drug-monitoring; nano-bio-chip; continuous-monitoring; anesthetics; carbon nanotubes; control-flow
Francesca Stradolini, Tugba Kilic, Alberto Di Consiglio, Mehmet Ozsoz, Giovanni De Micheli, Sandro Carrara (2018), Long-Term Drug Monitoring of Propofol and Fouling Effect on Pencil Graphite Electrodes, in Electroanalysis
, 30, 1-8.
Francesca Stradolini, Abuduwaili Tuoheti, Paolo Motto Ros, Danilo Demarchi, Sandro Carrara (2017), Raspberry Pi Based System for Portable and Simultaneous Monitoring of Anesthetics and Therapeutic Compounds, in Proceedings of the international IEEE conference NGCAS 2017
, Genoa (Italy)IEEE, Genoa (Italy).
Francesca Stradolini, Sofia Lydia Ntella, Abuduwaili Tuoheti, Danilo Demarchi, Alkis A. Hatzopoulos, Sandro Carrara (2017), Architecture and Procedures for pH and Temperature Monitoring in Medical Applications, in Proceedings of the IEEE Sensors Conference
, GlasgowIEEE, Glasgow.
Bruno Donato, Francesca Stradolini, Abuduwaili Tuoheti, Federico Angiolini, Danilo Demarchi, Giovanni De Micheli, Sandro Carrara (2017), Raspberry Pi Driven Flow-Injection System for Electrochemical Continuous Monitoring Platforms, in IEEE BioCAS Conference
, TurinIEEE, Turin.
Francesca Stradolini, Abuduwaili Tuoheti, Danilo Demarchi, Federico Angiolini, Giovanni De Micheli, Bruno Donato, Sandro Carrara (2017), Raspberry Pi Driven Flow-Injection System for Electrochemical Continuous Monitoring Platforms, in International conference BioCAS
, TurinIEEE, Turin.
Marius Schirmer, Francesca Stradolini, Sandro Carrara, Elisabetta Chicca (2016), FPGA-based Approach for Automatic Peak Detection in Cyclic Voltammetry, in 23rd IEEE International Conference on Electronics, Circuits and Systems (ICECS)
, Monte Carlo IEEE, Monte Carlo.
Stradolini Francesca, Lavalle Eleonora, De Micheli Giovanno, Motto Paolo Ros, Demarchi Danilo, Carrara Sandro (2016), Paradigm-Shifting Players for IoT: Smart-Watches for Intensive Care Monitoring, in Proceedings of the 6th International Conference on Wireless Mobile Communication and Healthcare
, European Alliance for Innovation, Milan (Italy).
Simalatsar Alena, Guidi Monia, Buclin Thierry (2016), Cascaded PID controller for anaesthesia delivery, in 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (E
, Orlando, FL, USAIEEE, Orlando.
Stradolini Francesca, Elboshra Tamador, Biscontini Armando, De Micheli Giovanni, Carrara Sandro (2016), Simultaneous monitoring of anesthetics and therapeutic compounds with a portable multichannel potentiostat, in 2016 IEEE International Symposium on Circuits and Systems (ISCAS)
, Montréal, QC, CanadaIEEE, Montreal.
Stradolini Francesca, Riario Stefano, Boero Cristina, Baj-Rossi Camilla, Taurino Irene, Surrel Gregoire, De Micheli Giovanni, Carrara Sandro (2016), Wireless Monitoring of Endogenous and Exogenous Biomolecules on an Android Interface, in IEEE Sensors Journal
, 16(9), 3163-3170.
Francesca Stradolini, Nadia Tamburrano, Thiébaud Modoux, Abuduwaili Tuoheti, Danilo Demarchi, Sandro Carrara, IoT for Telemedicine Practices enabled by an AndroidTM Application with Cloud System Integration, in IEEE Conference ISCAS 2018
, FlorenceIEEE, Florence.
Francesca Stradolini, Nima Aliakbari, Sattar Akbari Nakhjavani, Ioulia Tzouvadaki, Irene Taurino, Giovanni De Micheli, Sandro Carrara, Performance of Carbon Nano-Scale Allotropes in Detecting Midazolam and Paracetamol in Undiluted Human Serum, in IEEE Sensors Journal
Francesca Stradolini, Tuoheti Abuduwaili, Tugba Kilic, Danilo Demarchi, Sandro Carrara, Raspberry-Pi Based System for Propofol Monitoring, in Integration, the VLSI Journal
Motivation: Every year, 30,000 people undergo anesthesia and remain awake, still feeling pain while not being able to move, due faulty drug administration. Many more are put into uselessly deep or prolonged chemical coma. Proper anesthesia requires the achievement of a certain target plasma concentration of the drugs (e.g. propofol, fentanyl or midazolam, etc.). Today, such drugs are regularly injected by Target Controlled Infusion (TCI) systems, while the usual magnitude of prediction errors in control models reaches 20-30% due to the patients’ diversity. Therefore, continuous monitoring of anesthetic agents circulating in body fluids would contribute to better individualization of patients’ management. Project Focus: We propose to create a system with a semi-closed-loop control for anesthesia delivery based on the anesthetics monitoring in human fluids. Core of the system will include several electrochemical sensors for the real-time monitoring of anesthetics and sedatives, and complementary intelligent decision-making algorithms able to adjust dosages and delivery rates according to the sensor measurements, under the ultimate control of anesthesiologists.Expected outcome: We aim to produce a prototype of a device implementing semi-closed-loop anesthesia delivery based on real-time measurements of anesthetics concentration. The prototype will be ready for clinical development, after appropriate animal testing.Methods: the proposed semi-closed loop prototype is logically divided into four parts, all addressed by the activities of the present project. Part I (responsible Main Applicant: S. Carrara)It develops the setup emulating changes of drugs concentration in patients’ fluids (e.g., plasma concentration) and the array of sensors (S1, S2... Sn) for monitoring concentration of anesthetics (e.g. propofol, fentanyl and midazolam). Part II (responsible Main Applicant: S. Carrara)This part provides the readout for the sensors of Part I, the system for data verification, suitable connectivity to control algorithms of Part III, and the user interfaces for enabling anesthesiologists to take actions.Part III (responsible Co-applicant T. Buclin)This part studies proper pharmacokinetic (PK) prediction-models based on the monitored anesthetics (e.g., propofol) to be implemented in the drive-software of Part IVPart IV (responsible co-applicant J. Sifakis)This part builds appropriate drive-software algorithms to be implemented in programmable pumps, for dose adjustment based on classical proportional-integral-derivative (PID) and on the pharmacokinetic (PK) prediction-models developed in Part III