Nanomaterials to Fight Cancer: An Overview on Their Multifunctional Exploitability.

In recent years the worldwide research community has highlighted innumerable benefits of nanomaterials in cancer detection and therapy. Nevertheless, the development of cancer nanomedicines and other bionanotechnology requires a huge amount of considerations about the interactions of nanomaterials and biological systems, since long-term effects are not yet fully known. Open issues remain the determination of the nanoparticles distributions patterns and the internalization rate into the tumor while avoiding their accumulation in internal organs or other healthy tissues. The purpose of this work is to provide a standard overview of the most recent advances in nanomaterials to fight cancer and to collect trends and future directions to follow according to some critical aspects still present in this field. Complementary to the very recent review of Wolfram and Ferrari which discusses and classifies successful clinically-approved cancer nanodrugs as well as promising candidates in the pipeline, this work embraces part of their proposed classification system based on the exploitation of multifunctionality and extends the review to peer-reviewed journal articles published in the last 3 years identified through international databases.

Smart Portable Pen for Continuous Monitoring of Anaesthetics in Human Serum With Machine Learning.

Continuous monitoring of anaesthetics infusion is demanded by anaesthesiologists to help in defining personalized dose, hence reducing risks and side effects. We propose the first piece of technology tailored explicitly to close the loop between anaesthesiologist and patient with continuous drug monitoring. Direct detection of drugs is achieved with electrochemical techniques, and several options are present in literature to measure propofol (widely used anaesthetics). Still, the sensors proposed do not enable in-situ detection, they do not provide this information continuously, and they are based on bulky and costly lab equipment. In this paper, we present a novel smart pen-shaped electronic system for continuous monitoring of propofol in human serum. The system consists of a needle-shaped sensor, a quasi digital front-end, a smart machine learning data processing, in a single wireless battery-operated embedded device featuring Bluetooth Low Energy (BLE) communication. The system has been tested and characterized in real, undiluted human serum, at 37  °C. The device features a limit of detection of 3.8  µM, meeting the requirement of the target application, with an electronics system 59% smaller and 81% less power consuming w.r.t. the state-of-the-art, using a smart machine learning classification for data processing, which guarantees up to twenty continuous measure.

Continuous monitoring of propofol in human serum with fouling compensation by support vector classifier.

We present a new method for electrochemical sensing, which compensates the fouling effect of propofol through machine learning (ML) model. Direct and continuous monitoring of propofol is crucial in the development of automatic systems for control of drug infusion in anaesthesiology. The fouling effect on electrodes discourages the possibility of continuous online monitoring of propofol since polymerization of the surface produces sensor drift. Several approaches have been proposed to limit the phenomenon at the biochemical interface; instead, here, we present a novel ML-based calibration procedure. In this paper, we analyse a dataset of 600 samples acquired through staircase cyclic voltammetry (SCV), resembling the scenario of continuous monitoring of propofol, both in PBS and in undiluted human serum, to demonstrate that ML-based model solves electrode fouling of anaesthetics. The proposed calibration approach is based on Gaussian radial basis function support vector classifier (RBF-SVC) that achieves classification accuracy of 98.9% in PBS, and 100% in undiluted human serum. The results prove the ability of the ML-based model to correctly classify propofol concentration in the therapeutic range between 1µM and 60µM with levels of 10µM, continuously up to ten minutes, with one sample every 30s.

New Approach for Making Standard the Development of Biosensing Devices by a Modular Multi-Purpose Design.

The fast widening of biosensing applications, such as healthcare, drug delivery, food, and military industries, is increasing the need for generality and compatibility among different sensors. To address this challenge, we present here an innovative approach for the fast development of new electronic biosensing systems, linking a custom-designed front-end with a multi-purpose system. We envision an open tool to help designers to focus on the target molecule and related detection method instead of designing each time a dedicated electronic device. The architecture of the proposed system is based on a modular approach, where only the front-end and the software need to be custom re-designed according to the application. Considering current research and applying a rigorous definition of the technical requirements, the core of the system is designed to fit the highest number of biosensing methods. The flexibility of this approach is successfully demonstrated with three different types of biosensors, i.e., amperometric, ion-sensitive, and memristive.