Spheroid cultures of cancer cell lines or primary cells represent a more clinically relevant model for predicting therapy response compared to two-dimensional cell culture. However, current live-dead staining protocols used for treatment response in spheroid cultures are often expensive, toxic to the cells, or limited in their ability to monitor therapy response over an extended period due to reduced stability. In our study, we have developed a cost-effective method utilizing calcein-AM and Helix NP™ Blue for live-dead staining, enabling the monitoring of therapy response of spheroid cultures for up to 10 days. Additionally, we used ICY BioImage Analysis and Z-stacks projection to calculate viability, which is a more accurate method for assessing treatment response compared to traditional methods on spheroid size. Using the example of glioblastoma cell lines and primary glioblastoma cells, we show that spheroid cultures typically exhibit a green outer layer of viable cells, a turquoise mantle of hypoxic quiescent cells, and a blue core of necrotic cells when visualized using confocal microscopy. Upon treatment of spheroids with the alkylating agent temozolomide, we observed a reduction in the viability of glioblastoma cells after an incubation period of 7 days. This method can also be adapted for monitoring therapy response in different cancer systems, offering a versatile and cost-effective approach for assessing therapy efficacy in three-dimensional culture models.

UNASSIGNED: Accurate beam modelling is essential for dose calculation in stereotactic radiation therapy (SRT), such as CyberKnife treatment. However, the present deep learning methods only involve patient anatomical images and delineated masks for training. These studies generally focus on traditional intensity-modulated radiation therapy (RT) plans. Nevertheless, this paper aims to develop a deep CNN-based method for CyberKnife plan dose prediction about brain cancer patients. It utilized modelled beam information, target delineation, and patient anatomical information.
UNASSIGNED: This study proposes a method that adds beam information to predict the dose distribution of CyberKnife in brain cases. A retrospective dataset of 88 brain and abdominal cancer patients treated with the Ray-tracing algorithm was performed. The datasets include patients\' anatomical information (planning CT), binary masks for organs at risk (OARs) and targets, and clinical plans (containing beam information). The datasets were randomly split into 68, 6, and 14 brain cases for training, validation, and testing, respectively.
UNASSIGNED: Our proposed method performs well in SRT dose prediction. First, for the gamma passing rates in brain cancer cases, with the 2 mm/2% criteria, we got 96.7% ± 2.9% for the body, 98.3% ± 3.0% for the planning target volume, and 100.0% ± 0.0% for the OARs with small volumes referring to the clinical plan dose. Secondly, the model predictions matched the clinical plan\'s dose-volume histograms reasonably well for those cases. The differences in key metrics at the target area were generally below 1.0 Gy (approximately a 3% difference relative to the prescription dose).
UNASSIGNED: The preliminary results for selected 14 brain cancer cases suggest that accurate 3-dimensional dose prediction for brain cancer in CyberKnife can be accomplished based on accurate beam modelling for homogeneous tumour tissue. More patients and other cancer sites are needed in a further study to validate the proposed method fully.
UNASSIGNED: With accurate beam modelling, the deep learning model can quickly generate the dose distribution for CyberKnife cases. This method accelerates the RT planning process, significantly improves its operational efficiency, and optimizes it.

The efficacy of glioblastoma treatment is closely associated with complete tumor resection. However, conventional surgical techniques often result in incomplete removal, leading to poor prognosis. A major challenge is the accurate delineation of tumor margins from healthy tissues. Imaging-guided surgery, particularly using fluorescent probes, is a promising solution for intraoperative guidance. The recently developed \'always-on\' types of targeted fluorescence probes generate signals irrespective of their presence in tumor cells or in blood circulation, hampering their effectiveness. Here, we propose a novel activatable fluorescence imaging probe, Q-cRGD, that targets glioma cells via the specific binding of the cyclic Arg-Gly Asp-containing pentapeptide (cRGD) to integrins. The Q-cRGD probe was synthesized by conjugating a near-infrared (NIR) dye to a tryptophan quencher via a disulfide linkage, including a cRGD-targeting ligand. This activatable probe remained inactive until the redox-responsive cleavage of the disulfide linkage occurred within the target cell. The zwitterionic nature of NIR dyes minimizes nonspecific interactions with serum proteins, thereby enhancing the tumor-to-background signal ratio (TBR). An in vivo fluorescence imaging study demonstrated a TBR value of 2.65 within 3 h of the intravenous injection of Q-cRGD, confirming its potential utility in imaging-guided brain cancer surgery.

Intraoperative magnetic resonance imaging (iMRI) has witnessed significant growth in the field of neurosurgery, particularly in glioma surgery, enhancing image-guided neuronavigation and optimizing the extent of resection (EOR). Despite its extensive use in the treatment of gliomas, its utility in brain metastases (BMs) remains unexplored. This study examined the effect of iMRI on BM resection. This retrospective study was conducted at the neurosurgical center of the University Hospital of the Technical University of Munich and involved 25 patients with BM who underwent resection using 3-Tesla iMRI between 2018 and 2022. Volumetric measurements of the resected contrast-enhancing metastases were performed using preoperative, intraoperative, and postoperative MRI images. The Karnofsky Performance Score (KPS) and neurological status of the patients were assessed pre- and postoperatively. Local recurrence and in-brain progression were reported in patients who underwent follow-up MRI at 3 and 6 months postoperatively. In this cohort (n = 25, mean age 63.6 years), non-small-cell lung cancer (NSCLC) was the most common origin (28%). The mean surgical duration was 219.9 min, and that of iMRI was 61.7 min. Indications for iMRI were primarily associated with preoperative imaging, suggesting an unclear entity that is often suspicious for glioma. Gross total resection (GTR) was achieved in 21 patients (84%). Continued resection was pursued after iMRI in six cases (24%), resulting in an improved EOR of 100% in five cases and 97.6% in one case. Neurological status postoperatively remained stable in 60%, improved in 24%, and worsened in 16% of patients. No wound healing or postoperative complications were observed. Among the thirteen patients who underwent follow-up MRI 3 months postoperatively, one patient showed local recurrence at the site of resection, and seven patients showed in-brain progression. Of the eight patients who underwent a 6-month follow-up MRI, two showed local recurrence, while three exhibited in-brain progression. The observed favorable profiles of GTR, coupled with the notable absence of wound-healing problems and acute postoperative complications, affirm the safety and feasibility of incorporating iMRI into the neurosurgical workflow for resecting BM with specific indications. The real-time imaging capabilities of iMRI offer unparalleled precision, aiding meticulous tumor delineation and informed decision-making, ultimately contributing to improved patient outcomes. Although our experience suggests the potential benefits of iMRI as a safe tool for enhancing EOR, we acknowledge the need for larger prospective clinical trials. Comprehensive investigations on a broader scale are imperative to further elucidate the specific indications for iMRI in the context of BMs and to study its impact on survival. Rigorous prospective studies will refine our understanding of the clinical scenarios in which iMRI can maximize its impact, guiding neurosurgeons toward more informed and tailored decision-making.

Glioblastoma (IDH-wildtype) represents a formidable challenge in oncology, lacking effective chemotherapeutic or biological interventions. The metabolic reprogramming of cancer cells is a hallmark of tumor progression and drug resistance, yet the role of metabolic reprogramming in glioblastoma during drug treatment remains poorly understood. The dihydroorotate dehydrogenase (DHODH) inhibitor BAY2402234 is a blood-brain barrier penetrant drug showing efficiency in in vivo models of many brain cancers. In this study, we investigated the effect of BAY2402234 in regulating the metabolic phenotype of EGFRWT and EGFRvIII patient-derived glioblastoma cell lines. Our findings reveal the selective cytotoxicity of BAY2402234 toward EGFRWT glioblastoma subtypes with minimal effect on EGFRvIII patient cells. At sublethal doses, BAY2402234 induces triglyceride synthesis at the expense of membrane lipid synthesis and fatty acid oxidation in EGFRWT glioblastoma cells, while these effects are not observed in EGFRvIII glioblastoma cells. Furthermore, BAY2402234 reduced the abundance of signaling lipid species in EGFRWT glioblastoma. This study elucidates genetic mutation-specific metabolic plasticity and efficacy in glioblastoma cells in response to drug treatment, offering insights into therapeutic avenues for precision medicine approaches.

UNASSIGNED: Glioblastoma, a high-grade primary brain cancer, has a median survival of approximately 14 months. Post-mortem brain donation provides insight to pathogenesis along with spatial and temporal heterogeneity. Post-mortem brain biobanking programs are increasing in number and the need to understand and improve the associated human experience is pressing. This study aims to qualitatively explore the experiences of next of kin (NOK) following the death and brain donation of a loved one and to understand the impact such programs have on NOK carers.
UNASSIGNED: We interviewed 29 NOK following the death of their loved one and subsequent brain donation. Thematic analysis was conducted on the transcribed, qualitative interviews.
UNASSIGNED: Four themes were identified; (1) Brain donation is a straightforward decision grounded in altruism and pragmatism; (2) Supporting donors is a source of comfort, pride and empowerment; (3) Brain donation can provide meaning for suffering and tragedy and (4) Perceptions of procedures and processes when supporting a loved one to donate. Insights into areas for improvement, for example transporting donors following a home death and the role of the body bag were also noted.
UNASSIGNED: Supporting a loved one to donate their brain can be a positive experience providing a source of hope, empowerment and purpose for NOK. Data indicating areas for consideration are broadly relevant for improving the delivery of brain donation programs for future donors and their loved ones.
Understanding how loved ones feel about someone close to them donating their brain to research after their death from brain cancer The act of donating brain tissue after death from brain cancer is a huge gift to medical research and may have an impact on the ability of the scientific community to improve outcomes for people diagnosed with brain cancers. While we understand how valuable these donations are for research, we need more work to understand how these donations impact the people who donate and those who love and support them. This paper explores the experiences of people who have lost someone to brain cancer who then went on to donate their brain tissue after their death. Through the use of interviews, it explores the impact that the donation has on a loved one or next of kin from providing a source of comfort, empowerment, pride or an alternative to ‘senseless’ suffering and tragedy. It also provides areas that should be considered by people who are facilitating brain donations to ensure that any potential, harm or upset can be minimized.

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.