Recent Advances in Silver and Gold Nanoparticles-Based Colorimetric Sensors for Heavy Metal Ions Detection: A Review.

Silvetr and gold nanoparticles-based colorimetric sensors (Ag/Au-NPs-CSns) allow potential prospects for the development of efficient sensors owing to their unique shape- and size-dependent optical properties. In this review, recent (2020) advances in morphology-controllable synthesis, shape/size-dependent performance, sensing mechanism, challenges and prospects of Ag/Au-NPs-CSns for the detection of heavy metals are discussed. The size/shape-controlled synthesis of innovative Ag/Au-NPs-CSns is reviewed critically and the possible role of different parameters like temperature, time, pH, stabilizing/capping agents, reducing agents and concentration/nature of precursors are discussed. Then, we highlighted how the shape, size, optimum composition, functionalization, stabilizing/capping agents, surface structure and synergism influence the optical properties and sensing efficiency of Ag/Au-NPs-CSns. This review attempted to accentuate the efficiency of novel multimetallic Ag/AuNPs-CSns as compare to their monometallic counterparts and explained how the incorporation of multi-metals affects their performance. Besides, the sensing mechanisms of Ag/Au-NPs-CSns with special reference to recently published studies are discussed. Finally, challenges and prospects in the controllable-synthesis and practical applications of these sensors are studied. This mechanistic approach and timely review can be promisingly considered as a valuable reference and will help fuel new ideas for the development of novel colorimetric sensors. HighlightsA review of recent advances in Ag/Au-NPs-CSns for heavy metal ions detectionMorphology of Ag/Au-NPs-CSns regulate their optical properties/sensing efficiencyPromising Ag/Au-NPs-CSns can be synthesized by adjusting experimental parametersHybrid and functionalized Ag/Au-NPs-CSns have superior sensing performanceSize/shape transformation, aggregation/anti- and oxidation are sensing mechanisms.

SERS-based lateral flow immunoassay for sensitive and simultaneous detection of anti-SARS-CoV-2 IgM and IgG antibodies by using gap-enhanced Raman nanotags.

The lateral flow immunoassay (LFIA) has played a crucial role in early diagnosis during the current COVID-19 pandemic owing to its simplicity, speed and affordability for coronavirus antibody detection. However, the sensitivity of the commercially available LFIAs needs to be improved to better prevent the spread of the infection. Here, we developed an ultra-sensitive surface-enhanced Raman scattering-based lateral flow immunoassay (SERS-based LFIA) strip for simultaneous detection of anti-SARS-CoV-2 IgM and IgG by using gap-enhanced Raman nanotags (GERTs). The GERTs with a 1 nm gap between the core and shell were used to produce the "hot spots", which provided about 30-fold enhancement as compared to conventional nanotags. The COVID-19 recombinant antigens were conjugated on GERTs surfaces and replaced the traditional colloidal gold for the Raman sensitive detection of human IgM and IgG. The LODs of IgM and IgG were found to be 1 ng/mL and 0.1 ng/mL (about 100 times decrease was observed as compared to commercially available LFIA strips), respectively. Moreover, under the condition of common nano-surface antigen, precise SERS signals proved the unreliability of quantitation because of the interference effect of IgM on IgG.

Identification and structure-activity relationship exploration of uracil-based benzoic acid and ester derivatives as novel dipeptidyl Peptidase-4 inhibitors for the treatment of type 2 diabetes mellitus.

Our previously reported carboxyl-containing DPP-4 inhibitors were highly potent but were poorly bioavailable. Esters of the carboxyl analogs exhibited a significant DPP-4 potency loss albeit with enhanced oral absorption. Herein, we described identification and structure-activity relationship (SAR) exploration of a novel series of benzoic acid and ester derivatives as low single-digit nanomolar DPP-4 inhibitors. Importantly, the esters displayed comparable activities to the acids counterparts. Molecular simulation revealed that ester adopts a similar binding mode to acid. Moreover, the selected esters and acids demonstrated high selectivity and low cytotoxicity, as well as good metabolic stability. And more importantly, the esters possessed excellent pharmacokinetic profiles for oral administration. The best compound ester 19b demonstrated long DPP-4 inhibition in vivo, and robustly improved the glucose tolerance in normal and db/db mice while ensuring glucose-lowering potency in chronic treatment. Our results supported that the compound 19b can be served as a potential candidate for the treatment of type 2 diabetes.

Simultaneously and quantitatively analyze the heavy metals in Sargassum fusiforme by laser-induced breakdown spectroscopy.

As a nutritious and popular seafood among consumers, Sargassum fusiforme is susceptible to the toxic heavy metals because of its strong adsorption properties. In this study, laser-induced breakdown spectroscopy (LIBS) coupled with a simple framework (only remove some noise and low-intensity variables, and then combine with PLS algorithm) was used to establish the detection models to simultaneously and quantitatively analyze the content of heavy metals arsenic (As), chromium (Cd), cadmium (Cr), copper (Cu), mercury (Hg), lead (Pb) and zinc (Zn) in Sargassum fusiforme. As comparisons, three classic variable methods of successive projections algorithm (SPA), uninformative variable elimination (UVE) and variable importance in projection (VIP) were adopted. The final results showed that six of seven heavy metal models from the TV-PLSR model were optimal. These results demonstrate that the TV-PLSR framework combined with LIBS technique is an effective framework for quantitatively analyzing the heavy metals in Sargassum fusiforme.

Building a stable and accurate model for heavy metal detection in mulberry leaves based on a proposed analysis framework and laser-induced breakdown spectroscopy.

Laser-induced breakdown spectroscopy (LIBS) was used to rapidly detect heavy metals in mulberry leaves. For the purpose of increasing detection stability and accuracy, a novel analysis framework consisting of a Kohonen self-organizing map (SOM), a variable selection method using the successive projection algorithm (SPA) and uninformative variable elimination (UVE), and a consensus modeling strategy was proposed for processing LIBS data to determine copper (Cu) and chromium (Cr) content. Results showed that the best regression model for Cu and Cr content achieved the residual predictive deviation (RPD) values of 10.0494 and 8.3874, respectively, and root mean square error of prediction (RMSEP) values of 110.4550 and 41.4561, respectively. The proposed strategy provides a high-accuracy and rapid alternative to the traditional method for monitoring heavy metals in mulberry leaves, which could guarantee the quality of mulberry leaves and potentially be used in food-related industries.

Nondestructive measurement of pectin polysaccharides using hyperspectral imaging in mulberry fruit.

Pectin polysaccharide is an important phytochemical with potential biomedical applications. It is commonly measured by time-consuming destructive chemical methods. This work demonstrates the feasibility of using visible and near-infrared hyperspectral imaging (HSI) techniques to rapidly measure pectin polysaccharides in intact mulberry fruits. Based on spatial information provided by HSI images, the representative spectrum of each whole mulberry was accurately extracted without background. The effects of storage temperature on two varieties of mulberries for model establishment were studied. The performances of two spectral ranges obtained by Si and InGaAs CCD detectors for pectin prediction were compared. The best predictions were obtained from dilute alkali soluble pectin and total soluble pectin in Dashi mulberry fruit stored at room temperature, with residual predictive deviation values of 2.317 and 1.935, respectively. Our results show that HSI is a promising alternative to the chemical method to rapidly and nondestructively measure the pectin content.

Heavy metal detection in mulberry leaves: Laser-induced breakdown spectroscopy data.

Five copper or chromium stress levels were carried out on mulberry leaf, and 20 samples were collected for each metal stress level. A total of 100 samples (copper or chromium) were processed into uniform pressed pellet. The mulberry leaf pellet was placed on a sample platform of laser-induced breakdown spectroscopy (LIBS) system. A laser was used to ablate the sample pellet and generate the emission lines, the intensity and delay time of laser ablation were 80 mJ and 4 µs respectively. To reduce the acquisition errors, 16 different positions of each sample were ablated for 5 accumulation. Then, 80 spectra were collected per sample and the average of them was considered as the sample spectrum for subsequent analysis. Finally, a total of 200 spectra of copper and chromium in mulberry leaves with a wavelength range of 219-877 nm were obtained for calibration analysis [1].

Optimization of the benzamide fragment targeting the S2' site leads to potent dipeptidyl peptidase-IV inhibitors.

Our recently successful identification of benzoic acid-based DPP-4 inhibitors spurs the further quest for in-depth structure-activity relationships (SAR) study in S2' site DPP-4. Thus novel benzamide fragments were designed to target the S2' site to compromise lipophilicity and improve oral activity. Exploring SAR by introduction of a variety of amide and halogen on benzene ring led to identification of several compounds, exerting moderated to excellent DPP-4 activities, in which 4'-chlorine substituted methyl amide 17g showed most potent DPP-4 activity with the IC50 value of 1.6 nM. Its activity was superior to reference alogliptin. Docking study ideally verified and interpreted the obtained SAR of designed compounds. As a continuation, DPP-8/9 assays revealed the designed compounds exhibited good selectivity over DPP-8 and DPP-9. Subsequent cell-based test indicated compound 17g displayed low toxicity toward the LO2 cell line up to 100 µM. In vivo evaluation showed compound 17g robustly improved the glucose tolerance in normal mice. Importantly, 17g exhibited reasonable pharmacokinetic (PK) profiles for oral delivery. Overall, compound 17g has the potential to a safe and efficacious DPP-4 inhibitor for T2DM treatment.

Rapid generation of novel benzoic acid-based xanthine derivatives as highly potent, selective and long acting DPP-4 inhibitors: Scaffold-hopping and prodrug study.

A series of novel xanthine derivatives 2a-l incorporating benzoic acid moieties were rapidly generated by using strategy of scaffold-hopping from our previously reported scaffold uracil to xanthine, a scaffold of approved drug linagliptin. After systematic structure-activity relationship (SAR) study around benzoic acid moieties, 5 novel DPP-4 inhibitors with low picomolar potency range (IC50 < 1 nM) and excellent selectivity against various DPP-4 homologues were identified, in which the best one, compound 2f, with the IC50 value of 0.1 nM for DPP-4, showed 22-fold improvement in inhibitory activity compared to lead compound uracil 1, its activity was 45-fold more potent than alogliptin. 2e, 2f, 2i and 2k were selected for pharmacokinetic evaluation, and 2f and 2i showed the better pharmacokinetic profiles after iv administration, but poor oral bioavailability. To improve the oral pharmacokinetic profile, prodrug design approach was performed around 2f and 2i. Esters of 2f and 2i were synthesized and evaluated for stability, toxicity and pharmacokinetics. Compound 3e, the methyl ester of compound 2f, was identified to demonstrate good stability, low toxicity and improved oral bioavailability, with 3-fold higher blood concentration compared to 2f in rats. The following in vivo evaluations revealed 3e provided a sustained pharmacodynamics effect for 48h, and robustly improved glucose tolerance in normal ICR and db/db mice in dose-dependent manner. Chronic treatments investigations demonstrated that 3e achieved more beneficial effects on fasting blood glucose levels and glucose tolerance than alogliptin in type 2 diabetic db/db mice. The overall results have shown that compound 3e has the potential to efficacious, safety and long-acting treatment for T2DM.

Sample Consensus Model and Unsupervised Variable Consensus Model for Improving the Accuracy of a Calibration Model.

In the quantitative analysis of spectral data, small sample size and high dimensionality of spectral variables often lead to poor accuracy of a calibration model. We proposed two methods, namely sample consensus and unsupervised variable consensus models, in order to solve the problem of poor accuracy. Three public near-infrared (NIR) or infrared (IR) spectroscopy data from corn, wine, and soil were used to build the partial least squares regression (PLSR) model. Then, Monte Carlo sampling and unsupervised variable clustering methods of a self-organizing map were coupled with the consensus modeling strategy to establish the multiple sub-models. Finally, sample consensus and unsupervised variable consensus models were obtained by assigning the weights to each PLSR sub-model. The calculated results show that both sample consensus and unsupervised variable consensus models can significantly improve the accuracy of the calibration model compared to the single PLSR model. The effectiveness of these two methods points out a new approach to achieve a further accurate result, which can take full advantage of the sample information and valid variable information.

Feasibility of Laser-Induced Breakdown Spectroscopy and Hyperspectral Imaging for Rapid Detection of Thiophanate-Methyl Residue on Mulberry Fruit.

An effective and rapid way to detect thiophanate-methyl residue on mulberry fruit is important for providing consumers with quality and safe of mulberry fruit. Chemical methods are complex, time-consuming, and costly, and can result in sample contamination. Rapid detection of thiophanate-methyl residue on mulberry fruit was studied using laser-induced breakdown spectroscopy (LIBS) and hyperspectral imaging (HSI) techniques. Principal component analysis (PCA) and partial least square regression (PLSR) were used to qualitatively and quantitatively analyze the data obtained by using LIBS and HSI on mulberry fruit samples with different thiophanate-methyl residues. The competitive adaptive reweighted sampling algorithm was used to select optimal variables. The results of model calibration were compared. The best result was given by the PLSR model that used the optimal preprocessed LIBS-HSI variables, with a correlation coefficient of 0.921 for the prediction set. The results of this research confirmed the feasibility of using LIBS and HSI for the rapid detection of thiophanate-methyl residue on mulberry fruit.

Identification of novel uracil derivatives incorporating benzoic acid moieties as highly potent Dipeptidyl Peptidase-IV inhibitors.

Dipeptidyl Peptidase-IV (DPP-4) is a validated therapeutic target for type 2 diabetes. Aiming to interact with both residues Try629 and Lys554 in S2' site, a series of novel uracil derivatives 1a-l and 2a-i incorporating benzoic acid moieties at the N3 position were designed and evaluated for their DPP-4 inhibitory activity. Structure-activity relationships (SAR) study led to the identification of the optimal compound 2b as a potent and selective DPP-4 inhibitor (IC50 = 1.7 nM). Docking study revealed the additional salt bridge formed between the carboxylic acid and primary amine of Lys554 has a key role in the enhancement of the activity. Furthermore, compound 2b exhibited no cytotoxicity in human hepatocyte LO2 cells up to 50 µM. Subsequent in vivo evaluations revealed that the ester of 2b robustly improves the glucose tolerance in normal mice. The overall results have shown that compound 2b has the potential to a safe and efficacious treatment for T2DM.

Potential of Visible and Near-Infrared Hyperspectral Imaging for Detection of Diaphania pyloalis Larvae and Damage on Mulberry Leaves.

Mulberry trees are an important crop for sericulture. Pests can affect the yield and quality of mulberry leaves. This study aims to develop a hyperspectral imaging system in visible and near-infrared (NIR) region (400⁻1700 nm) for the rapid identification of Diaphania pyloalis larvae and its damage. The extracted spectra of five region of interests (ROI), namely leaf vein, healthy mesophyll, slight damage, serious damage, and Diaphania pyloalis larva at 400⁻1000 nm (visible range) and 900⁻1700 nm (NIR range), were used to establish a partial least squares discriminant analysis (PLS-DA) and least-squares support vector machines (LS-SVM) models. Successive projections algorithm (SPA), uninformation variable elimination (UVE), UVE-SPA, and competitive adaptive reweighted sampling were used for variable selection. The best models in distinguishing between leaf vein, healthy mesophyll, slight damage and serious damage, leaf vein, healthy mesophyll, and larva, slight damage, serious damage, and larva were all the SPA-LS-SVM models, based on the NIR range data, and their correct rate of prediction (CRP) were all 100.00%. The best model for the identification of all five ROIs was the UVE-SPA-LS-SVM model, based on visible range data, which had the CRP value of 97.30%. In summary, visible and near infrared hyperspectral imaging could distinguish Diaphania pyloalis larvae and their damage from leaf vein and healthy mesophyll in a rapid and non-destructive way.

Comparison of different CCD detectors and chemometrics for predicting total anthocyanin content and antioxidant activity of mulberry fruit using visible and near infrared hyperspectral imaging technique.

This study investigated the potential of using hyperspectral imaging technique in tandem with chemometrics for rapid and invasive predicting total anthocyanin content and antioxidant activity of mulberry fruit. Two calibration methods of partial least square regression and least-squares support vector machines and three wavelength selection algorithms of successive projections algorithm, uninformation variable elimination, and competitive adaptive reweighted sampling were applied. The best prediction models for the analysis of total anthocyanin content and antioxidant activity had Rval2 of 0.959 and 0.995 respectively. The performances of two CCD detectors named silicon (Si) and indium gallium arsenide (InGaAs) were compared. The results show that hyperspectral imaging has a great potential for the assessment of total anthocyanin content and antioxidant activity of mulberry fruit.