In this work, independent radial diffusion at arrayed nanointerfaces between two immiscible electrolyte solutions (nanoITIES) was achieved. The arrays were formed at nanopores fabricated by focused ion beam milling of silicon nitride (SiN) membranes, enabling the reproducible and systematic design of five arrays with different ratios of pore center-to-center distance (rc) to pore radius (ra). Voltammetry across water-1,6-dichlorohexane nanoITIES formed at these arrays was examined by the interfacial transfer of tetrapropylammonium ions. The diffusion-limited ion-transfer current increased with the ratio rc/ra, reaching a plateau for rc/ra ≥ 56, which was equivalent to the theoretical current for radial diffusion to an array of independent nanoITIES. As a result, mass transport to the nanoITIES arrays was greatly enhanced due to the decreased overlap of diffusion zones at adjacent nanoITIES, allowing each interface in the array to behave independently. When the rc/ra ratio increased from 13 to 56, the analytical performance parameters of sensitivity and limit of detection were improved from 0.50 (±0.02) A M(-1) to 0.76 (±0.02) A M(-1) and from 0.101 (±0.003) μM to 0.072 (±0.002) μM, respectively. These results provide an experimental basis for the design of arrayed nanointerfaces for electrochemical sensing.
We present a compact, cost-effective, label-free, real-time biosensor based on long-range surface plasmon polariton (LRSPP) gold (Au) waveguides for the detection of dengue-specific immunoglobulin M (IgM) antibody, and we demonstrate detection in actual patient blood plasma samples. Two surface functionalization approaches are proposed and demonstrated: a dengue virus serotype 2 (DENV-2) functionalized surface to capture dengue-specific IgM antibody in blood plasma and the reverse, a blood plasma functionalized surface to capture DENV-2. The results obtained via these two surface functionalization approaches are comparable to, or of greater quality, than those collected by conventional IgM antibody capture enzyme linked immunosorbent assay (MAC-ELISA). Our second functionalization approach was found to minimize nonspecific binding, thus improving the sensitivity and accuracy of the test. We also demonstrate reuse of the biosensors by regenerating the sensing surface down to the virus (or antibody) level or down to the bare Au.
Spatial segmentation is a critical procedure in mass spectrometry imaging (MSI)-based biochemical analysis. However, the commonly used unsupervised MSI segmentation methods may lead to inappropriate segmentation results as the MSI data is characterized by high dimensionality and low signal-to-noise ratio. This process can be improved by the incorporation of precise prior knowledge, which is hard to obtain in most cases. In this study, we show that the incorporation of partial or coarse prior knowledge from different sources such as reference images or biological knowledge may also help to improve MSI segmentation results. Here, we propose a novel interactive segmentation strategy for MSI data called iSegMSI, which incorporates prior information in the form of scribble-regularization of the unsupervised model to fine-tune the segmentation results. By using two typical MSI data sets (including a whole-body mouse fetus and human thyroid cancer), the present results demonstrate the effectiveness of the iSegMSI strategy in improving the MSI segmentations. Specifically, the method can be used to subdivide a region into several subregions specified by the user-defined scribbles or to merge several subregions into a single region. Additionally, these fine-tuned results are highly tolerant to the imprecision of the scribbles. Our results suggest that the proposed iSegMSI method may be an effective preprocessing strategy to facilitate the analysis of MSI data.
The onset of Covid-19 pandemic has resulted in the exponential growth of alcohol-based hand rub (ABHR)/hand sanitizer use. Reports have emerged of ABHR products containing methanol, a highly toxic compound to humans, exposing users to acute and chronic medical illnesses. While gas chromatography-mass spectrometry (GC-MS) remains the gold-standard method for the detection and identification of impurities in ABHRs, there exist limitations at widespread volume testing. This paper demonstrates the capability of an inexpensive portable pyroelectric linear array infrared spectrometer to rapidly test ABHR and compare the performance with a benchtop Fourier transform infrared spectrometer and HS-GC-MS. Multicomponent partial least square quantification models were built with performance found to be comparable between the two spectrometers and with the HS-GC-MS. Furthermore, the portable spectrometer was field-tested with real-world samples in Malaysia on both retail products (Group A) and freely deployed public dispensers (Group B) between May and November 2020. A total of 386 samples were tested. Only 75.2% of Group A met the criteria of safe and effective ABHR [no detectable methanol and alcohol concentration above 60% (v/v)], while <50% of Group B did. In addition, 7.4 and 18.8% of Group A and Group B, respectively, were found to contain methanol above permissible limits. The high percentage of sub-standard and methanol-containing samples combined with the frequent use of ABHR by the public highlights the need for and importance of a portable and rapid testing device for widespread screening of ABHR against falsified products and protects the general public.
High-resolution reconstruction has attracted increasing research interest in mass spectrometry imaging (MSI), but it remains a challenging ill-posed problem. In the present study, we proposed a deep learning model to fuse multimodal images to enhance the spatial resolution of MSI data, namely, DeepFERE. Hematoxylin and eosin (H&E) stain microscopy imaging was used to pose constraints in the process of high-resolution reconstruction to alleviate the ill-posedness. A novel model architecture was designed to achieve multi-task optimization by incorporating multi-modal image registration and fusion in a mutually reinforced framework. Experimental results demonstrated that the proposed DeepFERE model is able to produce high-resolution reconstruction images with rich chemical information and a detailed structure on both visual inspection and quantitative evaluation. In addition, our method was found to be able to improve the delimitation of the boundary between cancerous and para-cancerous regions in the MSI image. Furthermore, the reconstruction of low-resolution spatial transcriptomics data demonstrated that the developed DeepFERE model may find wider applications in biomedical fields.
Metabolic pathways are regarded as functional and basic components of the biological system. In metabolomics, metabolite set enrichment analysis (MSEA) is often used to identify the altered metabolic pathways (metabolite sets) associated with phenotypes of interest (POI), e.g., disease. However, in most studies, MSEA suffers from the limitation of low metabolite coverage. Random walk (RW)-based algorithms can be used to propagate the perturbation of detected metabolites to the undetected metabolites through a metabolite network model prior to MSEA. Nevertheless, most of the existing RW-based algorithms run on a general metabolite network constructed based on public databases, such as KEGG, without taking into consideration the potential influence of POI on the metabolite network, which may reduce the phenotypic specificities of the MSEA results. To solve this problem, a novel pathway analysis strategy, namely, differential correlation-informed MSEA (dci-MSEA), is proposed in this paper. Statistically, differential correlations between metabolites are used to evaluate the influence of POI on the metabolite network, so that a phenotype-specific metabolite network is constructed for RW-based propagation. The experimental results show that dci-MSEA outperforms the conventional RW-based MSEA in identifying the altered metabolic pathways associated with colorectal cancer. In addition, by incorporating the individual-specific metabolite network, the dci-MSEA strategy is easily extended to disease heterogeneity analysis. Here, dci-MSEA was used to decipher the heterogeneity of colorectal cancer. The present results highlight the clustering of colorectal cancer samples with their cluster-specific selection of differential pathways and demonstrate the feasibility of dci-MSEA in heterogeneity analysis. Taken together, the proposed dci-MSEA may provide insights into disease mechanisms and determination of disease heterogeneity.
A new sample preparation method is proposed for the extraction of pharmaceutical compounds (Metformin, Phenyl biguanide, and Phenformin) of varied hydrophilicity, dissolved in an aqueous sample. When in contact with an organic phase, an interfacial potential is imposed by the presence of an ion, tetramethylammonium (TMA+), common to each phase. The interfacial potential difference drives the transfer of ionic analytes across the interface and allows it to reach up to nearly 100% extraction efficiency and a 60-fold enrichment factor in optimized extraction conditions as determined by HPLC analysis.
Guanine (G), adenine (A), thymine (T), and cytosine (C) are the four basic constituents of DNA. Studies on DNA composition have focused especially on DNA damage and genotoxicity. However, the development of a rapid, simple, and multiplex method for the simultaneous measurement of the four DNA bases remains a challenge. In this study, we describe a graphite-based nanocomposite electrode (Au-rGO/MWCNT/graphite) that uses a simple electro-co-deposition approach. We successfully applied the developed sensor for multiplex detection of G, A, T, and C, using square-wave voltammetry. The sensor was tested using real animal and plant DNA samples in which the hydrolysis of T and C could be achieved with 8 mol L-1 of acid. The electrochemical sensor exhibited excellent sensitivity (G = 178.8 nA/μg mL-1, A = 92.9 nA/μg mL-1, T = 1.4 nA/μg mL-1, and C = 15.1 9 nA/μg mL-1), low limit of detection (G, A = 0.5 μg mL-1; T, C = 1.0 μg mL-1), and high selectivity in the presence of common interfering factors from biological matrixes. The reliability of the established method was assessed by method validation and comparison with the ultraperformance liquid chromatography technique, and a correlation of 103.7% was achieved.
Light-addressable potentiometric sensors (LAPS) are of great interest in bioimaging applications such as the monitoring of concentrations in microfluidic channels or the investigation of metabolic and signaling events in living cells. By measuring the photocurrents at electrolyte-insulator-semiconductor (EIS) and electrolyte-semiconductor structures, LAPS can produce spatiotemporal images of chemical or biological analytes, electrical potentials and impedance. However, its commercial applications are often restricted by their limited AC photocurrents and resolution of LAPS images. Herein, for the first time, the use of 1D semiconducting oxides in the form of ZnO nanorods for LAPS imaging is explored to solve this issue. A significantly increased AC photocurrent with enhanced image resolution has been achieved based on ZnO nanorods, with a photocurrent of 45.7 ± 0.1 nA at a light intensity of 0.05 mW, a lateral resolution as low as 3.0 μm as demonstrated by images of a PMMA dot on ZnO nanorods and a pH sensitivity of 53 mV/pH. The suitability of the device for bioanalysis and bioimaging was demonstrated by monitoring the degradation of a thin poly(ester amide) film with the enzyme α-chymotrypsin using LAPS. This simple and robust route to fabricate LAPS substrates with excellent performance would provide tremendous opportunities for bioimaging.
We present an electrochemical long period fiber grating (LPFG) sensor for electroactive species with an optically transparent electrode. The sensor was fabricated by coating indium tin oxide onto the surface of LPFG using a polygonal barrel-sputtering method. LPFG was produced by an electric arc-induced technique. The sensing is based on change in the detection of electron density on the electrode surface during potential application and its reduction by electrochemical redox of analytes. Four typical electroactive species of methylene blue, hexaammineruthenium(III), ferrocyanide, and ferrocenedimethanol were used to investigate the sensor performance. The concentrations of analytes were determined by the modulation of the potential as the change in transmittance around the resonance band of LPFG. The sensitivity of the sensor, particularly to methylene blue, was high, and the sensor responded to a wide concentration range of 0.001 mM to 1 mM.
Photodynamic therapy (PDT) is an alternative treatment for cancer that involves administration of a photosensitive drug or photosensitizer that localizes at the tumor tissue followed by in situ excitation at an appropriate wavelength of light. Tumour tissues are then killed by cytotoxic reactive oxygen species generated by the photosensitizer. Targeted excitation and photokilling of affected tissues is achieved through focal light irradiation, thereby minimizing systemic side effects to the normal healthy tissues. Currently, there are only a small number of photosensitizers that are in the clinic and many of these share the same structural core based on cyclic tetrapyrroles. This paper describes how metabolic tools are utilized to prioritize natural extracts to search for structurally new photosensitizers from Malaysian biodiversity. As proof of concept, we analyzed 278 photocytotoxic extracts using a hyphenated technique of liquid chromatography-mass spectrometry coupled with principal component analysis (LC-MS-PCA) and prioritized 27 extracts that potentially contained new photosensitizers for chemical dereplication using an in-house UPLC-PDA-MS-Photocytotoxic assay platform. This led to the identification of 2 new photosensitizers with cyclic tetrapyrrolic structures, thereby demonstrating the feasibility of the metabolic approach.
Since the declaration of COVID-19 as a pandemic in early 2020, multiple variants of the severe acute respiratory syndrome-related coronavirus (SARS-CoV-2) have been detected. The emergence of multiple variants has raised concerns due to their impact on public health. Therefore, it is crucial to distinguish between different viral variants. Here, we developed a machine learning web-based application for SARS-CoV-2 variant identification via duplex real-time polymerase chain reaction (PCR) coupled with high-resolution melt (qPCR-HRM) analysis. As a proof-of-concept, we investigated the platform's ability to identify the Alpha, Delta, and wild-type strains using two sets of primers. The duplex qPCR-HRM could identify the two variants reliably in as low as 100 copies/μL. Finally, the platform was validated with 167 nasopharyngeal swab samples, which gave a sensitivity of 95.2%. This work demonstrates the potential for use as automated, cost-effective, and large-scale viral variant surveillance.
Metabolomics and proteomics offer significant advantages in understanding biological mechanisms at two hierarchical levels. However, conventional single omics analysis faces challenges due to the high demand for specimens and the complexity of intrinsic associations. To obtain comprehensive and accurate system biological information, we developed a multiomics analytical method called Windows Scanning Multiomics (WSM). In this method, we performed simultaneous extraction of metabolites and proteins from the same sample, resulting in a 10% increase in the coverage of the identified biomolecules. Both metabolomics and proteomics analyses were conducted by using ultrahigh-performance liquid chromatography mass spectrometry (UPLC-MS), eliminating the need for instrument conversions. Additionally, we designed an R-based program (WSM.R) to integrate mathematical and biological correlations between metabolites and proteins into a correlation network. The network created from simultaneously extracted biomolecules was more focused and comprehensive compared to those from separate extractions. Notably, we excluded six pairs of false-positive relationships between metabolites and proteins in the network established using simultaneously extracted biomolecules. In conclusion, this study introduces a novel approach for multiomics analysis and data processing that greatly aids in bioinformation mining from multiomics results. This method is poised to play an indispensable role in systems biology research.
DNAzymes have emerged as an important class of sensors for a wide variety of metal ions, with florescence DNAzyme sensors as the most widely used in different sensing and imaging applications because of their fast response time, high signal intensity, and high sensitivity. However, the requirements of an external excitation light source and its associated power increase the cost and size of the fluorometer, making it difficult to be used for portable detections. To overcome these limitations, we report herein a DNAzyme sensor that relies on chemiluminescence resonance energy transfer (CRET) without the need for external light. The sensor is constructed by combining the functional motifs from both Pb2+-dependent 8-17 DNAzyme conjugated to fluorescein (FAM) and hemin/G-quadruplex that mimics horseradish peroxidase to catalyze the oxidation of luminol by H2O2 to yield chemiluminescence. In the absence of Pb2+, the hybridization between the enzyme and substrate strands bring the FAM and hemin/G-quadruplex in close proximity, resulting in CRET. The presence of Pb2+ ions can drive the cleavage on the substrate strand, resulting in a sharp decrease in the melting temperature of hybridization and thus separation of the FAM from hemin/G-quadruplex. The liberated CRET pair causes a ratiometric increase in the donor's fluorescent signal and a decrease in the acceptor signal. Using this method, Pb2+ ions have been measured rapidly (<15 min) with a low limit of detection at 5 nM. By removing the requirement of exogenous light excitation, we have demonstrated a simple and portable detection using a smartphone, making the DNAzyme-CRET system suitable for field tests of lake water. Since DNAzymes selective for other metal ions or targets, such as bacteria, can be obtained using in vitro selection, the method reported here opens a new avenue for rapid, portable, and ratiometric detection of many targets in environmental monitoring, food safety, and medical diagnostics.
A novel sequential three-dimensional gas chromatography-high-resolution time-of-flight mass spectrometry (3D GC-accTOFMS) approach for profiling secondary metabolites in complex plant extracts is described. This integrated system incorporates a nonpolar first-dimension (1Dnp) separation step, prior to a microfluidic heart-cut (H/C) of a targeted region(s) to a cryogenic trapping device, directly followed by the rapid reinjection of a trapped solute into a polar second-dimension (2DPEG) column for multidimensional separation (GCnp-GCPEG). For additional separation, the effluent from 2DPEG can then be modulated according to a comprehensive 2D GC process (GC×GC), using an ionic liquid phase as a third-dimension (3DIL) column, to produce a sequential GCnp-GCPEG×GCIL separation. Thus, the unresolved or poorly resolved components, or regions that require further separation, can be precisely selected and rapidly transferred for additional separation on 2D or 3D columns, based on the greater separation realized by these steps. The described integrated system can be used in a number of modes, but one useful approach is to target specific classes of compounds for improved resolution. This is demonstrated through the separation and detection of the oxygenated sesquiterpenes in hop ( Humulus lupulus L.) essential oil and agarwood ( Aquilaria malaccensis) oleoresin. Improved resolution and peak capacity were illustrated through the progressive comparison of the tentatively identified components for GCnp-GCPEG and GCnp-GCPEG×GCIL methods. Relative standard deviations of intraday retentions (1 tR, 2 tR,, and 3 tR) and peak areas of ≤0.01, 0.07, 0.71, and 7.5% were achieved. This analytical approach comprising three GC column selectivities, hyphenated with high-resolution TOFMS detection, should be a valuable adjunct for the improved characterization of complex plant samples, particularly in the area of plant metabolomics.
Organically (octyl amine, OA) surface modified electrocatalyst (OA-Pt/CB) was studied for its oxygen reduction reaction (ORR) activity via dc methods and its charge and mass transfer properties were studied via electrochemical impedance spectroscopy (EIS). Comparison with a commercial catalyst (TEC10V30E) with similar Pt content was also carried out. In EIS, both the catalysts showed a single time-constant with an emerging high-frequency semicircle of very small diameter which was fitted using suitable equivalent circuits. The organically modified catalyst showed lower charge-transfer resistance and hence, low polarization resistance in high potential region as compared to the commercial catalyst. The dominance of kinetic processes was observed at 0.925-1.000 V, whereas domination of diffusion based processes was observed at lower potential region for the organic catalyst. No effect due to the presence of carbon was observed in the EIS spectra. Using the hydrodynamic method, higher current penetration depth was obtained for the organically modified catalyst at 1600 rpm. Exchange current density and Tafel slopes for both the electrocatalysts were calculated from the polarization resistance obtained from EIS which was in correlation with the results obtained from dc methods.
2D nanosheets (NSs) have been widely used in drug-related applications. However, a comprehensive investigation into the cytotoxicity mechanism linked to the redox activity is lacking. In this study, with cytochrome c (Cyt c) as the model biospecies, the cytotoxicity of 2D NSs was evaluated systematically based on their redox effect with microfluidic techniques. The interface interaction, dissolution, and redox effect of 2D NSs on Cyt c were monitored with pulsed streaming potential (SP) measurement and capillary electrophoresis (CE). The relationship between the redox activity of 2D NSs and the function of Cyt c was evaluated in vitro with Hela cells. The results indicated that the dissolution and redox activity of 2D NSs can be simultaneously monitored with CE under weak interface interactions and at low sample volumes. Both WS2 NSs and MoS2 NSs can reduce Cyt c without significant dissolution, with reduction rates measured at 6.24 × 10-5 M for WS2 NSs and 3.76 × 10-5 M for MoS2 NSs. Furthermore, exposure to 2D NSs exhibited heightened reducibility, which prompted more pronounced alterations associated with Cyt c dysfunction, encompassing ATP synthesis, modifications in mitochondrial membrane potential, and increased reactive oxygen species production. These observations suggest a positive correlation between the redox activity of 2D NSs and their redox toxicity in Hela cells. These findings provide valuable insight into the redox properties of 2D NSs regarding cytotoxicity and offer the possibility to modify the 2D NSs to reduce their redox toxicity for clinical applications.
The translation of stacking techniques used in capillary electrophoresis (CE) to microchip CE (MCE) in order to improve concentration sensitivity is an important area of study. The success in stacking relies on the generation and control of the stacking boundaries which is a challenge in MCE because the manipulation of solutions is not as straightforward as in CE with a single channel. Here, a simple and rapid on-line sample concentration (stacking strategy) in a battery operated nonaqueous MCE device with a commercially available double T-junction glass chip is presented. A multi-stacking approach was developed in order to circumvent the issues for stacking in nonaqueous MCE. The cationic analytes from the two loading channels were injected under field-enhanced conditions and were focused by micelle-to-solvent stacking. This was achieved by the application of high electric fields along the two loading channels and a low electric field in the separation channel, with one ground electrode in the reservoir closest to the junction. At the junction, the stacked zones were re-stacked under field-enhanced conditions and then injected into the separation channels. The multi-stacking was verified under a fluorescence microscope using Rhodamine 6G as the analyte, revealing a sensitivity enhancement factor (SEF) of 110. The stacking approach was also implemented in the nonaqueous MCE with contactless conductivity detection of the anticancer drug tamoxifen as well as its metabolites. The multi-stacking and analysis time was 40 s and 110 s, respectively, the limit of detections was from 10 to 35 ng/mL, and the SEFs were 20 to 50. The method was able to quantify the target analytes from breast cancer patients.