Mechanical properties of human glioma

Glioblastoma mechanobiology at multiple length scales

Glioblastoma multiforme (GBM), a primary brain cancer, is one of the most aggressive forms of human cancer, with a very low patient survival rate. A characteristic feature of GBM is the diffuse infiltration of tumor cells into the surrounding brain extracellular matrix (ECM) that provide biophysical, topographical, and biochemical cues. In particular, ECM stiffness and composition is known to play a key role in controlling various GBM cell behaviors including proliferation, migration, invasion, as well as the stem-like state and response to chemotherapies. In this review, we discuss the mechanical characteristics of the GBM microenvironment at multiple length scales, and how biomaterial scaffolds such as polymeric hydrogels, and fibers, as well as microfluidic chip-based platforms have been employed as tissue mimetic models to study GBM mechanobiology. We also highlight how such tissue mimetic models can impact the field of GBM mechanobiology.

Atomic force microscopy in disease-related studies: Exploring tissue and cell mechanics

Despite significant progress in human medicine, certain diseases remain challenging to promptly diagnose and treat. Hence, the imperative lies in the development of more exhaustive criteria and tools. Tissue and cellular mechanics exhibit distinctive traits in both normal and pathological states, suggesting that "force" represents a promising and distinctive target for disease diagnosis and treatment. Atomic force microscopy (AFM) holds great promise as a prospective clinical medical device due to its capability to concurrently assess surface morphology and mechanical characteristics of biological specimens within a physiological setting. This review presents a comprehensive examination of the operational principles of AFM and diverse mechanical models, focusing on its applications in investigating tissue and cellular mechanics associated with prevalent diseases. The findings from these studies lay a solid groundwork for potential clinical implementations of AFM. RESEARCH HIGHLIGHTS: By examining the surface morphology and assessing tissue and cellular mechanics of biological specimens in a physiological setting, AFM shows promise as a clinical device to diagnose and treat challenging diseases.

Current Photodynamic Therapy for Glioma Treatment: An Update

Research on the development of photodynamic therapy for the treatment of brain tumors has shown promise in the treatment of this highly aggressive form of brain cancer. Analysis of both in vivo studies and clinical studies shows that photodynamic therapy can provide significant benefits, such as an improved median rate of survival. The use of photodynamic therapy is characterized by relatively few side effects, which is a significant advantage compared to conventional treatment methods such as often-used brain tumor surgery, advanced radiotherapy, and classic chemotherapy. Continued research in this area could bring significant advances, influencing future standards of treatment for this difficult and deadly disease.

Comparison of macroscale and microscale mechanical properties of fresh and fixed-frozen porcine colonic tissue

Mechanical changes to the microenvironment of the extracellular matrix (ECM) in tissue have been hypothesised to elicit a pathogenic response in the surrounding cells. Hence, 3D scaffolds are a popular method of studying cellular behaviour under conditions that mimic in vivo microenvironment. To create a 3D biomimetic scaffold that captures the in vivo ECM microenvironment a robust mechanical characterisation of the whole ECM at the microscale is necessary. This study examined the multiscale methods of characterising the ECM microenvironment using porcine colon tissue. To facilitate fresh tissue microscale mechanical characterisation, a protocol for sectioning fresh, unfixed, soft biological tissue was developed. Four experiments examined both the microscale and macroscale mechanics of both fresh (Fr) and fixed-frozen (FF) porcine colonic tissue using microindentation for microscale testing and uniaxial compression testing for macroscale testing. The results obtained in this study show a significant difference in elastic modulus between Fr and FF tissue at both the macroscale and microscale. There was an order of magnitude difference between the Fr and FF tissue at the microscale between each of the three layers of the colon tested i.e. the muscularis propria (MP), the submucosa (SM) and the mucosa (M). Macroscale testing cannot capture these regional differences. The findings in this study suggest that the most appropriate method for mechanically characterising the ECM is fresh microscale mechanical microindentation. These methods can be used on a range of biological tissues to create 3D biomimetic scaffolds that are more representative of the in vivo ECM, allowing for a more in-depth characterisation of the disease process.

Salvianolic acid B from Salvia miltiorrhiza bunge: A potential antitumor agent

Salvia miltiorrhiza Bunge (Lamiaceae) is a perennial herb widely found in China since ancient times with a high economic and medicinal value. Salvianolic acid B (Sal-B) is an important natural product derived from Salvia miltiorrhiza and this review summarizes the anticancer activity of Sal-B. Sal-B inhibits tumor growth and metastasis by targeting multiple cell signaling pathways. This review aims to review experimental studies to describe the possible anticancer mechanisms of Sal-B and confirm its potential as a therapeutic drug.

Deep Convolutional Neural Network-Based Brain Magnetic Resonance Imaging Applied in Glioma Diagnosis and Tumor Region Identification

The aim of this study was to explore the application value of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) based on a convolutional neural network (CNN) algorithm in glioma diagnosis and tumor segmentation. 66 patients with gliomas who were diagnosed and treated in the hospital were selected as the research objects. The patients were rolled into the high-grade glioma group (HGG, 46 cases) and the low-grade glioma group (LGG, 20 cases) according to the World Health Organization glioma grading standard. All patients received a conventional plain scan and a DCE-MRI. Parameters such as volume transfer constant (Ktrans), rate constant (Kep), extracellular volume (Ve), and mean plasma volume (Vp) were calculated, and the parameters of patients of each grade were analyzed. The efficacy of each parameter in diagnosing glioma was analyzed through a receiver operating characteristic curve. All images were segmented by the CNN algorithm. The CNN algorithm showed good performance in DCE-MRI image segmentation. The mean, standard deviation, kurtosis, and skewness of Ktrans and Ve, the standard deviation and skewness of Kep, and the mean and standard deviation of Vp were statistically considerable in differentiating HGG and LGG $\left(P<0.05\right)$ . ROC analysis showed that the standard deviation of Ktrans (0.885) had the highest diagnostic accuracy in distinguishing HGG and LGG. The values of Ktrans, Ve, and Vp were positively correlated with Ki-67 (r = 0.346, P = 0.014; r = 0.335, P = 0.017; r = 0.323, P = 0.022). In summary, the CNN-based DCE-MRI technology had high application value in glioma diagnosis and tumor segmentation.