Analytical and Bioanalytical Chemistry

Hydrogen-deuterium exchange mass spectrometry (HDX-MS) requires extraction of broad, time-dependent peptide isotope envelopes from noise and overlapping signals. The ExMS/ExMS2 lineage established a retention-time-aware framework for raw-data extraction and integrated HDX-MS analysis. Building on this foundation, msengine is an open-source Python engine that adapts mass-selection windows and retention-time boundaries to the observed data. Spectra are reconstructed as nonnegative combinations of the theoretical undeuterated (all-H) isotope distribution shifted across allowed deuteration states, without prescribing a population shape. The fitted weights, termed deuteration coefficients, describe each state's contribution and guide refinement of mass-selection windows. Spectral matches establish an initial retention interval, which is then expanded using chromatographic intensity to include weaker portions of the elution profile. Interference screening and final quality checks assess the recovered interval. In a 1:1 mixture of undeuterated and deuterated material, interval expansion changed the relative population intensities within the extracted envelope resulting in closer agreement to the 1:1 target, illustrating the importance of boundary selection. Across a four-timepoint experiment with the SET1/SWD2 complex collected in duplicate, msengine accepted an average of 901 peptide-charge targets per measurement versus 636 for ExMS2, a 41.7% increase, while improving replicate-centroid disagreement and retention-window widths between replicates. In a same-workstation benchmark, total wall time decreased from 71.9 to 7.3 min, with higher peak memory use. Together, these results demonstrate adaptive signal extraction across mass and retention time as a means to retain multimodal envelopes while increasing accepted-target yield, persistence, accuracy, and throughput.

Proteolysis can be completed in a single-step, rapid (5-30 minutes) reaction using hyperthermoacidic archaeal (HTA) proteases. Protocol simplicity and stability of HTA proteases in ambient storage conditions removes many logistical barriers to widespread deployment of proteomic analysis. HTA protease cleavage selectivity is complicated but quantitatively reproducible and generates peptides that negligibly overlap with trypsin. Tryptic and HTA protease peptides emphasize different proteins and provide complementary coverage of amino acid sequence and post-translational modification (PTM) sites. Digestion of a sample's separate aliquot with HTA protease is a minor addition to trypsin-based workflows, but LC-MS analysis of both sets of peptides is not practical for large cohorts. This manuscript evaluates the identifications and reproducibility of LC-MS analysis of tryptic and HTA protease peptide mixtures. Adding HTA protease peptides to 20% increased total protein identifications by 24% on average in neat plasma and identified 216 additional non-artifact modifications compared to conventional trypsin-only bottom-up proteomic analysis. This approach is reproducible and does not increase sample preparation or analysis time. Peptides from each protease present independent opportunities to quantify overlapping proteins and a quantification model was developed in which the protease with the most complete data is selected for each protein while the second protease is used to infer missing values. This cross-protease inference is more accurate than the alternative minimum, mean, or nearest neighbor imputations. Finally, biomarkers discovered through HTA protease peptides are amenable to rapid HTA protease only proteomic workflows thus streamlining translation from discovery to fieldable applications.

In the enzymatic toolbox of depollution, hydrolytic dehalogenases are of particular interest for the bioremediation of organohalogenated compounds. To better identify and characterize these enzymes, it is important to develop appropriate sensing methods. Fluorescent probes for dehalogenase detection have been developed in the past years, however, to this date none were available for application using the common FITC parameters (i.e. excitation laser at 488 nm). To address this limitation, we herein report a new fluorescent probe, COU4-Cl, excitable in the violet (405 nm) and blue (488 nm) wavelengths adapted for the detection and characterization of haloalkane dehalogenases. Its applicability is discussed and evaluated in comparison to the previously described COU2-Cl probe only excitable in the violet (405 nm). This study shows the advantages and complementarity of the two probes, and further demonstrate their application for the discrimination of dehalogenase activity in whole cell assays.

Variable-temperature electrospray ionization native mass spectrometry (vT-ESI nMS) provides a means to determine how temperature-dependent changes in protein and solution dynamics influence individual ligand-binding reactions within multiligand protein complexes. Here, the GroEL single-ring mutant (SR1), which binds up to seven nucleotides, serves as a model system due to its sensitivity to solution conditions. Native MS resolves individual ligand-bound populations, while vT-ESI extends these measurements across temperature (3-43 {degrees}C), enabling determination of binding affinities and the associated Gibbs free energy, enthalpy, entropy, heat capacity changes ({Delta}Cp), and enthalpy-entropy compensation (EEC) for each binding reaction. Temperature-dependent changes in van,t Hoff curvature produce distinct {Delta}Cp profiles, indicating changes in the dynamics governing ligand binding that may reflect contributions from protein conformational and protonation microstates. Temperature-dependent shifts in average charge state, consistent with changes in solvent-accessible surface area, indicate corresponding changes in protein conformation. Binding is predominantly enthalpy-driven below ~23 {degrees}C, with increasing entropic contributions at higher temperatures, while the seventh ADP binding reaction exhibits a distinct EEC profile. Comparison of the seventh ADP binding reaction in H2O and D2O reveals pronounced differences in van,t Hoff curvature,{Delta}Cp, and EEC, demonstrating that changes in the hydration environment alter the thermodynamic response. Collectively, these measurements show that temperature-dependent changes in protein and solution dynamics alter the distributions and thermodynamics of individual ligand-bound states and demonstrate the utility of vT-ESI nMS for resolving these effects in complex multiligand systems.

The acquisition of sufficient nitrogen for growth, as well as the disposal of highly toxic nitrogen-containing degradation products, represent serious metabolic challenges in all multicellular organisms and microbial ecosystems. Understanding these processes requires a broadly applicable system to accurately quantify the concentration of a wide spectrum of biological amines and inorganic nitrogen-containing compounds. To date, this has been challenging due to limited specificity, selectivity, breadth and/or sensitivity of assays. Here we present a simple, optimized method based on liquid chromatography and high-resolution mass spectrometry, with an accompanying analysis pipeline, for the accurate quantification of more than 30 of these compounds, including ammonia. We demonstrate the effectiveness of this method in quantifying relevant nitrogen-containing compounds in mouse and human gut content, bacterial cultures, algal symbiosis in a cnidarian and freshwater ecosystem samples. As a proof-of-concept, we applied the method to study the influence of the gut microbiome on cecal nitrogen distribution in mice with different gnotobiotic microbiomes. This revealed a profound influence of gut microbiota composition on amino acid, ammonia, and amine concentrations in the upper large intestine. Overall, we provide a broadly applicable approach that can be used across microbial ecology fields to generate novel insights into the biology of nitrogen assimilation, exchange, and disposal.

This paper predicts antimicrobial resistance (AMR) from matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectra using DRIAMS, evaluated across 13 species-antibiotic datasets spanning sensitive/resistant imbalance ratios of 2.95:1 to 141.21:1 and raw spectral similarities of 0.9166-0.9946. We propose HetSAGE, a heterogeneous graph neural network (GNN) that combines full-spectrum "raw" edges with consensus "biomarker" edges derived from a 4-method feature-selection vote and applies per-edge-type dropout to regularize the two views separately. HetSAGE outperforms a plain multilayer perceptron (MLP) only when spectral similarity is high, consistent with oversmoothing effects reported in the broader GNN literature; the biomarker view converges on features close to independently established clinical markers: within {+/-}1 Da in Staphylococcus aureus, Oxacillin, and within range in Klebsiella pneumoniae, Meropenem, confirming it is not a black box. At the same time, per-edge-type dropout protects this smaller signal from dilution by regularization tuned for the noisier raw spectrum. Together, these results indicate that heterogeneous graph structure is not a universal win for MALDI-TOF AMR prediction but a conditional one, tied to a measurable property of the data, spectral similarity, rather than to architecture alone. This caveat is largely unaddressed in current DRIAMS-based GNN work. It provides a concrete signal for practitioners deciding when a graph model is worth the added complexity over a plain MLP.

Detecting low-abundance bacterial peptides in samples dominated by human proteins using liquid chromatography tandem mass spectrometry (LC MS/MS) is challenging. Bacterial peptides are typically far less abundant than host-derived peptides, and sequence similarity between bacterial and human peptides can complicate confident attribution to a bacterial origin. To address this problem, we developed and benchmarked an in silico spectral library protein identification workflow for detecting Staphylococcus aureus USA300 peptides against a background of A375 cells. Peptide and protein identifications obtained at a 1% peptide-level false discovery rate were compared between the in silico spectral library search and a conventional sequence database search across a defined bacterial dilution series containing 0, 1%, 2%, 10%, 25%, 50%, and 100% bacterial protein. A human sequence similarity filter was applied to identifications from both search strategies to reduce the likelihood of retaining human derived or human like peptide sequences. Both search strategies showed concentration dependent bacterial detection and shared a practical detection limit of approximately 1 to 2% bacterial protein but exhibited distinct performance profiles. The in silico spectral library search produced fewer bacterial-like background identifications in the human-only control, whereas MS2Rescore substantially increased spectral library identifications at higher bacterial concentrations, exceeding database search at 100% bacterial protein. The partially overlapping peptide sets identified by the two strategies indicate that they provide complementary coverage of the bacterial proteome.

Paper-based colorimetric sensors offer a low-cost and accessible platform for point-of-care (POC) analysis, but enzyme activity loss during coating and drying can weaken analytical signals and require high enzyme loadings or complex immobilization procedures. Although surfactants are widely used to improve wettability in paper-based assays, their potential contribution to colorimetric performance beyond these effects remains unclear. Here, we investigated surfactant-assisted colorimetric signal enhancement in a glucose assay implemented on a 96-puddle paper plate (96-PPP) and identified Tween 20 as the most effective surfactant. Its effect on detection performance became more pronounced as glucose oxidase (GOx) loading decreased; at 0.1 mg/mL GOx, Tween 20 lowered the limit of detection (LoD) from 0.113 to 0.034 mg/mL (approximately 3.3-fold) over a working range of 0-5 mg/mL, despite no statistically significant change in the measured contact angle at this loading. Tween 20 had no appreciable effect on the reaction in solution but preserved 95% of the apparent reaction rate constant after drying, compared with 11% without it, and atomic force microscopy (AFM) revealed a more dispersed dried enzyme morphology on mica. Tween 20-containing sensors also showed slower signal decay during repeated wetting-drying cycles and thermal stress, retained 77% (vs 26%) of the response at 400 mM NaCl, and exhibited within-PPP and between-batch coefficients of variation (CVs) below 10% (vs 12.3-19.5%), while maintaining glucose selectivity over potentially interfering molecules. These results indicate that Tween 20 enhances paper-based glucose sensing beyond wettability, in part by retaining enzyme cascade activity during drying, although the contributions of the individual enzymes and the underlying mechanism remain to be established.

The modeling of aroma release in food science is a complex, multidisciplinary field integrating mass transfer theory and principles of physical chemistry. While mechanistic models have long been used to understand volatile release, their practical adoption has been hindered because existing implementations are often private, "in-house" tools that lack reproducibility and extensibility. This paper introduces digimouth, an open-source Python framework designed to provide a unified, modular, and reproducible environment for simulating and fitting aroma release kinetics. A key innovation of digimouth is its handling of events and stages. Events represent discrete, instantaneous changes in the system (such as opening a bottle or swallowing), while stages allow for multi-phase simulations where experimental conditions or physical dynamics change over time. To demonstrate the tool's utility, the authors implemented established in vitro convection models and performed sensitivity analyses on parameters such as air-water partition coefficients and mass transfer coefficients. The package also includes an optimization module based on the least-squares method to fit parameters to experimental data. Validation against real-time data from Proton Transfer Reaction-Time of Flight- Mass Spectrometry (PTR-ToF-MS) shows a satisfactory fit, with an average error of approximately 2%. Ultimately, digimouth facilitates robust model development and hypothesis testing, offering the food science community a collaborative tool to better understand the mechanisms of flavor release.

The rapid expansion of mRNA-based medicines has driven demand for robust, high-resolution analytical methods capable of characterising critical quality attributes including sequence identity, 5[prime] capping efficiency, and poly(A) tail length and heterogeneity. Here, we present two complementary liquid chromatography-mass spectrometry based workflows for mRNA characterisation, both based on site-specific digestion with the endoribonuclease MazF from Escherichia coli. A middle-up approach, optimised via specific 5[prime]-ACA cleavage enables rapid characterisation via oligoribonucleotide mass mapping providing complete sequence coverage and simultaneous assessment of 5[prime]-capping efficiency and 3[prime]-poly(A) tail length and heterogeneity. This workflow was successfully applied to three different mRNA constructs, NLuc, eGFP and FLuc mRNAs, achieving sequence coverages of 100%, 87%, and 74%, respectively. A complementary bottom-up approach, employing less specific 5[prime]-ACX cleavage enables tandem mass spectrometry-based sequencing of shorter oligoribonucleotides and detailed characterisation via mRNA sequence mapping. Applied to SARS-CoV-2 Spike Protein mRNA, this method yielded 50% sequence coverage based on unique oligoribonucleotides with MazF alone, extended to 90% by combining MazF, partial RNase T1, and partial RNase U2 digests. Together, these workflows provide a flexible, orthogonal platform for both high-throughput quality assessment and in-depth primary sequence characterisation of mRNA vaccines and therapeutics.

Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants with the potential to disrupt metabolic processes and contribute to carcinogenesis; however, molecular evidence linking alternative and emerging PFAS to ovarian cancer (OC) remains limited. We conducted a biomarker-based case-control study integrating exposureomics with multi-omics profiling. Plasma samples from 128 patients with OC and 128 controls were analyzed for 26 PFAS, untargeted metabolomics, lipidomics, and clinical phenotypes, while urine samples from an additional 100 patients and 100 controls underwent untargeted metabolomic and clinical profiling. Single-compound, burden-based, and mixture models were integrated with PFAS-omics association scans, cross-matrix metabolite prioritization, observational exposure-omics-clinical association-chain analyses, and repeated internal cross-validation. Higher PFAS burdens were consistently associated with OC case status, and DONA, GenX, PFNA, PFPeS, PFUdA, and PFTrDA constituted an OC-prioritized six-PFAS mixture. DONA and GenX exhibited the broadest lipid-related bridging signals, particularly across phospholipids, lysophospholipids, sphingolipids, neutral lipids, and acylcarnitines. Integrated plasma and urine analyses further identified five shared metabolites-elaidic acid, hippuric acid, betaine, corticosterone, and taurocholic acid-that provided incremental and complementary discrimination beyond reference models based on clinical variables and tumor markers in internal cross-validation. Collectively, these findings demonstrate that both alternative and emerging PFAS are associated with a distinct OC-related molecular phenotype involving coordinated lipidomic, metabolomic, and hematologic alterations, while highlighting DONA and GenX as the most prominent exposure-related signals.

Summary: Mass spectrometry resolution of glycosphingolipids requires advanced separation methods. The virtual-ly identical structures of glycolipid epimers cannot be discriminated by routine multiple reaction monitoring-high-performance liquid chromatography-electrospray ionization-tandem mass spectrometry (MRM-LC-ESI-MS/MS). Sep-aration can be achieved using field asymmetric (differential) ion mobility mass spectrometry (FAIMS/DMS). A major bottleneck to MRM-LC-ESI-DMS-MS/MS method development lies in manually optimizing the compound-dependent parameters of separation voltage (SV) and compensation voltage (CoV). To address this challenge, we developed intelligent Differential Mobility Spectrometry (iDMS). iDMS is a supervised deep neural network that evaluates and predicts the optimal ion mobility SV and CoV parameters for any user-requested monoglycosphingolipid from a training set of measured signal intensities composed of 12 lipids (6 stereoisomer pairs). We initially introduced iDMS as a series of python scripts. Here, we share desktop applications of both iDMS and its pre-processing input data assembly modules (DMS Data Extraction Toolkit). Both software assemblies enable users to rapidly deploy a validated machine learning alternative to manual DMS optimization. The applications are user-friendly, operate with-out need for any technical operating environment, make training dataset assembly quick and simple, and return measured, normalized, and predicted ionograms at every user-interrogated SV, evaluation of separability at each SV and CoV combination, and prediction of optimal resolution parameters to accelerate users MRM-LC-ESI-DMS-MS/MS deployment. Availability and implementation: Desktop iDMS and DMS Data Extraction Toolkit are native desktop applications for macOS (Apple Silicon), freely available for download at https://www.neurolipidomics.com/platforms.html under the GNU v3.0 license. The source code is available from GitHub and Zenodo at https://github.com/Neurolipidomics/iDMS, https://zenodo.org/records/22214408, https://github.com/Neurolipidomics/DMSDataExtractionToolkit, and https://zenodo.org/records/22777403. Bench-marking metrics are available at https://www.biorxiv.org/content/10.64898/2026.08.26.747394v1.

Central carbon metabolism coordinates energy production and carbon skeleton allocation across photosynthetic and heterotrophic organisms. Here, we established an optimized targeted metabolomics workflow for Chlamydomonas reinhardtii and Arabidopsis thaliana using a single-run HILIC-MS/MS assay with alternating positive and negative ionization. Our method provides robust multi-class coverage across 21 amino acids, 11 carboxylic acids, 4 sugars, 3 adenine nucleotides, 2 nucleotide sugars, 18 phosphorylated compounds in CCM, and Acetyl-CoA in a 20-minute run using both ESI modes. An optimal concentration of medronic acid with a BEH Amide column at high pH prevented metal adsorption, and low quantification limits (10 to 50 nM) for labile intermediates such as PEP, ATP and RUBP were achieved. Retention time variations remained below 1.3%, and over 90% of analytes showed high linearity (R2 > 0.990) across dynamic ranges spanning two to three orders of magnitude (typically 0.01-0.5 uM up to 15-20 uM). For sample preparation, glass-fiber filters (GF/C) outperformed nitrocellulose for harvesting C. reinhardtii. For A. thaliana, it was proved that no further grinding with micro pestle and extended cooling were necessary. A two-phase chloroform/methanol/water extraction was essential, as a single-phase extraction co-extracted green chlorophyll, which heavily suppressed phosphorylated signals. In Arabidopsis, glutamate, fumaric acid and citric acid were the dominant primary pools, indicating the importance of dilution and column washing for preventing carryover, and sensitive intermediates were reliably quantified at physiological levels. Together, this unified analytical and extraction strategy enables robust, artifact-free, high-throughput profiling of central metabolism in photosynthetic organisms.

GeneCapture is developing a novel, point-of-care, rapid AST technique that, when fully automated, can accomplish AST within 2 hours directly from a raw sample, representing a significant advancement in diagnostic capabilities. For this study, a panel of uropathogen isolates was collected from clinical specimens positive for urinary tract infection from various laboratories in the southern US. A selection of these isolates were then spiked into fresh clean-catch urine samples to produce mock samples covering a variety of antibiotic responses. Testing was performed on 20 strains of Escherichia coli, the cause of over 80% of UTIs, and 6 strains each of Klebsiella pneumoniae and Proteus mirabilis, the next most common causes. Each strain was tested against the susceptible, intermediate and resistant (S-I-R) breakpoints of up to 5 appropriate antibiotics including Amoxicillin-Clavulanate, Cefpodoxime, Ciprofloxacin, Nitrofurantoin and Trimethoprim-Sulfamethoxazole. The new method uses a phenotypic assessment of bacterial growth, comparing responses from bacteria treated with antibiotics at S-I-R breakpoint concentrations to untreated controls as the metric for determining susceptibility. Exposure to the drugs for only 90 minutes gave a Categorical Agreement of 98% to gold standard culture results across the resulting 247 bacteria/antibiotic pairs. Testing on separate days of nine of these strains in triplicate gave a reproducibility of 97%. The sequential steps of this novel AST - sample processing, growth, labeling, and counting - can readily be performed on an automated cartridge that is currently under development. The presented work, which took place in a laboratory setting, demonstrates the efficacy of the assay.

Hormone dynamics are central to a multitude of conditions in women's health; however, limitations in the cost, convenience, and multiplexing capacity of current hormone assays preclude routine longitudinal hormone testing. This limitation severely restricts our understanding in this historically neglected field, presenting a need for improved bioanalytical tools to support hormone-informed care. Silicon photonic resonator sensors have excellent multiplexing capacity and scalable fabrication, and offer strong potential to meet this important need. Here we demonstrate, for the first time, multiplexed silicon photonic detection of two urinary hormone biomarkers, follicle-stimulating hormone (FSH) and pregnanediol-3-glucuronide (PdG), using sub-wavelength grating ring resonators and a polydopamine-based, regenerable surface chemistry. We combine a sandwich immunoassay for FSH and an antigen-down competitive immunoassay for PdG on the same chip, both amplified using a commercially available quantum-dot-streptavidin conjugate and with on-chip regeneration over multiple measurement cycles. In artificial urine, this proof-of-principle multiplexed assay achieves limits of detection of 0.594 ng/mL for FSH and 19.1 ng/mL for PdG. This architecture is inherently scalable to tens of hormone targets on a millimetre-scale chip (a >10x multiplexing improvement over typical lateral-flow immunoassays) enabling future low-cost, quantitative at-home hormone panels that could transform research, diagnostics, and treatment in women's health.

Inhalation of airborne per- and polyfluoroalkyl substances (PFAS) represents an important yet understudied exposure route. While volatile organic compounds (VOCs) have been widely explored as biomarkers of toxicant exposure, their potential for early detection of PFAS-induced pulmonary toxicity remains largely unexplored. This study investigated VOC profiles in human alveolar (A549) and bronchial (BEAS-2B) epithelial cells following exposure to environmentally relevant PFAS concentrations (1, 5, and 10 ppb) for 12, 24, and 48 h. VOCs were analyzed using headspace solid-phase microextraction coupled with gas chromatography-Orbitrap mass spectrometry. We observed significant concentration- and time-dependent alterations in VOC profiles in both cell models. The principal VOCs associated with PFAS exposure include butanal, acetone, 2-butanone, 2-nonanone, (E)-2-nonenal, (E)-2-octenal, (E)-2-heptenal, 2,4-heptadienal, 2,4-nonadienal, 2,4-decadienal, 1-pentanol, and 1-hexanol. Cell type-specific regulations were observed, reflecting differential metabolic responses between transformed and non-transformed lung epithelial cells. Metabolite set enrichment analysis indicated perturbations in ketone body metabolism, consistent with altered cellular energy metabolism. Complementary qPCR analysis revealed altered expressions of CYP1A1, CYP2E1, ALDH1A1, and ALDH2, supporting a mechanistic link between PFAS exposure and oxidative stress, lipid peroxidation, and impaired aldehyde detoxification. These findings demonstrate that VOC profiling, combined with transcriptional analysis, can reveal PFAS-induced metabolic perturbations in pulmonary epithelial cells.

BACKGROUND: The protein 4.1a/4.1b ratio is an intrinsic molecular marker of mean red blood cell (RBC) age resulting from progressive deamidation of protein 4.1b during RBC circulation. Despite its potential for estimating mean RBC age from a single blood sample, its application has been limited by labor-intensive RBC membrane preparation, and its interindividual variability has not been characterized in a large human population. METHODS: We developed a low-volume method for direct RBC membrane isolation from whole blood using saponin permeabilization and differential centrifugation without prior RBC washing. Membrane proteins were separated by SDS-PAGE, and protein 4.1a/4.1b ratios were determined by densitometric analysis with Gaussian peak fitting. Assay conditions, sample-volume requirements, membrane protein recovery, and analytical reproducibility were evaluated. The method was subsequently applied to 114 anonymized residual samples from routine diagnostic testing selected for complete blood count parameters within the corresponding laboratory reference intervals. RESULTS: The optimized procedure used 50 uL of whole blood, 0.06% saponin, and centrifugation at 9000 g, although protein 4.1a/4.1b remained measurable from starting volumes as low as 5 uL. Mean within-subject CVs were 3.90% for electrophoretic/densitometric determination and 5.39% for membrane protein recovery; whole-procedure CVs in 3 subjects were 1.40% to 3.50%. In the population analysis, the mean protein 4.1a/4.1b ratio was 1.428 (SD 0.146), with an observed range of 1.052 to1.799 and a central 95% interval of 1.151 to 1.695. Ratios did not differ significantly between females and males (P=0.246) or among females aged 18 to 50 years, females aged >50 years, and males (P=0.151). The ratio showed modest positive correlations with chronological age in females (Spearman p=0.26, P=0.047) and males (p=0.33, P=0.015), while substantial interindividual variability remained across the age range. CONCLUSIONS: Direct isolation of RBC membranes from small volumes of whole blood enables reproducible determination of the protein 4.1a/4.1b ratio using a simplified workflow. Application of the assay to a comparatively large human population revealed substantial interindividual variation in this molecular marker of mean RBC age that was not explained by sex or chronological age alone. The method provides a practical approach for investigating biological variation in mean RBC age and its potential contribution to laboratory measurements influenced by RBC survival.

The perception of flavor during food consumption is a complex process governed by the dynamic release of aroma compounds, which is influenced by the interaction between the food matrix and human physiology. While mechanistic models have been developed to simulate these phenomena, their practical application has often been limited by the lack of flexible software tools capable of integrating diverse food structures and real-time physiological data. This study addresses this gap by incorporating in vivo mechanistic models for both liquid foods and melting solid matrices in a modular Python-based framework: digimouth. The modeling approach treats the oral, pharyngeal, and nasal cavities as a series of interconnected reactors with mass transfer governed by ordinary differential equations (ODEs). These models explicitly integrate key physiological parameters, including salivary flow, bidirectional respiratory airflow, and discrete events such as mastication and swallowing. For solid foods, the framework incorporates additional mechanisms for matrix dissolution, temperature-dependent melting, and dynamic surface area change. The models were validated using experimental data from 30 subjects who consumed aroma solutions and flavored gelatin gummy discs while monitored by PTR-MS (Proton Transfer Reaction - Mass Spectrometry). The simulations successfully reproduced the main kinetic features and release profiles observed experimentally for both types of matrices. Despite some limitations related to the self-reporting of physiological events and the non-inclusion of initial orthonasal mass transfer in the models, the developed software package proved to be a robust and evolutive tool. It offers a foundation for exploring inter-individual variability and the impact of complex food structures on sensory perception.

Diet can rapidly change the fecal metabolome, but less is known about recovery after the original diet is restored. We used untargeted UPLC-MS metabolomics to analyze 72 fecal samples from nine Pumi dogs during an owner-managed switch from dry food to raw food and back to dry food. Diet phase accounted for a large proportion of variation in both ionization modes. More than 13,000 LC-MS features changed at the first sampling point after the switch to raw food, with a similarly large response after return to dry food. Among features significant in both comparisons, more than 99% changed in opposite directions. At the final sampling point, no positive-mode (ESI+) features and only 13 negative-mode (ESI-) features differed from the second dry-food baseline under the same threshold. BARF-associated patterns persisted in analyses excluding individual dogs and in pedigree-adjusted candidate models, although individual feature effects depended on normalization. Putative metabolites from several biochemical classes differed in their response and recovery. The fecal metabolome therefore changed rapidly and returned largely toward baseline, with differences among dogs.

Phenylketonuria (PKU) is an autosomal recessive disorder caused by PAH gene defects and can cause irreversible neurological damage as early as the fetal period; however, non-invasive prenatal monitoring methods are lacking. In this study, Pah+/- female mice were mated with Pah+/-; male mice and wild-type male mice, respectively. Urine samples were collected longitudinally from pregnant mice at 10 time points during gestation from Day 1 to Day 19. Data-independent acquisition (DIA) quantitative proteomics combined with GO/KEGG analysis, WGCNA, and a dual-strategy LASSO-random forest approach was used to screen candidate biomarkers. The results showed that stable differentially expressed proteins between groups were detectable as early as Day 1, and the differences persisted throughout gestation and exhibited temporal dynamics. Permutation tests indicated that the probability of random generation of the differential protein combination was low (0.04-0.06), suggesting that it was not randomly generated. The intersection of the two strategies yielded five proteins, GINM1, PRG4, LY6A, NTM, and YIPF3, and after including GLO1, a total of six candidate biomarkers were obtained. The candidate proteins had interaction or co-expression associations with 14 proteins involved in PKU mechanisms, with GLO1 serving as a network hub. This study found that the maternal urine proteome can non-invasively reflect the systemic response induced by fetal-origin PKU, providing proof of concept and candidate biomarker resources for non-invasive early-pregnancy screening of PKU.