Numerical simulation study of nanoparticle diffusion in gray matter.

Purpose: Nanomedicine-based approaches have shown great potential in the treatment of central nervous system diseases. However, the fate of nanoparticles (NPs) within the brain parenchyma has not received much attention. The complexity of the microstructure of the brain and the invisibility of NPs make it difficult to study NP transport within the grey matter. Moreover, regulation of NP delivery is not fully understood. Methods: 2D interstitial system (ISS) models reflecting actual extracellular space (ECS) were constructed. A particle tracing model was used to simulate the diffusion of the NPs. The effect of NP size on NP diffusion was studied using numerical simulations. The diffusion of charged NPs was explored by comparing experimental and numerical simulation data, and the effect of cell membrane potential on the diffusion of charged NPs was further studied. Results: The model was verified using previously published experimental data. Small NPs could diffuse efficiently into the ISS. The diffusion of charged NPs was hindered in the ISS. Changes in cell membrane potential had little effect on NP diffusion. Conclusion: This study constructed 2D brain ISS models that reflected the actual ECS and simulated the diffusion of NPs within it. The study found that uncharged small NPs could effectively diffuse within the ISS and that the cell membrane potential had a limited effect on the diffusion of charged NPs. The model and findings of this study can aid the design of nanomedicines and nanocarriers for the diagnosis and treatment of brain diseases.

Assessing the magnetic resonance imaging in determining the depth of invasion of tongue cancer.

OBJECTIVES: To assess the magnetic resonance imaging (MRI) in predicting tumour's depth of invasion (DOI) of tongue cancer by comparing to pathology and to determine the cut-off value of MRI-derived DOI for lymph node metastasis. PATIENTS AND METHODS: In a retrospective analysis, 156 patients with newly diagnosed tongue cancer were included. Tumour's DOI was compared between MRI measurement and pathology by Pearson correlation coefficient and paired t test. The accuracy of MRI-derived DOI was compared to the pathological DOI. The relationship between MRI-derived DOI and cervical lymph node metastasis was calculated by receiver operating characteristic curve. RESULTS: Tumour's DOI was well correlated between MRI measurement and pathology with correlation coefficients of 0.77. MRI-derived DOI was 3.4 mm (28%) larger than pathology. The accuracy of MRI in deciding pathological DOI was 67.9%. The cut-off value of MRI-derived DOI was 10.5 mm for lymph node metastasis of tongue cancer. CONCLUSION: Magnetic resonance imaging can be used as a reference to determine tumour's DOI of tongue cancer. Tumour with MRI-derived DOI larger than 10.5 mm deserves simultaneous neck dissection at initial surgery.

The histogram analysis of apparent diffusion coefficient in differential diagnosis of parotid tumor.

OBJECTIVES: Use apparent diffusion coefficient (ADC) histogram to investigate whether the parameters of ADC histogram can distinguish between benign and malignant tumors and further differentiate the tumor subgroups. METHODS AND MATERIALS: This study retrospectively enrolls 161 patients with parotid gland tumors. Histogram parameters including mean, inhomogeneity, skewness, kurtosis and 10th, 25th, 50th, 75th, 90th percentiles are derived from ADC mono-exponential model. Mann-Whitney U test is used to compare the differences between benign and malignant groups. Kruskal-Wallis test with post-hoc Dunn-Bonferroni method is used for subgroup classification, then receiver operating characteristic curve analysis is performed in mean ADC value to obtain the appropriate cutoff values. RESULTS: Except for kurtosis and 90th percentile, there are significant differences in all other ADC parameters between benign and malignant groups. In subgroup classification of benign tumors, there are significant differences in all ADC parameters between pleomorphic adenoma and Warthin's tumor (area under curve 0.988; sensitivity 93.8%; specificity 94.7%; all ps < 0.05). Pleomorphic adenoma has high value in mean than basal cell adenoma (area under curve 0.819; sensitivity 76.9%; specificity 76.9%; p < 0.05). Basal cell adenoma has high values in mean (area under curve 0.897; sensitivity 92.3%; specificity 78.9%; all ps < 0.05) and 10th, 25th, 50th percentiles than Warthin's tumor. In subgroup classification of malignant tumors, low-risk parotid carcinomas have higher values than hematolymphoid tumors in mean (area under curve 0.912; sensitivity 84.6%; specificity 100%, all ps < 0.05) and 10th, 25th percentiles. CONCLUSION: ADC histogram parameters, especially mean and 10th, 25th percentiles, can potentially be an effective indicator for identifying and classifying parotid tumors.