Fast Methodology Research Articles

A Novel Spectrofluorimetric Method for Vibegron in the Newly FDA Approved Pharmaceutical Formulation and in Human Plasma: Analytical QbD Strategy for Method Development and Optimization.

Vibegron is a novel selective beta-3 adrenergic receptor agonist molecule, recently approved by US Food and Drug Administration (FDA) in tablet pharmaceutical formulation for treating overactive bladder syndrome. Such formulation necessitates the development of a simple, fast and cost-effective methodology capable of assaying the drug in various real samples with high sensitivity. Herein, a facile and robust spectrofluorimetric method was introduced, for the first time, for vibegron quantification based on analytical quality-by-design approach. The method involves drug reaction with dansyl chloride at pH 9.8, as a smart approach to overcome the non-fluorescent nature of vibegron, giving a highly fluorescent yellow derivative measured at 514nm after being excited at 345nm. Plausible reaction scheme between the drug and dansyl chloride was elucidated through studying the differences in their infrared (IR) spectra. Variables affecting fluorescence intensities were carefully screened and rationally optimized via preliminary scouting studies and central composite design for accurate and robust results. Full International Council for Harmonisation (ICH) validation protocol was followed where linearity was achieved in range of 20.0-400.0 ng/mL with minimum detectability of 3.6 ng/mL. The proposed method expressed good capability in assaying the marketed dosage forms with no excipient inference. Finally, the high sensitivity of such method paved the way for extending its application to quantify vibegron in spiked human plasma at concentrations around its real human plasma concentrations for further bioavailability studies.

Shape optimization for high efficiency metasurfaces: theory and implementation

Complex non-local behavior makes designing high efficiency and multifunctional metasurfaces a significant challenge. While using libraries of meta-atoms provide a simple and fast implementation methodology, pillar to pillar interaction often imposes performance limitations. On the other extreme, inverse design based on topology optimization leverages non-local coupling to achieve high efficiency, but leads to complex and difficult to fabricate structures. In this paper, we demonstrate numerically and experimentally a shape optimization method that enables high efficiency metasurfaces while providing direct control of the structure complexity through a Fourier decomposition of the surface gradient. The proposed method provides a path towards manufacturability of inverse-designed high efficiency metasurfaces.

A methodology to assess the phenology patterns of a eutrophic water body, using remote sensing data and principal component analysis

Chlorophyll-a concentrations in coastal and inland waters is acknowledged as an important factor to estimate Water Bodies Quality Status. Remote sensing analysis is a fast, cost-effective, and reliable methodology that can be used to assess the phenological patterns of aquatic ecosystems. The purpose of this study is to assess the phenology patterns of a eutrophic water body and present a novel methodology to fill in the missing values of the daily Surface Reflectance MODIS Aqua dataset. The proposed methodology is to apply Principal Component Analysis to generate the missing values from the chlorophyll-a concentrations and better assess Water Body's Trophic Status. The pilot study is Aitoliko Lagoon, a semi-closed Greek water body with eutrophication. The analysis reveals that sampling stations of swallow waters have higher variance in chlorophyll-a concentrations, favoring the deep waters sampling pixels for evaluating the water body's trophic status. The proposed PCA methodology showed a clear phenological pattern of chlorophyll-a with algae bloom from April and high chlorophyll-a concentrations until August, which is what is expected from a eutrophic lagoon and specifically for Aitoliko lagoon.

Fast and green methodology based on miniaturised liquid–liquid extraction with a hydrophobic natural deep eutectic solvent of opium alkaloids from poppy seed beverages

Currently, the development of environmentally friendly analytical methodologies is a challenge for analytical chemistry. Therefore, the search for solvents to extract analytes of interest from complex matrices has progressed considerably in recent years. In the present work, 15 hydrophobic natural deep eutectic solvents (HNADES) consisting of a mixture of different donors and acceptors in different ratios have been prepared and compared to evaluate the most efficient extraction of opium alkaloids (OA) from fortified water samples. After selecting the most effective one consisting in a mixture of thymol and camphor in the ratio 1:1 ([Thy]:[Cam] 1:1), a rapid analytical methodology to determine OA in poppy seed beverages was optimised and validated for the first time. It was based on a miniaturised liquid–liquid extraction (m-LLE) with 0.5 mL of sample and 0.5 mL of HNADES by vortexing for 1 min, followed by 1 min centrifugation and subsequent analysis by high-performance liquid chromatography with diode array detector (HPLC-DAD). The validation was successful, showing extraction efficiencies between 91 and 107 % for all analytes, low limits of quantification (0.1 µg/mL for all analytes, except for morphine, 0.4 µg/mL) and precision values below 15 %. In addition, the greenness of the method was evaluated, obtaining a 0.79 on the AGREEprep metric scale. Therefore, it was demonstrated that the methodology developed to analyse OAs in poppy seed beverage was efficient, fast, simple and environmentally friendly.

Laser Sintering by Spot and Linear Optics for Inkjet-Printed Thin-Film Conductive Silver Patterns with the Focus on Ink-Sets and Process Parameters

The implementation of the laser sintering for inkjet-printed nanoparticles and metal organic decomposition (MOD) inks on a flexible polymeric film has been analyzed in detail. A novel approach by implementing, next to a commonly 3.2 mm diameter spot laser optic, a line laser optic with a laser beam area of 2 mm × 80 mm, demonstrates the high potential of selective laser sintering to proceed towards a fast and efficient sintering methodology in printed electronics. In this work, a multiplicity of laser parameters, primary the laser speed and the laser power, have been altered systematically to identify an optimal process window for each ink and to convert the dried and non-conductive patterns into conductive and functional silver structures. For each ink, as well as for the two laser optics, a suitable laser parameter set has been found, where a conductivity without any damage to the substrate or silver layer could be achieved. In doing so, the margin of the laser speed for both optics is ranging in between 50 mm/s and 100 mm/s, which is compatible with common inkjet printing speeds and facilitates an in-line laser sintering approach. Considering the laser power, the typical parameter range for the spot laser lays in between 10 W and 50 W, whereas for the line optics the full laser power of 200 W had to be applied. One of the nanoparticle silver inks exhibits, especially for the line laser optic, a conductivity of up to 2.22 × 107 S‧m−1, corresponding to 36% of bulk silver within a few seconds of sintering duration. Both laser sintering approaches together present a remarkable facility to use the laser either as a digital tool for sintering of defined areas by means of a spot beam or to efficiently sinter larger areas by means of a line beam. With this, the utilization of a laser sintering methodology was successfully validated as a promising approach for converting a variety of inkjet-printed silver patterns on a flexible polymeric substrate into functionalized conductive silver layers for` applications in the field of printed electronics.

Fast predesign methodology of centrifugal compressor for PEMFCs combining a physics-based loss model and an interpretable machine learning method

For boosted hydrogen fuel cells, the centrifugal compressor greatly affects the system's efficiency, stack power and costs. However, the design of the centrifugal compressor is a lengthy and high-cost process. To tackle this issue, a new method combining a physics-based loss model and a machine learning method was proposed to achieve a fast and more accurate preliminary design step. A high-precision physics-based loss model was developed and validated for data generation. Furthermore, two gradient boosting decision tree (GBDT) models with Bayesian hyperparameter turning were established to construct mapping relationships between geometric parameters and aerodynamic performance. Then, the Sobol sensitivity analysis and Shapley Additive Explanations (SHAP)-based interpretability were used to explore the impact of the geometric parameters on the aerodynamic performances. Results showed that the outlet blade angle, impeller outlet diameter, impeller outlet width, inducer shroud diameter, and inducer blade angle (at the shroud) were the five most sensitive parameters. Additionally, some practical design recommendations were extracted using SHAP-based interpretability. The isentropic efficiencies of two redesign cases with optimal geometric parameters were increased by 2.59% and 1.85% compared with the baseline impellers. The performance prediction was completed within a time of 6 s using the proposed new predesign method. This study represents a significant new attempt to advance the preliminary design methodology of centrifugal compressors.

Use of NIR in COVID-19 Screening: Proof of Principles for Future Application.

The COVID-19 pandemic that affected the world between 2019 and 2022 showed the need for new tools to be tested and developed to be applied in global emergencies. Although standard diagnostic tools exist, such as the reverse-transcription polymerase chain reaction (RT-PCR), these tools have shown severe limitations when mass application is required. Consequently, a pressing need remains to develop a rapid and efficient screening test to deliver reliable results. In this context, near-infrared spectroscopy (NIRS) is a fast and noninvasive vibrational technique capable of identifying the chemical composition of biofluids. This study aimed to develop a rapid NIRS testing methodology to identify individuals with COVID-19 through the spectral analysis of swabs collected from the oral cavity. Swab samples from 67 hospitalized individuals were analyzed using NIR equipment. The spectra were preprocessed, outliers were removed, and classification models were constructed using partial least-squares for discriminant analysis (PLS-DA). Two models were developed: one with all the original variables and another with a limited number of variables selected using ordered predictors selection (OPS-DA). The OPS-DA model effectively reduced the number of redundant variables, thereby improving the diagnostic metrics. The model achieved a sensitivity of 92%, a specificity of 100%, an accuracy of 95%, and an AUROC of 94% for positive samples. These preliminary results suggest that NIRS could be a potential tool for future clinical application. A fast methodology for COVID-19 detection would facilitate medical diagnoses and laboratory routines, helping to ensure appropriate treatment.

Zirconium-based metal-organic framework composites for solid phase extraction of brevetoxin-A from seawater

Red tides are a type of natural marine disaster caused by harmful algae characterized by a high toxicity, wide distribution, and long duration. Since the concentration of algal toxins in seawater increases with the occurrence of red tides, algal toxins detected in seawater could be used to predict the occurrence and evolution of red tides. Brevetoxin-A (BTX-A) is a secondary metabolite produced by the harmful algae Karenia brevis, whose detection in seawater could form the basis of an accurate warning system for incoming red tides. However, due to the inherent complexity of the seawater matrix and the extremely low levels of BTX-A in seawater, the use of instruments for its direct detection is difficult. Therefore, there is an urgent need to develop a sample pretreatment method for the efficient enrichment of BTX-A in seawater. In this study, a metal-organic backbone material (UiO-66) and its composite with silica microspheres (SiO2@UiO-66) were successfully synthesized using the solvothermal method. The prepared SiO2@UiO-66 exhibited good hydrophilicity, water stability, and large specific surface area. Furthermore, it also exhibited hydrogen bonding and electrostatic interactions with BTX-A, had a strong affinity for BTX-A, and was able to efficiently adsorb BTX-A in complex matrices. Therefore, SiO2@UiO-66 showed potential as a novel packing material for the extraction of BTX-A from solid phase extraction columns. Combined with high performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS), a highly sensitive detection method for the determination of BTX-A in marine water was established. The established analytical method had a low detection limit (3.0 pg/mL), a wide linear range (10.0 -200.0 pg/mL), and a good linear relationship (R=0.9992). Combined with the Fujian Province Red Tide Monitoring and Early Warning Information 2021 issued by the Fujian Provincial Oceanic and Fisheries Bureau, the analytical method established herein was successfully applied to analyze and monitor the content of BTX-A in actual seawater samples. This highlights the proposed system's potential for use as an early warning factor in the monitoring of red tides, representing a simple and fast pretreatment methodology for the detection of BTX-A in seawater.

Investigating aqueous micellar systems enhancement tools for a sensitive spectrofluorimetric determination of anti-TB: Rifampicin.

Rifampicin is a frontline antibiotic in the management of tuberculosis. Since no spectrofluorimetric methods are reported for this drug, this approach was challenged to craft a sensitive, reliable, valid, fast, and green methodology. In recent years, fluorescence spectroscopy has received a lot of interest. Its benefits include ecological greenness and analytical performance. The pharmaceutical industries greatly like this approach because of its low energy and decreased solvent usage, which make it both economical and environmentally friendly. This methodology was based on utilizing the enhanced native fluorescence of the rifampicin at 341 nm after excitation at 241 nm in a beta-cyclodextrin micellar system. Modern developments in analytical chemistry have been applied to reduce risks to the workplace and environment by using distilled water as a dilution solvent for method application and optimization. The method was found excellent green with 97 eco-scale and 0.86 AGREE scores besides an 89.6 overall whiteness score. The range of linearity for rifampicin raw material was 0.2-1.5μg·mL-1, and the average recoveries for raw material and spiked plasma were 100.15% and 99.64%, respectively. The suggested technique worked well for the commercial oral syrup of Rimactane® and did not conflict with any common additives.

Design of Selective Nanoparticles of Layered Double Hydroxide (Mg/Al-LDH) for the Analysis of Anti-Inflammatory Non-Steroidal Agents in Environmental Samples, Coupled with Solid-Phase Extraction and Capillary Electrophoresis

A simple, fast, and low-cost pre-concentration methodology based on the application of solid-phase extraction coupled to layered double hydroxides (LDHs) and capillary electrophoresis was developed for the determination of naproxen (NPX), diclofenac (DFC), and ibuprofen (IBP) in environmental sample waters. A systematic study of the LDH composition was designed, including the effects of interlayer anions (NO3−, Cl−, CO32−, BenO−, and SDS−) and the effect of molar ratio (Mg:Al). The optimal composition of MgAl/Cl−-LDH (Mg:Al; 1.5:1.0) was coupled to an SPE system: pH (neutral pH), LDH amount (15 mg), and extraction capacity ranged from 79.71 to 83.11% for the three anti-inflammatory non-steroidal agents analyzed. A recovery rate of up to 80.87% was obtained when 0.01 M chloride acid in methanol was used as the eluent and 50 mL of sample was used. Under optimal conditions, the linear range of the calibration curve ranges from 18.02 to 200 μg L−1, with limits of detection ranging from 6.03 to 18.02 μg L−1 for the three NSAIDs. The precision of the methodology was evaluated in terms of inter- and intra-day repeatability, with %RSD < 10% in all cases. The proposed method was applied to analyze environmental water samples (bottle, tap, cistern, well, and river water samples). The developed method is a robust technique capable of combining with other analytical methods to quantitatively determine anti-inflammatory non-steroidal agents.

Identification and simultaneous quantification of potential genotoxic impurities in first-line HIV drug dolutegravir sodium using fast ultrasonication-assisted extraction method coupled with GC–MS and in-silico toxicity assessment

Dolutegravir (DLG) has become a distinctive first-line antiretroviral therapy for the treatment of HIV in most countries due to its affordability, high efficacy, and low drug-drug interactions. However, the evaluation of genotoxic impurities (GTIs) in DLG and their toxicity assessment has not been explored thoroughly. Thus, in this study, a simple, fast, and selective analytical methodology was developed for the identification and determination of 7 GTIs in the comprehensive, explicit route of synthesis for the dolutegravir sodium (DLG-Na) drug. A facile, fast ultrasonication-assisted liquid–liquid extraction procedure was adapted to isolate the GTIs in DLG-Na and then analyzed using the gas chromatography (GC)-electron impact (EI)/mass spectrometer (MS) quantification (using selective ion monitoring mode) technique. This EI-GC/MS method was validated as per the current requirements of ICH Q2 (R1) guidelines. Under optimal method conditions, excellent linearities were achieved with R between 0.9959 and 0.9995, and high sensitivity was obtained in terms of detection limits (LOD) between 0.15 to 0.63 µg/g, and quantification limits (LOQ) between 0.45 to 1.66 µg/g for the seven GTIs in DLG. The obtained recoveries ranged from 98.2 to 104.3 % at LOQ, 15 µg/g, and 18 µg/g concentration levels (maximum daily dose of 100 mg). This developed and validated method is rapid, easy to adopt, specific, sensitive, and accurate in estimating the seven GTIs in a relatively complex sodium matrix of the DLG-Na drug moiety. As a method application, two different manufactured samples of DLG-Na drug substances were analyzed for the fate of the GTIs and drug safety for the intended dosage applications. Moreover, an in-silico QSAR toxicity prediction assessment was carried out to prove scientifically the potential GTI nature of each impurity from the alerting functional groups.

New screen-printed electrodes for Raman spectroelectrochemistry. Determination of p-aminosalicylic acid

BackgroundThe availability of new surface enhanced Raman scattering (SERS) substrates is essential to develop quantitative analytical methods. Electrochemistry is an easy, fast and reproducible methodology to prepare SERS substrates on screen-printed electrodes (SPEs). ResultsThis work proposes new SPEs based on a three-electrode system all made of silver. Using the same ink for the whole electrode system facilitates the fabrication process, reduces production costs, and leads to excellent analytical performance. The results showed that Raman enhancement depends strongly on the type of silver ink. To demonstrate the capabilities of the new electrodes developed, 4-aminosalicylic acid was determined in complex matrices and in the presence of strong interfering compounds such as salicylic acid and acetylsalicylic acid. The proposed analytical method is based on the electrochemical surface oxidation enhanced Raman scattering (EC-SOERS) strategy. AgCl nanocrystals are generated on the working electrode surface, which amplify the Raman signal of 4-aminosalicylic acid. Good figures of merit were obtained both in the absence and in the presence of the interfering compounds, achieving a correct estimation of a 4-aminosalicylic test sample in complex matrices. SignificanceThe new SPEs have been demonstrated to be very sensitive and reproducible which, together to the high specificity of the Raman signal, makes this methodology very attractive for chemical analysis.

A lightweight deep learning architecture for malaria parasite-type classification and life cycle stage detection

Malaria is an endemic in various tropical countries. The gold standard for disease detection is to examine the blood smears of patients by an expert medical professional to detect malaria parasite called Plasmodium. In the rural areas of underdeveloped countries, with limited infrastructure, a scarcity of healthcare professionals, an absence of sufficient computing devices, and a lack of widespread internet access, this task becomes more challenging. A severe case of malaria can be fatal within one week, so the correct detection of the malaria parasite and its life cycle stage is crucial in treating the disease correctly. Though computer vision-based malaria detection has been adequately explored lately, the malaria life cycle stage classification is still a relatively unexplored field. In this paper, we introduce a fast and robust deep learning methodology to not only classify the malaria parasite-type detection but also the life cycle stage identification of the infected cell. The proposed deep learning architecture is more than twenty times lighter than the widely used DenseNet and has less than 0.4 million parameters, making it a good candidate to be used in the mobile applications of such economically challenged states for malaria detection. We have used four different publicly available malaria datasets to test the proposed architecture and gained significantly better results than the current state of the art on malaria parasite-type and malaria life cycle classification.

Development of a real-time ultrasonic monitoring tool for high-cycle thermal fatigue

Pipe networks within nuclear power plants (NPPs) are susceptible to high-cycle thermal fatigue (HCTF) failures especially in so-called mixing zones where fluids with different thermal and hydraulic properties interact. The 1998 incident at Civaux 1 NPP is a good example of such a failure and its potential consequences. Given the critical impact on safety of these NPP components, a non-invasive method for HCTF progression monitoring in real time is expected to be of great use. Previous ultrasonic monitoring work showed that it is possible to predict the through-thickness temperature distribution and its temporal evolution of a mild steel block to within ±2 °C, relative to a resistance temperature device. These predictions were achieved using time of flight measurements from an ultrasonic transducer placed on the accessible surface with the so-called inverse thermal model (ITM) to invert the data. However, experiments to date have been limited to slow (10 minute) thermal transients at low temperatures (T < 100 °C). Given that HCTF is driven by thermal gradients, the ITM method seems promising to monitor its progression. In this work the performance of the ITM method was investigated under more realistic conditions: faster (1 minute) thermal transients at higher temperatures (≈ 250-300 °C). A special high temperature electromagnetic acoustic transducer (EMAT) and a fast acquisition and inversion methodology were created to collect the data. The measurement setup and the collected data will be presented in this paper.

An immersed boundary fast meshfree integration methodology with consistent weight learning

An immersed boundary fast integration methodology featured by a consistent weight learning is proposed to accelerate Galerkin meshfree computation. In the proposed approach, the problem domain is embedded in a rectangular spatial domain discretized by regular distributions of meshfree nodes and integration sampling points with virtual integration cells. A trimming operation of the rectangular spatial domain by the physical problem boundary with nodal discretization yields the discrete model for meshfree computation, where the integration sampling points are grouped into the interior sampling points and the near boundary sampling points which form a training set. For the interior sampling points, a natural alignment between the influence domains of meshfree shape functions and virtual integration cells can be easily realized through employing integer support sizes, which ensures a satisfactory accuracy for the basis degree correspondent normal order Gauss integration. The background cells for the domain integration are completely avoided, which greatly simplifies the preprocessing for numerical integration. Meanwhile, a machine learning module is devised to optimize the weights of near boundary integration sampling points. The key step to construct this machine learning module for weight optimization is the selection of the variational integration consistency condition as the loss function, which guarantees the convergence of Galerkin meshfree formulation. The accuracy and efficiency of the proposed weight learning immersed boundary fast integration methodology is thoroughly validated through numerical results.

Fast preparation and thermoelectric performance of AgSTe based materials via plasma activated sintering

AgSTe based materials have recently been identified as promising thermoelectric materials that have favorable properties at room temperature. They are usually obtained by conventional processes involving melting, annealing, and sintering, which incur significant energy and time expenditures. The present investigation involved the synthesis of Ag20S7Te3 compounds by a fast non-equilibrium methodology, i.e., plasma activated sintering from raw elemental powders. The monoclinic Ag20S7Te3 phase crystal was successfully formed at a temperature of 673 K, accompanied by the presence of a secondary monoclinic phase, Ag2S. The Ag20S7Te3 phase predominated in the monoclinic form, constituting a significant percentage at temperature of 873 K. Furthermore, the thermoelectric figure of merit (zT) of the AgSTe phase at 873 K was determined to be 0.42. The rapid sample synthesis described herein has the potential to significantly reduce the duration required for material synthesis, hence presenting a conspicuous advantage in the context of manufacturing on a wide scale.

A Comprehensive Study for Determination of Free Fatty Acids in Selected Biological Materials: A Review.

The quality of oil is highly dependent on its free fatty acid (FFA) content, especially due to increased restrictions on renewable fuels. As a result, there has been a growing interest in free fatty acid determination methods over the last few decades. While various standard methods are currently available, such as the American Oil Chemists Society (AOCS), International Union of Pure and Applied Chemistry (IUPAC), and Japan Oil Chemists' Society (JOCS), to obtain accurate results, there is a pressing need to investigate a fast, accurate, feasible, and eco-friendly methodology for determining FFA in biological materials. This is owing to inadequate characteristics of the methods, such as solvent consumption and reproducibility, among others. This study aims to investigate FFA determination methods to identify suitable approaches and introduce a fresh perspective.

Emphasizing the Potential of Attenuated Total Reflectance–Fourier Transform Infrared (ATR-FTIR) Spectroscopy Combined with Chemometrics, for Classification of Greek Grape Marc Spirits

Grape marc spirits, such as the Greek tsipouro/tsikoudia, reflect the cultural heritage of winemaking traditions worldwide. This study explored the application of Attenuated Total Reflectance–Fourier Transform Infrared (ATR-FTIR) spectroscopy combined with chemometrics for its potential as a fast classification methodology for spirit characterization. ATR-FTIR spectra from thirty-nine products revealed distinctive bands corresponding to various chemical constituents, such as alcohols, organic acids, water, carbohydrates, and phenols. Principal Component Analysis (PCA) was performed on all acquired ATR-FTIR data and 78.50% of the total variance in the data was explained. Also, partial least squares–discriminant analysis (PLS-DA), used for the classification of products based on their major geographic origin, gave a correct classification of 89.5% for the north and 83.3% for the south of Greece. Classification of the type of distillations used was with 74.36% accuracy. Significant markers were identified through analysis, such as those associated with the O-H bending vibrations of phenols or alcohols, contributing to the discrimination of grape marc spirits from Crete when compared with the other four main geographical origin designations. By combining ATR-FTIR spectroscopy with chemometrics, this research gave insights into the origins and compositional variations of the spirits, providing an opportunity for a quality control assessment tool.

Green and fast quantification of Bisphenol A in instant noodles cup using iridium nanoparticle-modified glassy carbon electrode

This study presents a method for quantifying the endocrine-disrupting compound Bisphenol A (BPA) in food packages. Carbon-supported iridium nanoparticles were synthesized via formic acid reduction and used to modify a glassy carbon electrode, creating the Ir/C/GCE sensor. Employing this sensor in conjunction with square wave voltammetry, a fast and green electroanalytical methodology was developed and subsequently validated for quantifying BPA in instant noodle cup packages. The optimized and validated analytical method exhibited linearity, with low limits of detection and quantification recorded at 0.011 μmol L−1 and 0.037 μmol L−1, respectively. Besides this, it demonstrated good selectivity and accuracy, complying with major validation guidelines, including those from the International Council for Harmonisation, Eurachem, the European Union, and AOAC International. Furthermore, the environmental sustainability of the method was confirmed using the AGREE metric.

Synthetic peptides-based SPR biosensor evaluation towards canine Visceral leishmaniasis diagnosis: A simple and effective approach

A vital component of surface plasmon resonance (SPR) biosensors is the recognition element. It generates the sensor selectivity towards the target biomarker and plays an important role in defining the sensor’s analytical sensitivity. In serological diagnostics, recombinant proteins (purified antigens) have presented better selectivity compared to soluble crude antigens in SPR immunosensors. However, recombinant DNA technology to obtain proteins is only explored by specialized laboratories, and commercial recombinant proteins still have a high cost. Thus, with the aim of developing a much simpler and lower-cost method, we investigated the prospect of using synthetic peptides as recognition elements for constructing an SPR biosensor. Two synthetic peptides, named PEP13 and PEP16, were tested for the serological diagnosis of Canine Visceral Leishmaniasis (CVL), an endemic neglected disease that affects humans and dogs worldwide. Between the two peptides tested, PEP13 was more sensitive when evaluating its responses against purified antibodies in buffer solution (LOD = 1.05 nmol L-1), and it also discriminated the response better when applied in diluted serum samples of infected dogs compared to diluted healthy dogs’ samples. For this reason, the PEP13 immunosensor was used to analyze canine serum samples, precisely identifying all the positive (n = 7) and negative (n = 7) CVL cases (p = 0.00136) in less than 12 min. In brief, this study explored promising biostructures through a simple and fast methodology for serological diagnosis, addressing the suitability of synthetic peptides for use in biosensors in the urgent field of neglected diseases.