Multicomponent Assay Research Articles

Synergistic effect in simultaneous adsorption of cationic and anionic emerging contaminants by chitosan aerogels containing nanocellulose-modified tannic acid

Emerging contaminants (ECs) are present in the environment and can affect aquatic fauna and flora. Due to the difficulty in removing ECs from water, new technologies must be developed. Most studies report the adsorption of ECs in single systems and few works have investigated the uptake in multicomponent systems, which limits the applicability of adsorbents for real samples which usually contain different ECs. In this work, chitosan aerogel beads containing crystalline nanocellulose and modified tannic acid were synthesized and employed in the adsorption, in multicomponent systems, of the emerging contaminants VBB dye, CPC surfactant, and SC drug. The maximum removal potential in single systems was 1407, 698, and 934 mg·g−1 for VBB, CPC, and SC, respectively. The aerogels demonstrated high efficiency in the elimination of contaminants in real samples (>99 %). The CS@NC–mTA beads were able to remove the RR250 and RO122 anionic dyes in multicomponent systems, which were not adsorbed in single system, and achieved elevated removal capacities of 298 and 311 mg·g−1, respectively. In multicomponent assays with cationic contaminants, a synergistic effect was also observed, and the uptake of SC and CPC increased in 209.6 % and 40.9 % in the presence of the VBB dye. For the first time, a synergistic effect on the adsorption of cationic contaminants is reported. XPS analyses and adsorption data suggest that electrostatic, π-π, cation-exchange and H-bonds play important role in the process. These results indicate the great potential of the CS@NC–mTA beads in the water treatment for real samples.

Unravelling the Affinity of Alkali-Activated Fly Ash Cubic Foams towards Heavy Metals Sorption.

In this work, alkali-activated fly ash-derived foams were produced at room temperature by direct foaming using aluminum powder. The 1 cm3 foams (cubes) were then evaluated as adsorbents to extract heavy metals from aqueous solutions. The foams’ selectivity towards lead, cadmium, zinc, and copper ions was evaluated in single, binary, and multicomponent ionic solutions. In the single ion assays, the foams showed much higher affinity towards lead, compared to the other heavy metals; at 10 ppm, the removal efficiency reached 91.9% for lead, 83.2% for cadmium, 74.6% for copper, and 64.6% for zinc. The greater selectivity for lead was also seen in the binary tests. The results showed that the presence of zinc is detrimental to cadmium and copper sorption, while for lead it mainly affects the sorption rate, but not the ultimate removal efficiency. In the multicomponent assays, the removal efficiency for all the heavy metals was lower than the values seen in the single ion tests. However, the superior affinity for lead was preserved. This study decreases the existing knowledge gap regarding the potential of alkali-activated materials to act as heavy metals adsorbents under different scenarios.

Competitive kinetics of adsorption onto activated carbon for emerging contaminants with contrasting physicochemical properties.

Activated carbon (AC) can be used for the removal of emerging contaminants (e.g., drugs) in water and wastewater treatment plants. In the present study, we investigated the performance of two ACs (from coconut shell and Pinnus sp.) in the adsorption of caffeine, carbamazepine, and ricobendazole considering the compounds separately and in combination in batch-scale experiments. The concentrations of the drugs were determined by a validated method using solid-phase extraction with on-line ultra-high performance liquid chromatography-tandem mass spectrometry. The most mesoporous AC provided higher drug removal. The kinetic data were described by the pseudo-second-order, Elovich, and Weber-Morris models, while the adsorption isotherms showed a better fit to the Freundlich model, indicative of multilayer adsorption. The Dubinin-Radushkevich model was used as a first approach to estimate the mean adsorption energy (E) and the results indicate that chemisorption governed the adsorption process, with E higher than 8 kJ mol-1. In the multicomponent assays, the adsorption of caffeine showed the greatest hindrance caused by the presence of other drugs. Multicomponent assays are fundamental to evaluate the potential adsorption capacity in real water treatment plants. Our study suggests that drugs with different structures and physicochemical properties may interact differently with ACs, especially in multicomponent solutions, with important implications for the design (e.g., volumes and areas of treatment plants) and operation (e.g., water residence time) of the treatment plants.

A combined gene expression tool for parallel histological prediction and gene fusion detection in non-small cell lung cancer

Accurate histological classification and identification of fusion genes represent two cornerstones of clinical diagnostics in non-small cell lung cancer (NSCLC). Here, we present a NanoString gene expression platform and a novel platform-independent, single sample predictor (SSP) of NSCLC histology for combined, simultaneous, histological classification and fusion gene detection in minimal formalin fixed paraffin embedded (FFPE) tissue. The SSP was developed in 68 NSCLC tumors of adenocarcinoma (AC), squamous cell carcinoma (SqCC) and large-cell neuroendocrine carcinoma (LCNEC) histology, based on NanoString expression of 11 (CHGA, SYP, CD56, SFTPG, NAPSA, TTF-1, TP73L, KRT6A, KRT5, KRT40, KRT16) relevant genes for IHC-based NSCLC histology classification. The SSP was combined with a gene fusion detection module (analyzing ALK, RET, ROS1, MET, NRG1, and NTRK1) into a multicomponent NanoString assay. The histological SSP was validated in six cohorts varying in size (n = 11–199), tissue origin (early or advanced disease), histological composition (including undifferentiated cancer), and gene expression platform. Fusion gene detection revealed five EML4-ALK fusions, four KIF5B-RET fusions, two CD74-NRG1 fusion and three MET exon 14 skipping events among 131 tested cases. The histological SSP was successfully trained and tested in the development cohort (mean AUC = 0.96 in iterated test sets). The SSP proved successful in predicting histology of NSCLC tumors of well-defined subgroups and difficult undifferentiated morphology irrespective of gene expression data platform. Discrepancies between gene expression prediction and histologic diagnosis included cases with mixed histologies, true large cell carcinomas, or poorly differentiated adenocarcinomas with mucin expression. In summary, we present a proof-of-concept multicomponent assay for parallel histological classification and multiplexed fusion gene detection in archival tissue, including a novel platform-independent histological SSP classifier. The assay and SSP could serve as a promising complement in the routine evaluation of diagnostic lung cancer biopsies.

Research on Quality Markers of Moutan Cortex: Quality Evaluation and Quality Standards of Moutan Cortex

ObjectiveTo identify the quality markers of Moutan Cortex (MC) and establish the quality evaluation methods for multi-component assay and fingerprinting of MC. MethodsThe chemical constituents in MC were identified by HPLC-QTOF-MS. UPLC was employed for the multi-component assay and fingerprinting of MC. Furthermore, text mining was carried out to review the biosynthesis pathways and pharmacological and pharmacokinetic studies related to MC, and in silico target fishing was conducted to construct compound-target networks for MC. ResultsSixteen compounds were clearly identified in MC and their structures were confirmed through comparison with literature data. In addition, the biosynthetic pathways and component specificities of the identified compounds were summarized and confirmed by text mining. Pharmacological activities, including traditional usage and modern pharmacological studies were summarized. A total of 282 targets from Homo sapiens were fished for 13 compounds. In addition, pharmacokinetic studies of different compounds were synopsized. Finally, multi-component assay and fingerprint of MC were established. ConclusionEight major components are selected as quality markers of MC, such as oxypaeoniflorin, apiopaeonoside, albiflorin, paeonolide, paeoniflorin, 1,2,3,4,6-penta-O-galloyl-β-D-glucose, mudanpioside C and paeonol. These eight quality markers are successfully applied to the quality evaluation of MC, and could be useful in improving the current quality standards of MC.

Fluorescent probes for tracking the transfer of iron-sulfur cluster and other metal cofactors in biosynthetic reaction pathways.

Iron-sulfur (Fe-S) clusters are protein cofactors that are constructed and delivered to target proteins by elaborate biosynthetic machinery. Mechanistic insights into these processes have been limited by the lack of sensitive probes for tracking Fe-S cluster synthesis and transfer reactions. Here we present fusion protein- and intein-based fluorescent labeling strategies that can probe Fe-S cluster binding. The fluorescence is sensitive to different cluster types ([2Fe-2S] and [4Fe-4S] clusters), ligand environments ([2Fe-2S] clusters on Rieske, ferredoxin (Fdx), and glutaredoxin), and cluster oxidation states. The power of this approach is highlighted with an extreme example in which the kinetics of Fe-S cluster transfer reactions are monitored between two Fdx molecules that have identical Fe-S spectroscopic properties. This exchange reaction between labeled and unlabeled Fdx is catalyzed by dithiothreitol (DTT), a result that was confirmed by mass spectrometry. DTT likely functions in a ligand substitution reaction that generates a [2Fe-2S]-DTT species, which can transfer the cluster to either labeled or unlabeled Fdx. The ability to monitor this challenging cluster exchange reaction indicates that real-time Fe-S cluster incorporation can be tracked for a specific labeled protein in multicomponent assays that include several unlabeled Fe-S binding proteins or other chromophores. Such advanced kinetic experiments are required to untangle the intricate networks of transfer pathways and the factors affecting flux through branch points. High sensitivity and suitability with high-throughput methodology are additional benefits of this approach. We anticipate that this cluster detection methodology will transform the study of Fe-S cluster pathways and potentially other metal cofactor biosynthetic pathways.

A novel biosensor array with a wheel-like pattern for glucose, lactate and choline based on electrochemiluminescence imaging.

Electrochemiluminescence (ECL) imaging provides a superior approach to achieve array detection because of its ability for ultrasensitive multiplex analysis. In this paper, we reported a novel ECL imaging biosensor array modified with an enzyme/carbon nanotubes/chitosan composite film for the determination of glucose, choline and lactate. The biosensor array was constructed by integrating a patterned indium tin oxide (ITO) glass plate with six perforated poly(dimethylsiloxane) (PDMS) covers. ECL is generated by the electrochemical reaction between luminol and hydrogen peroxide that is produced by the enzyme catalysed oxidation of different substrates with molecular oxygen, and ECL images were captured by a charge-coupled device (CCD) camera. The separated electrochemical micro-cells enabled simultaneous assay of six samples at different concentrations. From the established calibration curves, the detection limits were 14 μM for glucose, 40 μM for lactate and 97 μM for choline, respectively. Moreover, multicomponent assays and cross reactivity were also studied, both of which were satisfied for the analysis. This biosensing platform based on ECL imaging shows many distinct advantages, including miniaturization, low cost, and multi-functionalization. We believe that this novel ECL imaging biosensor platform will have potential applications in clinical diagnostics, medicine and food inspection.

A Novel Strategy using Single Standards to Determine Multi-components in order to Quantify the Major Macrocyclin Diterpenoids and Triterpenes in the Roots of Euphobia kansui

It was hardly practical to control the quality of Chinese herbal medicines with different types of constituents, broad polarities, and a variety of UV absorptions utilizing a single standard to determine multi-components (SSDMC) method alone. To extend the application range of the SSDMC method, a new strategy was developed and named as WP-ERS-CSP-SSDMC. This new strategy combined three different techniques, namely wavelength program (WP), exogenous reference standard (ERS) and core-shell particle (CSP), to enhance the existing SSDMC method. Taking Kansui Radix (roots of Euphobia kansui) as an example, thirteen major components including three jatrophane diterpenoids, eight ingenane diterpenoids and two triterpenes which included three pairs of isomerides and three strong hydrophobic components were all simultaneously assayed using this new strategy with ethyl benzoate, a synthetic compound, employed as a single reference standard for the first time. A C8 column packed with 2.7 µm core-shell particles was optimized to separate these constituents in 25 minutes on a conventional HPLC instrument with a photodiode array detector on which a four-stage wavelength program was established. The WP-ERS-CSP-SSDMC method was validated to have the capacity to acquire more repeatable and consistent results than that obtained by traditional external standard methods. Through Plackett-Burmantest (PB) tests, eight primary chromatographic parameters of the method were investigated and it was found that the two factors, wavelength and flow rate, should be strictly restrained. At last 75 batches of Kansui Radix and its three different processing products were successfully analyzed and discriminated by applying the proposed method. This work provided a useful strategy for SSDMC method making the simultaneous multi-component assay of complex TCM system achievable, and an extensive application of multi-component quality control of TCM feasible.

DNAzyme-based microarray for highly sensitive determination of metal ions

A novel microarray for the multiplex determination of heavy metal ions in aqueous solution based on DNAzymes has been developed with good sensitivity and selectivity. In the present work, metal ion dependent DNAzymes of copper (Cu-Enz) and lead (Pb-Enz) were first associated with their corresponding DNA substrates (Cu-Sub and Pb-Sub) immobilized on the surface of aldehyde-coated slides. After introducing the corresponding metal ions, the DNA cleavage of the substrates caused by the DNAzymes took place, resulting in a dramatic change in fluorescent signal intensity. The proposed microarray method, which can be used as a multi-component assay with high efficiency, combines the high sensitivity and selectivity of DNAzymes with the high throughput and parallel analysis of microarray technology. The method reveals a sensitivity corresponding to 0.6ppb and 2ppb for Cu2+ and Pb2+, respectively, which is sufficient to detect them in drinking water. This approach may find potential applications in environmental monitoring, food safety monitoring, clinical toxicology, waste treatment, the cosmetic industry and industrial process monitoring.

Near-infrared spectroscopy: Promising diagnostic tool for viral infections

Although several methods, including enzyme-linked immunosorbent assay, polymerase chain reaction, immunofluorescent assay, and Western blotting, have been used for the diagnosis of viral infections, none of them is ideal in terms of cost-effectiveness, speed, and accuracy. Currently, the rate of outbreak of emerging viruses is increasing and therefore the development and establishment of analytical methods for such viral infections are becoming more important. Near-infrared (NIR) spectroscopy is a fast, multicomponent assay that enables non-invasive, non-destructive analysis. Recently, the diagnosis of viral infections using NIR spectroscopy has been attempted. In this review, the potential of the NIR method in the medical and virological fields is discussed.

Multicomponent Assay for Human Serum Using Mid-Infrared Transmission Spectroscopy Based on Component-Optimized Spectral Region Selected by a First Loading Vector Analysis in Partial Least-Squares Regression

Mid-infrared transmission spectroscopy with partial least-squares regression was used to determine the concentrations of blood components such as total protein, albumin, globulin, total cholesterol, HDL (high density lipoprotein) cholesterol, triglycerides, glucose, BUN (blood urea nitrogen), and uric acid in human serum. The optimal spectral region for each component was selected by first loading vector analysis. Positive peaks with positive value were assigned by first loading vector analysis. Because blood components in serum show a correlation among several components, a useful spectral region for predicting a particular component was selected such that its spectral feature was not overlapped by those of other components. Several regions with positive peaks by first loading vector were used to establish calibration models. The proposed method proved to be effective for a multicomponent assay and can also be used even when a single component spectrum in aqueous solution for all components is not known. Total protein, albumin, globulin, total cholesterol, triglycerides, and glucose have a mean percentage error of cross-validation (MPECV) of less than 5%. But HDL cholesterol, BUN, and uric acid have MPECVs between 12 and 18%. In terms of both the percentage error of cross-validation and clinically allowable error, six serum components, excepting HDL-cholesterol, BUN, and uric acid, were determined successfully.

Applicability of the Kubelka-Munk theory for the evaluation of reflectance spectra demonstrated for haemoglobin-free perfused heart tissue

Reflectance spectrometry is a useful tool for studying in vivo kinetic changes in the oxygen saturation of haemoglobin and myoglobin as well as the redox state of cytochromes. A method is given which allows the quantification of tissue reflectance spectra using multicomponent analysis. This method utilizes the Kubelka-Munk theory for modelling the measured tissue spectra. To test this approach, reflectance spectra of a haemoglobin-free perfused guinea pig heart were measured by a fast scanning spectrophotometer (100 spectra/s, spectral resolution 1.0 nm) and evaluated using the component absorbance spectra measured separately. A relative mean spectral residual error of 0.15% was achieved by least-squares fitting. Using statistical error propagation, oxygenation of myoglobin is obtained within a relative precision of 1%, and the redox state of cytochromes aa3 and c are determined simultaneously within a margin of 3%; the results for the redox-state of cytochrome b, however, are less precise. Special component error functions are presented to provide a reliability measure for the concentration prediction using this multicomponent assay. The consistency of the theory and the component absorptivity data is tested by regressing the actual concentrations obtained for each of the redox pair components during the various states of tissue oxygenation. A method is described for the recognition and reduction of systematic errors.

Multivariate calibration for near-infrared spectroscopic assays of blood substrates in human plasma based on variable selection using PLS-regression vector choices

A multicomponent assay for the blood substrates of total protein, glucose, total cholesterol, triglycerides and urea in human EDTA-plasma by FT-IR spectroscopy is described based on near-infrared spectra of human plasma recorded in a 1 mm quartz transmission cell. Partial least-squares was applied for multivariate calibration taking into account absorbance or logarithmized single beam spectra. Further data reduction was applied using the pairwise selection of spectral variables suggested by the weights of the optimum full spectrum PLS-regression vector. The standard errors of prediction for protein, cholesterol, triglycerides, glucose and urea are calculated by cross-validation for the population of 124 plasma samples of different patients. These values are compared for full spectrum and reduced spectral variable set regression.

Clinical Chemistry and near Infrared Spectroscopy: Multicomponent Assay for Human Plasma and its Evaluation for the Determination of Blood Substrates

A multicomponent assay for the blood substrates of total protein, glucose, total cholesterol, triglycerides and urea in human EDTA-plasma by FT-IR spectroscopy is described. Transmission near infrared spectra of plasma were recorded using a 1 mm cell. Partial least-squares was applied for multivariate calibration taking into account optimising the spectral ranges of absorbance or logarithmised single beam spectra. The standard errors of prediction for the population of 124 plasma samples of different patients calculated by cross-validation are in the range of clinical acceptance for protein, cholesterol and triglycerides, whereas glucose assay performance is slightly above the recommended level. The relative standard error of prediction for urea is 12.7% based on the mean concentration of the population studied. Within an error variance analysis, the contributions of the reference method and the spectrometric measurement to the average (RMS) prediction error have been estimated for each substrate giving evidence of the spectrometric method limitations. The potential for further improvement is discussed.

Comparison of different infrared measurement techniques in the clinical analysis of biofluids

An infrared spectroscopy-based multicomponent assay of biofluids such as whole blood, urine, and synovial fluid holds considerable promise for the clinical laboratory. The methodology is reagentless, fast and readily automated. Infrared spectroscopic analysis is not hindered by the physical state of the sample and measurements can be made in a variety of instrument configurations. In this review we present the spectra of physiological samples measured as liquids and dried films using transmission-, attenuated total reflection-, photoacoustic-and diffuse reflectance infrared spectroscopy. The advantages and limitations of each of these techniques are discussed in the context of developing a routine clinical analysis method for biofluid samples.

Multivariate calibration for physiological samples using infrared spectra with choice of different intensity data

A multicomponent assay of several blood substrates is presented for human plasma using mid-infrared spectra recorded by the attenuated total reflection technique. Two different sample populations were analysed: a pool plasma spiked with different amounts of glucose and a hospital population of samples from 126 different patients. Partial least-squares was used for multivariate calibration based on spectral intervals in the fingerprint region selected for optimum prediction modeling. Different data, either plasma absorbance spectra with compensation of the water signal or single beam spectra, were considered. Results are reported for protein, glucose, cholesterol, triglycerides and urea.

Multicomponent Assay for Blood Substrates in Human Plasma by Mid-Infrared Spectroscopy and its Evaluation for Clinical Analysis

An analytical multicomponent method for the blood substrates total protein, glucose, total cholesterol, triglycerides, and urea in human EDTA plasma by FT-IR spectroscopy is described. The spectra were obtained with the use of the attenuated total reflection technique. Partial least-squares was applied for multivariate calibration over optimized spectral ranges. The mean-square prediction errors for the population of 126 plasma samples of different patients calculated by cross-validation are in the range of clinical acceptance. Within an error variance analysis, the contributions of the reference method and the spectrometric measurement to the average (root mean square) prediction error have been estimated for each substrate, giving evidence of the limitations of the spectrometric method. The problem of the biocompatibility of the plasma has been investigated, and the protein adsorption onto the ATR crystal can be reduced to a constant and tolerable level by appropriate cleaning and rinsing. The potential for further improvement is discussed.

Forgetting factor algorithm and its application to the analysis of compound dosage forms

A forgetting factor algorithm was evaluated and introduced in the simultaneous multi-component assay of mixture. Three kinds of preparation, a compound injection of antipyrine, a three-acid powder and a four-acid powder, were analysed with satisfactory results. Estimates obtained by Kalman filtering and a discussion of the relationship between the forgetting factor algorithm and Kalman filtering are also given.

Application of Kalman filter spectrophotometry in the analysis of pharmaceutical preparations — I. Sulphamethoxypyrazine and trimethroprim in sulpha compound tablets

The treatment of measurement data to obtain results is crucial to analytical chemistry. As all real signals contain noise, techniques are necessary to extract the useful information from the noisy signal. Various filters are available and of these the Kalman filter offers a robust, simple way of treating noisy signals in a variety of situations. The filter has its origins in control theory [l-3] but has been applied to chemical systems in recent years. Seelig and Blount [4, 51 have applied the filter to anodic stripping voltammetry. Poulisse [6-81 has described the application of a filter to multicomponent assays using UV spectrophotometry, and Brown et al. [9-111 have applied a similar approach to overlapping electrochemical responses. The simultaneous assay of several components in a multicomponent mixture is increasingly important in analytical chemistry because of the simplifications it introduces in sample preparation. Although conventional multiple linear regression is used extensively in these situations, the Kalman filter approach offers an alternative with excellent performance. The assay of Sulphamethoxypyrazine compound tablets is used as an example of the application of a multicomponent Kalman filter in this paper.

Quantitative, multicomponent analysis of fatty acids from cholesteryl esters by chemical ionization reconstructed mass chromatography.

Reconstructed mass chromatography using methane as a carrier and reagent gas for chemical ionization gas chromatography-mass spectrometry of the derived methyl esters allows rapid, quantitative microdeterminations of complete cholesteryl ester fatty acid profiles. The sensitivity of this method is consistent with completely specific, multicomponent assay at the picomole level. Introduction of two homologues as internal standards, one into the intact biological specimen and the other after derivatization, provides a measure of the net efficiency of the processes of extraction and derivatization. This procedure may be extended readily to the determination of fatty acid profiles in most biological fluids.