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Analytical and Bioanalytical Chemistry
Journal Prestige (SJR): 0.978  Citation Impact (citeScore): 3 Number of Followers: 27  Hybrid journal (It can contain Open Access articles)ISSN (Print) 1618-2650 - ISSN (Online) 1618-2642 Published by Springer-Verlag  [2468 journals]
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- Correction to: Machine learning–assisted internal standard calibration
label‑free SERS strategy for colon cancer detection-
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PubDate: 2023-06-01
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- Correction to: A nanocomposite probe of polydopamine/molecularly imprinted
polymer/quantum dots for trace sarafloxacin detection in chicken meat-
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PubDate: 2023-06-01
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- An effective validation of analytical method for determination of a polar
complexing agent: the illustrative case of cytotoxic bleomycin-
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Abstract: The effectiveness of highly polar agents in cancer treatment is well recognized, but their physicochemical properties make their analytical determination a demanding task. Their analysis requires peculiar sample preparation and chromatographic separation, which heavily impacts the precision of such an analytical method. As a case study, we chose a polar cytotoxic bleomycin, which is a mixture of complexing congeners with relatively high molecular mass, a fact that creates an added challenge in regard to its detection via electrospray mass spectrometry. These issues combined lead to a deprived method performance, so the aim of this study is manifold, i.e., to optimize, validate, and establish quality performance measures for determination of bleomycin in pharmaceutical and biological specimens. Quantification of bleomycin is done at diametrically different concentration levels: at the concentrations relevant for analysis of pharmaceutical dosage forms it is based on a direct reversed-phase HPLC-UV detection, involving minimum sample pretreatment. On the contrary, analysis of bleomycin in biological specimens requires phospholipid removal and protein precipitation followed by HILIC chromatography with MS/MS detection of bleomycin A2 and B2 copper complexes being the predominant species. This study further attempts to solve the traceability issue in the absence of certified reference standards, determines measurement uncertainty, investigates BLM stability and method performance characteristics, and, last but not least, provides an explanatory example of how a method quality assurance procedure should be established in case of an exceedingly complex analytical method. Graphical abstract
PubDate: 2023-06-01
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- Improving the accuracy of quantitative spectroscopic analysis of leukocyte
by suppressing the influence of the “M” factors based on
“M + N” theory-
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Abstract: Leukocytes play a crucial role in clinical diagnosis. The noninvasive and immediate detection of this low blood component has both important application and academic significance. The “M + N” theory makes it obvious that suppressing the influence of N factors and reducing the influence of M factors are both necessary in order to accurately detect the low content of blood components like leukocytes. Therefore, based on the strategy of “fixing the influencing factors” in the “M + N” theory, this paper proposes the method of “partition modeling based on the content of large concentrations of non-target components.” Firstly, a dynamic spectral acquisition system was built to achieve the noninvasive acquisition of spectra. Then this paper applies the method proposed above to the modeling process of the samples. In order to lessen the impact of the M factors, the approach first divides the samples into groups based on the concentrations of the major blood components (platelets and hemoglobin). This reduces the range of fluctuation of the non-target components in each interval. The modeling of the leukocyte content was then carried out independently for each sample contained in each compartment. Compared with the result of modeling the sample directly, the related coefficient of the calibration set (Rc) improved by 11.70%, and the root mean square error (RMSEC) decreased by 76.97%; the related coefficient of the prediction set (Rp) improved by 32.68%, and the root mean square error (RMSEP) decreased by 52.80%. When the model was applied to predict all samples, the related coefficient (R-all) increased by 16.67%, and the root mean square error (RMSE-all) dropped by 63.00%. It was shown that, as compared to direct modeling of leukocyte concentration, the method of “partition modeling based on the content of large concentrations of non-target components” considerably increased the accuracy of quantitative analysis of leukocytes. The method can be used to analyze other blood components as well, offering a fresh approach and technique to increase the precision of spectral analysis of the blood’s small content components. Graphical
PubDate: 2023-06-01
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- XAI-enabled neural network analysis of metabolite spatial distributions
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Abstract: We used deep neural networks to process the mass spectrometry imaging (MSI) data of mouse muscle (young vs aged) and human cancer (tumor vs normal adjacent) tissues, with the aim of using explainable artificial intelligence (XAI) methods to rapidly identify biomarkers that can distinguish different classes of tissues, from several thousands of metabolite features. We also modified classic neural network architectures to construct a deep convolutional neural network that is more suitable for processing high-dimensional MSI data directly, instead of using dimension reduction techniques, and compared it to seven other machine learning analysis methods’ performance in classification accuracy. After ascertaining the superiority of Channel-ResNet10, we used a novel channel selection–based XAI method to identify the key metabolite features that were responsible for its learning accuracy. These key metabolite biomarkers were then processed using MetaboAnalyst for pathway enrichment mapping. We found that Channel-ResNet10 was superior to seven other machine learning methods for MSI analysis, reaching > 98% accuracy in muscle aging and colorectal cancer datasets. We also used a novel channel selection–based XAI method to find that in young and aged muscle tissues, the differentially distributed metabolite biomarkers were especially enriched in the propanoate metabolism pathway, suggesting it as a novel target pathway for anti-aging therapy. Graphical
PubDate: 2023-06-01
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- Ligand-targeted fishing of α-glucosidase inhibitors from Tribulus
terrestris L. based on chitosan-functionalized multi-walled carbon
nanotubes with immobilized α-glucosidase-
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Abstract: α-Glucosidase inhibitors in natural products are one of the promising drugs for the treatment of type 2 diabetes. However, due to the complexity of the matrix, it is challenging to comprehensibly clarify the specific pharmacodynamic substances. In this study, a novel high-throughput inhibitor screening strategy was established based on covalent binding of α-glucosidase on chitosan-functionalized multi-walled carbon nanotubes coupled with high-resolution mass spectrometry. The synthesized MWCNTs@CS@GA@α-Glu was characterized by TEM, SEM, FTIR, Raman, and TG. Performance studies showed that the microreactor exhibited stronger thermostability and pH tolerance than that of the free one while maintaining its inherent catalytic activity. Feasibility study applying a model mixture of known α-glucosidase ligand and non-ligands indicated the selectivity and specificity of the system. By integrating ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-QTOF-MS) with ion mobility mass spectrometry (IMS), 15 ligands were obtained and tentatively identified from Tribulus terrestris L., including 8 steroidal saponins, 4 flavonoids, and 3 alkaloids. These inhibitors were further validated by in vivo experiments and molecular docking simulation.
PubDate: 2023-06-01
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- Machine learning for optical chemical multi-analyte imaging
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Abstract: Simultaneous sensing of metabolic analytes such as pH and O2 is critical in complex and heterogeneous biological environments where analytes often are interrelated. However, measuring all target analytes at the same time and position is often challenging. A major challenge preventing further progress occurs when sensor signals cannot be directly correlated to analyte concentrations due to additional effects, overshadowing and complicating the actual correlations. In fields related to optical sensing, machine learning has already shown its potential to overcome these challenges by solving nested and multidimensional correlations. Hence, we want to apply machine learning models to fluorescence-based optical chemical sensors to facilitate simultaneous imaging of multiple analytes in 2D. We present a proof-of-concept approach for simultaneous imaging of pH and dissolved O2 using an optical chemical sensor, a hyperspectral camera for image acquisition, and a multi-layered machine learning model based on a decision tree algorithm (XGBoost) for data analysis. Our model predicts dissolved O2 and pH with a mean absolute error of < 4.50·10−2 and < 1.96·10−1, respectively, and a root mean square error of < 2.12·10−1 and < 4.42·10−1, respectively. Besides the model-building process, we discuss the potentials of machine learning for optical chemical sensing, especially regarding multi-analyte imaging, and highlight risks of bias that can arise in machine learning-based data analysis.
PubDate: 2023-06-01
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- Analysis of tri-benzeneboronic esters of monosaccharides formed in aqueous
solution by MALDI-TOF MS and DFT calculations-
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Abstract: The affinity interactions between boronic acids and sugars have been successfully exploited in many fields, such as the sensing of saccharides, selective enrichment of glycoconjugates, and drug delivery. However, despite multiple techniques having been adopted to investigate the reaction of boronate affinity, the pathway of boronate esters formation under aqueous conditions remains controversial. We report a MALDI-MS approach to investigate the interactions between phenylboronic acid and monosaccharides in neutral aqueous solution by using polylevodopa as an innovative substrate instead of conventional matrix. A series of unusual tri-benzeneboronic esters were then revealed. The mass spectrometry data indicate that they bear a dibenzenepyroboronate cyclic ester moiety with seven-membered ring or eight-membered ring. With the aid of theoretical computations, their most likely geometrical structures are elucidated, and these tri-benzeneboronic esters are proposed to be formed via a boroxine binding monosaccharide pathway. This work provides more insight into the mechanism of boronate affinity interaction between boronic acid and sugars and proves the developed MALDI-MS approach is promising for studying interactions between small molecules. Graphical
PubDate: 2023-06-01
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- Quantitative colorimetric sensing of heavy metal ions via analyte-promoted
growth of Au nanoparticles with timer or smartphone readout-
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Abstract: This work describes two new colorimetric nanosensors for label-free, equipment-free quantitative detection of nanomolar copper (II) (Cu2+) and mercury (II) (Hg2+) ions. Both are based on the analyte-promoted growth of Au nanoparticles (AuNPs) from the reduction of chloroauric acid by 4-morpholineethanesulfonic acid. For the Cu2+ nanosensor, the analyte can accelerate such a redox system to rapidly form a red solution containing dispersed, uniform, spherical AuNPs that is related to these particles’ surface plasmon resonance property. For the Hg2+ nanosensor, on the other hand, a blue mixture consisting of aggregated, ill-defined AuNPs with various sizes can be created, showing a significantly enhanced Tyndall effect (TE) signal (in comparison with that produced in the red solution of AuNPs). By using a timer and a smartphone to quantitatively measure the time of producing the red solution and the TE intensity (i.e., the average gray value of the corresponding image) of the blue mixture, respectively, the developed nanosensors are well demonstrated to achieve linear ranges of 6.4 nM to 100 μM and 6.1 nM to 1.56 μM for Cu2+ and Hg2+, respectively, with detection limits down to 3.5 and 0.1 nM, respectively. The acceptable recovery results obtained from the analysis of the two analytes in the complex real water samples including drinking water, tap water, and pond water ranged from 90.43 to 111.56%. Graphical abstract
PubDate: 2023-06-01
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- On- and off-line analysis by ICP-MS to measure the bioaccessible
concentration of elements in PM10 using dynamic versions of the simplified
bioaccessibility extraction test-
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Abstract: Two dynamic versions of the simplified bioaccessibility extraction test (SBET) were developed—an off-line procedure and an on-line procedure coupled directly to ICP-MS. Batch, on-line, and off-line procedures were applied to simulated PM10 samples prepared by loading NIST SRM 2711A Montana II Soil and BGS RM 102 Ironstone Soil onto 45-mm TX40 filters widely used in air quality monitoring. Three real PM10 samples were also extracted. A polycarbonate filter holder was used as an extraction unit for the dynamic procedures. Arsenic, Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn were determined in the extracts using an Agilent 7700 × ICP-MS instrument. The residual simulated PM10 samples following application of the SBET were subjected to microwave-assisted aqua regia digestion and a mass balance calculation performed with respect to digestion of a separate test portion of the SRM. Leachates were collected as subfractions for the off-line analysis or continuously introduced to the nebuliser of the ICP-MS for the on-line analysis. The mass balance was generally acceptable for all versions of the SBET. Recoveries obtained with the dynamic methods were closer to pseudototal values than those obtained in batch mode. Off-line analysis performed better than on-line analysis, except for Pb. Recoveries of bioaccessible Pb relative to the certified value in NIST SRM 2711A Montana II Soil (1110 ± 49 mg kg−1) were 99, 106, and 105% for the batch, off-line, and on-line methods, respectively. The study demonstrates that dynamic SBET can be used to measure bioaccessibility of potentially toxic elements in PM10 samples.
PubDate: 2023-06-01
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- Improved analysis of folpet and captan in foods using liquid
chromatography-triple quadrupole linear ion trap mass spectrometry:
applying mass filtering, collision, and trapping conditions-
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Abstract: Accurate and highly sensitive analysis of folpet and captan was accomplished using liquid chromatography-triple quadrupole linear ion trap mass spectrometry (LC-QqQIT) with selective ion mode; mass filtering, collision, and trapping condition. Dimensional mass spectrometry (MS3) parameters were optimized for the residue detection of folpet and captan in six food commodities (apples, tomatoes, sweet pepper, wheat flour, sesame seeds, and fennel seeds). The sample preparation method was based on the known QuEChERS protocol, except a mixture of acetonitrile/acetone was used for the sample extraction from the sesame seeds. The robustness and reliability of the developed MS3 method were demonstrated by performing a full validation, according to SANTE/11312/2021, at 0.01–0.25 mg/kg. Recovery ranged from 83 to 118% with a relative standard deviation below 19% in all the tested commodities, and limits of quantifications (LOQs) were 0.01 mg/kg in apples and tomatoes; 0.03 mg/kg in sweet pepper; and 0.05 mg/kg in wheat flour, sesame seeds, and fennel seeds. Monitoring results showed that about 90% of apples contained captan residue, and in sweet pepper, concentrations of captan and folpet as high as 1.57 and 0.97 mg/kg were found, respectively. The novel developed MS3 method enabled more reliable identification of these commonly problematic fungicides at lower LOQs than previously reported methods.
PubDate: 2023-06-01
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- The fluorescence regulation of a tri-functional oligonucleotide probe
HEX-OND in detecting Pb(II), cysteine, and K(I) based on two G-quadruplex
forms-
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Abstract: A novel tri-functional probe HEX-OND was developed for detecting Pb(II), cysteine (Cys), and K(I) by fluorescence quenching, recovery, and amplification strategies respectively, based on Pb(II)-induced chair-type G-quadruplex (CGQ) and K(I)-induced parallel G-quadruplex (PGQ). The thermodynamic mechanism was illustrated as that HEX-OND transformed into CGQ by associating equimolar Pb(II) (K1 = 1.10 ± 0.25 × 106 L/mol), forcing (G)2 spontaneously approaching and static-quenching HEX (5′-hexachlorofluorescein phosphoramidite) in the photo-induced electron transfer (PET) way by the van der Waals force and hydrogen bond (K2 = 5.14 ± 1.65 × 107 L/mol); the additional Cys recovered fluorescence in the molecular ratio of 2:1 via Pb(II)-precipitation induced CGQ destruction (K3 = 3.03 ± 0.77 × 109 L/mol); the equimolar K(I) induced HEX-OND transforming into PGQ (K4 = 3.53 ± 0.30 × 104 L/mol) and specifically associating with the equimolar N-methyl mesoporphyrin IX (NMM) by hydrophobic force (K5 = 3.48 ± 1.08 × 105 L/mol), leading to the fluorescence enhancement. Moreover, the practicability results showed that the detection limits reached a nanomolar level for Pb(II) and Cys and micromolar for K(I), with mere disturbances for 6, 10, and 5 kinds of other substances, respectively; no significant deviations of the real sample detection results were found between the well-understood methods with ours in detecting Pb(II) and Cys, and K(I) could be recognized and quantified even in the presence of Na(I) with 5000 and 600 fold respectively. The results demonstrated the triple-function, sensitivity, selectivity, and tremendous application feasibility of the current probe in sensing Pb(II), Cys, and K(I).
PubDate: 2023-06-01
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- ECL sensor for selective determination of citrate ions as a prostate
cancer biomarker using polymer of intrinsic microporosity-1
nanoparticles/nitrogen-doped carbon quantum dots-
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Abstract: Urine citrate analysis is relevant in the screening and monitoring of patients with prostate cancer and calcium nephrolithiasis. A sensitive, fast, easy, and low-maintenance electrochemiluminescence (ECL) method with conductivity detection for the analysis of citrate in urine is developed and validated by employing polymer of intrinsic microporosity-1 nanoparticles/nitrogen-doped carbon quantum dots (nano-PIM-1/N-CQDs). Using optimum conditions, the sensor was applied in ECL experiments in the presence of different concentrations of citrate ions. The ECL signals were quenched gradually by the increasing citrate concentration. The linear range of the relationship between the logarithm of the citrate concentration and ΔECL (ECL of blank − ECL of sample) was obtained between 1.0 × 10−7 M and 5.0 × 10−4 M. The limit of detection (LOD) was calculated to be 2.2 × 10−8 M (S/N = 3). The sensor was successfully applied in real samples such as human serum and patient urine. Graphical abstract
PubDate: 2023-06-01
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- Middle-out sequence confirmation of CRISPR/Cas9 single guide RNA (sgRNA)
using DNA primers and ribonuclease T1 digestion-
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Abstract: Accurate sequencing of single guide RNAs (sgRNAs) for CRISPR/Cas9 genome editing is critical for patient safety, as the sgRNA guides the Cas9 nuclease to target site-specific cleavages in DNA. An approach to fully sequence sgRNA using protective DNA primers followed by ribonuclease (RNase) T1 digestion was developed to facilitate the analysis of these larger molecules by hydrophilic interaction liquid chromatography coupled with high-resolution mass spectrometry (HILIC-HRMS). Without RNase digestion, top-down mass spectrometry alone struggles to properly fragment precursor ions in large RNA oligonucleotides to provide confidence in sequence coverage. With RNase T1 digestion of these larger oligonucleotides, however, bottom-up analysis cannot confirm full sequence coverage due to the presence of short, redundant digestion products. By combining primer protection with RNase T1 digestion, digestion products are large enough to prevent redundancy and small enough to provide base resolution by tandem mass spectrometry to allow for full sgRNA sequence coverage. An investigation into the general requirements for adequate primer protection of specific regions of the RNA was conducted, followed by the development of a generic protection and digestion strategy that may be applied to different sgRNA sequences. This middle-out technique has the potential to expedite accurate sequence confirmation of chemically modified sgRNA oligonucleotides. Graphical
PubDate: 2023-06-01
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- Development of dispersive liquid–liquid microextraction with solid-phase
evaporation as a novel hyphenated method prior to ion mobility
spectrometry and its application for trace analysis of fluoxetine-
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Abstract: For the hyphenating of dispersive liquid–liquid microextraction (DLLME) with nano mesoporous solid-phase evaporation (SPEV) as a novel method, MCM-41@SiO2 was synthesized and used as a nano mesoporous adsorbent for coating on the solid-phase fiber, preconcentration of fluoxetine antidepressant drug (as a model compound), and total evaporation of the extraction solvents obtained by the DLLME method. To detect the analyte molecules, a corona discharge ionization–ion mobility spectrometer (CD–IMS) was applied. In order to increase the extraction efficiency and the IMS signal of the fluoxetine drug, some variables including extraction solvent and its volume, disperser solvents and its volume, sample solution pH, desorption temperature, and evaporation time of the solvent from the solid-phase fiber were chosen and optimized. Some analytical parameters including limit of detection (LOD), limit of quantification (LOQ), linear dynamic range (LDR) with determination coefficient, and relative standard deviations (RSDs) were calculated under the optimized conditions. LOD (S/N = 3), 3 ng mL−1; LOQ (S/N = 10), 10 ng mL−1; LDR, 10–200 ng mL−1; and intra- and inter-day RSDs (n = 3), 2.5% and 9.6% for 10 ng mL−1, and 1.8% and 7.7% for 150 ng mL−1, respectively. To investigate the ability of the hyphenated method to determine fluoxetine in real samples, fluoxetine tablets, and some biological samples such as human urine and blood plasma were selected and the relative recovery values were calculated to be 85–110%. The accuracy of the proposed method was compared with that of the HPLC standard method.
PubDate: 2023-06-01
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- Nanozyme-based dual-signal sensing system for colorimetric and
photothermal detection of AChE activity in the blood of liver-injured mice
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Abstract: Acetylcholinesterase (AChE), a crucial enzyme related to liver function, is involved in numerous physiological processes such as neurotransmission and muscular contraction. The currently reported techniques for detecting AChE mainly rely on a single signal output, limiting their high-accuracy quantification. The few reported dual-signal assays are challenging to implement in dual-signal point-of-care testing (POCT) because of the need for large instruments, costly modifications, and trained operators. Herein, we report a colorimetric and photothermal dual-signal POCT sensing platform based on CeO2-TMB (3,3′,5,5′-tetramethylbenzidine) for the visualization of AChE activity in liver-injured mice. The method compensates for the false positives of a single signal and realizes the rapid, low-cost portable detection of AChE. More importantly, the CeO2-TMB sensing platform enables the diagnosis of liver injury and provides an effective tool for studying liver disease in basic medicine and clinical applications. Graphical abstract Rapid colorimetric and photothermal biosensor for sensitive detection of acetylcholinesterase (I) and acetylcholinesterase levels in mouse serum (II).
PubDate: 2023-06-01
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- Combining the amplification refractory mutation system and high-resolution
melting analysis for KRAS mutation detection in clinical samples-
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Abstract: The success of personalized medicine depends on the discovery of biomarkers that allow oncologists to identify patients that will benefit from a particular targeted drug. Molecular tests are mostly performed using tumor samples, which may not be representative of the tumor’s temporal and spatial heterogeneity. Liquid biopsies, and particularly the analysis of circulating tumor DNA, are emerging as an interesting means for diagnosis, prognosis, and predictive biomarker discovery. In this study, the amplification refractory mutation system (ARMS) coupled with high-resolution melting analysis (HRMA) was developed for detecting two of the most relevant KRAS mutations in codon 12. After optimization with commercial cancer cell lines, KRAS mutation screening was validated in tumor and plasma samples collected from patients with pancreatic ductal adenocarcinoma (PDAC), and the results were compared to those obtained by Sanger sequencing (SS) and droplet digital polymerase chain reaction (ddPCR). The developed ARMS-HRMA methodology stands out for its simplicity and reduced time to result when compared to both SS and ddPCR but showing high sensitivity and specificity for the detection of mutations in tumor and plasma samples. In fact, ARMS-HRMA scored 3 more mutations compared to SS (tumor samples T6, T7, and T12) and one more compared to ddPCR (tumor sample T7) in DNA extracted from tumors. For ctDNA from plasma samples, insufficient genetic material prevented the screening of all samples. Still, ARMS-HRMA allowed for scoring more mutations in comparison to SS and 1 more mutation in comparison to ddPCR (plasma sample P7). We propose that ARMS-HRMA might be used as a sensitive, specific, and simple method for the screening of low-level mutations in liquid biopsies, suitable for improving diagnosis and prognosis schemes. Graphical
PubDate: 2023-06-01
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- Improved LC–MS identification of short homologous peptides using
sequence-specific retention time predictors-
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Abstract: Peptides are an important group of compounds contributing to the desired, as well as the undesired taste of a food product. Their taste impressions can include aspects of sweetness, bitterness, savoury, umami and many other impressions depending on the amino acids present as well as their sequence. Identification of short peptides in foods is challenging. We developed a method to assign identities to short peptides including homologous structures, i.e. peptides containing the same amino acids with a different sequence order, by accurate prediction of the retention times during reversed phase separation. To train the method, a large set of well-defined short peptides with systematic variations in the amino acid sequence was prepared by a novel synthesis strategy called ‘swapped-sequence synthesis’. Additionally, several proteins were enzymatically digested to yield short peptides. Experimental retention times were determined after reversed phase separation and peptide MS2 data was acquired using a high-resolution mass spectrometer operated in data-dependent acquisition mode (DDA). A support vector regression model was trained using a combination of existing sequence-independent peptide descriptors and a newly derived set of selected amino acid index derived sequence-specific peptide (ASP) descriptors. The model was trained and validated using the experimental retention times of the 713 small food-relevant peptides prepared. Whilst selecting the most useful ASP descriptors for our model, special attention was given to predict the retention time differences between homologous peptide structures. Inclusion of ASP descriptors greatly improved the ability to accurately predict retention times, including retention time differences between 157 homologous peptide pairs. The final prediction model had a goodness-of-fit (Q2) of 0.94; moreover for 93% of the short peptides, the elution order was correctly predicted. Graphical abstract
PubDate: 2023-06-01
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- Preparation of a hydrophobic deep eutectic solvent and its application in
the detection of quinolone residues in cattle urine-
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Abstract: Enrichment for the detection of quinolone residues is usually cumbersome and requires large amounts of toxic organic reagents. Therefore, this study synthesized a low-toxicity hydrophobic deep eutectic solvent (DES) with dl-menthol and p-cresol, which was then characterized by Fourier transform infrared spectroscopy, nuclear magnetic resonance, and thermal analysis. A simple and rapid vortex-assisted liquid–liquid microextraction method was developed based on this DES for the extraction of eight quinolones from cattle urine. The optimal extraction conditions were screened by examining the DES volume, extraction temperature, vortex time, and salt concentration. Under the optimal conditions, the linear ranges of the eight quinolones were 1 ~ 100 μg/L with good linearity (r2 was 0.998 ~ 0.999), and the limits of detection and quantification were 0.08 ~ 0.30 μg/L and 0.27 ~ 0.98 μg/L, respectively. The average extraction recoveries of spiked cattle urine samples were 70.13 ~ 98.50% with relative standard deviations below 13.97%. This method can provide a reference for the pre-treatment of quinolone residue detection. Graphical
PubDate: 2023-05-27
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- Characterization of the natural peptidome of four leeches by integrated
proteogenomics and pseudotargeted peptidomics-
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Abstract: Animal-derived drugs are an indispensable part of folk medicine worldwide. However, their chemical constituents are poorly approached, which leads to the low level of the quality standard system of animal-derived drugs and further causes a chaotic market. Natural peptides are ubiquitous throughout the organism, especially in animal-derived drugs. Thus, in this study, we used multi-source leeches, including Hirudo nipponica (HN), Whitmania pigra (WP), Whitmania acranulata (WA), and Poecilobdella manillensis (PM), as a model. A strategy integrating proteogenomics and novel pseudotargeted peptidomics was developed to characterize the natural peptide phenotype and screen for signature peptides of four leech species. First, natural peptides were sequenced against an in-house annotated protein database of closely related species constructed from RNA-seq data from the Sequence Read Archive (SRA) website, which is an open-sourced public archive resource. Second, a novel pseudotargeted peptidomics integrating peptide ion pair extraction and retention time transfer was established to achieve high coverage and quantitative accuracy of the natural peptides and to screen for signature peptides for species authentication. In all, 2323 natural peptides were identified from four leech species whose databases were poorly annotated. The strategy was shown to significantly improve peptide identification. In addition, 36 of 167 differential peptides screened by pseudotargeted proteomics were identified, and about one-third of them came from the leucine-rich repeat domain (LRR) proteins, which are widely distributed in organisms. Furthermore, six signature peptides were screened with good specificity and stability, and four of them were validated by synthetic standards. Finally, a dynamic multiple reaction monitoring (dMRM) method based on these signature peptides was established and revealed that one-half of the commercial samples and all of the Tongxinluo capsules were derived from WP. All in all, the strategy developed in this study was effective for natural peptide characterization and signature peptide screening, which could also be applied to other animal-derived drugs, especially for modelless species that are less studied in protein database annotation. Graphical
PubDate: 2023-05-03
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