Search Results (454)

This review evaluates multi-omics applications in dairy research across nutrition, safety, and health. Through multi-omics integration, we reveal nutrient differences driven by species, rearing practices, and processing techniques, identify protein patterns and allergen profiles, and construct adulteration detection fingerprints and species-specific peptide markers, thereby improving the timeliness and accuracy of safety assessment. The coupling of metagenomics and metabolomics effectively predicts spoilage-related microbial risks, enabling better risk control. Furthermore, multi-omics approaches systematically elucidate the functional mechanisms of bioactive peptides (e.g., ACE-inhibitory peptides), clarify the prebiotic effects of functional oligosaccharides, and build interaction networks between dairy components and gut microbiota. The introduction of machine learning enables origin and shelf-life prediction, as well as the discovery of novel biomarkers, promoting personalized nutrition and precision fermentation strategies. However, the field is currently constrained by severe reproducibility issues arising from the absence of standardized operating procedures, excessive optimism regarding machine learning models that rarely generalize across laboratories or product matrices, and a persistent disconnect between laboratory-scale biomarker discovery and industrial implementation. Without rigorous cross-platform validation and openly shared multi-omics reference datasets, most published markers remain unfit for regulatory or industrial application. Future efforts should establish standardized workflows and expand the evidence base to drive the dairy industry toward safer, healthier, and more traceable directions. Full article

Andean crops can be efficiently incorporated into food industrialization after the characterization of their components. This study evaluated tarwi (Lupinus mutabilis Sweet) flours from three ecotypes: PNTF (punto negro), WTF (white), and MTF (moro), with a particle size of 125 µm, analyzing their color, proximate composition, amino acid profile, bioactive compounds, and spectroscopic, thermal, and microstructural properties. Significant differences among ecotypes were determined at p < 0.05. The white ecotype showed greater accumulation in Dx (50), while black point exhibited the highest Dx (90), indicating a higher proportion of large particles. Regarding color, WTF presented the highest lightness and whiteness index, PNTF intermediate values, and MTF the darkest coloration, with greenish tones in black point and reddish tones in moro. The MTF ecotype showed the highest protein content (56.28%) and higher levels of essential amino acids, with methionine being the limiting amino acid. It also contained phenolic compounds ranging from 29.97 to 35.49 mg GAE/100 g, flavonoids from 9.36 to 10.8 mg quercetin/100 g, and antioxidant capacity measured by DPPH ranging from 25.79 to 55.30 mg TE/100 g, particularly notable in MTF. PNTF stood out for its dietary fiber (5.93%) and carbohydrate (17.22%) content. Infrared spectroscopy analysis revealed a similar macromolecular fingerprint among the samples. Differential Scanning Calorimetry (DSC) and Thermogravimetric Analysis (TGA) indicated greater thermal stability in MTF. Scanning Electron Microscopy (SEM) revealed greater compaction of irregular particles in MTF and greater dispersion in PNTF. These results support the differentiated valorization of tarwi ecotypes as complementary raw materials for the development of high-value-added foods in the current food industry. Full article

Estimating the post-feeding developmental time of necrophagous larvae remains challenging because body size and behavior provide limited resolution after larvae cease feeding. This study integrated behavioral observation, attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR), chemometric regression modeling, transcriptomic analysis, and reverse transcription quantitative polymerase chain reaction (RT-qPCR) validation to characterize post-feeding development in Sarcophaga peregrina (Robineau-Desvoidy) under 25 °C. The post-feeding stage began at 84.83 ± 1.34 h after larviposition and lasted 38.21 ± 2.29 h. ATR-FTIR spectra from 0, 10, 20, 30, and 40 h post-feeding larvae showed temporal changes in the 1800–900 cm⁻¹ fingerprint region, mainly involving bands associated with proteins, lipids, polysaccharides, and chitin. Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and variable importance in projection (VIP) analyses identified discriminative spectral regions, while regression models further evaluated time-estimation performance. Among the models, partial least squares regression (PLS-R) showed the most balanced predictive ability, with the coefficient of determination in prediction (R²P) = 0.920 and the root mean square error of prediction (RMSEP) = 4.00 h in the prediction set. Transcriptomic analysis identified 4641 differentially expressed genes, and RT-qPCR confirmed temporal expression patterns of selected candidate genes. These molecular changes were mainly associated with cuticle/chitin remodeling, protein metabolism, and energy metabolism. Overall, ATR-FTIR combined with chemometric modeling provides a complementary biochemical approach for estimating post-feeding developmental time in S. peregrina under controlled 25 °C conditions, although validation at other temperatures is required before broader application. Full article

Recently, there has been great interest in AI-based approaches for de novo design of novel drug candidates. However, the generation of useful lead drug candidate compounds requires more than predicting engagement with the desired protein target. Candidate molecules must also be anchored in the real world of medicinal chemistry for their synthesis and modification as well as satisfying multiple drug development-related criteria. Here, we present Nevermore, an AI target-conditioned, database-grounded workflow for prioritizing candidate ligands from large compound libraries. Nevermore uses a geometry-aware protein–ligand affinity oracle to score target-specific binding and perform sparse integer edits in count-based Morgan fingerprint space. Nevermore then retrieves the most structurally similar molecules from public chemical databases. This design enables multi-objective search over predicted affinity and absorption, distribution, metabolism, excretion, and toxicity (ADMET) proxies while keeping all candidates anchored to valid database compounds. We evaluated Nevermore’s performance across three biologically distinct targets: Menin, a protein-interaction target relevant to leukemia; SARS-CoV-2 M^pro, a viral cysteine protease relevant to antiviral discovery; and epidermal growth factor receptor (EGFR), a kinase-superfamily oncology target with extensive experimentally tested compounds. Nevermore retrieved candidate sets with favorable predicted affinity–property trade-offs. These results support database-grounded fingerprint steering as a practical computational strategy for lead prioritization and for generating testable molecular hypotheses, although the prioritized candidates remain predictions, requiring follow-up experimental validation. Full article

This study examined Kefiran, an exopolysaccharide derived from milk kefir grains, as a novel biopolymer for encapsulating phenolic extracts from sunflower cake and its antimicrobial properties in the development of natural and functional food ingredients. Kefiran was obtained from kefir grains using three extraction protocols: hot water (M1), hot water with 30% trichloroacetic acid (M2), and mild heat combined with ultrasound at 60 °C (M3). The ultrasound-assisted method produced the highest carbohydrate concentration. Spectrophotometric assays (phenol–sulfuric and Bradford), Fourier transform infrared spectroscopy, scanning electron microscopy, thermogravimetric analysis, and water-holding capacity were employed to characterize the composition, structure, and morphology of the extracts, revealing well-preserved polysaccharide fingerprints and a highly porous microstructure, consistent with their potential application in food systems. Kefiran was then evaluated as an encapsulating agent in complex coacervation at pH 3.75, using three Kefiran-based wall formulations (M1, M2, and M3) with gum arabic and whey protein isolate (WPI) as co-wall materials, and their performance was compared with gum arabic and WPI controls. Across formulations, coacervate microcapsules achieved high encapsulation efficiencies (83–93%), tunable particle sizes, and predominantly negative zeta potentials, indicative of good colloidal stability. The Kefiran extract and coacervate microcapsules demonstrated significant antioxidant and antimicrobial activity against Staphylococcus aureus, Escherichia coli, Pseudomonas aeruginosa, and Candida albicans, with minimum inhibitory concentrations ranging from 250 to 1000 µg/mL. The findings support ultrasound-extracted Kefiran as a multifunctional biopolymer suitable for bioactive delivery and as a natural antimicrobial component in advanced functional food formulations. Full article

Chinese lacquerware is a multi-layered natural polymer composite whose characterization is complicated by burial degradation, organic–inorganic mixing, and the overlap of signals from lacquer, drying oils, proteins, polysaccharides, waxes, and pigments. This review evaluates analytical strategies for Chinese lacquerware by distinguishing three complementary levels of evidence: morphological and elemental observation, chemically specific molecular fingerprinting, and biomolecular source recognition. Microscopy, Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy, and scanning electron microscopy–energy dispersive spectroscopy (SEM-EDS) are useful for identifying stratigraphy, pigments, fillers, and functional groups, but they are often insufficient for assigning degraded organic matrices and trace additives independently. Pyrolysis–gas chromatography/mass spectrometry provides more specific molecular evidence through diagnostic marker classes, including alkyl catechols, alkyl phenols, nitrogen-containing pyrolysis products, anhydrosugars, long-chain aliphatics, aldehydes, and ketones. Immunological assays based on lacquer glycoproteins further complement chemical analysis by supporting biological source differentiation, although their reliability depends on protein preservation, extraction efficiency, and antibody specificity. Representative case studies, including a seventeenth-century Swedish lacquered pipe, the Nanyue Kingdom lacquered ear cup, and a Tang Dynasty lacquered leather artifact, show that robust interpretation requires cross-validation among stratigraphic, elemental, spectroscopic, chromatographic, immunological, and archaeological evidence. The review concludes that integrated analytical workflows can improve material identification, clarify manufacturing sequences, assess degradation uncertainty, and provide more reliable evidence for conservation decision-making and the reconstruction of historical lacquer craftsmanship. Full article

Anaerobic digestion (AD) of agro-industrial residues supports the green energy transition by converting organic matter into renewable biogas. Sugar beet pulp is a highly fermentable feedstock, although its process response may vary with chemical composition. This study examined how chemical composition affects mesophilic biogas-production kinetics of sugar beet pulp prepared under laboratory conditions from surplus sugar beet roots. The roots represented ten sugar beet varieties (A–J), and the prepared pulp was characterised for pH, dry matter, organic dry matter, mineral composition, and the relative shares of simple sugars, polysaccharides, protein, and fibre. Batch digestion tests were performed at 39 °C for 30 days. Production curves were analysed using complementary kinetic models (modified Gompertz and a two-fraction first-order model) to capture the lag phase and the contributions of rapidly and slowly degradable substrate pools. Biogas yields ranged from 126 to 141 m³ Mg⁻¹ fresh matter with 50–55% CH₄, corresponding to 64.3–76.1 m³ CH₄ Mg⁻¹ organic dry matter, while organic matter conversion reached 71.2–82.4%. Varieties enriched in simple sugars exhibited a higher share of the fast-degradable fraction and shorter lag phases, indicating faster onset and stronger methane formation. In contrast, higher fibre contents reduced the slow-fraction rate constant and lowered overall conversion, consistent with hydrolysis-limited degradation of the structural carbohydrate matrix. The mineral ion background, particularly K and Na, indicated moderate ionic buffering and stable operation without inhibition. The novelty of this work lies in integrating detailed compositional profiling with dual kinetic modelling to translate chemical fingerprints into tentative process-relevant implications. These implications include feeding strategy, organic loading control and hydraulic retention time selection, and they require further validation in continuous or semi-continuous AD systems. Full article

Background: The translocation of diet-derived antigens from the maternal intestine to breast milk represents a primary gateway for neonatal immune priming, yet the structural basis for why certain proteins survive this transit while others do not remains poorly understood. This review introduces the “Survivor Peptide” hypothesis, proposing that specific food allergens possess intrinsic “stability architectures” that enable them to resist maternal digestion and navigate the gut–mammary axis to reach the infant in an immunologically active form. Methods: We analyzed the current literature regarding the detection and structural characteristics of food allergens in human milk. Integrating evidence from 26 major sources, we performed an in silico structural analysis of five representative “survivor” proteins: Gal d 1 (egg white), Bos d 5 (cow’s milk), Gal d 6 (egg yolk), Tri a 19 (wheat), and tropomyosin (Der p 10-mite/shellfish). High-resolution 3D models were retrieved from the Protein Data Bank and AlphaFold2, and then visualized in UCSF ChimeraX to map stability anchors, including disulfide bonds and hydrophobic clusters, against solvent-accessible IgE-binding epitopes. Results: We identified and categorized allergens into distinct Molecular Resilience Architectures: the “Covalent Cage” (Gal d 1), defined by dense disulfide stapling, the “Glycoprotein Shield” (Gal d 6), utilizing yolk-matrix structural anchors, the “Topological Shield” (Bos d 5), characterized by a stable β-barrel, and “Coiled-Coil Rigidity” (Der p 10). These frameworks protect large, immunogenic fragments that maintain the spatial arrangement required for IgE cross-linking. Conclusions: Allergen persistence in the gut–mammary axis is dictated by a protein’s intrinsic structural architecture. Identifying these stability fingerprints provides a unified theory for allergen persistence and offers a path for refining component-resolved diagnostics and neonatal oral tolerance strategies. Full article

Computer-aided drug discovery increasingly depends on virtual-screening workflows that remain reliable under severe class imbalance, chemical redundancy and early-recognition constraints. In this study, we developed a leakage-aware prioritization workflow for two cancer-relevant targets, pyruvate kinase M2 (PKM2) and mitogen-activated protein kinase 1 (MAPK1/ERK2), using the LIT-PCBA benchmark. The workflow combines canonical-SMILES curation, duplicate and label-conflict auditing, scaffold-aware validation, a non-learning nearest-active Tanimoto baseline, imbalance-aware machine-learning models, repeated-seed robustness analysis, isotonic probability calibration, ensemble-disagreement estimation, absorption, distribution, metabolism, excretion and toxicity (ADMET)-aware triage, molecular docking, and residue-level contact analysis. Benchmark enrichment is interpreted alongside calibration, ADMET filtering, docking and residue-contact evidence, rather than as a standalone discovery claim. PKM2 emerged as the clearer machine-learning case, with scaffold-aware tree models improving early recognition beyond the nearest-active similarity baseline and yielding top-ranked candidates supported by calibrated activity scores, ADMET profiles, docking scores, and residue-contact fingerprints. MAPK1 provided a biologically relevant contrast target, where ligand-neighborhood similarity remained competitive and downstream structural triage became more decisive than ligand-based ranking alone. These results support a conservative drug-discovery workflow in which leakage-aware benchmarking, calibration, uncertainty, and molecular-level triage remain visible throughout candidate prioritization. Full article

The INFOGEST protocol is a standardised in vitro digestion model widely utilised to evaluate the digestibility and bioaccessibility of nutrients in diverse food matrices. This review focuses on its application since 2020 (after the publication of the INFOGEST 2.0 model) to milk and dairy products, which often serve as a suitable food matrix in digestion studies. By analysing 50 studies selected using a semi-automated method, this review highlights its strong performance in reproducing general digestive trends, including peptide fingerprint profiling, consistent high-protein digestibility, and matrix-dependent lipid and mineral bioaccessibility. The model is particularly effective in evaluating structural modifications of dairy products and their impact on digestive behaviour. However, its application remains skewed toward bovine systems, limiting broader relevance to other dairy matrices. Methodological variability, including protocol modifications and emerging semi-dynamic adaptations, poses challenges to reproducibility. Furthermore, reliance on simplified downstream models constrains the physiological interpretation of bioactivity and nutrient absorption. Future progress requires harmonised dynamic extensions, expanded use of advanced biological systems, and inclusion of diverse dairy matrices. Collectively, these advances will support a shift from descriptive bioaccessibility toward more predictive assessments of nutrient bioavailability. This six-year, non-topic-dependent bibliometric analysis contextualises the expanding adoption of INFOGEST 2.0 as reflected in its versatility and evolving scope, positioning it as a cornerstone tool for advancing our understanding of dairy nutrition, digestion-derived bioactivity, and ultimately, the relationship between dairy consumption and human health. Full article

Cancer remains a leading cause of mortality worldwide, largely due to dysregulated apoptotic signaling and the persistent activation of oncogenic pathways. However, natural products are a promising source of multi-target anticancer agents. In this study, we investigated the anticancer activity and underlying mechanisms of Clausena harmandiana root extract and its major carbazole alkaloid, 7-methoxyheptaphylline, both in vitro and in vivo. High-Performance Liquid Chromatography (HPLC) chemical fingerprinting confirmed the presence of bioactive coumarins and carbazole alkaloids in the extract. Cytotoxicity assays demonstrated that the extract significantly reduced the viability of human colorectal adenocarcinoma (HT-29), human hepatocellular carcinoma (HepG2), human lung adenocarcinoma (A549–Luc2), and murine Lewis lung carcinoma (3LL–Luc2) cells in a dose- and time-dependent manner. Our mechanistic investigations revealed the activation of JNK signaling, downregulation of anti-apoptotic proteins (Bcl-2, Bcl-xL, and Mcl-1), and increased cleaved caspase-3 expression, indicating that mitochondrial apoptosis was induced. Notably, 7-methoxyheptaphylline markedly suppressed STAT3 phosphorylation in a concentration-dependent manner, comparable to the STAT3 inhibitor JSI-124. In a syngeneic 3LL–Luciferase2 lung cancer mouse model, oral administration of C. harmandiana capsules significantly reduced tumor growth and bioluminescence intensity compared with controls. These in vivo findings were consistent with the inhibition of STAT3 signaling and induction of apoptosis observed in vitro. Collectively, our results demonstrate that C. harmandiana exerts broad-spectrum anticancer activity through coordinated modulation of the JNK–STAT3 axis, leading to caspase-dependent apoptosis. These findings highlight its potential as a promising candidate for the development of STAT3-targeted anticancer therapies. Full article

The transition to a post-antibiotic era necessitates novel interventions to mitigate gastrointestinal inflammation and optimize metabolic efficiency in monogastric animals. This review evaluates the Hermetia illucens (BSF) lipid matrix as an evolutionary signal sensor rather than merely a caloric substrate. The BSF lipid fingerprint—rich in lauric acid and bioactive co-factors—exerts a synergistic “entourage effect,” which is proposed to thermodynamically disrupt pathogenic membranes and engage GPR84/PPARγ crosstalk to silence sterile inflammation. Metabolically, medium-chain fatty acids bypass the CPT-1 bottleneck, enabling rapid mitochondrial ATP rescue that supports intestinal tight junction restoration. This targeted immunomodulation is hypothesized to underpin an “immune-energy sparing” effect—redirecting bioenergetic fluxes from inflammatory antagonism toward muscle protein deposition—a phenomenon that correlates with improved feed conversion ratios in vivo. Full article

Background: Nitrous oxide (N₂O) is increasingly used as a recreational drug, leading to neurological and systemic toxicities. However, due to the rapid elimination and minimal alteration of nitrogen oxides, the short direct detection window complicates the assessment of N₂O exposure. Method: In this study, we investigated the effects of chronic N₂O exposure on plasma metabolites using an untargeted metabolomics approach in a mouse model. C57BL/6 mice were exposed to 90,000 ppm N₂O (1 h, twice daily for 28 days) or room air. Plasma samples were analyzed via UHPLC -Triple TOF -MS. Orthogonal partial least squares discriminant analysis (OPLS-DA) and receiver operating characteristic (ROC) curves were used to identify differential metabolites. Result: A total of 35 differential metabolites were identified. Eight metabolites with an area under the curve (AUC) > 0.90 were selected as candidate biomarkers, including up-regulated SOPC and PC(16:0/16:0) (suggesting disrupted phospholipid remodeling and membrane integrity), and down-regulated DL-tryptophan, creatine, ectoine, indole, His-Ser, and Ile-Pro. Pathway enrichment analysis revealed significant alterations in glycine, serine and threonine metabolism; phenylalanine, tyrosine and tryptophan biosynthesis; protein digestion and absorption; and tryptophan metabolism. Conclusions: Our data indicate that chronic N₂O exposure disrupts multiple amino acid-related metabolic pathways (e.g., tryptophan-kynurenine pathway) and phospholipid homeostasis. The identified metabolite changes, along with vitamin B₁₂, homocysteine, and methylmalonic acid, may constitute a specific metabolic fingerprint for N₂O exposure. These findings help reveal the intrinsic mechanistic links underlying metabolic disorders induced by N₂O exposure. Full article

Selective inhibition of poly(ADP-ribose) polymerase 1 (PARP1) may reduce the hematologic toxicity associated with dual PARP1/PARP2 inhibition. We performed molecular dynamics simulations for five selective inhibitors in complexes with PARP1 and PARP2, using three independent 50 ns runs per complex after docking and equilibration, followed by protein–ligand interaction fingerprint and statistical analyses. All complexes remained dynamically stable, with ligand root-mean-square deviation values generally within 0.3 nm. Comparative analysis identified three αF-helix residue pairs with nominally reduced interaction frequencies in PARP2: Asn767/Ala336, Leu769/Gly338, and Asp770/Asp339 (p < 0.05). After Benjamini–Hochberg correction for multiple comparisons, Leu769/Gly338 remained significant (q < 0.05), indicating that this pair represents the most statistically robust interaction difference within this region. Using palacaparib as the most selective inhibitor, these differences were associated with weakened or lost hydrophobic, van der Waals, and cation–π interactions in PARP2. Selective binding of modern PARP1 inhibitors appears to be associated with αF-helix-dependent interaction patterns, providing a mechanistic basis for the rational design of next-generation selective inhibitors with improved selectivity and potentially reduced toxicity. Full article

Background/Objectives: Early transient endocrine dysfunction after simultaneous pancreas-kidney transplantation (SPK) frequently triggers urgent investigations to exclude thrombosis, pancreatitis, or rejection, yet many recipients recover during the index admission. We tested whether pre-transplant day zero (D0) serum Fourier-transform infrared spectroscopy (FTIRS) captures a biochemical fingerprint associated with a Start&Stop trajectory (initial insulin independence followed by transient dysfunction with recovery). Methods: In a single-center retrospective case-control study nested within 104 consecutive SPK recipients with available D0 serum, 12 Start&Stop cases were matched 1:1 to 12 No-Stop controls. Serum FTIR spectra went through structured quality control and standardized preprocessing. A Naïve Bayes classifier with Fast Correlation-Based Filter (FCBF) feature selection was evaluated using leave-one-out cross-validation (LOOCV) and label-permutation analysis. Results: Under LOOCV, the primary FTIRS model (Savitzky-Golay second derivative; 600–900 and 2800–3400 cm⁻¹) achieved excellent discrimination (ROC-AUC 1.00) with accuracy 0.958 and F1 score 0.958. Discrimination collapsed under label permutation (ROC-AUC 0.461), supporting a non-random label-spectrum association. Discriminant information mapped mainly to carbohydrate/glycoprotein-associated bands (~946–1161 cm⁻¹), protein structural contributions near the amide III region (~1300 cm⁻¹), and lipid/protein stretching modes (~2865–3163 cm⁻¹), consistent with a multicomponent systemic biochemical state. Conclusions: In this exploratory matched case-control cohort, pre-transplant D0 serum FTIRS signatures were associated with the subsequent Start&Stop phenotype after SPK. These findings should be interpreted as recipient-side exploratory risk-stratification signals rather than clinically actionable decision tools. Larger multicenter validation in unselected cohorts, with standardized endpoint adjudication, preanalytical control, fully nested model development and inter-instrument harmonization, is required before clinical implementation or population-level risk calibration. Full article