Automated ELISA On-Chip for the Detection of Anti-SARS-CoV-2 Antibodies.

The COVID-19 pandemic has been the most critical public health issue in modern history due to its highly infectious and deathly potential, and the limited access to massive, low-cost, and reliable testing has significantly worsened the crisis. The recovery and the vaccination of millions of people against COVID-19 have made serological tests highly relevant to identify the presence and levels of SARS-CoV-2 antibodies. Due to its advantages, microfluidic-based technologies represent an attractive alternative to the conventional testing methodologies used for these purposes.

Characterization of a novel automated microfiltration device for the efficient isolation and analysis of circulating tumor cells from clinical blood samples.

The detection and analysis of circulating tumor cells (CTCs) may enable a broad range of cancer-related applications, including the identification of acquired drug resistance during treatments. However, the non-scalable fabrication, prolonged sample processing times, and the lack of automation, associated with most of the technologies developed to isolate these rare cells, have impeded their transition into the clinical practice. This work describes a novel membrane-based microfiltration device comprised of a fully automated sample processing unit and a machine-vision-enabled imaging system that allows the efficient isolation and rapid analysis of CTCs from blood.

High-Throughput Automated Microscopy of Circulating Tumor Cells.

Circulating tumor cells (CTCs) have the potential of becoming the gold standard marker for cancer diagnosis, prognosis and monitoring. However, current methods for its isolation and characterization suffer from equipment variability and human operator error that hinder its widespread use. Here we report the design and construction of a fully automated high-throughput fluorescence microscope that enables the imaging and classification of cancer cells that were labeled by immunostaining procedures.

Adaptive Neural Network Control for the Trajectory Tracking of the Furuta Pendulum.

The purpose of this paper is to introduce a novel adaptive neural network-based control scheme for the Furuta pendulum, which is a two degree-of-freedom underactuated system. Adaptation laws for the input and output weights are also provided. The proposed controller is able to guarantee tracking of a reference signal for the arm while the pendulum remains in the upright position.

A composite controller for trajectory tracking applied to the Furuta pendulum.

In this paper, a new composite scheme is proposed, where the total control action is composed of the sum of a feedback-linearization-based controller and an energy-based compensation. This new proposition is applied to the rotary inverted pendulum or Furuta pendulum. The Furuta pendulum is a well-known underactuated mechanical system with two degrees of freedom.