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Exosomes derived from mesenchymal stem cells (MSCs) have been considered as an alternative for cell therapy of acute spinal cord injury (ASCI). However, the underlying mechanism remains unclear. Here, ASCI mouse model and hypoxic cell model were established to evaluate the effects of MSCs and MSCs-derived exosomes (MSCs-exo). The results showed that both MSCs and MSCs-exo inhibited the production of ROS and ferrous iron, upregulated the expression of ferroptosis suppressor FSP1, and enhanced repair of neurological function in the ASCI mice. Besides, MSCs and MSCs-exo attenuated hypoxia-induced neuronal cell ferroptosis and increased cell proliferation. Further study demonstrated that lncGm36569 was enriched in the MSCs-exo. Through bioinformatics analysis and luciferase assay, we confirmed that lncGm36569 acted as a competitive RNA of miR-5627-5p to induce FSP1 upregulation. Furthermore, overexpression of miR-5627-5p reversed the therapeutic effects of lncGm36569 on neuronal cell ferroptosis. In conclusion, MSCs-exosomes lncGm36569 inhibited neuronal cell ferroptosis through miR-5627-5p/FSP1 axis, thereby attenuating neuronal dysfunction.

BackgroundIsocitrate dehydrogenase (IDH) mutation and 1p19q codeletion status have been identified as significant markers for therapy and prognosis in lower-grade glioma (LGG). The current study aimed to construct a combined machine learning-based model for predicting the molecular subtypes of LGG, including (1) IDH wild-type astrocytoma (IDHwt), (2) IDH mutant and 1p19q non-codeleted astrocytoma (IDHmut-noncodel), and (3) IDH-mutant and 1p19q codeleted oligodendroglioma (IDHmut-codel), based on multiparametric magnetic resonance imaging (MRI) radiomics, qualitative features, and clinical factors.MethodsA total of 335 patients with LGG (WHO grade II/III) were retrospectively enrolled. The sum of 5,929 radiomics features were extracted from multiparametric MRI. Selected robust, non-redundant, and relevant features were used to construct a random forest model based on a training cohort (n = 269) and evaluated on a testing cohort (n = 66). Meanwhile, preoperative MRIs of all patients were scored in accordance with Visually Accessible Rembrandt Images (VASARI) annotations and T2-fluid attenuated inversion recovery (T2-FLAIR) mismatch sign. By combining radiomics features, qualitative features (VASARI annotations and T2-FLAIR mismatch signs), and clinical factors, a combined prediction model for the molecular subtypes of LGG was built.ResultsThe 17-feature radiomics model achieved area under the curve (AUC) values of 0.6557, 0.6830, and 0.7579 for IDHwt, IDHmut-noncodel, and IDHmut-codel, respectively, in the testing cohort. Incorporating qualitative features and clinical factors into the radiomics model resulted in improved AUCs of 0.8623, 0.8056, and 0.8036 for IDHwt, IDHmut-noncodel, and IDHmut-codel, with balanced accuracies of 0.8924, 0.8066, and 0.8095, respectively.ConclusionThe combined machine learning algorithm can provide a method to non-invasively predict the molecular subtypes of LGG preoperatively with excellent predictive performance.

CD204 is a specific marker of tumor‐associated macrophages (TAMs) in glioma. However, the expression levels of CD204 and its involvement in glioma are not fully understood. In this large‐scale study, we assessed the expression and function of CD204 in whole‐grade glioma molecularly and clinically. In total, 1323 glioma samples, including 301 microarray data and 325 RNA‐seq data from the Chinese Glioma Genome Atlas (CGGA) dataset and 697 RNA‐seq data from The Cancer Genome Atlas (TCGA) dataset, were utilized. The statistical analysis and graphical work were mainly performed using the R software. Univariate and multivariate Cox analysis demonstrated that CD204 was an independent prognosticator in glioma patients. CD204 expression was positively correlated with the grade of malignancy. CD204 was consistently upregulated in wild‐type isocitrate dehydrogenase glioma and highly expressed in mesenchymal glioblastoma. Gene ontology of CD204‐related genes showed that CD204 was most enriched in inflammatory response and immune response. It was associated with the stromal and immune populations, especially the monocytic lineage, fibroblasts, and T cells. Circos plots revealed that CD204 was closely associated with many immune checkpoint regulators, especially TIM‐3. CD204 expression is consistent with the malignant phenotype of glioma and independently predicts poor outcomes in glioma patients. Additionally, CD204+ TAMs, collaborating with other checkpoint members, may contribute to the dysfunction of T cells. These findings suggest that CD204 may be a promising target for glioma immunotherapy.

Background: To develop and validate a deep learning signature (DLS) from diffusion tensor imaging (DTI) for predicting overall survival in patients with infiltrative gliomas, and to investigate the biological pathways underlying the developed DLS. Methods: The DLS was developed based on a deep learning cohort (n = 688). The key pathways underlying the DLS were identified on a radiogenomics cohort with paired DTI and RNA-seq data (n=78), where the prognostic value of the pathway genes was validated in public databases (TCGA, n = 663; CGGA, n = 657). Findings: The DLS was associated with survival (log-rank P < 0.001) and was an independent predictor (P < 0.001). Incorporating the DLS into existing risk system resulted in a deep learning nomogram predicting survival better than either the DLS or the clinicomolecular nomogram alone, with a better calibration and classification accuracy (net reclassification improvement 0.646, P < 0.001). Five kinds of pathways (synaptic transmission, calcium signaling, glutamate secretion, axon guidance, and glioma pathways) were significantly correlated with the DLS. Average expression value of pathway genes showed prognostic significance in our radiogenomics cohort and TCGA/CGGA cohorts (log-rank P < 0.05). Interpretation: DTI-derived DLS can improve glioma stratification by identifying risk groups with dysregulated biological pathways that contributed to survival outcomes. Therapies inhibiting neuron-to-brain tumor synaptic communication may be more effective in high-risk glioma defined by DTI-derived DLS. Funding: A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

The 2016 WHO classification of central nervous system tumors has included four molecular subgroups under medulloblastoma (MB) as sonic hedgehog (SHH), wingless (WNT), Grade 3, and Group 4. We aimed to develop machine learning models for predicting MB molecular subgroups based on multi-parameter magnetic resonance imaging (MRI) radiomics, tumor locations, and clinical factors. A total of 122 MB patients were enrolled retrospectively. After selecting robust, non-redundant, and relevant features from 5,529 extracted radiomics features, a random forest model was constructed based on a training cohort (n= 92) and evaluated on a testing cohort (n= 30). By combining radiographic features and clinical parameters, two combined prediction models were also built. The subgroup can be classified using an 11-feature radiomics model with a high area under the curve (AUC) of 0.8264 for WNT and modest AUCs of 0.6683, 0.6004, and 0.6979 for SHH, Group 3, and Group 4 in the testing cohort, respectively. Incorporating location and hydrocephalus into the radiomics model resulted in improved AUCs of 0.8403 and 0.8317 for WNT and SHH, respectively. After adding gender and age, the AUCs for WNT and SHH were further improved to 0.9097 and 0.8654, while the accuracies were 70 and 86.67% for Group 3 and Group 4, respectively. Prediction performance was excellent for WNT and SHH, while that for Group 3 and Group 4 needs further improvements. Machine learning algorithms offer potentials to non-invasively predict the molecular subgroups of MB.

This study aimed to explore the effects and function of microRNA-101a-3p (miR-101a-3p) in epilepsy. Rat model of pilocarpine-induced epilepsy was established and the seizure frequency was recorded. Expression of miR-101a-3p and c-Fos in hippocampus tissues of Rat models were detected by qRT-PCR and western blot. Besides, we established a hippocampal neuronal culture model of acquired epilepsy using Mg

Background: The 2016 WHO Classification of Central Nervous System tumors has included four molecular subgroups under medulloblastoma (MB) as sonic hedgehog (SHH), wingless (WNT), Grade 3 and Group 4. We aimed to develop machine learning models for predicting MB molecular subgroups based on multimodal magnetic resonance imaging (MRI) radiomics, tumor locations and clinical factors. Methods: 122 MB patients were enrolled retrospectively. After selecting robust, nonredundant and relevant features from 5529 extracted radiomics features, a random forest model was constructed based on a training cohort (n = 92) and evaluated on a testing cohort (n = 30). By combining the radiographic features and clinical parameters, two combined prediction models were also built. Findings: The subgroup can be classified using an 11-feature radiomics model with a high area under curve (AUC) of 0.8264 for WNT and modest AUCs of 0.6683, 0.6004 and 0.6979 for SHH, Group 3 and Group 4 in testing cohort, respectively. Incorporating location and hydrocephalus into the radiomics model resulted in improved AUCs of 0.8403 and 0.8317 for WNT and SHH, respectively. After adding gender and age, the AUCs for WNT and SHH were further improved to 0.9097 and 0.8654, while the accuracies were 70% and 86.67% for Group 3 and Group 4, respectively. Interpretation: The prediction performance was excellent for WNT and SHH, while that for Group 3 and Group 4 needs further improvements. Machine learning algorithms offer potentials to non-invasively predict molecular subgroups of MB. Funding Statement: This research was supported by the National Natural Science Foundation of China (No. 81702465, 61571432 and U1804172), the Science and Technology Program of Henan Province (No. 182102310113, 192102310123 and 192102310050). Declaration of Interests: The authors declare no potential conflicts of interest. Ethics Approval Statement: The Human Scientific Ethics Committee of the First Affiliated Hospital of Zhengzhou University has approved the protocol of this study (No. 2019-KY-176).

During neurological surgery, neurosurgeons have to transform the two-dimensional (2D) sectional images into three-dimensional (3D) structures at the cognitive level. The complexity of the intracranial structures increases the difficulty and risk of neurosurgery. Mixed reality (MR) applications reduce the obstacles in the transformation from 2D images to 3D visualization of anatomical structures of central nervous system. In this study, the holographic image was established by MR using computed tomography (CT), computed tomography angiography (CTA) and magnetic resonance imaging (MRI) data of patients. The surgeon's field of vision was superimposed with the 3D model of the patient's intracranial structure displayed on the mixed reality head-mounted display (MR-HMD). The neurosurgeons practiced and evaluated the feasibility of this technique in neurosurgical cases. We developed the segmentation image masks and texture mapping including brain tissue, intracranial vessels, nerves, tumors, and their relative positions by MR technologies. The results showed that the three-dimensional imaging is in a stable state in the operating room with no significant flutter and blur. And the neurosurgeon's feedback on the comfort of the equipment and the practicality of the technology was satisfactory. In conclusion, MR technology can holographically construct a 3D digital model of patient's lesions and improve the anatomical perception of neurosurgeons during craniotomy. The feasibility of the MR-HMD application in neurosurgery is confirmed.

Inflammation and immunoreaction markers were correlated with the survival of patients in many tumors. However, there were no reports investigating the relationships between preoperative hematological markers and the prognosis of medulloblastoma (MB) patients based on the molecular subgroups (WNT, SHH, Group 3, and Group 4). A total 144 MB patients were enrolled in the study. The differences of preoperative hematological markers among molecular subgroups of MB were compared by One-way ANOVA method. Kaplan-Meier method was used to calculate the curves of progression free survival (PFS) and overall survival (OS). The comparison of survival rates in different groups were conducted by the Log-rank test. Multivariate analysis was used to evaluate independent prognostic factors. Increased preoperative NLR (neutrophil-to-lymphocyte ratio, PFS, P = 0.004, OS, P P = 0.028, OS, P = 0.003) predicted poor prognosis in patients with MB, while preoperative MLR (monocyte-to-lymphocyte ratio), MPV (mean platelet volume), PDW (platelet distribution width), and AGR (albumin-to-globulin ratio) were revealed no predictive value on the prognosis of patients with MB. Furthermore, high preoperative NLR and PLR predicted unfavorable prognosis in childhood MB patients. However, preoperative NLR and PLR were not associated with the prognosis in adult MB patients. Multivariate analysis demonstrated preoperative NLR (PFS, P = 0.029, OS, P = 0.005) and PLR (PFS, P = 0.023, OS, P = 0.005) were the independent prognostic factors in MB patients. Emphatically, the levels of preoperative NLR and PLR in Group 3 MB were significantly higher than those in WNT MB. High preoperative NLR was associated with unfavorable OS in Group 3 (P = 0.032) and Group 4 (P = 0.027) tumors. Similarly, increased preoperative PLR predicted poor PFS (P = 0.012) and OS (P = 0.009) in Group 4 tumors. Preoperative NLR and PLR were the potential prognostic markers for MB patients. Preoperative NLR and PLR were significantly associated with the survival of Group 3 and Group 4 tumors.