Conventional manufacturing methods for polydimethylsiloxane (PDMS)-based microdevices require multiple steps and elements that increase cost and production time. Also, these PDMS microdevices are mostly limited to single use, and it is difficult to recover the contents inside the microchannels or perform advanced microscopy visualization due to their irreversible sealing method. Herein, we developed a novel manufacturing method based on polymethylmethacrylate (PMMA) plates adjusted using a mechanical pressure-based system. One conformation of the PMMA plate assembly system allows the reproducible manufacture of PDMS replicas, reducing the cost since a precise amount of PDMS is used, and the PDMS replicas show uniform dimensions. A second form of assembling the PMMA plates permits pressure-based sealing of the PDMS layer with a glass base. By reversibly sealing the microdevice without using plasma for bonding, we achieve chip on/off configurations, which allow the user to open and close the device and reuse it in an easy-to-use way. No deformation was observed on the structures of the PDMS microchannels when a range of 10 to 18 kPa pressure was applied using the technique. Furthermore, the functionality of the proposed system was successfully validated by the generation of microdroplets with reused microdevices via three repetitions.

Microcontact printing using PDMS embossing tools and its variations have aroused the interest of a wide spectrum of research fields, hence the feasibility of defining micro and nanoscale patterns. In this work, we have proposed and demonstrated a novel lithography method based on grayscale patterns printed in a flexographic photopolymer mold and transferred to epoxy resin and a single PDMS stamp to obtain different microprint pattern structures. The geometry of the patterns can be modified by adjusting the layout and grayscale of the stamp patterns. The functionality of this contact printing methodology was validated by generating human induced pluripotent stem cells (hiPSC) patterns. These specific micropatterns can be very useful for achieving complex differentiation in cell lines such as hiPSC. Microfabrication through the new technique provides a promising alternative to conventional lithography for constructing complex aligned surfaces; these structures could be used as components of biological patterns or microfluidic devices.

Nowadays, image analysis has a relevant role in most scientific and research areas. This process is used to extract and understand information from images to obtain a model, knowledge, and rules in the decision process. In the case of biological areas, images are acquired to describe the behavior of a biological agent in time such as cells using a mathematical and computational approach to generate a system with automatic control. In this paper, MCF7 cells are used to model their growth and death when they have been injected with a drug. These mammalian cells allow understanding of behavior, gene expression, and drug resistance to breast cancer. For this, an automatic segmentation method called GEMA is presented to analyze the apoptosis and confluence stages of culture by measuring the increase or decrease of the image area occupied by cells in microfluidic devices. In vitro, the biological experiments can be analyzed through a sequence of images taken at specific intervals of time. To automate the image segmentation, the proposed algorithm is based on a Gabor filter, a coefficient of variation (CV), and linear regression. This allows the processing of images in real time during the evolution of biological experiments. Moreover, GEMA has been compared with another three representative methods such as gold standard (manual segmentation), morphological gradient, and a semi-automatic algorithm using FIJI. The experiments show promising results, due to the proposed algorithm achieving an accuracy above 90% and a lower computation time because it requires on average 1 s to process each image. This makes it suitable for image-based real-time automatization of biological lab-on-a-chip experiments.

No abstract