Lateral flow assays (LFAs) have been extensively explored in nucleic acid testing (NAT) for medical diagnostics, food safety analysis and environmental monitoring. However, the amount of target nucleic acid in a raw sample is usually too low to be directly detected by LFAs, necessitating the process of amplification. Even though cost-effective paper-based amplification techniques have been introduced, they have always been separately performed from LFAs, hence increasing the risk of reagent loss and cross-contaminations. To date, integrating paper-based nucleic acid amplification into colorimetric LFA in a simple, portable and cost-effective manner has not been introduced. Herein, we developed an integrated LFA with the aid of a specially designed handheld battery-powered system for effective amplification and detection of targets in resource-poor settings. Interestingly, using the integrated paper-based loop-mediated isothermal amplification (LAMP)-LFA, we successfully performed highly sensitive and specific target detection, achieving a detection limit of as low as 3 × 10(3) copies of target DNA, which is comparable to the conventional tube-based LAMP-LFA in an unintegrated format. The device may serve in conjunction with a simple paper-based sample preparation to create a fully integrated paper-based sample-to-answer diagnostic device for point-of-care testing (POCT) in the near future.
A shelf-stable loop-mediated isothermal amplification (LAMP) reagent for Burkholderia pseudomallei detection is described. The coupling of LAMP reagents with the indirect colorimetric indicator and consequently its lyophilization enable the simple evaluation of results without the need for any advance laboratory instruments. The reagents were found to have a stable shelf life of at least 30 days with well-maintained sensitivity and specificity.
Gold nanorods (Au NRs) are elongated nanoparticles with unique optical properties which depend on their shape anisometry. The Au NR-based longitudinal localized surface plasmon resonance (longitudinal LSPR) band is very sensitive to the surrounding local environment and upon the addition of target analytes, the interaction between the analytes and the surface of the Au NRs leads to a change in the longitudinal LSPR band. This makes it possible to devise Au NR probes with application potential to the detection of toxic metal ions with an improved limit of detection, response time, and selectivity for the fabrication of sensing devices. The effective surface modification of Au NRs helps in improving their selectivity and sensitivity toward the detection of toxic metal ions. In this review, we discuss different methods for the preparation of surface modified Au NRs for the detection of toxic metal ions based on the LSPR band of the Au NRs and the types of interactions between the surface of Au NRs and metal ions. We summarize the work that has been done on Au NR-based longitudinal LSPR detection of environmentally toxic metal ions, sensing mechanisms, and the current progress in various modified Au NR-based longitudinal LSPR sensors for toxic metal ions. Finally, we discuss the applications of Au NR-based longitudinal LSPR sensors to real sample analysis and some of the future challenges facing longitudinal LSPR-based sensors for the detection of toxic metal ions toward commercial devices.
Reporting biomolecular interactions has become part and parcel of many applications of science towards an in-depth understanding of disease and gene regulation. Apart from that, in diagnostic applications where biomolecules (antibodies and aptamers) are vastly applied, meticulous monitoring of biomolecular interaction is vital for clear-cut diagnosis. Several currently available methods of analyzing the interaction of the ligands with the appropriate analytes are aided by labeling using fluorescence or luminescence techniques. However, labeling is cumbersome and can occupy important binding sites of interactive molecules to be labeled, which may interfere with the conformational changes of the molecules and increase non-specificity. Optical-based sensing can provide an alternative way as a label-free procedure for monitoring biomolecular interactions. Optical sensors affiliated with different operating principles, including surface plasmon changes, scattering and interferometry, can impart a huge impact for in-house and point-of-care applications. This optical-based biosensing permits real-time monitoring, obviating the use of hazardous labeling molecules such as radioactive tags. Herein, label-free ways of reporting biomolecular interactions by various optical biosensors were gleaned.
Mucins and mucin-type glycoproteins, collectively referred to as mucin-type O-glycans, are implicated in many important biological functions and pathological conditions, including malignancy. Presently, there is no reliable method to measure the total mucin-type O-glycans of a sample, which may contain one or more of these macromolecules of unknown structures. We report the development of an improved microassay that is based on the binding of lectins to the unique and constant GalNAc-Ser/Thr structural feature of mucin-type O-glycans. Since the sugar-amino acid linkage in the mucin-type O-glycans is invariably cryptic, we first chemically removed the heterogeneous peripheral and core saccharides of model glycoconjugates before examining for their interactions using an enzyme-linked lectin assay (ELLA). Desialylation of the model glycoconjugates led to maximal binding of the lectins but additional treatments such as Smith degradation did not result in increased binding. Of the lectins tested for their ability to probe the desialylated O-glycans, jacalin showed the highest sensitivity followed by champedak galactose binding (CGB) lectin and Vicia villosa agglutinin. Further improvement in the sensitivity of ELLA was achieved by using microtiter plates that were pre-coated with the CGB lectin, which increased the specificity of the assay to mucin-type O-glycans. Finally, the applicability of the developed sandwich ELLA to crude samples was demonstrated by estimating trace quantities of the mucin-type O-glycans in the human serum.
The discovery that synthetic short chain nucleic acids are capable of selective binding to biological targets has made them to be widely used as molecular recognition elements. These nucleic acids, called aptamers, are comprised of two types, DNA and RNA aptamers, where the DNA aptamer is preferred over the latter due to its stability, making it widely used in a number of applications. However, the success of the DNA selection process through Systematic Evolution of Ligands by Exponential Enrichment (SELEX) experiments is very much dependent on its most critical step, which is the conversion of the dsDNA to ssDNA. There is a plethora of methods available in generating ssDNA from the corresponding dsDNA. These include asymmetric PCR, biotin-streptavidin separation, lambda exonuclease digestion and size separation on denaturing-urea PAGE. Herein, different methods of ssDNA generation following the PCR amplification step in SELEX are reviewed.
Salbutamol ¿2-(tert-butylamino)-1-[4-hydroxy-3- (hydroxymethyl)phenyl]ethanol¿, also known as albuterol, is clinically the most widely used beta 2-adrenoceptor agonist in the treatment of bronchial asthma. During this study, we evaluated liquid-liquid extraction (LLE) and solid-phase extraction (SPE) in order to develop a reliable extraction method followed by analysis using liquid chromatography and gas chromatography. An assay is described which involves SPE as the clean-up method followed by gas chromatography-mass spectrometry to determine salbutamol levels in human serum after oral administration. The SPE method requires the use of a hyper-cross-linked styrene-divinylbenzene bonded phase (ENV+) without involving any sample pre-treatment to obtain 60-65% recoveries for salbutamol and terbutaline as the internal standard. Distilled water and 1% trifluoroacetic acid in methanol were found to be the most suitable washing solvent and eluting solvent, respectively. A detection limit of 2 ng mL-1 was achieved by derivatization with N-methyl-N-trimethylsilyltrifluoroacetamide to form trimethylsilyl (TMS)-salbutamol (m/z 369) and TMS-terbutaline (m/z 356). The relationship between the ratio of the peak area of salbutamol to that of the internal standard and concentration was linear for the range tested (2-200 ng mL-1) and the correlation of coefficient was 0.9999 with a y-intercept not significantly different from zero. The inter-day relative standard deviation (RSD) was < 10% for all three concentrations. The intra-day RSD was 14% for 2 ng mL-1. This assay was then successfully applied to human serum samples obtained from clinical trials after oral administration of salbutamol.
Solid-phase extraction (SPE) of organochlorine pesticide residues from environmental water samples was evaluated using octadecyl (C18)-bonded porous silica. The efficiency of SPE of these pesticide residues from reagent water samples at 1-5 micrograms dm-3 levels was compared with those obtained by solvent extraction with hexane and Freon TF (trichlorotrifluoroethane). Average recoveries exceeding 80% for these organochlorine pesticides were obtained via the SPE method using small cartridges containing 100 mg of 40 microns C18-bonded porous silica. The average recovery by solvent extraction with hexane and Freon TF exceeded 90% in both instances. It was concluded that the recoveries and precision for the SPE of organochlorine pesticides were poorer than those for the solvent extraction method. Organochlorine pesticide residue levels in environmental water samples from two major rivers flowing through predominantly rice-growing areas were monitored by gas chromatography using the solvent extraction method with hexane. Exceptionally high levels of organochlorine pesticide residues such as BHC, DDT, heptachlor, endosulfan and dieldrin were found in these water samples.
Lactate measurement is vital in clinical diagnostics especially among trauma and sepsis patients. In recent years, it has been shown that saliva samples are an excellent applicable alternative for non-invasive measurement of lactate. In this study, we describe a method for the determination of lactate concentration in saliva samples by using a simple and low-cost cotton fabric-based electrochemical device (FED). The device was fabricated using template method for patterning the electrodes and wax-patterning technique for creating the sample placement/reaction zone. Lactate oxidase (LOx) enzyme was immobilised at the reaction zone using a simple entrapment method. The LOx enzymatic reaction product, hydrogen peroxide (H2O2) was measured using chronoamperometric measurements at the optimal detection potential (-0.2 V vs. Ag/AgCl), in which the device exhibited a linear working range between 0.1 to 5 mM, sensitivity (slope) of 0.3169 μA mM(-1) and detection limit of 0.3 mM. The low detection limit and wide linear range were suitable to measure salivary lactate (SL) concentration, thus saliva samples obtained under fasting conditions and after meals were evaluated using the FED. The measured SL varied among subjects and increased after meals randomly. The proposed device provides a suitable analytical alternative for rapid and non-invasive determination of lactate in saliva samples. The device can also be adapted to a variety of other assays that requires simplicity, low-cost, portability and flexibility.
Biosensor chips for immune-based assay systems have been investigated for their application in early diagnostics. The development of such systems strongly depends on the effective protein immobilization on polymer substrates. In order to achieve this complex heterogeneous interaction the polymer surface must be functionalized with chemical groups that are reactive towards proteins in a way that surface functional groups (such as carboxyl, -COOH; amine, -NH2; and hydroxyl, -OH) chemically or physically anchor the proteins to the polymer platform. Since the proteins are very sensitive towards their environment and can easily lose their activity when brought in close proximity to the solid surface, effective surface functionalization and high level of control over surface chemistry present the most important steps in the fabrication of biosensors. This paper reviews recent developments in surface functionalization and preparation of polymethacrylates for protein immobilization. Due to their versatility and cost effectiveness, this particular group of plastic polymers is widely used both in research and in industry.
The development of molecularly imprinted polymer nanoparticles (MIP-NPs), which specifically bind biomolecules, is of great interest in the area of biosensors, sample purification, therapeutic agents and biotechnology. Polymerisation techniques such as precipitation polymerisation, solid phase synthesis and core shell surface imprinting have allowed for significant improvements to be made in developing MIP-NPs which specifically recognise proteins. However, the development of MIP-NPs for protein templates (targets) still require lengthy optimisation and characterisation using different ratios of monomers in order to control their size, binding affinity and specificity. In this work we successfully demonstrated that differential scanning fluorimetry (DSF) can be used to rapidly determine the optimum imprinting conditions and monomer composition required for MIP-NP design and polymerisation. This is based on the stability of the protein template and shift in apparent melting points (Tm) upon interaction with different functional acrylic monomers. The method allows for the characterisation of molecularly imprinted nanoparticles (MIP-NPs) due to the observed differences in melting point profiles between, protein-MIP-NPs complexes, pre-polymerisation mixtures and non-imprinted nanoparticles (NIP-NPs) without the need for prior purification. The technique is simple, rapid and can be carried out on most quantitative polymerase chain reaction (qPCR) thermal cyclers which have the required filters for SYPRO
Partial least squares-discriminant analysis (PLS-DA) is a versatile algorithm that can be used for predictive and descriptive modelling as well as for discriminative variable selection. However, versatility is both a blessing and a curse and the user needs to optimize a wealth of parameters before reaching reliable and valid outcomes. Over the past two decades, PLS-DA has demonstrated great success in modelling high-dimensional datasets for diverse purposes, e.g. product authentication in food analysis, diseases classification in medical diagnosis, and evidence analysis in forensic science. Despite that, in practice, many users have yet to grasp the essence of constructing a valid and reliable PLS-DA model. As the technology progresses, across every discipline, datasets are evolving into a more complex form, i.e. multi-class, imbalanced and colossal. Indeed, the community is welcoming a new era called big data. In this context, the aim of the article is two-fold: (a) to review, outline and describe the contemporary PLS-DA modelling practice strategies, and (b) to critically discuss the respective knowledge gaps that have emerged in response to the present big data era. This work could complement other available reviews or tutorials on PLS-DA, to provide a timely and user-friendly guide to researchers, especially those working in applied research.
Preparation of selective magnetic adsorbents for dispersive micro-solid phase extraction often involves multi-step reactions which are time consuming. This study demonstrates a simplified method for the synthesis of a magnetic adsorbent, which is selective towards the adsorption of mercury(ii) ions (Hg2+). In this method, the incorporation of a metal capturing ligand (3-oxo-1,3-diphenylpropyl-2-(naphthalen-2-ylamino) ethylcarbamodithioate) and the coating of magnetic particles with silica gel was performed in a single step. This adsorbent was then used in solid-phase microextraction for the preconcentration of Hg2+ in water. In this study, a mercury analyzer was used to quantify the Hg2+. Under optimized conditions, the developed analytical method achieved a low detection limit (4.0 ng L-1), satisfactory enrichment factor (96.4) and wide linearity range (50.0-5000 ng L-1) with a good coefficient of determination (0.9985) and good repeatability (<7%). The preconcentration factor of this method was 100. This proposed method was also successfully utilized for the determination of Hg2+ in drinking water, tap water and surface water with good recovery (>91%) and high intra-day and inter-day precision.
In response to our review paper [L. C. Lee et al., Analyst, 2018, 143, 3526-3539], we present a study that compares empirical differences between PLS1-DA and PLS2-DA algorithms in modelling a colossal ATR-FTIR spectral dataset. Over the past two decades, partial least squares-discriminant analysis (PLS-DA) has gained wide acceptance and huge popularity in the field of applied research, partly due to its dimensionality reduction capability and ability to handle multicollinear and correlated variables. To solve a K-class problem (K > 2) using PLS-DA and high-dimensional data like infrared spectra, one can construct either K one-versus-all PLS1-DA models or only one PLS2-DA model. The aim of this work is to explore empirical differences between the two PLS-DA algorithms in modeling a colossal ATR-FTIR spectral dataset. The practical task is to build a prediction model using the imbalanced, high dimensional, colossal and multi-class ATR-FTIR spectra of blue gel pen inks. Four different sub-datasets were prepared from the principal dataset by considering the raw and asymmetric least squares (AsLS) preprocessed forms: (a) Raw-global region; (b) Raw-local region; (c) AsLS-global region; and (d) AsLS-local region. A series of 50 models which includes the first 50 PLS components incrementally was constructed repeatedly using the four sub-datasets. Each model was evaluated using six different variants of v-fold cross validation, autoprediction and external testing methods. As a result, each PLS-DA algorithm was represented by a number of figures of merit. The differences between PLS1-DA and PLS2-DA algorithms were assessed using hypothesis tests with respect to model accuracy, stability and fitting. On the other hand, confusion matrices of the two PLS-DA algorithms were inspected carefully for assessment of model parsimony. Overall, both the algorithms presented satisfactory model accuracy and stability. Nonetheless, PLS1-DA models showed significantly higher accuracy rates than PLS2-DA models, whereas PLS2-DA models seem to be much more stable compared to PLS1-DA models. Eventually, PLS2-DA also proved to be less prone to overfitting and is more parsimonious than PLS1-DA. In conclusion, the relatively high accuracy of the PLS1-DA algorithm is achieved at the cost of rather low parsimony and stability, and with an increased risk of overfitting.