays. Wound healing and transwell assays were used to investigate the capacities of invasion and migration of GBM cells. Additionally, we constructed an intracranial xenograft model of GBM to study the in vivo role of CLDN3. Our study devoted to investigate the function of CLDN3 in the pathogenesis and progression of GBM. Our study revealed that CLDN3 was upregulated in GBM and could stimulate tumor cell growth and epithelial-mesenchymal transition (EMT) in both laboratory and animal models. We also discovered that CLDN3 expression could be triggered by transforming growth factor-ß (TGF-ß) and reduced by specific inhibitors of the TGF-ß signaling pathway, such as ITD-1. Further analysis revealed that increased CLDN3 levels enhanced TGF-ß-induced growth and EMT in GBM cells, while reducing CLDN3 levels weakened these effects. Our study demonstrated the function of CLDN3 in facilitating GBM growth and metastasis and indicated its involvement in the tumorigenic effects of TGF-ß. Developing specific inhibitors of CLDN3 might, therefore, represent a promising new approach for treating this devastating disease.

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Accurate stroke assessment and consequent favorable clinical outcomes rely on the early identification and quantification of aneurysmal subarachnoid hemorrhage (aSAH) in non-contrast computed tomography (NCCT) images. However, hemorrhagic lesions can be complex and difficult to distinguish manually. To solve these problems, here we propose a novel Hybrid 2D/3D UNet deep-learning framework for automatic aSAH identification and quantification in NCCT images. We evaluated 1824 consecutive patients admitted with aSAH to four hospitals in China between June 2018 and May 2022. Accuracy and precision, Dice scores and intersection over union (IoU), and interclass correlation coefficients (ICC) were calculated to assess model performance, segmentation performance, and correlations between automatic and manual segmentation, respectively. A total of 1355 patients with aSAH were enrolled: 931, 101, 179, and 144 in four datasets, of whom 326 were scanned with Siemens, 640 with Philips, and 389 with GE Medical Systems scanners. Our proposed deep-learning method accurately identified (accuracies 0.993-0.999) and segmented (Dice scores 0.550-0.897) hemorrhage in both the internal and external datasets, even combinations of hemorrhage subtypes. We further developed a convenient AI-assisted platform based on our algorithm to assist clinical workflows, whose performance was comparable to manual measurements by experienced neurosurgeons (ICCs 0.815-0.957) but with greater efficiency and reduced cost. While this tool has not yet been prospectively tested in clinical practice, our innovative hybrid network algorithm and platform can accurately identify and quantify aSAH, paving the way for fast and cheap NCCT interpretation and a reliable AI-based approach to expedite clinical decision-making for aSAH patients.

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Background: 2021 World Health Organization (WHO) Central Nervous System (CNS) tumor classification increasingly emphasizes the important role of molecular markers in glioma diagnoses. Preoperatively non-invasive "integrated diagnosis" will bring great benefits to the treatment and prognosis of these patients with special tumor locations that cannot receive craniotomy or needle biopsy. Magnetic resonance imaging (MRI) radiomics and liquid biopsy (LB) have great potential for non-invasive diagnosis of molecular markers and grading since they are both easy to perform. This study aims to build a novel multi-task deep learning (DL) radiomic model to achieve preoperative non-invasive "integrated diagnosis" of glioma based on the 2021 WHO-CNS classification and explore whether the DL model with LB parameters can improve the performance of glioma diagnosis. Methods: This is a double-center, ambispective, diagnostical observational study. One public database named the 2019 Brain Tumor Segmentation challenge dataset (BraTS) and two original datasets, including the Second Affiliated Hospital of Nanchang University, and Renmin Hospital of Wuhan University, will be used to develop the multi-task DL radiomic model. As one of the LB techniques, circulating tumor cell (CTC) parameters will be additionally applied in the DL radiomic model for assisting the "integrated diagnosis" of glioma. The segmentation model will be evaluated with the Dice index, and the performance of the DL model for WHO grading and all molecular subtype will be evaluated with the indicators of accuracy, precision, and recall. Discussion: Simply relying on radiomics features to find the correlation with the molecular subtypes of gliomas can no longer meet the need for "precisely integrated prediction." CTC features are a promising biomarker that may provide new directions in the exploration of "precision integrated prediction" based on the radiomics, and this is the first original study that combination of radiomics and LB technology for glioma diagnosis. We firmly believe that this innovative work will surely lay a good foundation for the "precisely integrated prediction" of glioma and point out further directions for future research. Clinical trail registration: This study was registered on ClinicalTrails.gov on 09/10/2022 with Identifier NCT05536024.

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OBJECTIVE: The effect of surgical clipping (SC) and endovascular coiling (EC) on the incidence of delayed cerebral ischemia (DCI) in patients with aneurysmal subarachnoid hemorrhage (aSAH) has always been a controversial topic. Hence, it is necessary to reanalyze the effects of the 2 surgical methods on DCI, which determines the choice of the most favorable method for patients who are suitable for both surgical modalities. METHODS: A multicenter retrospective observational cohort study was performed to evaluate all consecutive patients with aSAH admitted to 5 medical centers in China between April 2019 and June 2021. Univariable and multivariable analyses were used to confirm risk factors of DCI after aSAH. A 1:1 propensity score matching model was generated in the EC and SC groups to reduce the influence of all confounding factors on DCI. RESULTS: A total of 412 patients were included, and 115 patients (27.9%) developed DCI. After propensity score matching for controlling demographic information, past medical history, admission clinical status, aneurysm characteristics, and inflammatory factors associated with DCI, 133 patients with SC and 133 patients with EC treatment were matched. The results of the matched cohorts indicate a significantly lower incidence of DCI when patients received EC than SC (31.9% vs. 20%; adjusted odds ratio, 1.87; 95% confidence interval, 1.08-3.29; P = 0.027). CONCLUSIONS: The study found that the patients who received SC treatment had a higher incidence of DCI than did those who received EC and suggested that ruptured intracerebral aneurysm is preferentially coiled rather than clipped if the aneurysm is suitable for both surgical modalities.

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Introduction: Spontaneous subarachnoid hemorrhage (SAH), is a disorder that may be fatal and is primarily caused by a ruptured brain aneurysm. Despite significant leaps forward in the methods to produce aneurysms, the long-term outcomes did not much improve. Pioglitazone is a medication that has been authorized by the FDA as an agonist for the peroxisome proliferator-activated receptor-gamma (PPARγ). Pioglitazone or PPARγ has neuroprotective benefits in animal experiments both during and after traumatic brain injury (TBI) and SAH. Nevertheless, the treatment impact of Pioglitazone on humans is still unknown at this time. As a result, we will conduct a randomized, double-blind, placebo-controlled trial to explore the impact of pioglitazone on SAH. Methods/Design: This trial will recruit 400 patients with SAH from four Chinese hospitals. These patients will be equally and randomly assigned to Pioglitazone and placebo control groups for up to 30 days. Scores on the modified Rankin scale (mRS) are the primary outcomes. The secondary outcomes are a 30-day all-cause mortality rate, 6 months of Montreal cognitive assessment (Mo-CA), delayed cerebral ischemia, the requirement for intensive care, the incidence of sepsis, etc. All serious adverse events (SAEs) were recorded during the hospital. Every primary and safety analysis was conducted based on the intention-to-treat technique. The participants were given either a matching placebo or 15 mg of pioglitazone, with dose titrated to a target of 45 mg daily. Data on the therapeutic use of pioglitazone after SAH will be provided as a consequence of the findings of this experiment. In addition, this pilot trial is the first to prospectively investigate the effectiveness and safety of pioglitazone in patients with SAH. Ethics and dissemination: Ethics approval was obtained from the Medical Ethics Committee of 904th Hospital of Joint Logistic Support Force of PLA (Wuxi Taihu Hospital, approval No. 20220701). The findings of the trial will be presented at conferences, discussed in relevant patient groups, and published in peer-reviewed journals. Clinical Trial Registration: clinicaltrials.gov, identifier ChiCTR2200062954.

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Immunogenic cell death (ICD) is a type of regulated cell death (RCD) and is correlated with the progression, prognosis, and therapy of tumors, including glioma. Numerous studies have shown that the immunotherapeutic and chemotherapeutic agents of glioma might induce ICD. However, studies on the comprehensive analysis of the role of ICD-related genes and their correlations with overall survival (OS) in glioma are lacking. The genetic, transcriptional, and clinical data of 1896 glioma samples were acquired from five distinct databases and analyzed in terms of genes and transcription levels. The method of consensus unsupervised clustering divided the patients into two disparate molecular clusters: A and B. All of the patients were randomly divided into training and testing groups. Employing the training group data, 14 ICD-related genes were filtered out to develop a risk-score model. The correlations between our risk groups and prognosis, cells in the tumor microenvironment (TME) and immune cells infiltration, chemosensitivity and cancer stem cell (CSC) index were assessed. A highly precise nomogram model was constructed to enhance and optimize the clinical application of the risk score. The results demonstrated that the risk score could independently predict the OS rate and the immunotherapeutic response of glioma patients. This study analyzed the ICD-related genes in glioma and evaluated their role in the OS, clinicopathological characteristics, TME and immune cell infiltration of glioma. Our results may help in assessing the OS of glioma and developing better immunotherapeutic strategies.

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Glioblastoma multiforme (GBM) is the intracranial malignancy with the highest rates of morbidity and mortality. Chemotherapy is often ineffective against GBM due to the presence of the blood-brain barrier (BBB); however, the application of nanotechnology is expected to overcome this limitation. Poly(lactic-co-glycolic acid) (PLGA) is a degradable and nontoxic functional polymer with good biocompatibility that is widely used in the pharmaceutical industry. Previous studies have shown that the ability of PLGA nanoparticles (NPs) to penetrate the BBB is largely determined by their size; however, determination of the optimal PLGA NP size requires further research. Here, we report a tandutinib-based prodrug (proTan), which responds to the GBM microenvironment, that was combined with NPs to overcome the BBB. AMD3100-PLGA NPs loaded with proTan inhibited tumor growth and effectively prolonged the survival of tumor-bearing mice.

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As in many other cancers, highly malignant proliferation and disordered cell division play irreplaceable roles in the exceedingly easy recurrence and complex progression of glioblastoma multiforme (GBM); however, mechanistic studies of the numerous regulators involved in this process are still insufficiently thorough. The role of BCAS3 has been studied in other cancers, but its role in GBM is unclear. Here, our goal was to investigate the expression pattern of BCAS3 in GBM and its potential mechanism of action. Using TCGA database and human GBM samples, we found that BCAS3 expression was up-regulated in GBM, and its high expression predicted poor prognosis. To further investigate the relationship between BCAS3 and GBM characteristics, we up-regulated and down-regulated BCAS3 expression in GBM to detect its effect on cell proliferation and cell cycle. At the same time, we established U87 cells stably overexpressing BCAS3 and generated an intracranial xenograft model to investigate the Potential role of BCAS3 in vivo. Finally, based on in vitro cell experiments and in vivo GBM xenograft models, we observed that BCAS3 significantly regulates GBM cell proliferation and cell cycle and that this regulation is associated with p53/GADD45α Signaling pathway. Taken together, our findings suggest that BCAS3 is inextricably linked to the progression of GBM and that targeting BCAS3 may have therapeutic effects in GBM patients.

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Backgrounds: As a most widely used machine learning method, tree-based algorithms have not been applied to predict delayed cerebral ischemia (DCI) in elderly patients with aneurysmal subarachnoid hemorrhage (aSAH). Hence, this study aims to develop the conventional regression and tree-based models and determine which model has better prediction performance for DCI development in hospitalized elderly patients after aSAH. Methods: This was a multicenter, retrospective, observational cohort study analyzing elderly patients with aSAH aged 60 years and older. We randomly divided the multicentral data into model training and validation cohort in a ratio of 70-30%. One conventional regression and tree-based model, such as least absolute shrinkage and selection operator (LASSO), decision tree (DT), random forest (RF), and eXtreme Gradient Boosting (XGBoost), was developed. Accuracy, sensitivity, specificity, area under the precision-recall curve (AUC-PR), and area under the receiver operating characteristic curve (AUC-ROC) with 95% CI were employed to evaluate the model prediction performance. A DeLong test was conducted to calculate the statistical differences among models. Finally, we figured the importance weight of each feature to visualize the contribution on DCI. Results: There were 111 and 42 patients in the model training and validation cohorts, and 53 cases developed DCI. According to AUC-ROC value in the model internal validation, DT of 0.836 (95% CI: 0.747-0.926, p = 0.15), RF of 1 (95% CI: 1-1, p < 0.05), and XGBoost of 0.931 (95% CI: 0.885-0.978, p = 0.01) outperformed LASSO of 0.793 (95% CI: 0.692-0.893). However, the LASSO scored a highest AUC-ROC value of 0.894 (95% CI: 0.8-0.989) than DT of 0.764 (95% CI: 0.6-0.928, p = 0.05), RF of 0.821 (95% CI: 0.683-0.959, p = 0.27), and XGBoost of 0.865 (95% CI: 0.751-0.979, p = 0.69) in independent external validation. Moreover, the LASSO had a highest AUC-PR value of 0.681 than DT of 0.615, RF of 0.667, and XGBoost of 0.622 in external validation. In addition, we found that CT values of subarachnoid clots, aneurysm therapy, and white blood cell counts were the most important features for DCI in elderly patients with aSAH. Conclusions: The LASSO had a superior prediction power than tree-based models in external validation. As a result, we recommend the conventional LASSO regression model to predict DCI in elderly patients with aSAH.

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Glioblastoma (GBM) is the most common and aggressive primary malignant brain tumor. The unregulated expression of Claudin-4 (CLDN4) plays an important role in tumor progression. However, the biological role of CLDN4 in GBM is still unknown. This study aimed to determine whether CLDN4 mediates glioma malignant progression, if so, it would further explore the molecular mechanisms of carcinogenesis. Our results revealed that CLDN4 was significantly upregulated in glioma specimens and cells. The inhibition of CLND4 expression could inhibit mesenchymal transformation, cell invasion, cell migration and tumor growth in vitro and in vivo. Moreover, combined with in vitro analysis, we found that CLDN4 can modulate tumor necrosis factor-α (TNF-α) signal pathway. Meanwhile, we also validated that the transforming growth factor-ß (TGF-ß) signal pathway can upregulate the expression of CLDN4, and promote the invasion ability of GBM cells. Conversely, TGF-ß signal pathway inhibitor ITD-1 can downregulate the expression of CLDN4, and inhibit the invasion ability of GBM cells. Furthermore, we found that TGF-ß can promote the nuclear translocation of CLDN4. In summary, our findings indicated that the TGF-ß/CLDN4/TNF-α/NF-κB signal axis plays a key role in the biological progression of glioma. Disrupting the function of this signal axis may represent a new treatment strategy for patients with GBM.

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Glioblastoma multiforme (GBM) is the most common and most fatal primary malignant brain tumour in adults. The average survival time of patients after diagnosis is only 12-15 months. And its characteristics of excessive proliferation and apoptosis evasion play a crucial role in the poor prognosis of patients. Therefore, it is worth investigating the molecular mechanism of GBM to find an effective therapeutic target to overcome the dilemma. In the current study, Transmembrane BAX inhibitor motif containing 1 (TMBIM1) was highly expressed in GBM tissues and high TMBIM1 expression in GBM cell lines (U87 and U251) could promote cell proliferation and inhibit cell cycle arrest. In addition, TMBIM1 could significantly attenuate GBM cell apoptosis and decrease the sensitivity of GBM cells to temozolomide (TMZ). In terms of the molecular mechanism, we revealed that TMBIM1 interferes with the p38/MAPK pathway by inhibiting p38 phosphorylation to promote cell proliferation and attenuate cell apoptosis. In vivo experiments showed that the survival time of mice in TMBIM1 knockdown group was significantly prolonged. Our discovery provided an important basis for future intensive molecular mechanism research in GBM and presented a potential target for the treatment of GBM.

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Recent studies showed that molecule interacting with CasL2 (MICAL2) could be a novel tumor growth factor, and it is closely associated with tumor growth and invasion. However, the role it plays in glioblastoma (GBM) and its potential mechanisms are currently unknown. Our study is designed to identify the effect of MICAL2 on GBM cells and the potential mechanisms behind it. Here, we found that MICAL2 interacts with TGF receptor-type I (TGFRI) and promotes the proliferation and migration of glioblastoma through the TGF-ß/p-Smad2/EMT-like signaling pathway. MICAL2-knockdown inhibited the proliferation of glioblastoma cells, which was related to cell cycle arrest and downregulation of DNA replication. The invasion abilities of U87 and U251 cells were reduced after the knockdown of MICAL2. MICAL2 promoted the growth of GBM in nude mice. High MICAL2 predicts poor outcome of GBM patients. MICAL2 could be identified as a novel promising therapeutic target for human GBM.