Solid CaCO3 Formation in Glioblastoma Multiforme and its Treatment with Ultra-Nanoparticulated NPt-Bionanocatalysts.

BACKGROUND: Glioblastoma multiforme (GBM), the most prevalent form of central nervous system (CNS) cancer, stands as a highly aggressive glioma deemed virtually incurable according to the World Health Organization (WHO) standards, with survival rates typically falling between 6 to 18 months. Despite concerted efforts, advancements in survival rates have been elusive. Recent cutting-edge research has unveiled bionanocatalysts with 1% Pt, demonstrating unparalleled selectivity in cleaving C-C, C-N, and C-O bonds within DNA in malignant cells. The application of these nanoparticles has yielded promising outcomes. OBJECTIVE: The objective of this study is to employ bionanocatalysts for the treatment of Glioblastoma Multiforme (GBM) in a patient, followed by the evaluation of obtained tissues through electronic microscopy. METHODS: Bionanocatalysts were synthesized using established protocols. These catalysts were then surgically implanted into the GBM tissue through stereotaxic procedures. Subsequently, tissue samples were extracted from the patient and meticulously examined using Scanning Electron Microscopy (SEM). RESULTS AND DISCUSSION: Detailed examination of biopsies via SEM unveiled a complex network of small capillaries branching from a central vessel, accompanied by a significant presence of solid carbonate formations. Remarkably, the patient subjected to this innovative approach exhibited a three-year extension in survival, highlighting the potential efficacy of bionanocatalysts in combating GBM and its metastases. CONCLUSION: Bionanocatalysts demonstrate promise as a viable treatment option for severe cases of GBM. Additionally, the identification of solid calcium carbonate formations may serve as a diagnostic marker not only for GBM but also for other CNS pathologies.

Temporal lobe epilepsy: Evaluation of central and systemic immune-inflammatory features associated with drug resistance.

Neuroinflammation is probably one of the factors involved in drug resistance in people with epilepsy. Finding peripheral markers reflecting the intensity of neuroinflammation could be of great help to decide for which patients anti-inflammatory treatment might be an option. In this context, peripheral cytokines levels and lymphocyte phenotypes were assessed by ELISA and flow cytometry in 3 groups of subjects: drug resistant patients with temporal lobe epilepsy (DR-TLE), non DR-TLE patients and healthy controls. The same parameters were assessed in brain tissue in the DR-TLE group. Differences in the peripheral immune-inflammatory status between the 3 groups of subjects, and correlations between the central and peripheral immune-inflammatory status in DR-TLE patients were evaluated. Forty-one patients with DR-TLE, ten with non-DR-TLE and twenty controls were included. In the periphery, decrease in regulatory cells were observed in DR-TLE patients compared to controls. In addition, significant increase of IL-6 and IL-5 was observed in patients with epilepsy (particularly DR-TLE patients). Two groups of DR-TLE patients with significant differences in several central inflammatory parameters were identified in a cluster analysis. The inflammatory cluster was associated with a peripheral increase of CD4+CD38+ cells and different significant correlations between central and systemic inflammatory parameters were observed. Although their interpretation is not immediate, they demonstrate a clear dialogue between central and peripheral inflammatory reactions. In conclusion, our results add new elements to better understand the interactions between the central and peripheral compartments in patients with DR-TLE, and to help better define treatment options in this group of patients.

Medical Imaging Lesion Detection Based on Unified Gravitational Fuzzy Clustering.

We develop a swift, robust, and practical tool for detecting brain lesions with minimal user intervention to assist clinicians and researchers in the diagnosis process, radiosurgery planning, and assessment of the patient's response to the therapy. We propose a unified gravitational fuzzy clustering-based segmentation algorithm, which integrates the Newtonian concept of gravity into fuzzy clustering. We first perform fuzzy rule-based image enhancement on our database which is comprised of T1/T2 weighted magnetic resonance (MR) and fluid-attenuated inversion recovery (FLAIR) images to facilitate a smoother segmentation. The scalar output obtained is fed into a gravitational fuzzy clustering algorithm, which separates healthy structures from the unhealthy. Finally, the lesion contour is automatically outlined through the initialization-free level set evolution method. An advantage of this lesion detection algorithm is its precision and its simultaneous use of features computed from the intensity properties of the MR scan in a cascading pattern, which makes the computation fast, robust, and self-contained. Furthermore, we validate our algorithm with large-scale experiments using clinical and synthetic brain lesion datasets. As a result, an 84%-93% overlap performance is obtained, with an emphasis on robustness with respect to different and heterogeneous types of lesion and a swift computation time.