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Background

Visceral Leishmaniasis is a neglected tropical disease with a high rate of infection and mortality in affected areas. Around 50,000 to 90,000 new cases of visceral leishmaniasis (VL) are estimated every year. Individuals asymptomatic for the disease should also be considered in epidemiological surveillance of the disease, as they can help spread the parasite. Thus, the development of low-cost diagnosis methods that allow the identification of infected and asymptomatic individuals is required, especially in developing countries where this disease is endemic.

Results

In this work, we developed an immunosensor for recognizing anti-Leishmania antibodies in asymptomatic individuals and avoiding cross-reaction with Chagas disease (CD). For that, we used carbon-based screen-printed electrodes, modified with graphene oxide and gold. Reproducibility was assessed by calculating the relative standard deviation (RSD < 5 %) from cyclic voltammograms of [Fe(CN)₆]^3-/4− using three different electrodes, screen-printed carbon electrodes (DPR-110) and graphene modified screen-printed electrodes (DPR-110 GPH) were purchased from DropSens (Oviedo, Asturias, Spain).

Significance

As an electrochemical methodology, we use cyclic voltammetry. After the tests were carried out, we considered that carbon electrodes adsorbed with reduced graphene oxide and modified with gold nanoparticles were the best platforms for detecting anti-Leishmania antibodies. In the study carried out, the limit of quantification (LOQ) for anti-Leishmania antibodies was established at 16.75 mg/mL, while the limit of detection (LOD) was 5.58 mg/mL. These limits indicate the minimum antibody concentration values that can be quantified and detected accurately and reliably in the analyzed sera.

Tin contamination in waters due to mining and natural activities in high concentrations can threaten human health. This research presents the development of a sensitive and selective colorimetric sensor in aqueous, paper, and gel-based to detect Sn²⁺. The development of such sensors is promising, with attractive advantages such as intense color, fast naked-eye response, and simple continuous fabrication. The addition of Sn²⁺ ions will change the color of the medium because curcumin (Cur) interacts with Sn²⁺, causing a decrease in free Cur, silver nanoparticles (AgNPs) becoming less stable, and a change in particle size. Colorimetric changes in Sn²⁺ were achieved by visual inspection within 10 min for aqueous-based and 20 min for paper and gel-based. The good linear relationship (R² = 0.9999) between Sn²⁺ and Δ absorption with a detection limit of up to 66.99 µg/L. This method is relatively scalable in determining Sn²⁺ and shows good recovery between 80 and 105 %. This colorimetric sensor gives good sensitivity to Sn²⁺ metal ions which is expected to become the basic technology for developing in-situ sensors to monitor Sn²⁺ levels in tin industrial waste.

With a rise in the prominence of cannabis usage, due to its widespread availability and varying legal status, there has been an increased emphasis on the differentiation of cannabinoids present within cannabis using various analytical techniques. The present study aimed to exploit the capability of Raman microscopy to collect high-quality spectra of seven cannabinoid analytical standards, followed by their classification using linear discriminant analysis (LDA) and a novel transfer learning approach. Additionally, the experimental Raman spectra of delta-9-tetrahydrocannabinol (Δ9-THC), cannabidiol (CBD), and cannabichromene (CBC) were compared to simulated spectra from density functional theory calculations (DFT) to connect the spectral features to the underlying vibrational motions. A microscopical approach enabled the determination of the optimal sampling areas to collect Raman spectra for the nonacidic and acidic cannabinoids. An initial visualization of the data using principal component analysis (PCA) confirmed the spectral differences observable by visual comparisons of the spectra of the cannabinoid standards. The application of LDA implemented with a 5-fold cross-validation with 10 repeats, resulted in a classification accuracy of 99.83 %. For the transfer learning approach, the artificial intelligence (AI) model training was conducted in less than 10 min in a graphical processing unit (GPU) environment. All seven cannabinoids were successfully classified into respective classes based on scalograms transformed from Raman spectra, with 100 % classification accuracy. The average prediction probability for correct classification was 99.31 %. The classification outcome provided by the AI model included both prediction labels and probability, which provided a comprehensive evaluation of the samples.

Paper-based analytical devices (PADs) are becoming increasingly popular for drug analysis in various matrices. These affordable, portable platforms come with a range of benefits, such as their user-friendly design, straightforward operation, and minimal need for sample preparation. This study evaluates the potential applications of PADs in drug testing with a particular emphasis on the adaptability of PADs in terms of their ability to handle various types of samples during the testing process. In this study, the design, manufacturing, and performance of PADs for drug detection, as well as the influence that different biological samples have on the performance of PAD performance were investigated. PADs are discussed in this paper, with respect to their applications in forensic sample analysis, point-of-care testing and illicit drug testing. The study demonstrates the flexibility of PADs as platforms for drug testing in varied matrices. The authors highlight the fact that PADs are inexpensive, portable, user-friendly, and need minimum sample preparation, which makes them effective instruments in a variety of situations, including research, law enforcement, and healthcare. On the whole, this paper highlights the versatility of PADs, emphasizing their role as adaptable platforms for drug testing within different matrices. Scientists and professionals can utilize these cutting-edge devices to improve the efficiency and accessibility of drug analysis.

To quantify phytocannabinoids in hemp, liquid chromatography diode array detector (LC-DAD) methods are favored, but their selectivity depends on baseline separation of all phytocannabinoids and unknown compounds in an extract. Therefore, development of a LC-DAD method with a different selectivity has become highly desirable. Currently, most LC-DAD methods use the water/acetonitrile eluting system, while this study aimed to use the water/methanol eluting system. A systematic investigation of various chromatographic parameters on LC separation of eighteen phytocannabinoids, the maximum number that has been quantified in hemp so far, plus two potential internal standards, led to a four-step isocratic mobile phase that was able to baseline separate the twenty compounds with a significantly different eluting order from published methods. Although changes in the mobile phase composition caused baseline drifts, consequent difficulty in quantification was avoided through detection at wavelengths longer than 230 nm. Subsequently, the method was validated according to the ISO 17025 guidelines, calibrated between 0.04 and 50 µg/mL, and used to analyze phytocannabinoids in nine strains of hemp flowers that were extracted using methanol between 0.04 and 50 % (w/w). Extraction recovery was tracked in real-time by spiking one of the two potential internal standards, i.e., abnormal cannabidiol (ACBD), a cannabinoid not naturally present in hemp. Method selectivity was further assessed by electrospray ionization time-of-flight mass spectrometry (ESI/TOFMS), indicating minimum interferences. In addition, five untargeted/unknown phytocannabinoids were identified by ESI/TOFMS, including two structural isomers of Δ⁹-tetrahydrocannabinol (Δ⁹-THC), two structural isomers of Δ⁹-tetrahydrocannabinolic acid (Δ⁹-THCA), and one structural isomer of Δ⁹-THC acetate.

Poisoning metabolites within mushrooms typically exert varying physiological and biological effects; however, their distributions within mushrooms are seldom investigated. This study focused on imaging illudin S, a representative poisoning metabolite within Omphalotus illudens mushroom, using desorption electrospray ionization–tandem mass spectrometry (DESI–MS/MS) coupled with liquid chromatography–electrospray ionization–tandem mass spectrometry (LC–ESI–MS/MS). Initially, LC–ESI–MS/MS conditions were established for the selective detection of illudin S (m/z 247→159, positive mode) from extracted mushroom. Based on this MS/MS condition, each mushroom was divided into nine sections, and each section was subjected to specific DESI–MS/MS mode imaging (pixel size: 200 × 200 μm). The results revealed high concentrations of illudin S in the stalks of the mushrooms growing on trees. Further, re-evaluations using the LC–MS/MS assay revealed a seven-fold difference in the illudin S between the stalk and other regions. This study marks the first attempt at assessing toxic metabolite distributions within poisonous mushrooms, thus offering valuable insights including the efficient utilization of illudin analogs in biological applications.

Almond oils extracted from roasted kernels at different roasting times at 150 °C were analyzed to quantify quality parameters such as acidity, peroxide value, K₂₃₂, K₂₇₀, antioxidant activity and the oxidative stability index. The roasting process induced oxidation of the chemical compounds in the oil, resulting in increased acidity, peroxide value, K₂₃₂ and K₂₇₀. The antioxidant activity exhibited a decreasing trend over time, while the oxidative stability showed only slight changes. Excitation-emission matrices (EEMs) were directly scanned on almond oil samples. The combination of the EEMs with parallel factor analysis (PARAFAC) provided qualitative information about the main fluorophores and their evolution with the roasting time. Quantitative information was obtained using unfolded partial least squares (U-PLS), demonstrating the effectiveness of the fluorescence technique in combination with multivariate analysis to quantify analytical parameters in almond oils. Prediction models were developed, and subjected to external validation. The coefficients of determination in the external validation were higher than 0.94 for all parameters except k270.

Eu-MOFs have sparked widespread interest as a dependable, convenient, time-saving, highly sensitive, and specialised platform for analytical applications due to their strong skeletons, extremely high surface area, changeable pore sizes, easy functionalization, and great stability. This review will provide remarkable insight into the synthesis, structure, and properties of Eu-MOFs. Here, we will also focus on Eu-MOF applications as chemosensors, biosensors, selective gas adsorption, and chromatographic stationary phases in order to meet the needs of the materials used for analysis. Hence, this study will offer a comprehensive overview of existing research on the analytical applications of Eu-MOFs. We expect that this study will provide complete knowledge for researchers to recreate the procedures and contribute to future discoveries in this prominent field.

Designer fuel fraud consists in the smuggling of modified diesel blends as engine lubricant oils and their illegal trade avoiding payment of the excise duty applied to energy products. The fraudulent mixture contains regular diesel fuel plus a heavier hydrocarbon fraction, originating from waste automotive lubricant or cheap, residual base oils.

Raman spectroscopy was tested as a rapid in-situ screening method to separate regular diesel fuel samples from those suspected to contain a heavier component, and thus demanding a more extensive characterization. The Raman fingerprint region from the screened sample is matched to purposely created spectral libraries of compliant and non-compliant diesel fuels using the instrumental search algorithm. Overall, 177 compliant fuel samples and 28 non-compliant samples (all designer fuels with a confirmed heavier fraction and/or anomalous distillation parameters) were measured. The designer fuels were all positively identified, with ∼18 % false positives.

Subsequently, the Raman data-set was studied by Principal Component Analysis (PCA) and then classified as either compliant or non-compliant using Linear Discriminant Analysis (LDA). PCA using up to three principal components for data visualization shows only an incipient separation but still a partial overlap between compliant and non-compliant samples. LDA, on the opposite, performed superiorly in the binary classification task, with no false negatives and less than 4 % false positives.

A novel and major cannabinoid (epicannabidiol hydrate) present in hemp plants and oils was isolated and characterized by a combination of flash chromatography and liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (LC/Q-TOF-MS). This novel cannabinoid differs from cannabidiol (CBD) by the absence of a double bond in the terpene ring and the presence of an additional hydroxyl group in the same terpene ring. The isolation procedure involved the use of normal phase chromatography with a silica gel preparative column, followed by reversed phase chromatography with a C18 preparative column. In this way, other major cannabinoids present in the samples, such as cannabidiol and Δ9-tetrahydrocannabinol, were separated and the focus was placed on the novel cannabinoid compound. Exact accurate masses were obtained for the compound of interest at m/z = 333.2424 in positive ion mode and m/z = 331.2279 in negative ion mode. Additional MS-MS analysis in negative ion mode revealed the position of the additional hydroxyl group in the molecule. Finally, the structural characterization was corroborated with ¹H NMR and ¹³C NMR analysis, thus verifying the exact chemical structure of this novel cannabinoid, which has not previously been reported in hemp samples.