Tumor treating fields in glioblastoma: long-term treatment and high compliance as favorable prognostic factors.

Introduction: Tumor treating fields (TTFields) have earned substantial attention in recent years as a novel therapeutic approach with the potential to improve the prognosis of glioblastoma (GBM) patients. However, the impact of TTFields remains a subject of ongoing debate. This study aimed to offer real-world evidence on TTFields therapy for GBM, and to investigate the clinical determinants affecting its efficacy. Methods: We have reported a retrospective analysis of 81 newly diagnosed Chinese GBM patients who received TTFields/Stupp treatment in the Second Affiliated Hospital of Zhejiang University. Overall survival (OS) and progression-free survival (PFS) were analyzed using Kaplan-Meier method. Cox regression models with time-dependent covariates were utilized to address non-proportional hazards and to assess the influence of clinical variables on PFS and OS. Results: The median PFS and OS following TTFields/STUPP treatment was 12.6 months (95% CI 11.0-14.1) and 21.3 months (95% CI 10.0-32.6) respectively. Long-term TTFields treatment (>2 months) exhibits significant improvements in PFS and OS compared to the short-term treatment group (≤2 months). Time-dependent covariate COX analysis revealed that longer TTFields treatment was correlated with enhanced PFS and OS for up to 12 and 13 months, respectively. Higher compliance to TTFields (≥ 0.8) significantly reduced the death risk (HR=0.297, 95%CI 0.108-0.819). Complete surgical resection and MGMT promoter methylation were associated with significantly lower risk of progression (HR=0.337, 95% CI 0.176-0.643; HR=0.156, 95% CI 0.065-0.378) and death (HR=0.276, 95% CI 0.105-0.727; HR=0.249, 95% CI 0.087-0.710). Conclusion: The TTFields/Stupp treatment may prolong median OS and PFS in GBM patients, with long-term TTFields treatment, higher TTFields compliance, complete surgical resection, and MGMT promoter methylation significantly improving prognosis.

Adherence to oxidative balance score is inversely associated with the prevalence of stroke: results from National Health and Nutrition Examination Survey 1999-2018.

Introduction: The relationship between oxidative balance score (OBS), an emerging integrative metric for assessing individual redox homeostasis, and the prevalence of stroke in the general population remains unknown. We aimed to explore these relationships in the National Health and Nutrition Examination Survey (NHANES). We investigated the relationship between the oxidative balance score (OBS) and stroke prevalence using NHANES data from 1999-2018. Methods: We included eligible individuals from NHANES 1999-2018. OBS calculations were based on previously validated methods, and stroke diagnoses were based on self-reports in questionnaires. Multivariable logistic regression analyses were used to examine the independent associations of overall, dietary, and lifestyle OBS with stroke prevalence. In addition, restricted cubic spline (RCS), stratified analysis, and sensitivity analysis were used. Results: We included 25,258 participants aged 20-85 years, in which the prevalence of stroke was 2.66%. After adjusting for all confounders, overall and dietary OBS, but not lifestyle OBS, were inversely associated with the prevalence of stroke [odds ratios and 95% confidence intervals of 0.97 (0.96, 0.99) and 0.98 (0.96, 0.99) for overall and dietary OBS, respectively, both p < 0.05]. In addition, there was a dose-response relationship between overall and dietary OBS and stroke prevalence. The RCS showed that these relationships were linear. Stratified analyses indicated that socioeconomic status (SES) significantly influenced the relationship between all OBS and stroke prevalence. Conclusion: Dietary OBS, but not lifestyle OBS, had an inverse relationship with the prevalence of stroke in the general population. SES significantly influenced the protective effect of OBS against stroke. These findings emphasize the importance of integrated antioxidant properties from diet for stroke prevention.

Convolutional neural network-based magnetic resonance image differentiation of filum terminale ependymomas from schwannomas.

PURPOSE: Preoperative diagnosis of filum terminale ependymomas (FTEs) versus schwannomas is difficult but essential for surgical planning and prognostic assessment. With the advancement of deep-learning approaches based on convolutional neural networks (CNNs), the aim of this study was to determine whether CNN-based interpretation of magnetic resonance (MR) images of these two tumours could be achieved. METHODS: Contrast-enhanced MRI data from 50 patients with primary FTE and 50 schwannomas in the lumbosacral spinal canal were retrospectively collected and used as training and internal validation datasets. The diagnostic accuracy of MRI was determined by consistency with postoperative histopathological examination. T1-weighted (T1-WI), T2-weighted (T2-WI) and contrast-enhanced T1-weighted (CE-T1) MR images of the sagittal plane containing the tumour mass were selected for analysis. For each sequence, patient MRI data were randomly allocated to 5 groups that further underwent fivefold cross-validation to evaluate the diagnostic efficacy of the CNN models. An additional 34 pairs of cases were used as an external test dataset to validate the CNN classifiers. RESULTS: After comparing multiple backbone CNN models, we developed a diagnostic system using Inception-v3. In the external test dataset, the per-examination combined sensitivities were 0.78 (0.71-0.84, 95% CI) based on T1-weighted images, 0.79 (0.72-0.84, 95% CI) for T2-weighted images, 0.88 (0.83-0.92, 95% CI) for CE-T1 images, and 0.88 (0.83-0.92, 95% CI) for all weighted images. The combined specificities were 0.72 based on T1-WI (0.66-0.78, 95% CI), 0.84 (0.78-0.89, 95% CI) based on T2-WI, 0.74 (0.67-0.80, 95% CI) for CE-T1, and 0.81 (0.76-0.86, 95% CI) for all weighted images. After all three MRI modalities were merged, the receiver operating characteristic (ROC) curve was calculated, and the area under the curve (AUC) was 0.93, with an accuracy of 0.87. CONCLUSIONS: CNN based MRI analysis has the potential to accurately differentiate ependymomas from schwannomas in the lumbar segment.

Predicted factors of surgical site infection in glioblastoma patients: A meta-analysis.

Surgical site infection (SSI) is one of the common postoperative complications after craniotomy for glioblastoma patients. Previous studies have investigated the risk factors for SSI in patients with glioblastoma. Whereas big differences in research results exist, and the correlation coefficients of different research results are quite different. A meta-analysis was conducted to examine the risk factors related to surgical site infection in patients with glioblastoma. We searched English databases to collect case-control studies or cohort studies published before 15 October 2023 including PubMed, Web of Science, Embase. The risk of bias of the included studies was assessed via Newcastle-Ottawa Scale. The analysis was performed using RevMan 5.4.1 tool. A total of 4 articles (n = 2222) were selected in this meta-analysis. The following risk factors were presented to be correlated with SSI in glioblastoma: irradiation (OR = 1.88, 95% CI [0.46, 7.60]), more than 3 surgeries (OR = 2.99, 95% CI [1.47, 6.08]). Occurrence of SSI is influenced by a variety of factors. Thus, we should pay close attention to high-risk subjects and take crucial targeted interventions to lower the SSI risk following craniotomy. Owing to the limited quality and quantity of the included studies, more rigorous studies with adequate sample sizes are needed to verify the conclusion.

IFN-γ-STAT1-mediated CD8+ T-cell-neural stem cell cross talk controls astrogliogenesis after spinal cord injury.

BACKGROUND: Spinal cord injury (SCI) causes nearly all patients to suffer from protracted disabilities. An emerging therapeutic strategy involving the recruitment of endogenous neural stem cells (NSCs) has been developed. However, endogenous NSCs in the adult spinal cord differentiate into mostly astrocytes after traumatic injury, forming glial scars, which is a major cause of regeneration failure in SCI. Thus, understanding which factors drive the activation and differentiation of endogenous NSCs after SCI is critical for developing therapeutic drugs. METHODS: The infiltration, state, and location of CD8+ T cells in spinal cord after traumatic injury were analyzed by flow cytometry and immunofluorescence (IF) staining. The Basso Mouse Scale (BMS) scores and rotarod testing were used for motor behavioral analysis. NSCs were co-cultured with CD8+ T cells. EdU assay was used to detect proliferative cells. Western blotting was used to analyze the expression levels of STAT1, p-STAT1, and p27. ChIP-seq and ChIP-qRT-PCR analyses were used to detect the downstream of STAT1. Nestin-CreERT2::Ai9 transgenic mice were used to genetic lineage tracing of Nestin+ NSCs after SCI in vivo. RESULTS: A prolonged increase of activated CD8+ T cells occurs in the injured spinal cords. The behavioral analysis demonstrated that the administration of an anti-CD8 antibody promotes the recovery of locomotor function. Then, we discovered that CD8+ T cells suppressed the proliferation of NSCs and promoted the differentiation of NSCs into astrocytes by the IFN-γ-STAT1 pathway in vitro. ChIP-seq and ChIP-qRT-PCR analysis revealed that STAT1 could directly bind to the promoters of astrocyte marker genes GFAP and Aldh1l1. Genetic lineage tracing of Nestin+ NSCs demonstrated that most NSCs differentiated into astrocytes following SCI. Depleting CD8+ T cells reduced the differentiation of NSCs into astrocytes and instead promoted the differentiation of NSCs into oligodendrocytes. CONCLUSION: In conclusion, CD8+ T cells suppressed the proliferation of NSCs and promoted the differentiation of NSCs into astrocytes by the IFN-γ-STAT1-GFAP/Aldhl1l axis. Our study identifies INF-γ as a critical mediator of CD8+ T-cell-NSC cross talk and a potential node for therapeutic intervention in SCI.

Emerging diagnostic markers and therapeutic targets in post-stroke hemorrhagic transformation and brain edema.

Stroke is a devastating condition that can lead to significant morbidity and mortality. The aftermath of a stroke, particularly hemorrhagic transformation (HT) and brain edema, can significantly impact the prognosis of patients. Early detection and effective management of these complications are crucial for improving outcomes in stroke patients. This review highlights the emerging diagnostic markers and therapeutic targets including claudin, occludin, zonula occluden, s100ß, albumin, MMP-9, MMP-2, MMP-12, IL-1ß, TNF-α, IL-6, IFN-γ, TGF-ß, IL-10, IL-4, IL-13, MCP-1/CCL2, CXCL2, CXCL8, CXCL12, CCL5, CX3CL1, ICAM-1, VCAM-1, P-selectin, E-selectin, PECAM-1/CD31, JAMs, HMGB1, vWF, VEGF, ROS, NAC, and AQP4. The clinical significance and implications of these biomarkers were also discussed.

Diagnostic performance evaluation of adult Chiari malformation type I based on convolutional neural networks.

PURPOSE: This study aimed to evaluate the diagnostic performance of convolutional neural network (CNN) models in Chiari malformation type I (CMI) and to verify whether CNNs can identify the morphological features of the craniocervical junction region between patients with CMI and healthy controls (HCs). To date, numerous indicators based on manual measurements are used for the diagnosis of CMI. However, the corresponding postoperative efficacy and prognostic evaluations have remained inconsistent. From a diagnostic perspective, CNN models may be used to explore the relationship between the clinical features and image morphological parameters. METHODS: This study included a total of 148 patients diagnosed with CMI at our institution and 205 HCs were included. T1-weighted sagittal magnetic resonance imaging (MRI) images were used for the analysis. A total of 220 and 355 slices were acquired from 98 patients with CMI and 155 HCs, respectively, to train and validate the CNN models. In addition, median sagittal images obtained from 50 patients with CMI and 50 HCs were selected to test the models. We applied original cervical MRI images (CI) and images of posterior cranial fossa and craniocervical junction area (CVI) to train the CI- and CVI-based CNN models. Transfer learning and data augmentation were used for model construction and each model was retrained 10 times. RESULTS: Both the CI- and CVI-based CNN models achieved high diagnostic accuracy. In the validation dataset, the models had diagnostic accuracy of 100% and 97% (p = 0.005), sensitivity of 100% and 98% (p = 0.016), and specificity of 100% (p = 0.929), respectively. In the test dataset, the accuracy was 97% and 96% (p = 0.25), sensitivity was 97% and 92% (p = 0.109), and specificity was 100% (p = 0.123), respectively. For patients with cerebellar subungual herniation less than 5 mm, three out of the 10 CVI-based retrained models reached 100% sensitivity. CONCLUSIONS: Our results revealed that the CNN models demonstrated excellent diagnostic performance for CMI. The models had higher sensitivity than the application of cerebellar tonsillar herniation alone and could identify features in the posterior cranial fossa and craniocervical junction area of patients. Our preliminary experiments provided a feasible method for the diagnosis and study of CMI using CNN models. However, further studies are needed to identify the morphologic characteristics of patients with different clinical outcomes, as well as patients who may benefit from surgery.

Role of Calcium Signaling Pathway-Related Gene Regulatory Networks in Ischemic Stroke Based on Multiple WGCNA and Single-Cell Analysis.

BACKGROUND: This study is aimed at investigating the changes in relevant pathways and the differential expression of related gene expression after ischemic stroke (IS) at the single-cell level using multiple weighted gene coexpression network analysis (WGCNA) and single-cell analysis. METHODS: The transcriptome expression datasets of IS samples and single-cell RNA sequencing (scRNA-seq) profiles of cerebrovascular tissues were obtained by searching the Gene Expression Omnibus (GEO) database. First, gene pathway scoring was calculated via gene set variation analysis (GSVA) and was imported into multiple WGCNA to acquire key pathways and pathway-related hub genes. Furthermore, SCENIC was used to identify transcription factors (TFs) regulating these core genes using scRNA-seq data. Finally, the pseudotemporal trajectory analysis was used to analyse the role of these TFs on various cell types under hypoxic and normoxic conditions. RESULTS: The scores of 186 KEGG pathways were obtained via GSVA using microarray expression profiles of 40 specimens. WGCNA of the KEGG pathways revealed the two following pathways: calcium signaling pathway and neuroactive ligand-receptor interaction pathways. Subsequently, WGCNA of the gene expression matrix of the samples revealed the calcium signaling pathway-related genes (AC079305.10, BCL10, BCL2A1, BRE-AS1, DYNLL2, EREG, and PTGS2) that were identified as core genes via correlation analysis. Furthermore, SCENIC and pseudotemporal analysis revealed JUN, IRF9, ETV5, and PPARA score gene-related TFs. Jun was found to be associated with hypoxia in endothelial cells, whereas Irf9 and Etv5 were identified as astrocyte-specific TFs associated with oxygen concentration in the mouse cerebral cortex. CONCLUSIONS: Calcium signaling pathway-related genes (AC079305.10, BCL10, BCL2A1, BRE-AS1, DYNLL2, EREG, and PTGS2) and TFs (JUN, IRF9, ETV5, and PPARA) were identified to play a key role in IS. This study provides a new perspective and basis for investigating the pathogenesis of IS and developing new therapeutic approaches.