On-Site Therapeutic Drug Monitoring of Paracetamol Analgesic in Non-Invasively Collected Saliva for Personalized Medicine.

Paracetamol or acetaminophen is the main non-opioid analgesic recommended for mild pain by the World Health Organization (WHO) analgesic ladder. However, the high levels used of paracetamol are associated with the hepatotoxicity and nephrotoxicity caused by accumulation of toxic metabolites. The sensor is produced on a polyester substrate containing a full electrochemical device with working, auxiliary, and reference electrodes in which, guiding personalized medicine solutions are not reported. Temporal paracetamol profiles in human saliva are performed with the subject taking different amounts of commercial analgesic pills. The variation of saliva paracetamol levels is demonstrated to be interference free from electroactive interfering species and human saliva constituents. In addition, the sensor displays to be useful as a disposable device for the fast detection of paracetamol in untreated raw saliva following pill intake. The maximum concentration (Cmax ) and half-life time (t1/2 ) for paracetamol are 143.27 µm and 110 min. The results demonstrate the potential of a simple strategy with electrochemical devices for noninvasive personalized therapy toward guiding drug interventions through tracking of active substance, detecting, and correcting insufficiency of absorption to meet individual needs avoiding overdoses, side effects, and intoxication.

Flexible and integrated dual carbon sensor for multiplexed detection of nonylphenol and paroxetine in tap water samples.

Multiplex detection of emerging pollutants is essential to improve quality control of water treatment plants, which requires portable systems capable of real-time monitoring. In this paper we describe a flexible, dual electrochemical sensing device that detects nonylphenol and paroxetine in tap water samples. The platform contains two voltammetric sensors, with different working electrodes that were either pretreated or functionalized. Each working electrode was judiciously tailored to cover the concentration range of interest for nonylphenol and paroxetine, and square wave voltammetry was used for detection. An electrochemical pretreatment with sulfuric acid on the printed electrode enabled a selective detection of nonylphenol in 1.0-10 × 10-6 mol L-1 range with a limit of detection of 8.0 × 10-7 mol L-1. Paroxetine was detected in the same range with a limit of detection of 6.7 × 10-7 mol L-1 using the printed electrode coated with a layer of carbon spherical shells. Simultaneous detection of the two analytes was achieved in tap water samples within 1 min, with no fouling and no interference effects. The long-term monitoring capability of the dual sensor was demonstrated in phosphate buffer for 45 days. This performance is statistically equivalent to that of high-performance liquid chromatography (HPLC) for water analysis. The dual-sensor platform is generic and may be extended to other water pollutants and clinical biomarkers in real-time monitoring of the environment and health conditions. Silver pseudo-reference electrodes for paroxetine (REP) and nonylphenol (REN), working electrodes for paroxetine (WP) and nonylphenol (WN), and auxiliary electrode (AE). USP refers to the University of Sao Paulo. "Red" is reduced form and "Oxi" is oxidized form of analytes.

Sustainable plant-wearable sensors for on-site, rapid decentralized detection of pesticides toward precision agriculture and food safety.

The synergy between eco-friendly biopolymeric films and printed devices leads to the production of plant-wearable sensors for decentralized analysis of pesticides in precision agriculture and food safety. Herein, a simple method for fabrication of flexible, and sustainable sensors printed on cellulose acetate (CA) substrates has been demonstrated to detect carbendazim and paraquat in agricultural, water and food samples. The biodegradable CA substrates were made by casting method while the full electrochemical system of three electrodes was deposited by screen-printing technique (SPE) to produce plant-wearable sensors. Analytical performance was assessed by differential pulse (DPV) and square wave voltammetry (SWV) in a linear concentration range between 0.1 and 1.0 µM with detection limits of 54.9 and 19.8 nM for carbendazim and paraquat, respectively. The flexible and sustainable non-enzymatic plant-wearable sensor can detect carbendazim and paraquat on lettuce and tomato skins, and also water samples with no interference from other pesticides. The plant-wearable sensors had reproducible response being robust and stable against multiple flexions. Due to high sensitivity and selectivity, easy operation and rapid agrochemical detection, the plant-wearable sensors can be used to detect biomarkers in human biofluids and be used in on-site analysis of other hazardous chemical substances.

Wearable sensors made with solution-blow spinning poly(lactic acid) for non-enzymatic pesticide detection in agriculture and food safety.

On-site monitoring the presence of pesticides on crops and food samples is essential for precision and post-harvest agriculture, which demands nondestructive analytical methods for rapid, low-cost detection that is not achievable with gold standard methods. The synergy between eco-friendly substrates and printed devices may lead to wearable sensors for decentralized analysis of pesticides in precision agriculture. In this paper we report on a wearable non-enzymatic electrochemical sensor capable of detecting carbamate and bipyridinium pesticides on the surface of agricultural and food samples. The low-cost devices (

Low-cost bacterial nanocellulose-based interdigitated biosensor to detect the p53 cancer biomarker.

Low-cost sensors to detect cancer biomarkers with high sensitivity and selectivity are essential for early diagnosis. Herein, an immunosensor was developed to detect the cancer biomarker p53 antigen in MCF7 lysates using electrical impedance spectroscopy. Interdigitated electrodes were screen printed on bacterial nanocellulose substrates, then coated with a matrix of layer-by-layer films of chitosan and chondroitin sulfate onto which a layer of anti-p53 antibodies was adsorbed. The immunosensing performance was optimized with a 3-bilayer matrix, with detection of p53 in MCF7 cell lysates at concentrations between 0.01 and 1000 Ucell. mL-1, and detection limit of 0.16 Ucell mL-1. The effective buildup of the immunosensor on bacterial nanocellulose was confirmed with polarization-modulated infrared reflection absorption spectroscopy (PM-IRRAS) and surface energy analysis. In spite of the high sensitivity, full selectivity with distinction of the p53-containing cell lysates and possible interferents required treating the data with a supervised machine learning approach based on decision trees. This allowed the creation of a multidimensional calibration space with 11 dimensions (frequencies used to generate decision tree rules), with which the classification of the p53-containing samples can be explained.