Staff member

Maria José López Martínez

Staff member publications

Mencattini, A, Rizzuto, V, Antonelli, G, Di Giuseppe, D, D'Orazio, M, Filippi, J, Comes, MC, Casti, P, Corrons, JLV, Garcia-Bravo, M, Segovia, JC, Manu-Pereira, MD, Lopez-Martinez, MJ, Samitier, J, Martinelli, E, (2023). Machine learning microfluidic based platform: Integration of Lab-on-Chip devices and data analysis algorithms for red blood cell plasticity evaluation in Pyruvate Kinase Disease monitoring Sensors And Actuators A-Physical 351, 114187

Microfluidics represents a very promising technological solution for conducting massive biological experiments. However, the difficulty of managing the amount of information available often precludes the wide potential offered. Using machine learning, we aim to accelerate microfluidics uptake and lead to quantitative and reliable findings. In this work, we propose complementing microfluidics with machine learning (MLM) approaches to enhance the diagnostic capability of lab-on-chip devices. The introduction of data analysis methodologies within the deep learning framework corroborates the possibility of encoding cell morphology beyond the standard cell appearance. The proposed MLM platform is used in a diagnostic test for blood diseases in murine RBC samples in a dedicated microfluidics device in flow. The lack of plasticity of RBCs in Pyruvate Kinase Disease (PKD) is measured massively by recognizing the shape deformation in RBCs walking in a forest of pillars within the chip. Very high accuracy results, far over 85 %, in recognizing PKD from control RBCs either in simulated and in real experiments demonstrate the effectiveness of the platform.

JTD Keywords: Blood disease, Deep transfer learning, Deficiency, Deformability, Machine learning microfluidics, Video analysis

Rizzuto, V, Mencattini, A, Alvarez-González, B, Di Giuseppe, D, Martinelli, E, Beneitez-Pastor, D, Mañú-Pereira, MD, Lopez-Martinez, MJ, Samitier, J, (2021). Combining microfluidics with machine learning algorithms for RBC classification in rare hereditary hemolytic anemia Scientific Reports 11, 13553

Combining microfluidics technology with machine learning represents an innovative approach to conduct massive quantitative cell behavior study and implement smart decision-making systems in support of clinical diagnostics. The spleen plays a key-role in rare hereditary hemolytic anemia (RHHA), being the organ responsible for the premature removal of defective red blood cells (RBCs). The goal is to adapt the physiological spleen filtering strategy for in vitro study and monitoring of blood diseases through RBCs shape analysis. Then, a microfluidic device mimicking the slits of the spleen red pulp area and video data analysis are combined for the characterization of RBCs in RHHA. This microfluidic unit is designed to evaluate RBC deformability by maintaining them fixed in planar orientation, allowing the visual inspection of RBC's capacity to restore their original shape after crossing microconstrictions. Then, two cooperative learning approaches are used for the analysis: the majority voting scheme, in which the most voted label for all the cell images is the class assigned to the entire video; and the maximum sum of scores to decide the maximally scored class to assign. The proposed platform shows the capability to discriminate healthy controls and patients with an average efficiency of 91%, but also to distinguish between RHHA subtypes, with an efficiency of 82%.

JTD Keywords: chip, disease, Red-blood-cell

Paoli, R, Badiola-Mateos, M, Lopez-Martinez, MJ, Samitier, J, Di Giuseppe, D, Martinelli, E, (2021). Rapid manufacturing of multilayered microfluidic devices for organ on a chip applications Sensors 21, 1382

© 2021 by the authors. Licensee MDPI, Basel, Switzerland. Microfabrication and Polydimethylsiloxane (PDMS) soft-lithography techniques became popular for microfluidic prototyping at the lab, but even after protocol optimization, fabrication is yet a long, laborious process and partly user-dependent. Furthermore, the time and money required for the master fabrication process, necessary at any design upgrade, is still elevated. Digital Manufacturing (DM) and Rapid-Prototyping (RP) for microfluidics applications arise as a solution to this and other limitations of photo and soft-lithography fabrication techniques. Particularly for this paper, we will focus on the use of subtractive DM techniques for Organ-on-a-Chip (OoC) applications. Main available thermoplastics for microfluidics are suggested as material choices for device fabrication. The aim of this review is to explore DM and RP technologies for fabrication of an OoC with an embedded membrane after the evaluation of the main limitations of PDMS soft-lithography strategy. Different material options are also reviewed, as well as various bonding strategies. Finally, a new functional OoC device is showed, defining protocols for its fabrication in Cyclic Olefin Polymer (COP) using two different RP technologies. Different cells are seeded in both sides of the membrane as a proof of concept to test the optical and fluidic properties of the device.

JTD Keywords: digital manufacturing, microfluidics, organ on a chip, rapid prototyping, Digital manufacturing, Microfluidics, Organ on a chip, Rapid prototyping