Development of paper-based color test-strip for drug detection in aquatic environment: Application to oxytetracycline

The wide use of antibiotics in aquaculture has led to the emergence of resistant microbial species. It should be avoided/minimized by controlling the amount of drug employed in fish farming. For this purpose, the present work proposes test-strip papers aiming at the detection/semi-quantitative determination of organic drugs by visual comparison of color changes, in a similar analytical procedure to that of pH monitoring by universal pH paper. This is done by establishing suitable chemical changes upon cellulose, attributing the paper the ability to react with the organic drug and to produce a color change. Quantitative data is also enabled by taking a picture and applying a suitable mathematical treatment to the color coordinates given by the HSL system used by windows. As proof of concept, this approach was applied to oxytetracycline (OXY), one of the antibiotics frequently used in aquaculture. A bottom-up modification of paper was established, starting by the reaction of the glucose moieties on the paper with 3-triethoxysilylpropylamine (APTES). The so-formed amine layer allowed binding to a metal ion by coordination chemistry, while the metal ion reacted after with the drug to produce a colored compound. The most suitable metals to carry out such modification were selected by bulk studies, and the several stages of the paper modification were optimized to produce an intense color change against the concentration of the drug. The paper strips were applied to the analysis of spiked environmental water, allowing a quantitative determination for OXY concentrations as low as 30 ng/mL. In general, this work provided a simple, method to screen and discriminate tetracycline drugs, in aquaculture, being a promising tool for local, quick and cheap monitoring of drugs.

Host-Tailored Sensors for Leucomalachite Green Potentiometric Measurements

A new biomimetic sensor for leucomalachite green host-guest interactions and potentiometric transduction is presented. The artificial host was imprinted in methacrylic acid or acrylamido-2-methyl-1-propanesulfonic acid-based polymers. Molecularly imprinted particles were dispersed in 2-nitrophenyloctyl ether and trapped in poly(vinyl chloride). The potentiometric sensors exhibited a near-Nernstian response in steady state evaluations, with slopes and detection limits ranging from 45.8 to 81.2 mV and 0.28 to 1.01 , respectively. They were independent from the pH of test solutions within 3 to 5. Good selectivity was observed towards drugs that may contaminate water near fish cultures, such as oxycycline, doxycycline, enrofloxacin, trimethoprim, creatinine, chloramphenicol, and dopamine. The sensors were successfully applied to field monitoring of leucomalachite green in river samples. The method offered the advantages of simplicity, accuracy, applicability to colored and turbid samples, and automation feasibility.

Biomimetic sensors of molecularly-imprinted polymers for chlorpromazine determination

A new man-tailored biomimetic sensor for Chlorpromazine host-guest interactions and potentiometric transduction is presented. The artificial host was imprinted within methacrylic acid, 2-vinyl pyridine and 2-acrylamido-2-methyl-1-propanesulfonic acid based polymers. Molecularly imprinted particles were dispersed in 2-nitrophenyloctyl ether and entrapped in a poly(vinyl chloride) matrix. Slopes and detection limits ranged 51–67 mV/decade and 0.46–3.9 μg/mL, respectively, in steady state conditions. Sensors were independent from the pH of test solutions within 2.0–5.5. Good selectivity was observed towards oxytetracycline, doxytetracycline, ciprofloxacin, enrofloxacin, nalidixic acid, sulfadiazine, trimethoprim, glycine, hydroxylamine, cysteine and creatinine. Analytical features in flowing media were evaluated on a double-channel manifold, with a carrier solution of 5.0 × 10−2 mol/L phosphate buffer. Near-Nernstian response was observed over the concentration range 1.0 × 10−4 to 1.0 × 10−2 mol/L. Average slopes were about 48 mV/decade. The sensors were successfully applied to field monitoring of CPZ in fish samples, offering the advantages of simplicity, accuracy, automation feasibility and applicability to complex samples.

Molecularly-Imprinted Materials for Potentiometric Transduction: Application to the Antibiotic Enrofloxacin

Enrofloxacin (ENR) is an antimicrobial used both in humans and in food producing species. Its control is required in farmed species and their surroundings in order to reduce the prevalence of antibiotic resistant bacteria. Thus, a new biomimetic sensor enrofloxacin is presented. An artificial host was imprinted in specific polymers. These were dispersed in 2-nitrophenyloctyl ether and entrapped in a poly(vinyl chloride) matrix. The potentiometric sensors exhibited a near-Nernstian response. Slopes expressing mV/Δlog([ENR]/M) varied within 48–63. The detection limits ranged from 0.28 to 1.01 µg mL−1. Sensors were independent from the pH of test solutions within 4–7. Good selectivity was observed toward potassium, calcium, barium, magnesium, glycine, ascorbic acid, creatinine, norfloxacin, ciprofloxacin, and tetracycline. In flowing media, the biomimetic sensors presented good reproducibility (RSD of ± 0.7%), fast response, good sensitivity (47 mV/Δlog([ENR]/M), wide linear range (1.0 × 10−5–1.0 × 10−3 M), low detection limit (0.9 µg mL−1), and a stable baseline for a 5 × 10−2 M acetate buffer (pH 4.7) carrier. The sensors were used to analyze fish samples. The method offered the advantages of simplicity, accuracy, and automation feasibility. The sensing membrane may contribute to the development of small devices allowing in vivo measurements of enrofloxacin or parent-drugs.

TEC4SEA - A Modular Platform for Research, Test and Validation of Technologies Supporting a Sustainable Blue Economy

This paper presents the TEC4SEA research infrastructure created in Portugal to support research, development, and validation of marine technologies. It is a multidisciplinary open platform, capable of supporting research, development, and test of marine robotics, telecommunications, and sensing technologies for monitoring and operating in the ocean environment. Due to the installed research facilities and its privileged geographic location, it allows fast access to deep sea, and can support multidisciplinary research, enabling full validation and evaluation of technological solutions designed for the ocean environment. It is a vertically integrated infrastructure, in the sense that it possesses a set of skills and resources which range from pure conceptual research to field deployment missions, with strong industrial and logistic capacities in the middle tier of prototype production. TEC4SEA is open to the entire scientific and enterprise community, with a free access policy for researchers affiliated with the research units that ensure its maintenance and sustainability. The paper describes the infrastructure in detail, and discusses associated research programs, providing a strategic vision for deep sea research initiatives, within the context of both the Portuguese National Ocean Strategy and European Strategy frameworks.

An Exact Schedulability Test for Global FP Using State Space Pruning

Presented at 23rd International Conference on Real-Time Networks and Systems (RTNS 2015). 4 to 6, Nov, 2015, Main Track. Lille, France.

Are coffee silverskin extracts safe for topical use? An in vitro and in vivo approach

Recent changes in regulatory requirements and social views on animal testing have incremented the development of reliable alternative tests for predicting skin and ocular irritation potential of products based on new raw materials. In this regard, botanical ingredients used in cosmetic products are among those materials, and should be carefully reviewed concerning the potential presence of irritant constituents. In particular, cosmetic products used on the face, in vicinity of the eyes or that may come in contact with mucous membranes, should avoid botanical ingredients that contain, or are suspected to contain, such ingredients. In this study, we aimed to evaluate the effect of a new cosmetic ingredient, namely, coffee silverskin (CS), with an in vitro skin and ocular irritation assay using reconstructed human epidermis, EpiSkin™, and human corneal epithelial model, SkinEthics™ HCE, and an in vivo assay. Three different extracts of CS were evaluated. The histology of the models after extracts applications was analysed. The in vitro results demonstrated that extracts were not classified as irritant and the histological analyses proved that extracts did not affect both models structure. The content of caffeine, 5-hydroxymethyl furfural and chlorogenic acid was quantified after the epidermal assay. The in vivo test carried out with the most promising extract (hydroalcoholic) showed that, with respect to irritant effects, these extracts can be regarded as safe for topical application.

MnFe2O4@CNT-N as novel electrochemical nanosensor for determination of caffeine, acetaminophen and ascorbic acid

For the first time, a glassy carbon electrode (GCE) modified with novel N-doped carbon nanotubes (CNT-N) functionalized with MnFe2O4 nanoparticles (MnFe2O4@CNT-N) has been prepared and applied for the electrochemical determination of caffeine (CF), acetaminophen (AC) and ascorbic acid (AA). The electrochemical behaviour of CF, AC and AA on the bare GCE, CNT-N/GCE and MnFe2O4@CNT-N/GCE were carefully investigated using cyclic voltammetry (CV) and square-wave voltammetry (SWV). Compared to bare GCE and CNT-N modified electrode, the MnFe2O4@CNT-N modified electrode can remarkably improve the electrocatalytic activity towards the oxidation of CF, AC and AA with an increase in the anodic peak currents of 52%, 50% and 55%, respectively. Also, the SWV anodic peaks of these molecules could be distinguished from each other at the MnFe2O4@CNT-N modified electrode with enhanced oxidation currents. The linear response ranges for the square wave voltammetric determination of CF, AC and AA were 1.0 × 10−6 to 1.1 × 10−3 mol dm−3, 1.0 × 10−6 to 1.0 × 10−3 mol dm−3 and 2.0 × 10−6 to 1.0 × 10−4 mol dm−3 with detection limit (S/N = 3) of 0.83 × 10−6, 0.83 × 10−6 and 1.8 × 10−6 mol dm−3, respectively. The sensitivity values at the MnFe2O4@CNT-N/GCE for the individual determination of AC, AA and CF and in the presence of the other molecules showed that the quantification of AA and CF show no interferences from the other molecules; however, AA and CF interfered in the determination of AC, with the latter molecule showing the strongest interference. Nevertheless, the obtained results show that MnFe2O4@CNT-N composite material acted as an efficient electrochemical sensor towards the selected biomolecules.

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.

Exploring cinnamic acid scaffold: development of promising neuroprotective lipophilic antioxidants

New lipophilic hydroxycinnamic acid based derivatives were designed and synthesized and their antioxidant and neuroprotective activities evaluated. The chemical modification introduced in the cinnamic acid scaffold leads to compounds with amplified lipophilicity and in general with increased antioxidant activity when compared to natural models (caffeic and ferulic acids). The compounds did not display cytotoxicity and present a significant neuroprotective effect against 6-OH-DA induced damage to SH-SY5Y cells. Compound 6 stands out as an efficient radical scavenger and iron(II) chelator that ensures drug-like properties. Moreover, neuroprotection against oxidative damage was observed even at low concentration (1 μM). Therefore, compound 6 developed by a biology-oriented approach displays a combination of important features for a further optimization process that will generate a new effective antioxidant with therapeutic application for oxidative-stress-related events, namely neurodegenerative diseases.

Using neural networks and support vector regression to relate marchetti dilatometer test parameters and maximum shear modulus

In the last two decades, small strain shear modulus became one of the most important geotechnical parameters to characterize soil stiffness. Finite element analysis have shown that in-situ stiffness of soils and rocks is much higher than what was previously thought and that stress-strain behaviour of these materials is non-linear in most cases with small strain levels, especially in the ground around retaining walls, foundations and tunnels, typically in the order of 10−2 to 10−4 of strain. Although the best approach to estimate shear modulus seems to be based in measuring seismic wave velocities, deriving the parameter through correlations with in-situ tests is usually considered very useful for design practice.The use of Neural Networks for modeling systems has been widespread, in particular within areas where the great amount of available data and the complexity of the systems keeps the problem very unfriendly to treat following traditional data analysis methodologies. In this work, the use of Neural Networks and Support Vector Regression is proposed to estimate small strain shear modulus for sedimentary soils from the basic or intermediate parameters derived from Marchetti Dilatometer Test. The results are discussed and compared with some of the most common available methodologies for this evaluation.