Pediatric diffuse midline gliomas (DMG) with H3-K27M-altered are aggressive brain tumors that arise during childhood. Despite advances in genomic knowledge and the significant number of clinical trials testing new targeted therapies, patient outcomes are still insufficient. Immune checkpoint blockades with small molecules, such as aptamers, are opening new therapeutic options that represent hope for this orphan disease. Here, we demonstrated that a TIM-3 aptamer as monotherapy increased the immune infiltration and elicited a strong specific immune response with a tendency to improve the overall survival of treated DMG-bearing mice. Importantly, combining TIM-3 Apt with radiotherapy increased the overall median survival and led to long-term survivor mice in two pediatric DMG orthotopic murine models. Interestingly, TIM-3 aptamer administration increased the number of myeloid populations and the pro-inflammatory ratios of CD8: Tregs in the tumor microenvironment as compared to non-treated groups after radiotherapy. Importantly, the depletion of T-cells led to a major loss of the therapeutic effect achieved by the combination. This work uncovers TIM-3 targeting as an immunotherapy approach to improve the radiotherapy outcome in DMGs and offers a strong foundation for propelling a phase I clinical trial using radiotherapy and TIM-3 blockade combination as a treatment for these tumors.

Despite being the leading cause of childhood mortality, pediatric gliomas have been relatively understudied, and the repurposing of immunotherapies has not been successful. Whole transcriptome sequencing, single-cell sequencing, and sequential multiplex immunofluorescence were used to identify an immunotherapy strategy evaluated in multiple preclinical glioma models. MAPK-driven pediatric gliomas have a higher interferon signature relative to other molecular subgroups. Single-cell sequencing identified an activated and cytotoxic microglia population designated MG-Act in BRAF-fused MAPK-activated pilocytic astrocytoma (PA), but not in high-grade gliomas or normal brain. TIM3 is expressed on MG-Act and on the myeloid cells lining the tumor vasculature but not normal brain. TIM3 expression becomes upregulated on immune cells in the PA microenvironment and anti-TIM3 reprograms ex vivo immune cells from human PAs to a pro-inflammatory cytotoxic phenotype. In a genetically engineered murine model of MAPK-driven low-grade gliomas, anti-TIM3 treatment increased median survival over IgG and anti-PD1 treated mice. ScRNA sequencing data during the therapeutic window of anti-TIM3 demonstrates enrichment of the MG-Act population. The therapeutic activity of anti-TIM3 is abrogated in the CX3CR1 microglia knockout background. These data support the use of anti-TIM3 in clinical trials of pediatric low-grade MAPK-driven gliomas.

Soloxolone amides are semisynthetic triterpenoids that can cross the blood-brain barrier and inhibit glioblastoma growth both in vitro and in vivo. Here we investigate the impact of these compounds on processes associated with glioblastoma invasiveness and therapy resistance. Screening of soloxolone amides against glioblastoma cells revealed the ability of compound 7 (soloxolone para-methylanilide) to inhibit transforming growth factor-beta 1 (TGF-β1)-induced glial-mesenchymal transition Compound 7 inhibited morphological changes, wound healing, transwell migration, and expression of mesenchymal markers (N-cadherin, fibronectin, Slug) in TGF-β1-induced U87 and U118 glioblastoma cells, while restoring their adhesiveness. Confocal microscopy and molecular docking showed that 7 reduced SMAD2/3 nuclear translocation probably by direct interaction with the TGF-β type I and type II receptors (TβRI/II). In addition, 7 suppressed stemness of glioblastoma cells as evidenced by inhibition of colony forming ability, spheroid growth, and aldehyde dehydrogenase (ALDH) activity. Furthermore, 7 exhibited a synergistic effect with temozolomide (TMZ) on glioblastoma cell viability. Using N-acetyl-L-cysteine (NAC) and flow cytometry analysis of Annexin V-FITC-, propidium iodide-, and DCFDA-stained cells, 7 was found to synergize the cytotoxicity of TMZ by inducing ROS-dependent apoptosis. Further in vivo studies showed that 7, alone or in combination with TMZ, effectively suppressed the growth of U87 xenograft tumors in mice. Thus, 7 demonstrated promising potential as a component of combination therapy for glioblastoma, reducing its invasiveness and increasing its sensitivity to chemotherapy.

Brain cancer is one of the deadliest diseases, although many efforts have been made to treat it, there is no comprehensive and effective treatment approach yet. In recent years, the use of network-based analysis to identify important biological genes and pathways involved in various complex diseases, including brain cancer, has attracted the attention of researchers. The goal of this manuscript is to perform a comprehensive analysis of the various results presented related to brain cancer. For this purpose, firstly, based on the CORMINE medical database, collected all the genes related to brain cancer with a valid P-value. Then the structural and functional relationships between the above gene sets have been identified based on the STRING database. Next, in the PPI network, hub centrality analysis was performed to determine the proteins that have many connections with other proteins. After the modularization of the network, the module with the most hub vertices is considered as the most relevant module to the formation and progression of brain cancer. Since the driver vertices play an important role in biological systems, the edges of the selected module were oriented, and by analyzing the controllability of complex networks, a set of five proteins with the highest control power has been identified. Finally, based on the drug-gene interaction, a set of drugs effective on each of the driver genes has been obtained, which can potentially be used as new combination drugs. Validation of the hub and driver proteins shows that they are mainly essential proteins in the biological processes related to the various cancers and therefore the drugs that affect them can be considered as new combination therapy. The presented procedure can be used for any other complex disease.

Emerging Industry 5.0 designs promote artificial intelligence services and data-driven applications across multiple places with varying ownership that need special data protection and privacy considerations to prevent the disclosure of private information to outsiders. Due to this, federated learning offers a method for improving machine-learning models without accessing the train data at a single manufacturing facility. We provide a self-adaptive framework for federated machine learning of healthcare intelligent systems in this research. Our method takes into account the participating parties at various levels of healthcare ecosystem abstraction. Each hospital trains its local model internally in a self-adaptive style and transmits it to the centralized server for universal model optimization and communication cycle reduction. To represent a multi-task optimization issue, we split the dataset into as many subsets as devices. Each device selects the most advantageous subset for every local iteration of the model. On a training dataset, our initial study demonstrates the algorithm\'s ability to converge various hospital and device counts. By merging a federated machine-learning approach with advanced deep machine-learning models, we can simply and accurately predict multidisciplinary cancer diseases in the human body. Furthermore, in the smart healthcare industry 5.0, the results of federated machine learning approaches are used to validate multidisciplinary cancer disease prediction. The proposed adaptive federated machine learning methodology achieved 90.0%, while the conventional federated learning approach achieved 87.30%, both of which were higher than the previous state-of-the-art methodologies for cancer disease prediction in the smart healthcare industry 5.0.

UNASSIGNED: The brainstem tumour known as diffuse intrinsic pontine glioma (DIPG), also known as pontine glioma, infiltrative brainstem glioma is uncommon and virtually always affects children. A pontine glioma develops in the brainstem\'s most vulnerable region (the \"pons\"), which regulates a number of vital processes like respiration and blood pressure. It is particularly challenging to treat due to its location and how it invades healthy brain tissue. The hunt for a solution is continually advancing thanks to advances in modern medicine, but the correct approach is still elusive. With a particular focus on brain tumours that are incurable or recur, research is ongoing to discover fresh, practical approaches to target particular areas of the brain.
UNASSIGNED: To successfully complete this task, a thorough literature search was carried out in reputable databases like Google Scholar, PubMed, and ScienceDirect.
UNASSIGNED: The present article provides a comprehensive analysis of the notable advantages of lipid nanoparticles compared to alternative nanoparticle formulations. The article delves into the intricate realm of diverse lipid-based nanoparticulate delivery systems, which are used in Diffuse Intrinsic Pontine Glioma (DIPG) which thoroughly examines preclinical and clinical studies, providing a comprehensive analysis of the effectiveness and potential of lipid nanoparticles in driving therapeutic advancements for DIPG.
UNASSIGNED: There is strong clinical data to support the promising method of using lipid-based nanoparticulate drug delivery for brain cancer treatment, which shows improved outcomes.

Patients affected by glioma frequently suffer of epileptic discharges, however the causes of brain tumor-related epilepsy (BTRE) are still not completely understood. We investigated the mechanisms underlying BTRE by analyzing the effects of exosomes released by U87 glioma cells and by patient-derived glioma cells. Rat hippocampal neurons incubated for 24 h with these exosomes exhibited increased spontaneous firing, while their resting membrane potential shifted positively by 10-15 mV. Voltage clamp recordings demonstrated that the activation of the Na+ current shifted towards more hyperpolarized voltages by 10-15 mV. To understand the factors inducing hyperexcitability we focused on exosomal cytokines. Western Blot and ELISA assays show that TNF-α is present inside glioma-derived exosomes. Remarkably, incubation with TNF-α fully mimicked the phenotype induced by exosomes, with neurons firing continuously, while their resting membrane potential shifted positively. RT-PCR revealed that both exosomes and TNF-α induced over-expression of the voltage-gated Na channel Nav1.6, a low-threshold Na+ channel responsible for hyperexcitability. When neurons were preincubated with Infliximab, a specific TNF-α inhibitor, the hyperexcitability induced by exosomes and TNF-α were drastically reduced. We propose that Infliximab, an FDA approved drug to treat rheumatoid arthritis, could ameliorate the conditions of glioma patients suffering of BTRE.

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Gamma Knife radiosurgery (GKRS) is a well-established technique in radiation therapy (RT) for treating small-size brain tumors. It administers highly concentrated doses during each treatment fraction, with even minor dose errors posing a significant risk of causing severe damage to healthy tissues. It underscores the critical need for precise and meticulous precision in GKRS. However, the planning process for GKRS is complex and time-consuming, heavily reliant on the expertise of medical physicists. Incorporating deep learning approaches for GKRS dose prediction can reduce this dependency, improve planning efficiency and homogeneity, streamline clinical workflows, and reduce patient lagging times. Despite this, precise Gamma Knife plan dose distribution prediction using existing models remains a significant challenge. The complexity stems from the intricate nature of dose distributions, subtle contrasts in CT scans, and the interdependence of dosimetric metrics. To overcome these challenges, we have developed a \"Cascaded-Deep-Supervised\" Convolutional Neural Network (CDS-CNN) that employs a hybrid-weighted optimization scheme. Our innovative method incorporates multi-level deep supervision and a strategic sequential multi-network training approach. It enables the extraction of intra-slice and inter-slice features, leading to more realistic dose predictions with additional contextual information. CDS-CNN was trained and evaluated using data from 105 brain cancer patients who underwent GKRS treatment, with 85 cases used for training and 20 for testing. Quantitative assessments and statistical analyses demonstrated high consistency between the predicted dose distributions and the reference doses from the treatment planning system (TPS). The 3D overall gamma passing rates (GPRs) reached 97.15% ± 1.36% (3 mm/3%, 10% threshold), surpassing the previous best performance by 2.53% using the 3D Dense U-Net model. When evaluated against more stringent criteria (2 mm/3%, 10% threshold, and 1 mm/3%, 10% threshold), the overall GPRs still achieved 96.53% ± 1.08% and 95.03% ± 1.18%. Furthermore, the average target coverage (TC) was 98.33% ± 1.16%, dose selectivity (DS) was 0.57 ± 0.10, gradient index (GI) was 2.69 ± 0.30, and homogeneity index (HI) was 1.79 ± 0.09. Compared to the 3D Dense U-Net, CDS-CNN predictions demonstrated a 3.5% improvement in TC, and CDS-CNN\'s dose prediction yielded better outcomes than the 3D Dense U-Net across all evaluation criteria. The experimental results demonstrated that the proposed CDS-CNN model outperformed other models in predicting GKRS dose distributions, with predictions closely matching the TPS doses.

Brain tumors have a poor prognosis. Early, accurate diagnosis and treatment are crucial. Although brain surgical biopsy can provide an accurate diagnosis, it is highly invasive and risky and is not suitable for follow-up examination. Blood-based liquid biopsies have a low detection rate of tumor biomarkers and limited evaluation ability due to the existence of the blood-brain barrier (BBB). The BBB is composed of brain capillary endothelial cells through tight junctions, which prevents the release of brain tumor markers to the human peripheral circulation, making it more difficult to diagnose, predict prognosis, and evaluate therapeutic response through brain tumor markers than other tumors. Focused ultrasound (FUS)-enabled liquid biopsy (sonobiopsy) is an emerging technique using FUS to promote the release of tumor markers into the circulatory system and cerebrospinal fluid, thus facilitating tumor detection. The feasibility and safety data from both animal models and clinical trials support sonobiopsy as a great potential in the diagnosis of brain diseases.