ACS Measurement Science Au最新文献

Phytocannabinoids are a diverse class of bioactive compounds produced by Cannabis sativa, including both major and a growing number of minor constituents with pharmacological relevance. However, their comprehensive annotation in untargeted high-resolution mass spectrometry (HRMS) data sets remains a significant analytical challenge due to their structural similarity, low abundance, and the complexity of plant matrices. In this study, we present a comparative evaluation of Kendrick Mass Defect (KMD)-based filtering workflows for the efficient untargeted annotation of minor phytocannabinoids. Three data processing strategies were implemented using Compound Discoverer: (i) KMD filtering before the "Compound Detection" tool, (ii) KMD filtering after the "Compound Detection" tool, and (iii) a pseudo-KMD approach based on the generation of expected compounds. These workflows were tested and compared using a data set comprising 50 Cannabis inflorescence samples analyzed in an untargeted fashion, taking into account the phytocannabinoid coverage, false positive rates, computation burden, and versatility. A total of 61 phytocannabinoids were annotated, including a full series of alkyl homologues (C1-C7), cis/trans isomers, O-methylated derivatives, and sesquicannabinoids. Statistical analyses revealed meaningful chemical differentiation based on seed origin, chemovar classification, and reproductive strategy (dioecious vs monoecious), highlighting the biological significance of minor cannabinoids. Overall, the results demonstrate that KMD filtering significantly enhances the throughput and accuracy of untargeted HRMS workflows for structurally related classes of compounds.

Monitoring chemistry in the central nervous system in vivo is an important route to better understand chemical signals and metabolism underlying brain function. Sampling from the brain extracellular space allows for in depth and multiplexed analysis of this compartment. Miniaturized sampling devices prepared by using microfabrication are emerging as tools that allow high spatial resolution measurements with low invasiveness. In this work, we describe a microfabrication method for Parylene-C-based push-pull sampling probes. Parylene-C is an attractive material because its biocompatibility and flexibility may allow for chronic measurements. Probes with 3 mm long shanks and 45 × 166 μm cross sections were prepared and tested for suitability for in vivo experiments. Two designs, one with push-pull orifices on the probe tip and the other with the orifices recessed ∼40 mm within a sheath of Parylene-C, were investigated. The sheathed probes successfully flowed in tofu (n = 5) and ex vivo brain tissue (n = 5) for at least 3 h at 100 nL/min. In contrast, only 60% (n = 5) of the end-on probes flowed successfully in tofu. In vitro recoveries for the sheathed and design were 66 ± 2 and 91 ± 7%, respectively, in stirred solution. The sheathed probes were tested by sampling from the cortex of anesthetized rats. In 13 of 19 experiments, push-pull fractions were successfully collected. Of the six failures, all had a sheath shallower than the 30 μm design depth, suggesting that this depth is necessary for reliable sampling flow. Basal concentrations of 20 neurotransmitters and metabolites determined by liquid chromatography with tandem mass spectrometry (LC-MS/MS) were comparable with concentrations recorded using microdialysis sampling in prior work. Infusion of 2.0 mM nipecotic acid through the probe resulted in a significant increase in GABA with little effect on other compounds. Untargeted metabolomics analysis by LC-MS/MS identified 141 metabolites within the push-pull perfusates. The results demonstrate the feasibility of using Parylene-C push-pull probes with a sheathed tip design for multiplexed and temporally resolved monitoring of brain chemistry in vivo.

This study examines how Mg²⁺ ions affect the hybridization between surface-immobilized peptide nucleic acid (PNA) probes and microRNA targets (miR125 and miR141), which is important for the development of nucleic acid-based biosensors utilizing surface plasmon resonance (SPR). The results show that appropriate concentrations of Mg²⁺ significantly enhance microRNA hybridization with PNA probes, whereas Na⁺ does not yield similar results. Kinetic analysis demonstrated that 30 and 100 mM concentrations of Mg²⁺ facilitate the interaction between the PNA probe and its microRNA target by effectively screening the negative charges of the microRNA molecules as they approach the surface. These Mg²⁺ levels also stabilize the heteroduplexes formed on the surface by reducing the dissociation rate. However, a higher Mg²⁺ concentration (300 mM) was found to hinder the surface-confined hybridization. In comparison, Na⁺ showed a considerably smaller role in improving the hybridization. Melting curve analysis in solution indicated that the increase in T _m of PNA/miRNA heteroduplexes in the presence of Mg²⁺ does not fully explain the enhanced surface interaction, underscoring the role of surface confinement. These findings demonstrate that optimizing the Mg²⁺ concentration can significantly improve the sensitivity and efficiency of PNA- and SPR-based microRNA biosensors. This optimization is particularly relevant for diagnostic and research applications involving the analysis of low concentrations of microRNAs in biofluids.

Acetylcholine (ACh) is a neurotransmitter that plays critical roles in human health and diseases. To better understand ACh signaling in the brain, developing analytical capabilities for its selective and quantitative measurement in real time is essential. While electrochemical amperometry offers exceptional temporal resolution, most in vivo electrochemical ACh sensors have limited selectivity, such as against choline (the product of ACh hydrolysis) and ascorbic acid. Here, we report a micropipet-supported ITIES (interface between two immiscible electrolyte solutions), which demonstrated the selective detection of ACh against choline, ascorbic acid, and other neurotransmitters using chronoamperometry. The detection of ACh is based on its ion transfer across a water/oil interface, which was formed between an aqueous solution and a 2-nitrophenyl octyl ether (NPOE) solution containing an ACh ionophore, termed water/NPOE-ionophore ITIES. The ionophore was heptakis-(2,6-di-O-methyl)-β-cyclodextrin (DM-β-CD). The addition of DM-β-CD in the NPOE phase resulted in an easier ACh transfer at the water/NPOE-ionophore ITIES compared with that at the ITIES without the ionophore, suggesting the presence of ionophore-facilitated ion transfer in addition to the direct ion transfer at the water/NPOE-ionophore ITIES. We observed a linear detection of ACh on the water/NPOE-ionophore ITIES. When implanted in the cortex of the brain of a live mouse, the water/NPOE-ionophore ITIES tracked the dynamic concentration changes of the injected ACh in the brain. The measuring techniques are broadly applicable to quantifying real-time ACh release in the brain with negligible interference, enabling a better understanding of neurological disorders and diseases.

The COVID-19 pandemic has presented significant challenges for the effectiveness of existing diagnostic tools in detecting and monitoring infections. Currently, there is an increased emphasis on the potential challenges faced by individuals during their postrecovery phase. The impact of post-COVID conditions (PCC) has substantially influenced perspectives on disease management, fostering a positive trend toward personal healthcare. Here, we report a ZnO-modified, non-Faradaic impedimetric biosensor for the rapid detection of TRAIL and D-dimer across clinically relevant ranges. The dual-analyte platform demonstrated great sensitivity (LOD: 3.4 pg/mL for TRAIL, 8.9 ng/mL for D-dimer), with high specificity in human plasma. Optimized surface chemistry and impedance analysis enabled reliable signal acquisition from 5 μL samples in 5 min. Beyond detection, we introduce the PCCODE (Post-COVID Co-dysregulation Evaluator) threshold-based classifier model using quantified concentration output values-TRAIL <50 pg/mL, D-dimer >1000 ng/mL-to encode biomarker signals in four binary states. This logic-driven system was constructed using exogenously spiked plasma samples and validated through signal-mapped heatmaps, allowing stratification of healthy, inflammation, immune dysregulation, and post-COVID categories. Together, the biosensor and classifier framework enable real-time, mechanism-informed stratification of PCC, marking a significant advance toward point-of-care diagnostics.

Post-translational modifications have emerged as a key biomolecular process in the onset of neurodegenerative disorders, such as the hyperphosphorylation of tau protein in Alzheimer's disease (AD). High levels of phosphorylation are related to tau malfunction, self-assembly into amyloids forming neurofibrillary tangles in the brain, and cellular toxicity. These molecular processes are also reflected in human biofluids, allowing us to use tau phosphorylation as a biomarker of the disease onset and progression. However, it is not yet clear what structural changes the tau protein undergoes upon phosphorylation and what the early self-assembly steps are that lead to the formation of the final amyloid species. This knowledge gap is related in large part to the experimental challenge in achieving a multiparametric physical-chemical characterization of nanoscale size and heterogeneous amyloid at the single-molecule level. Here, we employ high-resolution and advanced atomic force microscopy methods to study the effect of phosphorylation on the tau pathway of self-assembly and on the physical-chemical properties of the heterogeneous amyloid species formed, down to the single-oligomer level. We report the correlative analysis of single-oligomer structural and chemical properties and achieve, for the first time, the quantitative determination of their phosphorylation state. Our findings reveal that hyperphosphorylation results in the formation of smaller, stiffer, and more adhesive oligomers, which might be critical for their pathological role in AD. This nanoresolved information might be, in turn, useful to understand the early molecular mechanisms of disease, as well as to improve the detection of pathological tau species in biofluids as diagnostic biomarkers.

The global burden of multidrug-resistant tuberculosis (MDR-TB) underscores the urgent need for novel therapeutics with distinct mechanisms of action. Here, we report a comparative evaluation of four antimicrobial peptides (AMPs) derived from the amphibian peptide B1CTcu5, integrating experimental validation with molecular modeling to elucidate structure-activity relationships. Among them, W-B1CTcu5, featuring a single N-terminal tryptophan substitution, exhibited the most potent antimycobacterial activity (MIC = 3.2 μg/mL) against Mycobacterium tuberculosis (MTB) combined with high structural stability, persistent membrane interaction, and multitarget affinity against key MTB proteins, including the porin MspA, the transporter CpnT, and the cell wall enzyme Ag85B. In contrast, analogs with reduced hydrophobic anchoring or dynamic instability demonstrated diminished efficacy despite partial membrane insertion or surface affinity. Molecular dynamics simulations revealed that peptides with low root-mean-square deviation and minimal residue fluctuation retained compact, α-helical conformations and maintained productive bilayer engagement, which are traits correlated with antimicrobial performance. However, the hemolytic properties of W-B1CTcu5 highlight a therapeutic trade-off between potency and host toxicity. Together, these findings emphasize the predictive power of dynamic structural descriptors in AMP design, and identify W-B1CTcu5 as a promising, yet optimization-requiring, scaffold for future design of anti-TB AMPs.

Accurate evaluation and comparison of site-normalized catalytic activity (turnover frequency, TOF) in heterogeneous catalysis require consideration of catalyst nanoparticle (NP) size and geometry. In this study, we systematically quantify the impact of NP geometry on the fraction of surface atoms across FCC, BCC, and HCP crystal structures with various geometries and evaluate the absolute and relative errors introduced by assuming spherical NPs. Using catalytic H₂ combustion (CHC) over an octahedron Ni catalyst supported on γAl₂O₃ as a model experiment, we demonstrate that assuming spherical single-crystal Ni NPs underestimates the fraction of the surface atoms and overestimates TOF by 86%. This discrepancy arises from the miscalculation of surface site availability in spherical approximations. These findings emphasize the need for geometry-specific models to ensure reliable TOF calculations and accurate catalyst performance comparisons in heterogeneous catalysis. We work provide a framework for geometry-dependent TOF calculations, offering new insights into morphology-controlled catalyst design and facet-specific reactivity optimization.

Ammonia is a critical impurity in hydrogen fuel due to its irreversible poisoning effect on proton exchange membrane fuel cells. Therefore, international standards (e.g., ISO 14687) set a stringent threshold of 100 nmol/mol. Furthermore, with the growing potential use of ammonia as a hydrogen carrier, its accurate quantification is becoming increasingly important. However, the presence of trace humidity poses analytical challenges, as ammonia may interact with water or interfaces, thereby affecting its detectability. Therefore, the goal of this work is to enable accurate trace ammonia quantification for hydrogen purity measurements through fundamental studies of the methodological challenges. Here, low-pressure sampling (ultra)-long-path Optical-Feedback Cavity-Enhanced Absorption Spectroscopy (OF-CEAS) was applied with an effective optical path length of approximately 6.17 km. We studied three average amounts of ammonia: (38.2 ± 0.8) nmol/mol, (74.8 ± 0.7) nmol/mol, and (112.1 ± 1.2) nmol/mol. Furthermore, these amounts were investigated at trace-humidity levels ranging from 0.8 to 8.5 ppm_V. We observed a systematic, nonlinear, and humidity-dependent positive measurement bias of up to + (1.0 ± 0.2) nmol/mol at the maximum investigated trace-humidity volume fraction of 8.5 ppm_V. This bias was not caused by spectral interference but rather by water-induced accumulation of ammonia within the optical cavity. Moreover, time-resolved measurements in the presence of trace ammonia showed that water desorption follows first-order kinetics, whereas water adsorption followed mixed-order kinetics with an apparent reaction order of 1.57 ± 0.03. Distinct hydration states of surface-bound ammonia were identified, whereas under dry conditions and with increasing amounts of ammonia, enhanced surface adhesion through intermolecular clustering was observed. In addition, the presence of ammonium species within the sorption layer was indirectly confirmed by our experiments. In conclusion, we provide a deeper insight into trace-level ammonia-water interactions and establish a framework for optimizing methodologies, particularly for (ultra)-long-path optical gas measurement systems.

One of the ways to improve the performance of electrospray ionization (ESI) mass spectrometry (MS) is to introduce additives to the sample solution. Alternatively, such additives can be introduced in the gaseous form directly into the electrospray plume. Normally, only one additive can be introduced via one method. Here, we present a flexible automated system that enables dynamic switching among several gas-phase additives, which can alternately be introduced to the ion source compartment over a short period of time. These additives include vapors of acids and solvents. We show that different gaseous additives enhance the signals of different analytes to a varied extent. The enhancement factors were in the range ∼ 2-15. We applied the gas-phase additive switching in real sample analysis, where it enhanced signal intensities and broadened the detection range. The automated system also enables the dosing of vapors at different concentrations. Unlike conventional approaches that saturate the electrospray plume with a fixed vapor level, controlled dosing of acid or solvent vapor levels enables optimization of the protein signal intensity and facilitates structural probing. Overall, it is possible to systematically vary both the type and the concentration of vapor additives using a single setup, improving the analytical performance and versatility of ESI-MS. The proposed setup is compatible with liquid chromatography.