Force-Sensorless Identification and Classification of Tissue Biomechanical Parameters for Robot-Assisted Palpation.

The implementation of robotic systems for minimally invasive surgery and medical procedures is an active topic of research in recent years. One of the most common procedures is the palpation of soft tissues to identify their mechanical characteristics. In particular, it is very useful to identify the tissue's stiffness or equivalently its elasticity coefficient. However, this identification relies on the existence of a force sensor or a tactile sensor mounted at the tip of the robot, as well as on measuring the robot velocity. For some applications it would be desirable to identify the biomechanical characteristics of soft tissues without the need for a force/tactile nor velocity sensors. An estimation of such quantities can be obtained by a model-based state observer for which the inputs are only the robot joint positions and its commanded joint torques. The estimated velocities and forces can then be employed for closed-loop force control, force reflection, and mechanical parameters estimation. In this work, a closed-loop force control is proposed based on the estimated contact forces to avoid any tissue damage. Then, the information from the estimated forces and velocities is used in a least squares estimator of the mechanical parameters. Moreover, the estimated biomechanical parameters are employed in a Bayesian classifier to provide further help for the physician to make a diagnosis. We have found that a combination of the parameters of both linear and nonlinear viscoelastic models provide better classification results: 0% misclassifications against 50% when using a linear model, and 3.12% when using only a nonlinear model, for the case in which the samples have very similar mechanical properties.

Loop-mediated isothermal amplification-based electrochemical sensor for detecting SARS-CoV-2 in wastewater samples.

The current pandemic COVID-19 caused by the coronavirus SARS-CoV-2, has generated different economic, social and public health problems. Moreover, wastewater-based epidemiology could be a predictor of the virus rate of spread to alert on new outbreaks. To assist in epidemiological surveillance, this work introduces a simple, low-cost and affordable electrochemical sensor to specifically detect N and ORF1ab genes of the SARS-CoV-2 genome. The proposed sensor works based on screen-printed electrodes acting as a disposable test strip, where the reverse transcription loop-mediated isothermal amplification (RT-LAMP) reaction takes place. Electrochemical detection relies upon methylene blue as a redox intercalator probe, to provide a diffusion-controlled current encoding the presence and concentration of RT-LAMP products, namely amplicons or double-stranded DNA. We test the performance of the sensor by testing real wastewater samples using end-point and time course measurements. Results show the ability of the electrochemical test strip to specifically detect and quantify RT-LAMP amplicons below to ~ 2.5 × 10-6 ng/µL exhibiting high reproducibility. In this sense, our RT-LAMP electrochemical sensor is an attractive, efficient and powerful tool for rapid and reliable wastewater-based epidemiology studies.