Local delivery of carboplatin-loaded hydrogel and calcium carbonate enables two-stage drug release for limited-dose radiation to eliminate mouse malignant glioma.

Postoperative radiotherapy remains the gold standard for malignant glioma treatment. Clinical limitations, including tumor growth between surgery and radiotherapy and the emergence of radioresistance, reduce treatment effectiveness and result in local disease progression. This study aimed to develop a local drug delivery system to inhibit tumor growth before radiotherapy and enhance the subsequent anticancer effects of limited-dose radiotherapy. We developed a compound of carboplatin-loaded hydrogel (CPH) incorporated with carboplatin-loaded calcium carbonate (CPCC) to enable two-stage (peritumoral and intracellular) release of carboplatin to initially inhibit tumor growth and to synergize with limited-dose radiation (10 Gy in a single fraction) to eliminate malignant glioma (ALTS1C1 cells) in a C57BL/6 mouse subcutaneous tumor model. The doses of carboplatin in CPH and CPCC treatments were 150 µL (carboplatin concentration of 5 mg/mL) and 15 mg (carboplatin concentration of 4.1 µg/mg), respectively. Mice receiving the combination of CPH-CPCC treatment and limited-dose radiation exhibited significantly reduced tumor growth volume compared to those receiving double-dose radiation alone. Furthermore, combining CPH-CPCC treatment with limited-dose radiation resulted in significantly longer progression-free survival than combining CPH treatment with limited-dose radiation. Local CPH-CPCC delivery synergized effectively with limited-dose radiation to eliminate mouse glioma, offering a promising solution for overcoming clinical limitations.

Comparing spatial distributions of ALA-PpIX and indocyanine green in a whole pig brain glioma model using 3D fluorescence cryotomography.

Significance: ALA-PpIX and second-window indocyanine green (ICG) have been studied widely for guiding the resection of high-grade gliomas. These agents have different mechanisms of action and uptake characteristics, which can affect their performance as surgical guidance agents. Elucidating these differences in animal models that approach the size and anatomy of the human brain would help guide the use of these agents. Herein, we report on the use of a new pig glioma model and fluorescence cryotomography to evaluate the 3D distributions of both agents throughout the whole brain. Aim: We aim to assess and compare the 3D spatial distributions of ALA-PpIX and second-window ICG in a glioma-bearing pig brain using fluorescence cryotomography. Approach: A glioma was induced in the brain of a transgenic Oncopig via adeno-associated virus delivery of Cre-recombinase plasmids. After tumor induction, the pro-drug 5-ALA and ICG were administered to the animal 3 and 24 h prior to brain harvest, respectively. The harvested brain was imaged using fluorescence cryotomography. The fluorescence distributions of both agents were evaluated in 3D in the whole brain using various spatial distribution and contrast performance metrics. Results: Significant differences in the spatial distributions of both agents were observed. Indocyanine green accumulated within the tumor core, whereas ALA-PpIX appeared more toward the tumor periphery. Both ALA-PpIX and second-window ICG provided elevated tumor-to-background contrast (13 and 23, respectively). Conclusions: This study is the first to demonstrate the use of a new glioma model and large-specimen fluorescence cryotomography to evaluate and compare imaging agent distribution at high resolution in 3D.

Multifaceted bioinformatic analysis of m6A-related ferroptosis and its link with gene signatures and tumour-infiltrating immune cells in gliomas.

Whether N6-Methyladenosine (m6A)- and ferroptosis-related genes act on immune responses to regulate glioma progression remains unanswered. Data of glioma and corresponding normal brain tissues were fetched from the TCGA database and GTEx. Differentially expressed genes (DEGs) were identified for GO and KEGG enrichment analyses. The FerrDb database was based to yield ferroptosis-related DEGs. Hub genes were then screened out using the cytoHubba database and validated in clinical samples. Immune cells infiltrating into the glioma tissues were analysed using the CIBERSORT R script. The association of gene signature underlying the m6A-related ferroptosis with tumour-infiltrating immune cells and immune checkpoints in low-grade gliomas was analysed. Of 6298 DEGs enriched in mRNA modifications, 144 were ferroptosis-related; NFE2L2 and METTL16 showed the strongest positive correlation. METTL16 knockdown inhibited the migrative and invasive abilities of glioma cells and induced ferroptosis in vitro. NFE2L2 was enriched in the anti-m6A antibody. Moreover, METTL16 knockdown reduced the mRNA stability and level of NFE2L2 (both p < 0.05). Proportions of CD8+ T lymphocytes, activated mast cells and M2 macrophages differed between low-grade gliomas and normal tissues. METTL16 expression was negatively correlated with CD8+ T lymphocytes, while that of NFE2L2 was positively correlated with M2 macrophages and immune checkpoints in low-grade gliomas. Gene signatures involved in the m6A-related ferroptosis in gliomas were identified via bioinformatic analyses. NFE2L2 interacted with METTL16 to regulate the immune response in low-grade gliomas, and both molecules may be novel therapeutic targets for gliomas.

Precision meets generalization: Enhancing brain tumor classification via pretrained DenseNet with global average pooling and hyperparameter tuning.

Brain tumors pose significant global health concerns due to their high mortality rates and limited treatment options. These tumors, arising from abnormal cell growth within the brain, exhibits various sizes and shapes, making their manual detection from magnetic resonance imaging (MRI) scans a subjective and challenging task for healthcare professionals, hence necessitating automated solutions. This study investigates the potential of deep learning, specifically the DenseNet architecture, to automate brain tumor classification, aiming to enhance accuracy and generalizability for clinical applications. We utilized the Figshare brain tumor dataset, comprising 3,064 T1-weighted contrast-enhanced MRI images from 233 patients with three prevalent tumor types: meningioma, glioma, and pituitary tumor. Four pre-trained deep learning models-ResNet, EfficientNet, MobileNet, and DenseNet-were evaluated using transfer learning from ImageNet. DenseNet achieved the highest test set accuracy of 96%, outperforming ResNet (91%), EfficientNet (91%), and MobileNet (93%). Therefore, we focused on improving the performance of the DenseNet, while considering it as base model. To enhance the generalizability of the base DenseNet model, we implemented a fine-tuning approach with regularization techniques, including data augmentation, dropout, batch normalization, and global average pooling, coupled with hyperparameter optimization. This enhanced DenseNet model achieved an accuracy of 97.1%. Our findings demonstrate the effectiveness of DenseNet with transfer learning and fine-tuning for brain tumor classification, highlighting its potential to improve diagnostic accuracy and reliability in clinical settings.

Letter to editor: The efects of dabrafenib and/or trametinib treatment in Braf V600­mutant glioma: a systematic review and meta-analysis.

The systematic review and meta-analysis titled "The Effects of Dabrafenib and/or Trametinib Treatment in BRAF V600-Mutant Glioma" provides a critical evaluation of these targeted therapies for a challenging subset of gliomas. This review is notable for its comprehensive data integration, offering a robust assessment of the efficacy and safety of dabrafenib and trametinib. By focusing on BRAF V600 mutations, it contributes valuable insights into personalized treatment strategies. However, limitations include study heterogeneity and a lack of long-term follow-up data, which hinder the generalizability and complete understanding of treatment effects. Additionally, while the review emphasizes therapeutic potential, it requires a thorough evaluation of adverse effects. Future research should address these limitations by providing more consistent data, longer follow-up, and a balanced view of treatment risks and benefits.

Immune landscape of oncohistone-mutant gliomas reveals diverse myeloid populations and tumor-promoting function.

Histone H3-mutant gliomas are deadly brain tumors characterized by a dysregulated epigenome and stalled differentiation. In contrast to the extensive datasets available on tumor cells, limited information exists on their tumor microenvironment (TME), particularly the immune infiltrate. Here, we characterize the immune TME of H3.3K27M and G34R/V-mutant gliomas, and multiple H3.3K27M mouse models, using transcriptomic, proteomic and spatial single-cell approaches. Resolution of immune lineages indicates high infiltration of H3-mutant gliomas with diverse myeloid populations, high-level expression of immune checkpoint markers, and scarce lymphoid cells, findings uniformly reproduced in all H3.3K27M mouse models tested. We show these myeloid populations communicate with H3-mutant cells, mediating immunosuppression and sustaining tumor formation and maintenance. Dual inhibition of myeloid cells and immune checkpoint pathways show significant therapeutic benefits in pre-clinical syngeneic mouse models. Our findings provide a valuable characterization of the TME of oncohistone-mutant gliomas, and insight into the means for modulating the myeloid infiltrate for the benefit of patients.

Blood based metabolic markers of glioma from pre-diagnosis to surgery.

Gliomas are highly complex and metabolically active brain tumors associated with poor prognosis. Recent reports have found altered levels of blood metabolites during early tumor development, suggesting that tumor development could be detected several years before clinical manifestation. In this study, we performed metabolite analyses of blood samples collected from healthy controls and future glioma patients, up to eight years before glioma diagnosis, and on the day of glioma surgery. We discovered that metabolites related to early glioma development were associated with an increased energy turnover, as highlighted by elevated levels of TCA-related metabolites such as fumarate, malate, lactate and pyruvate in pre-diagnostic cases. We also found that metabolites related to glioma progression at surgery were primarily high levels of amino acids and metabolites of amino acid catabolism, with elevated levels of 11 amino acids and two branched-chain alpha-ketoacids, ketoleucine and ketoisoleucine. High amino acid turnover in glioma tumor tissue is currently utilized for PET imaging, diagnosis and delineation of tumor margins. By examining blood-based metabolic progression patterns towards disease onset, we demonstrate that this high amino acid turnover is also detectable in a simple blood sample. These findings provide additional insight of metabolic alterations during glioma development and progression.

High-throughput fluorescence lifetime imaging flow cytometry.

Flow cytometry is a vital tool in biomedical research and laboratory medicine. However, its accuracy is often compromised by undesired fluctuations in fluorescence intensity. While fluorescence lifetime imaging microscopy (FLIM) bypasses this challenge as fluorescence lifetime remains unaffected by such fluctuations, the full integration of FLIM into flow cytometry has yet to be demonstrated due to speed limitations. Here we overcome the speed limitations in FLIM, thereby enabling high-throughput FLIM flow cytometry at a high rate of over 10,000 cells per second. This is made possible by using dual intensity-modulated continuous-wave beam arrays with complementary modulation frequency pairs for fluorophore excitation and acquiring fluorescence lifetime images of rapidly flowing cells. Moreover, our FLIM system distinguishes subpopulations in male rat glioma and captures dynamic changes in the cell nucleus induced by an anti-cancer drug. FLIM flow cytometry significantly enhances cellular analysis capabilities, providing detailed insights into cellular functions, interactions, and environments.

Eslicarbazepine induces apoptosis and cell cycle arrest in C6 glioma cells in vitro and suppresses tumor growth in an intracranial rat model.

BACKGROUND: Glioblastoma multiforme (GBM) is the most malignant brain tumor, with a poor prognosis and life expectancy of 14-16 months after diagnosis. The standard treatment for GBM consists of surgery, radiotherapy, and chemotherapy with temozolomide. Most patients become resistant to treatment after some time, and the tumor recurs. Therefore, there is a need for new drugs to manage GBM. Eslicarbazepine (ESL) is a well-known antiepileptic drug belonging to the dibenzazepine group with anticancer potentials. In this study, for the first time, we evaluated the potential effects of ESL on C6 cell growth, both in vitro and in vivo, and examined its molecular effects. METHODS: To determine the effect of ESL on the c6 cell line, cell viability, proliferation, and migration were evaluated by MTT assay, colony formation, and wound healing assay. Also, apoptosis and cell cycle were examined by flow cytometry, qRT-PCR, and western blotting. In addition, an intracranial model in Wistar rats was used to investigate the effect of ESL in vivo, and the tumor size was measured using both Caliper and MRI. RESULTS: The obtained results are extremely consistent and highly encouraging. C6 cell viability, proliferation, and migration were significantly suppressed in ESL-treated C6 cells (p < 0.001), as determined by cell-based assays. ESL treatment led to significant enhancement of apoptosis (p < 0.01), as determined by flow cytometry, and upregulation of genes involved in cell apoptosis, such as the Bax/Bcl2 ratio at RNA (p < 0.05) and protein levels (5.37-fold). Flow cytometric analysis of ESL-treated cells revealed G2/M phase cell cycle arrest. ESL-treated cells demonstrated 2.49-fold upregulation of p21 alongside, 0.22-fold downregulation of cyclin B1, and 0.34-fold downregulation of cyclin-dependent kinase-1 at the protein level. Administration of ESL (30 mg/kg) to male rats bearing C6 intracranial tumors also suppressed the tumor volume and weight (p < 0.01). CONCLUSIONS: Based on these novel findings, ESL has the potential for further experimental and clinical studies in glioblastoma.

Radiogenomic profiling of global DNA methylation associated with molecular phenotypes and immune features in glioma.

BACKGROUND: The radiogenomic analysis has provided valuable imaging biomarkers with biological insights for gliomas. The radiogenomic markers for molecular profile such as DNA methylation remain to be uncovered to assist the molecular diagnosis and tumor treatment. METHODS: We apply the machine learning approaches to identify the magnetic resonance imaging (MRI) features that are associated with molecular profiles in 146 patients with gliomas, and the fitting models for each molecular feature (MoRad) are developed and validated. To provide radiological annotations for the molecular profiles, we devise two novel approaches called radiomic oncology (RO) and radiomic set enrichment analysis (RSEA). RESULTS: The generated MoRad models perform well for profiling each molecular feature with radiomic features, including mutational, methylation, transcriptional, and protein profiles. Among them, the MoRad models have a remarkable performance in quantitatively mapping global DNA methylation. With RO and RSEA approaches, we find that global DNA methylation could be reflected by the heterogeneity in volumetric and textural features of enhanced regions in T2-weighted MRI. Finally, we demonstrate the associations of global DNA methylation with clinicopathological, molecular, and immunological features, including histological grade, mutations of IDH and ATRX, MGMT methylation, multiple methylation-high subtypes, tumor-infiltrating lymphocytes, and long-term survival outcomes. CONCLUSIONS: Global DNA methylation is highly associated with radiological profiles in glioma. Radiogenomic global methylation is an imaging-based quantitative molecular biomarker that is associated with specific consensus molecular subtypes and immune features.

Diffusion-weighted MR image analysis based on gamma distribution model for differentiating benign and malignant brain tumors.

BACKGROUND: Considering the invasiveness of the biopsy method, we attempted to evaluate the ability of the gamma distribution model using magnetic resonance imaging images to stage and grade benign and malignant brain tumors. METHODS: A total of 42 patients with malignant brain tumors (including glioma, lymphoma, and choroid plexus papilloma) and 24 patients with benign brain tumors (meningioma) underwent diffusion-weighted imaging using five b-values ranging from 0 to 2000 s/mm2 with a 1.5 T scanner. The gamma distribution model is expected to demonstrate the probability of water molecule distribution based on the apparent diffusion coefficient. For all tumors, the apparent diffusion coefficient, shape parameter (κ), and scale parameter (θ) were calculated for each b-value. In the staging step, the fractions (ƒ1, ƒ2, ƒ3) expected to reflect the intracellular, and extracellular diffusion and perfusion were investigated. Diffusion <1 × 10-4 mm2/s (ƒ1), 1 × 10-4 mm2/s < Diffusion > 3 × 10-4 mm2/s (ƒ2), and Diffusion >3 × 10-4 mm2/s (ƒ3); in the grading step, fractions were determined to check heavily restricted diffusion. Diffusion lower than 0.3 × 10-4 mm2/s (ƒ11). Diffusion lower than 0.5 × 10-4 mm2/s (ƒ12). Diffusion lower than 0.8 × 10-4 mm2/s (ƒ13). RESULTS: The findings were analyzed using nonparametric statistics and receiver operating characteristic curve diagnostic performance. Gamma model parameters (κ, ƒ1, ƒ2, ƒ3) showed a satisfactory difference in differentiating meningioma from glioma. For b value = 2000 s/mm2, ƒ1 had a better diagnostic performance than κ and apparent diffusion coefficient (sensitivity, 88%; specificity, 68%; P < .001). The best diagnostic performance was related to ƒ3 in b = 2000 s/mm2 (area under the curve = 0.891, sensitivity = 83%, specificity = 80%, P < .001). In the grading step, ƒ12 (area under the curve = 0.870, sensitivity = 92%, specificity = 72%, P < .001) had the best diagnostic performance in differentiating high-grade from low-grade gliomas with b = 2000 s/mm2. CONCLUSION: The findings of our study highlight the potential of using a gamma distribution model with diffusion-weighted imaging based on multiple b-values for grading and staging brain tumors. Its potential integration into routine clinical practice could advance neurooncology and improve patient outcomes through more accurate diagnosis and treatment planning.

Prediction of Glioma enhancement pattern using a MRI radiomics-based model.

Contrast-MRI scans carry risks associated with the chemical contrast agents. Accurate prediction of enhancement pattern of gliomas has potential in avoiding contrast agent administration to patients. This study aimed to develop a machine learning radiomics model that can accurately predict enhancement pattern of gliomas based on T2 fluid attenuated inversion recovery images. A total of 385 cases of pathologically-proven glioma were retrospectively collected with preoperative magnetic resonance T2 fluid attenuated inversion recovery images, which were divided into enhancing and non-enhancing groups. Predictive radiomics models based on machine learning with 6 different classifiers were established in the training cohort (n = 201), and tested both in the internal validation cohort (n = 85) and the external validation cohort (n = 99). Receiver-operator characteristic curve was used to assess the predictive performance of these radiomics models. This study demonstrated that the radiomics model comprising of 15 features using the Gaussian process as a classifier had the highest predictive performance in both the training cohort and the internal validation cohort, with the area under the curve being 0.88 and 0.80, respectively. This model showed an area under the curve, sensitivity, specificity, positive predictive value and negative predictive value of 0.81, 0.98, 0.61, 0.82, 0.76 and 0.96, respectively, in the external validation cohort. This study suggests that the T2-FLAIR-based machine learning radiomics model can accurately predict enhancement pattern of glioma.

Impact of Microsurgery and Postoperative Radiotherapy on Neurological Function in Intramedullary Spinal Cord Gliomas.

OBJECTIVE: To assess the clinical efficacy of combined microsurgery and postoperative radiotherapy for the treatment of intramedullary spinal gliomas and its impact on neurological function. STUDY DESIGN: An observational study. Place and Duration of the Study: Department of Neurosurgery, Baoding No.1 Central Hospital, Hebei, China, between January 2020 and 2023. METHODOLOGY: Sixty patients diagnosed with spinal cord intramedullary gliomas were divided equally into an experimental and control group. The control group received microsurgical treatment, and the experimental group received microsurgical treatment combined with postoperative radiotherapy. The treatment effectiveness, neurological function, and follow-up results of the two groups were compared. RESULTS: After treatment, the clinical efficacy of the experimental group treatment was significantly better than that of the control group (p <0.05). The National Institutes of Health Stroke Scale (NIHSS) scores were significantly lower, and the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire-30 (EORTC QLQ-C30) scores were significantly higher in the experimental group than in the control group (p <0.05). The 1-3-year survival rate and median survival time of the experimental group were significantly higher than those of the control group (p <0.05). The incidence of complications was 3.33% in the experimental group and 6.67% in the control group, but the difference was not statistically significant (p >0.05). The postoperative recurrence rate was significantly lower in the experimental (0%) than in the control group (13.33%, p <0.05). CONCLUSION: Combined microsurgery and postoperative radiotherapy was found to be more effective than microsurgery alone. It was also more conducive to the recovery of neurological function and improved the patient's quality of life. KEY WORDS: Intramedullary spinal cord glioma, Microsurgery, Neurological function, Radiotherapy.

Preliminary studies on changes in the amount of tryptophan metabolites in human glioma tissues.

BACKGROUND: We have developed and validated methods for the determination of three major tryptophan metabolites metabolized by the kynurenine pathway, namely kynurenine (KYN), 3-hydroxykynurenine (3-HK), and 3-hydroxyanthranilic acid (3-HAA). KYN and 3-HK were determined using RP-HPLC-UV, and 3-HAA using RP-HPLC-FL. We then developed a comparative method based on CE-UV. The developed methods were validated and 36 samples of human brain glioma tissue homogenates were assayed in all 4 grades of malignancy, and the concentration levels of assayed metabolites were compared with available clinical data. RESULTS: Each of the methods is characterized by high precision, accuracy and repeatability, and the determined LOQ values indicate the possibility of performing quantitative analysis on the available samples of human glioma tumors (36 samples in grades G1-G4). The concentration values of selected metabolites obtained using HPLC methods were subjected to statistical analysis and preliminary clinical data processing. We found statistically significant differences in the concentrations of KYN, 3-HK and 3-HAA between the various grades of the disease, and characterized these differences more precisely by means of the Dunn-Bonferroni post hoc test. We did not find that the patient's environment or habits significantly affected the metabolites concentration of the study samples population. In addition, we showed a high positive correlation between KYN, 3-HK and 3-HAA, which appears to be a characteristic that describes metabolic changes of Trp in relation to KYN, 3-HK and 3-HAA, and indicates potential diagnostic value. SIGNIFICANCE: The preliminary studies carried out contribute new knowledge on the molecular basis of human brain glioma. They also provide valuable information useful for the development of glioma diagnostics, differentiation of disease grades and assessment of the patient's condition. The obtained relationships between metabolite concentrations and the grade of malignancy of the disease and correlations between metabolite concentrations constitute the basis for further broader biochemical and clinical analysis.

Update on the intriguing roles of AQP4 expression and redistribution in the progression and treatment of glioma.

Aquaporin 4 (AQP4) is abundant in the human brain and has an important role in brain homeostasis and diseases. AQP4 expression has been found to be associated with glioma malignancies. However, the complete understanding of the biological processes and curative importance of AQP4 in glioma remains unclear. The impact of AQP4 subcellular mislocalization on glioma progression and the precise mechanisms regarding AQP4 translocation in glioma need further investigation. In this review, we update recent findings about disturbed AQP4 expression in glioma and explore targeting AQP4 to modulate the glioma progression. Thereafter we discuss some possible mechanisms of action of AQP4 translocations in glioma. The present article offers an appropriate introduction to the potential involvement of AQP4 in the emergence and progression of glioma. Both comprehensive research into the mechanisms and systematically intervention studies focusing on AQP4 are essential. By embracing this strategy, we can obtain a new and insightful outlook on managing cancerous glioma. Although the observations summarized in this review should be confirmed with more studies, we believe that they could provide critical information for the design of more focused research that will allow for systematic and definitive evaluation of the role of AQP4 in glioma treatments.

Pre-Clinical Models for CAR T-Cell Therapy for Glioma.

Immunotherapy represents a transformative shift in cancer treatment. Among myriad immune-based approaches, chimeric antigen receptor (CAR) T-cell therapy has shown promising results in treating hematological malignancies. Despite aggressive treatment options, the prognosis for patients with malignant brain tumors remains poor. Research leveraging CAR T-cell therapy for brain tumors has surged in recent years. Pre-clinical models are crucial in evaluating the safety and efficacy of these therapies before they advance to clinical trials. However, current models recapitulate the human tumor environment to varying degrees. Novel in vitro and in vivo techniques offer the opportunity to validate CAR T-cell therapies but also have limitations. By evaluating the strengths and weaknesses of various pre-clinical glioma models, this review aims to provide a roadmap for the development and pre-clinical testing of CAR T-cell therapies for brain tumors.

Emerging and Biological Concepts in Pediatric High-Grade Gliomas.

Primary central nervous system tumors are the most frequent solid tumors in children, accounting for over 40% of all childhood brain tumor deaths, specifically high-grade gliomas. Compared with pediatric low-grade gliomas (pLGGs), pediatric high-grade gliomas (pHGGs) have an abysmal survival rate. The WHO CNS classification identifies four subtypes of pHGGs, including Grade 4 Diffuse midline glioma H3K27-altered, Grade 4 Diffuse hemispheric gliomas H3-G34-mutant, Grade 4 pediatric-type high-grade glioma H3-wildtype and IDH-wildtype, and infant-type hemispheric gliomas. In recent years, we have seen promising advancements in treatment strategies for pediatric high-grade gliomas, including immunotherapy, CAR-T cell therapy, and vaccine approaches, which are currently undergoing clinical trials. These therapies are underscored by the integration of molecular features that further stratify HGG subtypes. Herein, we will discuss the molecular features of pediatric high-grade gliomas and the evolving landscape for treating these challenging tumors.

A comparative study of PEO-PBO content on the targeting and anti-glioma activity of annonaceous acetogenins-loaded nanomicelles.

Annonaceous acetogenins (ACGs) have great potential in the treatment of gliomas, but are extremely insoluble and difficult for delivery in vivo. Poly(ethylene oxide)-b-poly(butylene oxide) (PEO-PBO) is an amphiphilic polymer and can reduce the clearance of nanoparticles by mononuclear phagocyte system. To explore an efficient and safe nanomedicine for glioma, ACGs-loaded nanomicelles (ACGs/EB-NCs) was constructed using PEO-PBO as a carrier, and the effect of PEO-PBO content on the targeting and anti-glioma activity were also compared. ACGs/EB5-NCs, ACGs/EB10-NCs and ACGs/EB20-NCs, the three nanomicellels prepared with different ACGs/EB feeding ratios, had average particle sizes of 148.8±0.5 nm, 32.7±4.1 nm, and 27.1±0.3 nm, respectively. The three ACGs/EB-NCs were spherical in shape, with drug loading content close to the theoretical drug loading content, encapsulation efficiency greater than 97 %, and good stability in physiological media. The cumulative release rates of ACGs/EB5-NCs, ACGs/EB10-NCs and ACGs/EB20-NCs were 78.2 %, 63.4 %, and 56.3 % within 216 hours, respectively. The inhibitory effects of three ACGs/EB-NCs on U87 MG cells were similar and stronger than free ACGs (P<0.05), with half inhibitory concentration of 0.17, 0.18, and 0.16 ng/mL (P>0.05), respectively. In U87 MG tumor­bearing mice, ACGs/EB5-NC, ACGs/EB10-NCs and ACGs/EB20-NCs showed a similar tumor inhibition rate of 61.1±5.9 %, 56.2±8.6 % and 64.3±9.4 % (P>0.05), with good safety. Three ACGs/EB-NCs exhibited excellent liver escape ability and tumor targeting ability, with the tumor targeting index greater than 1.5. Three ACGs/EB-NCs were successfully prepared with strong anti-glioma activity and tumor targeting properties, which are expected to provide new options for the clinical treatment of gliomas. The content of PEO-PBO in micelles did not have a significant effect on the tumor targeting and anti-glioma activity of ACGs/EB-NCs.

[An MRI multi-sequence feature imputation and fusion mutual-aid model based on sequence deletion for differentiation of high-grade from low-grade glioma].

OBJECTIVE: To evaluate the performance of magnetic resonance imaging (MRI) multi-sequence feature imputation and fusion mutual model based on sequence deletion in differentiating high-grade glioma (HGG) from low-grade glioma (LGG). METHODS: We retrospectively collected multi-sequence MR images from 305 glioma patients, including 189 HGG patients and 116 LGG patients. The region of interest (ROI) of T1-weighted images (T1WI), T2-weighted images (T2WI), T2 fluid attenuated inversion recovery (T2_FLAIR) and post-contrast enhancement T1WI (CE_T1WI) were delineated to extract the radiomics features. A mutual-aid model of MRI multi-sequence feature imputation and fusion based on sequence deletion was used for imputation and fusion of the feature matrix with missing data. The discriminative ability of the model was evaluated using 5-fold cross-validation method and by assessing the accuracy, balanced accuracy, area under the ROC curve (AUC), specificity, and sensitivity. The proposed model was quantitatively compared with other non-holonomic multimodal classification models for discriminating HGG and LGG. Class separability experiments were performed on the latent features learned by the proposed feature imputation and fusion methods to observe the classification effect of the samples in twodimensional plane. Convergence experiments were used to verify the feasibility of the model. RESULTS: For differentiation of HGG from LGG with a missing rate of 10%, the proposed model achieved accuracy, balanced accuracy, AUC, specificity, and sensitivity of 0.777, 0.768, 0.826, 0.754 and 0.780, respectively. The fused latent features showed excellent performance in the class separability experiment, and the algorithm could be iterated to convergence with superior classification performance over other methods at the missing rates of 30% and 50%. CONCLUSION: The proposed model has excellent performance in classification task of HGG and LGG and outperforms other non-holonomic multimodal classification models, demonstrating its potential for efficient processing of non-holonomic multimodal data.