Analytical and Bioanalytical Chemistry最新文献

Protein determination is of importance to characterise insect composition and quality. While different factors (kp) to convert nitrogen to protein have been proposed to report the concentration of crude protein (CP) in insect applications, the N × 6.25 is still utilised by many commercial laboratories. Different data-dependent conversion factors have been proposed to report the concentration of CP in different insect species, including black soldier fly larvae (BSFL). The objective of this paper was to evaluate the effect of using different nitrogen to protein conversion factors (kp) to predict CP in BSFL from different commercial sources using near-infrared spectroscopy. The coefficient of determination in cross-validation (R² _CV) and the standard error in cross-validation (SECV) for the prediction of CP% in the BSFL were 0.75 (SECV, 4.51%), 0.75 (SECV, 4.03%), and 0.75 (SECV, 3.43%), using the kp_6.25, kp_5.65, and kp_4.76, respectively. The study showed that the different kp used can affect the cross-validation statistics (SECV) for the prediction of CP in BSFL using NIR spectroscopy. Additionally, the lower accuracies obtained for the prediction of CP are not only associated with the kp used to calculate the CP but also with the amount of chitin in the BSFL. Understanding the variables, such as reference data, that influence the calibration results using NIR spectroscopy is of importance to better provide consistent QC methods for the industry. The limitations of this study are the few numbers of samples used to develop the calibration models, although different waste streams and larvae stages were evaluated.

Comprehensive two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GC×GC-TOFMS) is a powerful technique suited for resolving complex mixtures of volatile organic compounds (VOCs). In this study, GC×GC-TOFMS was combined with an image-based chromatographic fingerprinting strategy to characterize the fecal volatilome of subjects affected by non-celiac gluten/wheat sensitivity (NCGWS) under dietary intervention. Fifty participants underwent a dietary protocol consisting of a gluten-free diet followed by gluten reintroduction while receiving probiotics or placebo. Headspace solid-phase microextraction (HS-SPME) conditions were specifically optimized to maximize the extraction coverage of volatiles, while a combined untargeted-targeted fingerprinting approach (UT fingerprinting) enabled the mapping of approximately 1000 volatile features in fecal samples. Chemometric analyses, including principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), highlighted informative differences in VOC profiles induced by gluten reintroduction and probiotic administration. Around 270 VOCs were annotated, expanding the existing knowledge on the human fecal volatilome by 20%, predominantly comprising microbial fermentation-derived metabolites such as short-chain fatty acid esters, aldehydes, alcohols, terpenes, and aromatic hydrocarbons. PLS-DA models discriminated probiotic-treated individuals from placebo recipients with 89-90% accuracy, confirming the impact of probiotic supplementation on gut metabolic signatures. The integration of GC×GC-TOFMS and image-based chromatographic fingerprinting effectively captured subtle metabolic variations, demonstrating its suitability in biomarker discovery for dietary interventions and personalized nutrition studies.

Efficient separation of cancer cells from whole blood is vital for early cancer diagnosis. However, disease progression often causes complications that reduce survival, such as cholangitis in liver cancer due to bile-duct compression, with Escherichia coli (E. coli) and Klebsiella as common pathogens. To address this, we developed a sheathless microfluidic sorter that integrates inertial focusing with surface acoustic wave (SAW) manipulation to simultaneously isolate cancer cells and bacteria. Specifically, we introduce a label-free, SAW-driven microfluidic platform that couples passive inertial focusing with active SAW manipulation to enable concurrent separation of cancer cells and bacteria; Cr/Au interdigital transducers (IDTs) are patterned on a 128° Y-cut LiNbO₃ substrate. The system first employs a contraction-expansion microchannel to achieve preliminary separation. Larger particles are then deflected by a slanted interdigital transducer (S-IDT), improving bacterial sorting purity. Small particles are enriched at the first outlet (purity > 96%), and the remaining targets are further refined by a focusing interdigital transducer (F-IDT). Tests with polystyrene (PS) particles achieved >96% purity, and in clinical samples, sorting efficiency and purity exceeded 93% for HepG2 cells and E. coli, with HepG2 viability above 94%. This compact platform unifies active and passive mechanisms, offering promise for early diagnosis and real-time monitoring of complications.

Visualizing the spatial distribution of proteins in biological tissue sections is fundamental to proteomic analysis, as they provide intuitive and comprehensive information for understanding their function. Matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) is a powerful tool for characterizing the spatial distribution of biomolecules. Due to the low sensitivity and insufficient mass resolution of time-of-flight (TOF) mass spectrometers in the high mass range, conventional proteomics strategies typically employ tryptic digestion to cleave proteins into peptides (500-3500 Da) prior to detection. However, existing in situ digestion methods often require long digestion times (> 2h) and are prone to molecule diffusion, leading to the loss of intrinsic spatial information. The present study presented a rapid in situ tryptic digestion method using our self-developed automatic ultrasonic matrix sprayer, SoniCoat. Following digestion with trypsin sprayed with SoniCoat, the tissue was directly coated with α-cyano-4-hydroxycinnamic acid (CHCA) without incubation. Results demonstrated that this method yielded more peptide signals compared with conventional incubation-based approaches and commercial devices. In addition, high-quality images were obtained without molecule diffusion. The established protocol was subsequently applied to a mouse model of liver cancer, revealing spatial distribution differences of proteins between diseased and healthy regions. Our protocol holds promise for supporting the discovery of protein biomarkers for cancer diagnosis.

Mass spectrometry-based lipidomics has emerged as a crucial field for unraveling the complexity of biological systems through comprehensive profiling of lipid species with unparalleled sensitivity and specificity. Among the available techniques, matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI imaging, or MALDI-IMS) stands out as a powerful spatial lipidomics tool, enabling the visualization of distribution and localization of individual lipid classes and species directly within biological tissues. The spatial context of lipids provided by MALDI imaging is especially valuable for studying nutritional and developmental processes in heterogenic tissues. The reliability and quality of MALDI imaging data critically depend on sample preparation, which must preserve both tissue morphology and analyte integrity. Here we introduce a sample preparation protocol for Drosophila melanogaster larval tissue, addressing key limitations of standard protocols such as thin-sectioning. Owing to the small size of Drosophila tissues and the flat morphology of organs such as the fat body, our approach enables direct imaging of the inner organs of the larvae without sectioning. This not only streamlines the workflow and minimizes tissue disruption but also allows simultaneous analysis of all major internal organs in a single measurement. Our approach not only facilitates high-quality MALDI-IMS data acquisition from complex larval tissues but also enables the localization of a broad spectrum of lipid species complementary to data gained by established techniques like liquid chromatography mass spectrometry (LC-MS). These advances position MALDI imaging, when coupled with our optimized preparation workflow, as an indispensable technique for spatially resolved lipidomics studies in small model organisms such as Drosophila larvae.

Effective monitoring of CO₂ leaks from carbon capture and storage (CCS) sites is vital to mitigate environmental risks, including soil acidification and ecosystem disruption. This study presents a novel, sustainable biosensor based on betanin from Beta vulgaris (beetroot) extract for pH-responsive colorimetric detection of soil CO₂ leakage, coupled with smartphone imaging for quantitative analysis. Central to the investigation is a comparative evaluation of Academy Color Encoding System (ACES) AP0 and AP1 primaries against sRGB and DaVinci Wide Gamut color spaces, highlighting AP1's advantages in perceptual uniformity, reduced metamerism, and enhanced computational efficiency for resolving subtle color gradients (effective LOD ~ 0.015 wt% or ~150 ppm using PLS on G-channel), while AP0's expansive virtual gamut excels in archival fidelity but introduces practical artifacts, resulting in higher effective LOD (~0.017 wt% or ~170 ppm). Sensors were fabricated with dialysis membranes, calibrated in a simulated chamber across 0-4.0 wt% CO₂ levels, and imaged using a Realme 9 smartphone. RGB data were extracted via Python libraries (Pillow, OpenCV, NumPy, Colormath, Colour-Science), with machine learning models (partial least squares (PLS), multiple linear regression (MLR), and support vector regression (SVR)) optimized through K-fold cross-validation. The optimal ACES AP1 PLS model on the green channel demonstrated superior performance (R² = 0.9984, RMSE = 0.0519, MAPE = 17.80%), achieving <5% deviation from UV-Vis benchmarks (base LOD ~ 0.0024 wt% or ~24 ppm) during field validation at an Indonesian CCS facility. This low-cost, portable system advances CCS surveillance by integrating natural biosensors with advanced colorimetry, offering scalable, real-time solutions for environmental monitoring.

Aptamer-based pH switches hold significant potential for targeting cancer cells, which exhibit distinct pH microenvironments. Studying the operation mechanisms of such switches is a prerequisite for their rational design. However, the thermodynamics of aptamer-based pH switches remains challenging to explore due to aptamer target instability, laboriousness, uncertainty, or unavailability. This work introduces a novel approach that uses short nucleic acid mimetics to probe the pH-dependent behavior of aptamer switches without requiring the target molecule. By mimicking ATP binding, these probes enable the analysis of thermodynamic transitions in a model ATP aptamer-based pH-dependent system. Two complementary approaches-free energy scanning and melting curve fitting-were developed to determine effective dissociation constants and the corresponding Gibbs free energy changes for ATP binding to a set of pH switches. The results showed that the proposed techniques are applicable to reliably determine the Gibbs free energy changes of specified switch states with high precision. The mimetic-based strategy circumvented the limitations of conventional aptamer target application and demonstrated consistency with prior studies. This method provides a versatile framework for the rational analysis of environment-responsive molecular devices, advancing applications in diagnostics and targeted therapies.

The sensitive detection of luteolin is crucial for monitoring its levels in botanical sources, assessing its clinical pharmacokinetics, and guaranteeing the efficacy of luteolin-containing products. In this study, a high-performance electrochemical sensing platform for luteolin detection was developed based on a gold nanoparticle (AuNPs)/Ti₃C₂T_x-MXene composite. Ti₃C₂T_x-MXene was first etched from the Ti₃AlC₂-MAX phase using hydrofluoric acid and further functionalized with polyethylenimine to endow it with a positive surface charge. This modification facilitated the electrostatic self-assembly of negatively charged AuNPs onto the MXene surface, forming a stable AuNPs-MXene composite with enhanced electrical conductivity and a large effective surface area. The electrochemical behavior of luteolin at the AuNPs-MXene-modified glassy carbon electrode was systematically investigated. Cyclic voltammetry and density functional theory calculations confirmed an adsorption-controlled, reversible redox process involving two electrons and two protons at the catechol hydroxyl groups of luteolin. Molecular electrostatic potential maps and highest occupied molecular orbital distribution further verified the catechol hydroxyls as the primary redox-active sites. Under optimal conditions, the sensor displayed a wide linear response range from 1.0 × 10^-4 μM to 100 μM and a low detection limit of 0.034 nM, outperforming most previously reported electrochemical sensors for luteolin. Its practical application was validated by determining luteolin in commercial Duyiwei capsules and chrysanthemum samples via the standard addition method. This work not only offers a reliable tool for luteolin detection but also extends the application of MXene-based composites in the electrochemical sensing of flavonoids.