Wearable Electrochemical Sensors: Toward Biochemical Lab on the Body
University of California, San Diego
Wearable sensors have received a major recent attention owing to their considerable promise for monitoring the wearer’s health and wellness [1,2]. These devices have the potential to continuously and non-invasively collect vital and rich information from a person’s body and provide this information in a timely fashion. Capture such continuous molecular data from the human body will provide guidance towards optimal personal health and nutrition. This presentation will discuss our recent efforts toward filling the gaps toward obtaining biochemical information, beyond that given by common wrist-watch mobility trackers. Such real-time molecular information is achieved using advanced wearable electrochemical biosensors integrated directly on the epidermis or within the mouth. The design, operation and applications of such wearable bioelectronic sensors will be described, along with future prospects and challenges.
 “Wearable Chemical Sensors: Present Challenges and Future Prospects” A. J. Bandodkar, I. Jeerapan, J. Wang, ACS Sensors, 2016, 1, 464.
 “Wearable biosensors for healthcare monitoring”, J. Kim, A. S. Campbell, B. Esteban-Fernández de Ávila, and J. Wang, Nature Biotechnology, 2019, 37, 389.
The importance of Machine Learning in the exploitation of Organ-on-chip experiments
University of Rome Tor Vergata
A fascinating technological solution for conducting novel, reproducible, and massive biological experiments is represented by Organ-on-Chips (OoCs) microfluidic devices where specific aspects or characteristics of tissues or organs are mimicked. However, the difficulty of managing the vast amount of information available for this kind of device often limits its usefulness [1,2]. To accelerate the uptake of OoCs and lead to quantitative and reliable findings, an approach exploiting machine learning algorithms coupled with time-lapse microscopy and microfluidic devices is introduced. In this talk, we will discuss its potentialities through various case studies [2,3], corroborated by the investigation of robustness to external sources of variability related to technological set-up, to heterogeneity of the samples, and subjectivity due to operator-dependent procedures.
 “Mencattini, A., Mattei, F., Schiavoni, G., Gerardino, A., Businaro, L., Di Natale, C., Martinelli, E.”, From petri dishes to organ on chip platform: The increasing importance of machine learning and image analysis, 2019, Frontiers in Pharmacology, 10, 100
”Mencattini, A., Di Giuseppe, D., Comes, M.C., Casti, P., Corsi, F., Bertani, F.R., Ghibelli, L., Businaro, L., Di Natale, C., Parrini, M.C., Martinelli, E.”, Discovering the hidden messages within cell trajectories using a deep learning approach for in vitro evaluation of cancer drug treatments, 2020, Scientific Reports, 10, 1, 7653 .
 “Comes, M.C., Filippi, J., Mencattini, A., Casti, P., Cerrato, G., Sauvat, A., Vacchelli, E., De Ninno, A., Di Giuseppe, D., D’Orazio, M., Mattei, F., Schiavoni, G., Businaro, L., Di Natale, C., Kroemer, G., Martinelli, E.”, Multi-scale generative adversarial network for improved evaluation of cell–cell interactions observed in organ-on-chip experiments, 2021, Neural Computing and Applications, 33, 8, 3671-3689.