Responsiveness of voltage-gated calcium channels in SH-SY5Y human neuroblastoma cells on quasi-three-dimensional micropatterns formed with poly (l-lactic acid)

Introduction: In this study, quasi-three-dimensional (3D) microwell patterns were fabricated with poly (l-lactic acid) for the development of cell-based assays, targeting voltage-gated calcium channels (VGCCs). Methods and materials: SH-SY5Y human neuroblastoma cells were interfaced with the microwell patterns and found to grow as two dimensional (2D), 3D, and near two dimensional (N2D), categorized on the basis of the cells’ location in the pattern. The capability of the microwell patterns to support 3D cell growth was evaluated in terms of the percentage of the cells in each growth category. Cell spreading was analyzed in terms of projection areas under light microscopy. SH-SY5Y cells’ VGCC responsiveness was evaluated with confocal microscopy and a calcium fluorescent indicator, Calcium GreenTM-1. The expression of L-type calcium channels was evaluated using immunofluorescence staining with DM-BODIPY. Results: It was found that cells within the microwells, either N2D or 3D, showed more rounded shapes and less projection areas than 2D cells on flat poly (l-lactic acid) substrates. Also, cells in microwells showed a significantly lower VGCC responsiveness than cells on flat substrates, in terms of both response magnitudes and percentages of responsive cells, upon depolarization with 50 mM K+. This lower VGCC responsiveness could not be explained by the difference in L-type calcium channel expression. For the two patterns addressed in this study, N2D cells consistently exhibited an intermediate value of either projection areas or VGCC responsiveness between those for 2D and 3D cells, suggesting a correlative relation between cell morphology and VGCC responsiveness. Conclusion: These results suggest that the pattern structure and therefore the cell growth characteristics were critical factors in determining cell VGCC responsiveness and thus provide an approach for engineering cell functionality in cell-based assay systems and tissue engineering scaffolds.

The Application of Fluorescent Nanocrystals to Study the Distribution of Neuropeptides in the Aedes Aegypti Mosquito

Semiconductor nanocrystals, also known as quantum dots (QDs), have been used in studies involving mice and human tissues, but never before in research on insects. We used QDs to study the distribution of two neuropeptides in the Aedes aegypti mosquito, the vector of both dengue and yellow fever. These neuropeptides play a significant role in the production of juvenile hormone, a hormone that controls biting behavior, metamorphosis, and reproduction throughout the life of the mosquito. The two neuropeptides allatostatin-C (AS-C) and allatotropin (AT) function as inhibitory (AS-C) and stimulatory (AT) regulators of juvenile hormone synthesis in the corpus allatum gland. In other insects, they also affect heart rate, gut movement, and nutrient uptake. Conjugating these neuropeptides to quantum dots via a streptavidinlbiotin link, we were able to expose the mosquito corpus allatum and abdomen to allatostatin-C and allatotropin and then to visualize their distribution under UV light using confocal and compound light microscopy. Histological sections of the whole mosquito, incubations of tissues with conjugates (in vitro), and microinjections of conjugates into the mosquito (in vivo) were performed. The results showed that quantum dots can be used to detect neuropeptide distribution in the mosquito. The more we understand about these neuropeptides and juvenile hormone, the more we can contribute to stopping the spread of infectious diseases, such as dengue and yellow fever.

Cascade artificial neural networks technique for solving ellipsometry problems

Ellipsometry is a well known optical technique used for the characterization of reflective surfaces in study and films between two media. It is based on measuring the change in the state of polarization that occurs as a beam of polarized light is reflected from or transmitted through the film. Measuring this change can be used to calculate parameters of a single layer film such as the thickness and the refractive index. However, extracting these parameters of interest requires significant numerical processing due to the noninvertible equations. Typically, this is done using least squares solving methods which are slow and adversely affected by local minima in the solvable surface. This thesis describes the development and implementation of a new technique using only Artificial Neural Networks (ANN) to calculate thin film parameters. The new method offers a speed in the orders of magnitude faster than preceding methods and convergence to local minima is completely eliminated.