Search Results (443)

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

Compound–target interaction (CTI) prediction plays a critical role in drug discovery and the functional study of food-derived bioactive compounds. However, traditional experimental methods for CTI identification are limited by high costs, long cycle times, and high false-positive rates, highlighting an urgent need for more efficient approaches. Machine learning (ML) has become a revolutionary tool to address these challenges. In this review, we focus on recent developments in ML-based CTI prediction. We first systematically outline the commonly used public databases and feature extraction methods for both compounds (molecular fingerprints) and proteins (sequence-derived features), followed by elaborating on four types of ML approaches, including classical supervised learning, matrix factorization, graph topology-based inference, and deep neural network frameworks. In particular, this review explores the emerging application of these computational approaches in identifying targets of food-derived bioactive compounds, underscoring its significant potential to advance functional food research. Moreover, we analyze key challenges, such as limited model interpretability, high data dependency, and insufficient multi-source information integration, and put forth future prospects to improve the prediction of food-derived CTIs, thereby facilitating their application in functional food research. Full article

Background: Identifying potential targets for bioactive compounds is crucial for elucidating the mechanisms of action and drug development. Methods: This study presents ComTarget, a computational tool that integrates 3D molecular shape similarity analysis (based on combined 3D descriptors, C3DD) with reverse docking to predict protein targets for small molecules. ComTarget screens against a library of 4429 unique protein targets derived from 26,272 PDB complexes. Results: Validation on benchmark datasets (DEKOIS 2.0 and DUDE-Z) demonstrated that the C3DD molecular similarity calculation method effectively enriches active ligands by capturing critical 3D shape information not evident from chemical topology alone. It outperformed conventional 2D fingerprint methods and offered a favorable balance between shape sensitivity and computational efficiency, serving as a rapid pre-screening filter within the integrated workflow. For FDA-approved drugs (e.g., Imatinib, Aspirin) and natural products (e.g., Berberine). ComTarget identified targets consistent with reported therapeutic targets or putative off-targets in the literature, while also revealing potential targets aligned with the compounds’ pharmacological mechanisms. Conclusions: As a local program, ComTarget offers flexibility in computational resources customization and is freely available for polypharmacology studies, drug repurposing, and adverse reaction prediction. Full article

Background/Objectives: Selective inhibition of JAK1 remains a major challenge in cytokine-signaling therapeutics due to the high structural similarity of the JAK family. Here, we present an integrated computational framework that combines large-scale binding-site conformational analysis, ensemble docking, and protein–ligand interaction fingerprinting (PLIF) to elucidate the structural determinants of JAK1 selectivity and prioritize JAK1-biased scaffolds. Methods: A curated set of JAK1 and JAK2 catalytic-domain structures was clustered to capture binding-site diversity, and representative conformers were evaluated using >2300 annotated ligands. Docking performance was assessed via AUC, early enrichment metrics, and structural pose validation against experimentally resolved complexes. The workflow was subsequently applied to a library of ~6000 drug-like compounds to prioritize candidates with predicted JAK1 preference. Results: Across the ensemble, the most predictive features reliably separated active from inactive ligands (AUC = 0.78–0.82) and captured subtle, systematic rank shifts supporting the reported JAK1 bias. Interaction fingerprint analysis revealed a conserved hinge-binding motif required for potency, alongside a JAK1-enriched hotspot adjacent to Glu aD.55 that contributes to isoform discrimination. Applied to a library of ~6000 drug-like molecules, the workflow yielded 174 candidates predicted to exhibit preferential JAK1 recognition and reduced JAK2 engagement. Conclusions: These findings define the structural and physicochemical features underlying JAK1 selectivity and illustrate how ensemble-based modeling can guide the discovery of next-generation selective kinase inhibitors. Full article

Background/Objectives: Natural compounds occupy a pharmacologically rich chemical space, characterized by abundant scaffolds, extensive functional group elaboration, and defined stereochemistry. In this context, Helichrysum italicum, a Mediterranean medicinal plant, represents a valuable source of polyphenols with multiple biological and pharmacological activities. Methods: Here, we introduce an inverse molecular docking fingerprint approach to systematically investigate eight major Helichrysum italicum polyphenols, including $\alpha$-pyrones (arzanol, ethylpyrone), flavonols (gnaphaliin, kaempferol, quercetin), and flavanones (naringenin, pinocembrin, hesperetin). More than 40,000 human protein structures from the Protein Data Bank were screened to generate target-based inverse docking score fingerprints for each compound. Results: Hierarchical clustering of these fingerprints revealed shared binding patterns among structurally related polyphenols and enabled hypothesis generation regarding potential synergistic effects. Notably, favorable interactions were identified with PPARG and CARM1, supporting therapeutic relevance in inflammation and cancer, alongside additional targets associated with neurodegeneration and bone metabolism. Conclusions: This study establishes inverse docking fingerprints as a robust, mechanism-oriented method for natural product research and highlights Helichrysum italicum polyphenols as starting points for medicinal chemistry and drug discovery. Full article

Brewer’s spent grain (BSG) is an abundant lignocellulosic by-product whose valorization can support circular bioeconomy strategies. This study evaluated BSG bioconversion by Aspergillus oryzae ATCC 10124 under solid-state fermentation (SSF) to produce lignocellulolytic enzymes and release second-generation (2G) sugars relevant to biorefinery applications. SSF was monitored over 0–10 days, and FPase, endo-cellulase, β-glucosidase, xylanase, mannanase, amylase, and ligninolytic enzyme activities were quantified. Enzymatic crude extracts were further assessed in SDS-PAGE analysis. Glucose, cellobiose, xylose and arabinose release and consumption were tracked throughout fermentation, and substrate transformation was supported by FTIR. The secretome exhibited a predominantly hydrolytic profile, with maximal hemicellulolytic and cellulolytic activity around days 2–4, as well as sustained amylase activity. Ligninolytic activity was not detected. Sugar profiles indicated rapid early hydrolysis of glucose, followed by progressive pentose release. The stabilization and decline were consistent with fungal uptake. Changes in the carbohydrate fingerprint and SDS–PAGE banding supported structural polysaccharide remodeling and hydrolytic protein secretion. Thus, this SSF platform confirmed certain potential for low-cost cellulolytic and hemicellulolytic enzyme generation. However, because sugar accumulation was temporary and followed by consumption, this system is best interpreted as a biological pretreatment and enzyme-generation step that supports subsequent downstream valorization. Full article

Serum contains diverse proteins whose concentrations vary with pathological conditions such as cancer, liver disease, neurological disorder, and infections. Conventional methods like serum protein electrophoresis (SPEP) and enzyme-linked immunosorbent assay (ELISA) are gold standards for protein identification; however, they are time-consuming and can miss abnormal serum protein levels. Inspired by chemical nose sensing based on selective sensor–analyte interactions, we synthesized five pyrene-conjugated fluorescent polymers (PFPs) with distinct side-chain head groups to construct a multichannel fluorescence sensor array. These polymers were screened for sensitivity to changes in serum protein levels using linear discriminant analysis (LDA), a machine learning method. This process led to the successful discovery of two PFPs that effectively detect protein level fluctuations. These PFPs provided a sensitive sensor array capable of generating a high-content response pattern (fingerprint) with six fluorescence channels. This sensor array successfully discriminated protein level fluctuations in serum with 98% jackknife classification accuracy and 95% unknown identification accuracy. This polymer sensor array holds strong potential as a diagnostic tool for serum-based samples and can be extended to other applications related to protein identification. Full article

Compost supplementation is widely used to improve yield and crop consistency in the cultivation of white button mushroom (Agaricus bisporus), yet practical alternatives to conventional protein-rich supplements and rapid candidate-screening approaches are still needed. In this study, plant- and byproduct-based supplements were first compared by sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE) to obtain qualitative fingerprints of extractable protein fractions, and were then evaluated in Phase III cultivation under both bag-based screening conditions and in a large-scale pull-mat system. Supplements differed notably in protein banding patterns and cultivation performance. In the bag trials, lupin grist and corn pellet produced the largest yield increases relative to the non-supplemented control, whereas in the commercial pull-mat trials lupin grist was the best-performing supplement, reaching 240.77 kg t⁻¹ compost. Under the present conditions, SDS-PAGE was useful as a qualitative screening aid for prioritizing candidates for cultivation trials, but not as a stand-alone predictor of yield. These results identify lupin grist as a practically relevant supplement candidate for commercial A. bisporus production. Full article

Moringa oleifera leaves are recognized as a nutrient-dense plant material of compositional and nutritional interest. This study aimed to characterize the nutritional and physicochemical properties of M. oleifera dried leaves through nutritional assessment and spectroscopic fingerprinting. Amino acid profiling, antioxidant activity assessment using ferric reducing antioxidant power (FRAP), 2,2-diphenyl-1-picrylhydrazyl (DPPH), 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS), and oxygen radical absorbance capacity (ORAC) assays, chromatographic analysis of organic acids and sugars, color measurement, techno-functional characterization, and vibrational spectroscopy including Fourier Transform infrared with attenuated total reflectance (FT-IR/ATR) and Raman were employed. The crude protein content was 16.13 ± 0.43%. Moringa leaves contained all essential amino acids, with notably high tryptophan content (amino acid score, AAS = 200.00%). The amino acids limiting the nutritional value of the protein were primarily sulfur-containing amino acids (AAS = 49.57%) and lysine (AAS = 49.79%). Histidine, leucine, and valine also showed levels below the reference protein. Antioxidant activity exhibited solvent-dependent patterns: the 80% ethanolic extract demonstrated significantly higher FRAP activity (27.05 ± 1.05 mg Trolox Equivalent (TxE)/g dry matter (DM)) and ORAC values (107.24 ± 6.80 mg TxE/g DM), while no statistically significant differences between extracts were observed for DPPH, ABTS, or total phenolic content. Chromatographic profiling identified fructose and glucose as the predominant sugars, alongside citric, succinic, lactic, and acetic acids. The leaves exhibited favorable techno-functional properties, including high water holding capacity and water solubility index. Spectroscopic analysis revealed bands consistent with proteins, lipids, carbohydrates, and glycoside-related structures, while the preserved green-yellow coloration (hue angle 101.68°) indicated retention of pigment-related features during processing. These findings provide compositional and physicochemical characteristics of Moringa leaves relevant to their evaluation as a plant-derived food material. Full article

Aim: This study investigates how Buyang Huanwu Decoction (BHD) protects against cerebral ischemic damage by targeting the NLRP3/Caspase-1 pathway. Methods: The fingerprint of BHD was analyzed by HPLC-UV. Migratory chemicals in BHD-containing cerebrospinal fluid (BHD-CCSF) were analyzed by ultra-performance liquid chromatography-quadrupole-time of flight-mass spectrometry (UPLC-Q-TOF-MS). The effects of BHD on the NLRP3/Caspase-1 pathway, IL-18 and IL-1β levels in oxygen and glucose deprivation/reperfusion (OGD/R) cells were assessed by Western blot and ELISA. Cerebral infarction severity in permanent middle cerebral artery occlusion (pMCAO) mice was assessed by mNSS scores and staining. Protein and mRNA levels of the NLRP3/Caspase-1 pathway and inflammatory factors (IL-18, IL-1β) were measured. Results: BHD-containing serum (BHD-CS), BHD-CCSF, and Calycosin (Cal) reduced NLRP3, Caspase-1, ASC, GSDMD proteins, IL-18 and IL-1β in OGD/R cells. In pMCAO mice, BHD decreased pathway-related proteins and mRNA and inflammatory factors and alleviated brain injury. Conclusions: BHD ameliorates cerebral ischemia by inhibiting the NLRP3/Caspase-1 pathway, thereby suppressing pyroptosis and inflammation. Full article

Raman spectroscopy offers unique molecular fingerprinting capability for cancer diagnosis and monitoring, yet its biomedical application is fundamentally limited by weak intrinsic signals and complex biological backgrounds. Composite Raman probes, particularly surface-enhanced Raman scattering (SERS)—based systems, overcome these limitations through synergistic electromagnetic and chemical enhancement combined with functional integration. By engineering plasmonic nanostructures, interfacial electronic states, and molecular architectures, composite Raman probes achieve synergistic electromagnetic and chemical enhancement while incorporating biorecognition units, reporter molecules, and protective coatings to improve stability, specificity, and biocompatibility. In recent years, these probes have evolved from simple signal tags into multifunctional platforms capable of ultrasensitive tumor biomarker detection, high-contrast imaging, surgical guidance, therapy monitoring, and dynamic analysis of the tumor microenvironment (TME). This review systematically summarizes recent advances in composite Raman probes for oncological applications, with an emphasis on material design strategies, enhancement mechanisms, and stimulus-responsive regulation. Representative applications at both molecular and tissue levels are highlighted, including nucleic acid, protein, and exosome detection, as well as in vivo imaging and microenvironmental sensing. Finally, current challenges and future perspectives toward clinical translation are discussed, aiming to provide guidance for the rational design of next-generation Raman probes for precision oncology. Full article

Molecular representation learning (MRL) has garnered significant attention due to its pivotal role in downstream applications such as molecular property prediction and drug discovery. In most MRL approaches, molecules are encoded into 2D topological graphs via graph neural network (GNN), which suffers from over-smoothing issues and limited receptive fields. Furthermore, most GNN models fail to utilize the 3D spatial structural information that determines molecular physicochemical properties and biological activity. To this end, here we propose multimodal contrast-enhanced molecular representation learning (MCMRL). This approach utilizes both the 2D topological information and 3D structural information of molecules for contrastive learning to enhance molecular graph representations. Further, it integrates additional molecular fingerprint information and feature fusion techniques to incorporate multimodal knowledge, yielding more reliable and generalizable molecular representations. MCMRL is pre-trained on ~10 million unlabeled molecules from PubChem, followed by various downstream benchmark tasks. Experimental results demonstrate that MCMRL achieves superior performance in 9 out of 13 benchmark tests for molecular property prediction, validating its effectiveness in molecular representation learning. Furthermore, potential molecular drugs binding to biological target protein DRD2 screened by MCMRL representation show promising affinity score, which also demonstrates the efficacy of the proposed method. Full article