搜索结果：Nature computational science

T-cell receptor (TCR) cross-reactivity, whereby a single TCR recognizes multiple peptide-MHC (pMHC) ligands, is essential for immune surveillance but also represents a major safety challenge for TCR-based therapeutics because of severe off-target toxicities. Rapid advances in immune-repertoire sequencing, structural biology, and immunopeptidomics have accelerated computational efforts to characterize cross-reactive recognition at scale. However, most existing methods are adapted from conventional TCR-pMHC specificity prediction and remain insufficiently aligned with the one-to-many, structurally plastic, and context-dependent nature of cross-reactivity. Here, we review the main data resources supporting TCR cross-reactivity modeling, including sequence- and structure-centric databases, and highlight persistent limitations, such as scarce explicitly annotated cross-reactive TCRs, strong biases toward viral epitopes and common HLA alleles, limited paired αβ-chain information, and a lack of rigorously validated negative examples. We then compare the major computational paradigms-sequence-based, structure-based, machine-learning, and multimodal approaches-with emphasis on their respective strengths and limitations in interpretability, generalization, and scalability. Finally, we discuss emerging translational applications and outline key priorities for the field, including dedicated datasets, rigorous benchmarking, and biologically grounded multimodal models to enable safer and more clinically actionable TCR-based immunotherapies.

Amoebiasis, caused by Entamoeba histolytica, remains a global health concern due to its asymptomatic nature and potential for severe complications such as liver abscesses. To investigate molecular recognition mechanisms, this study focused on the binding affinity and detection limit of aptamers targeting E. histolytica actin (EhActin), a cytoskeletal protein selected as a target molecule. A genomic tRNA-based sequence library was computationally screened to identify potential high-affinity aptamer candidates, followed by molecular docking and molecular dynamics simulations to evaluate interaction profiles. Recombinant EhActin (rEhActin) was expressed in Escherichia coli LEMO21 using a pET28a(+) vector system and used in ELONA to quantify the interaction between a biotinylated DNA aptamer and rEhActin. The limit of detection was also assessed using ELONA with E. histolytica protein extract. Among the shortlisted aptamers, APT29 demonstrated favorable in silico and in vitro binding characteristics. Molecular dynamics analysis supported overall structural stability of the APT29-EhActin complex, although hydrogen bond interactions fluctuated over time. ELONA showed a dissociation constant (Kd) of 44.72 nM for APT29, and this aptamer achieved a limit of detection of 0.4 µg/well of E. histolytica protein extract. These findings suggest APT29 as a promising molecular probe for further biophysical and structural studies of aptamer-target interactions involving E. histolytica, with potential relevance to future diagnostic development for amoebiasis.

Pancreatic cancer remains one of the most lethal malignancies, characterised by aggressive progression, metabolic adaptation, and resistance to therapy. Ferroptosis, an iron-dependent form of regulated cell death driven by lipid peroxidation, has emerged as a critical mechanism influencing tumour survival and therapeutic response. However, the role of ferroptosis suppressor genes (FSGs) in pancreatic cancer remains incompletely understood. In this study, FSGs were systematically retrieved from FerrDb V2 and subjected to multi-step filtering to identify a curated set of 196 protein-coding genes. Genomic alteration analysis using cBioPortal across 21 pancreatic cancer studies (n = 5189 samples) identified seven recurrently altered genes (TP53, HSF1, PARP10, ZFP36, SIRT2, ECH1, and ENPP2) with alteration frequencies ≥ 3%. Co-occurrence analysis revealed predominantly cooperative alteration patterns among these genes, suggesting functional complementarity. Survival analysis demonstrated that alterations in FSGs were significantly associated with reduced overall survival in pancreatic cancer, with several genes (ECH1, ZFP36, SIRT2, and ENPP2) showing particularly strong adverse prognostic effects. In contrast, no significant survival associations were observed in oesophageal and gastric cancers, indicating a tumour-specific dependency on ferroptosis-related mechanisms. KEGG pathway enrichment analysis of the broader FSG set revealed significant involvement in pathways related to metabolic regulation (AMPK-mTOR signalling), autophagy, hypoxia response (HIF-1 signalling), and oncogenic signalling (PI3K-Akt pathway). Integration of these findings suggests that ferroptosis suppressor genes contribute to pancreatic cancer progression by promoting metabolic adaptation and resistance to oxidative stress. In conclusion, this study identifies key ferroptosis suppressor genes with prognostic relevance in pancreatic cancer and highlights their integration within critical metabolic and stress-response pathways. The tumour-specific nature of these associations underscores the importance of biological context and supports the potential of FSGs as prognostic biomarkers and potential therapeutic targets in ferroptosis-based strategies.

In the present study, optoelectronic, thermoelectric (TE), and elastic characteristics of LiHfIrZ Heusler alloys (HAs) (Z = Ge and Si) are studied through first-principles calculations and semi-classical Boltzmann transport theory. The structural properties were studied using the generalized gradient approximation (GGA) with the Perdew-Burke-Ernzerhof (PBE) functional, while electronic and transport properties were analysed using the Tran-Blaha modified Becke-Johnson potential. The calculated elastic constants, thermodynamic parameters, and formation energy confirm the mechanical, thermodynamic, and structural stability of the HAs, as well as their anisotropic character. Moreover, the alloys exhibit semiconducting behaviour. The calculated band gaps (Eg) of LiHfIrSi and LiHfIrGe are 1.72 and 0.84 eV, respectively. The semiconducting nature of both HAs has been established by their band structure (BS) and density of states (DOS) calculations. The optical analysis reveals a high dielectric response in the low-energy region (~ 2 eV), which decreases with increasing photon energy. Our HAs exhibit strong reflectivity in the ultraviolet (UV) region, indicating significant interaction with high-energy electromagnetic radiation, while appreciable optical conductivity is observed in the visible energy range. Moreover, both HAs displayed an increasing power factor, highlighting their potential for high-temperature TE applications. Notably, LiHfIrSi achieves a maximum ZT of 0.72 at 1200 K. Given the limited studies on these HAs, this study provides a valuable basis for future theoretical and experimental investigations.

Antibiotic resistance is a global threat requiring new potential antimicrobial sources. Secondary metabolites from cold-adapted microorganisms may provide unique antimicrobial compounds. Ethyl-acetate extract of Alcaligenes pakistanensis LTP10 from Passu glacier was explored for antimicrobial potential against clinical isolates. Cytotoxicity was determined by Brine shrimp lethality assay. The extract is analyzed by LC-MS/MS and the data is processed by MZmine. Important compounds were evaluated by molecular docking and in silico study. The extract demonstrated activity against clinical isolates Staphylococcus aureus, Escherichia coli, Salmonella enterica, Pseudomonas aeruginosa, and Candida albicans with zone of inhibition ranging from 16 mm to 24 mm, with no killing of nauplii suggested non-cytotoxic nature. LC-MS/MS analysis established presence of important putative antimicrobial metabolites (E)-3-(acetyloxymethyl)-5-(2-formyl-4-hydroxy-5,5,8a-trimethyl-1,4,4a,6,7,8-hexahydronaphthalen-1-yl)pent-2-enoic acid, cyclizidine-F, neovasipyridone-G, paenibacillin-A, and tricholomenyn-A. Molecular docking study of these metabolites by AutoDock Vina against dihydrofolate reductase of S. aureus demonstrated binding affinities from -6.8 kcal/mol to -8.5 kcal/mol, while against enoyl-ACP reductase of E. coli showed binding affinity values from -6.3 kcal/mol to -7.9 kcal/mol. In silico analysis predicted considerable absorption, distribution, metabolism, excretion, safety, and druglikeness properties. These results suggest that metabolites from A. pakistanensis LTP10 possess antimicrobial potential and warrant advanced post-docking validation via molecular dynamics, free-energy, and mechanistic analyses for future antibiotic development.

Capturing carbon monoxide (CO) via ligand complexation offers a strategy to mitigate and control CO emissions. In this study, the interaction of CO with N-heterocyclic carbenes (NHCs) and their isoelectronic boron-substituted analogs (NHBs) is examined using density functional theory. Computed geometries, natural bond orbital (NBO) analysis, topology analysis, electron localization functions (ELF), and charge density difference (CDD) maps are employed to characterize the nature of CO binding. The boron-based systems consistently formed more stable CO adducts than traditional NHC ligands. Building upon this, a mixed ligand approach combining NHC and NHB on either side of CO resulted in exceptional stabilization for 1NO2-CO-2NH2 and 1NO2-CO-2CH3 complexes, with the reaction mechanism suggesting that NHC facilitates initial CO capture, followed by complex stabilization through NHB coordination. Charge analyses showed a clear redistribution of electron density in CO upon coordination, with reduced C-O bond order and distinct C-C and B-O bonds, while Natural orbitals for chemical valence (NOCV), and Charge decomposition analysis (CDA) confirmed predominant covalent interaction with an electron transfer from NHB to NHC fragment through CO and π-backdonation from NHC to CO. Such charge transfer and back bonding exist in pure NHC complexes; however, the extent of orbital interaction is significantly enhanced in the pure NHB and NHC-NHB mixed systems. These findings highlight the superior ability of NHC and NHB ligand combinations, particularly those with electron-withdrawing and electron-donating substituents, to trap CO, offering valuable insights into the design of main-group frameworks for toxic gas capture applications.

The inductive effect is generally taught as being transmitted through the σ-bond framework in organic molecules, diminishing with each bond. This historical position does not align with more recent studies, and we show that the inductive effect in neutral molecules is effectively limited to one bond. Evidence of onward transmission (e.g., 13C NMR chemical shifts) should be viewed with scepticism. In charged species, where an electron-donating functional group is coupled with a suitably disposed electron-withdrawing substituent, the effect is transmitted over more bonds. These examples should be considered as polarizability rather than 'purely' inductive. Examples that are commonly described as a through-space field effect are better explained by polarizability/orbital perturbation. This work presents a more complete view of the inductive effect and its interface with polarizability.

Traditional cognitive science has historically confined the mind within the cranium. While the "second brain" metaphor underscores the autonomy of the enteric nervous system, it remains entrenched in a neurocentric paradigm. Here, we propose a transformative framework: the gut microbiota may function as a constitutively relevant contributor to specific embodied cognitive architectures. We contend that cognition, emotion, and behavior are not fully understandable in brain-isolated terms. Instead, these processes emerge from a sustained, bidirectional dialogue between the host and its symbiotic microbial ecosystem. Integrating 4E cognition theory, we systematically delineate how gut microbiota functions as an embedded signaling system-producing cognitively active metabolites, such as short-chain fatty acids and neuroactive substances-to shape interoceptive states and neural function via neural, immune, and metabolic/endocrine interfaces. We establish a rigorous evidential chain, categorized as "deprivation, replacement, observation, and intervention," synthesizing germ-free animal models, fecal microbiota transplantation, human multi-omics, and clinical interventions. These data-drawn from animal models that establish causal necessity and sufficiency, human cohort studies that reveal systematic ecological associations, and proof-of-concept intervention trials that demonstrate clinical plasticity-converge to support the view that microbiota-derived processes may be constitutively relevant to the realization of specific embodied cognitive architectures, especially those organized through interoceptive prediction, affective appraisal, and vagal-metabolic signaling, rather than functioning as merely transient or incidental regulators. The multi-level nature of this evidence base, spanning causal mechanisms in controlled settings to ecological validity in human populations, provides a robust foundation for reframing the gut microbiota as a symbiotic co-constructor of the embodied mind. Ultimately, we move beyond the linear "gut-brain axis" model to outline a multispecies framework for understanding the embodied architectures within which interoceptive, affective, and related cognitive processes unfold. This paradigm shift offers a novel biological foundation for the mind and enables precision interventions for mental health, such as psychobiotics and targeted ecological remodeling. Looking forward, we envision a unified "microbiota-mind" model that integrates computational modeling and ethical frameworks. This endeavor challenges the traditional concept of a "self" bounded by the skin, providing a roadmap for the future of precision psychiatry and cognitive science.

Cell-in-cell (CIC) structures are among the most intriguing cellular phenomena occasionally observed in human cancer specimens. Once regarded as incidental findings, accumulating evidence has linked specific CIC subtypes, particularly entosis, to tumor progression and patient prognosis. Despite growing interest, systematic investigation of entosis remains limited due to the labor-intensive nature of manual identification and analysis. Computational approaches are, therefore, needed to enable scalable and reproducible entosis detection. In this study, we developed and evaluated five morphology-driven deformable segmentation models alongside a YOLOv8 deep learning-based detection framework for automated entotic cell identification in BxPC3 (pancreatic) and MCF7 (breast) cancer cell lines. The deformable models were designed to capture complementary morphological characteristics, including entropy, spatial proximity, contour topology, and circularity. Comparative evaluation showed that deformable Models A and C achieved the highest sensitivity, with recall values ranging from 0.91 to 0.94 and F1-scores between 0.81 and 0.83, demonstrating robust performance across heterogeneous entotic morphologies. YOLOv8 achieved high overall accuracy (0.97) and specificity (0.98), indicating strong background discrimination, but exhibited lower recall (0.65) and F1-score (0.59), reflecting a conservative detection profile under extreme class imbalance, where entotic events comprised approximately 1% of all observed cells. While deformable models provided higher sensitivity and detailed morphological segmentation, YOLOv8 offered advantages in computational efficiency and rapid inference. Together, these findings highlight the complementary strengths of morphology-driven segmentation and deep learning-based detection, and support the future development of scalable hybrid frameworks for automated entosis analysis.

Low-code platforms like OpenAI custom GPTs (cGPTs) promise easy development of specialized AI assistants for complex bioinformatics and clinical tasks, allowing researchers to integrate proprietary data into intuitive chatbot interfaces. However, these commercial frameworks operate as opaque "black boxes," fundamentally clashing with open-science values and principles of reproducibility. To audit their hidden configurations, we performed "jailbreak" (prompt injection) attacks. We found that all tested cGPTs were critically vulnerable, leading to the leakage of private system instructions, full knowledge base files, and proprietary API details. This systemic failure poses severe security and privacy risks, particularly when handling sensitive patient data, clinical notes, or proprietary basic science assets. While low-code tools lower the barrier to AI adoption, their commercial nature and security flaws warrant extreme caution, forcing biomedical researchers to weigh convenience against the non-negotiable standards of data integrity and security.

High-energy heavy-ion particle accelerators have long served as proxies for the harsh space radiation environment, enabling both fundamental life-science research and applied testing of flight hardware. Traditionally, monoenergetic high-energy heavy-ion beams have been employed for practicality, providing valuable datasets that underpin radiation risk and predictive computational models. However, such beams cannot fully reproduce the mixed-field nature of space radiation, motivating the development of realistic analogs for improved risk assessment and countermeasure evaluation in preparation for future deep-space missions to Moon or Mars. Spearheaded by developments at the NASA Space Radiation Laboratory, the GSI Helmholtzzentrum für Schwerionenforschung, supported by the European Space Agency (ESA), has established advanced space radiation simulation capabilities in Europe. Here, we present the design, optimization, and in-silico benchmarking of GSI's hybrid active-passive Galactic Cosmic Ray (GCR) simulator, together with a computationally optimized phase-space particle source for Geant4, which is available to external users for their own simulation studies and experimental planning.

Synthesis of bifunctional cytisine-squaramide derivatives bearing a single amino acid moiety has revealed an unexpected and intriguing chemical challenge. During modification of cytisine squaramates with α-amino acids, base-sensitive amido esters readily underwent hydrolysis, forming poorly soluble amido-acid side products that resisted standard purification and initially obscured their identity. Persistent observation of these elusive precipitates prompted a deliberate co-crystallization approach, which unambiguously revealed their supramolecular nature using single-crystal X-ray diffraction. With this insight, optimized purification strategies allowed isolation of analytically pure Cyt-SQ-OH and its derivatives, which were characterized by complementary spectroscopic techniques, X-ray crystallography and computational studies. Furthermore, the DFT-optimized parameters of all compounds were determined, providing additional insight into their structural and electronic properties. This work highlights the interplay between reactivity, solubility, and supramolecular assembly in cytisine-squaramide-amino acid hybrids, providing a robust platform for future exploration of multifunctional conjugates with potential applications in medicinal chemistry, molecular recognition, and materials science.

Metal-organic frameworks with coordinatively unsaturated metal sites (open metal sites) capable of engaging in orbital interactions with π-acidic gases are of interest for enabling ambient-temperature gas separations, such as hydrogen isotope separations. In view of the weakly π-acidic nature of H2, we sought to strengthen π-backbonding-mediated H2 adsorption through pore confinement effects. Toward that end, we synthesized and characterized the ultramicroporous metal-organic framework CuxZn5-xCl4-yHz(bbta)3 (CuIZn-MFU-4; H2bbta = 1H,5H-benzo(1,2-d:4,5-d')bistriazole), featuring π-basic trigonal pyramidal CuI sites that reside within 7 Å of one another at their closest. Gas adsorption measurements reveal an H2 adsorption enthalpy of -38 kJ/mol, exceeding that of the larger-pore analog (CuIZn-MFU-4l; -33 kJ/mol) and representing the strongest H2 adsorption yet achieved in a metal-organic framework. The stronger H2 adsorption in CuIZn-MFU-4 is attributed to a combination of pore confinement effects and the increased σ-accepting nature of the CuI sites caused by a more electron-withdrawing bbta2- linker, as supported by structural, spectroscopic, and computational evidence. With the strongest H2 adsorption, equilibrium isotope effects in CuIZn-MFU-4 lead to a D2/H2 selectivity (as estimated by ideal adsorbed solution theory) of 1.35 even at 298 K, approaching the values reported below 200 K for conventional porous materials.

Accurate classification of terrestrial habitats is critical for biodiversity conservation, ecological monitoring, and land use planning. Several habitat classification schemes are in use, typically based on analysis of satellite imagery and validation by field ecologists. Here, a methodology is presented for classification of habitats based solely on ground-level imagery (photographs), offering improved validation and enhanced ability to classify habitats at scale (e.g., using imagery from citizen science). In collaboration with Natural England, a public sector organisation with responsibility for nature/biodiversity conservation in England, this study develops a classification system that applies deep learning to ground-level habitat photographs, categorising each image into one of 16 distinct classes following the established 'Living England' framework. Images were pre-processed using resizing, normalisation, and augmentation techniques, while resampling was used to balance classes in the training data and enhance model robustness. A custom deep learning classifier based on the DeepLabV3-ResNet101 architecture was developed and fine-tuned to assign a habitat class label to ground-level photographs. Using five-fold cross-validation, the model demonstrated strong overall performance across 16 habitat classes, with accuracy and F1-scores varying between classes. This approach supports robust, scalable habitat classification based on balanced and well-prepared training data. Across all folds, the model achieved a mean F1-score of 0.63, with some habitat classes such as Bare Sand (BS) and Coniferous Woodland (CW) reaching values above 0.87. High performance was achieved for visually distinct habitats and lower performance for visually mixed or ambiguous classes. These findings demonstrate the potential of this approach for ecological monitoring. Ground-level imagery is easily obtained and accurate computational methods for habitat classification based on such data have many potential applications. To support use by practitioners, a simple web application is also provided that allows classification of uploaded images using the trained model.

In this study, the structural, electronic, and biological characteristics of newly synthesized transition metal complexes FeABNF, CoABNF, and NiABNF, resulting from the chelation compounds albendazole (AB) and nifuroxazide (NF), were studied. The synthesized compounds were found to have excellent yields with high percentage yields (∼80%). Thermal analysis showed that these complexes were highly stable with decomposition temperatures above 300°C. Molar conductivity tests revealed that FeABNF is a 1:1 electrolyte with a value of 38.95 Ω-1·cm2·mol-1, NiABNF is a 1:2 electrolyte with a value of 88.17 Ω-1·cm2·mol-1, while CoABNF is a nonelectrolyte with a value of 9.86 Ω-1·cm2·mol-1. FT-IR analysis proved that these complexes are bidentate due to their N and O donor atoms. The electronic spectra and magnetic moments confirmed that these compounds had octahedral geometry. The DFT calculation and biological assays showed that the complexes exhibited enhanced antimicrobial activity compared to the free ligands. The investigated compounds exhibited variable antibacterial and antifungal activities, with the metal complexes generally showing enhanced activity compared to the free ligands. However, the degree of activity was found to depend on the nature of the metal ion and the tested microbial strain. The most potent antibacterial action was exhibited by NiABNF and CoABNF complexes, which exhibited 28-30-mm inhibition zones and 91%-94% activity index, and good antifungal activity against Candida albicans, 20 mm. Anti-inflammatory activity order was determined as the NiABNF complex with IC50: 56.97 μM and 93% inhibition. These findings were supported at the molecular docking level, as the NiABNF complex had the highest binding affinity to DNA gyrase B (PDB: 4DUH, -8.90 kcal/mol) and SARS-CoV-2 main protease (6LU7, -9.40 kcal/mol) via hydrogen bonding and hydrophobic interactions, which make it a promising therapeutic agent. Both experimental and computational studies reported the same order of bioactivity: NiABNF > CoABNF > FeABNF > free ligands.

The classification of resting-state functional magnetic resonance imaging (rs-fMRI) data presents unique challenges in the detection and diagnosis of neuropsychiatric disorders such as Attention-Deficit/Hyperactivity Disorder (ADHD). Traditional classification approaches often prove inadequate when handling complex spatiotemporal patterns and high-dimensional fMRI data, particularly when significant variations exist both between and within diagnostic groups. To address these limitations, we introduce a novel classification framework that integrates three complementary analytical components: elastic registration for curve alignment, geometric curve length computation for capturing signal variability, and sparse principal component analysis for dimensionality reduction. Extensive simulation studies show that our proposed method significantly outperforms existing approaches, especially in scenarios where groups exhibit distinct variation patterns rather than mean differences in their functional curves. When applied to the ADHD-200 dataset, our method achieves classification accuracy rates substantially exceeding conventional approaches. The proposed framework's ability to capture subtle variability differences while maintaining computational efficiency makes it particularly valuable for biomarker discovery and clinical applications in neuropsychiatric research. Our approach's focus on signal variability rather than mean activation patterns offers new insights into the dynamic nature of brain activity differences in ADHD and provides a promising foundation for analyzing other neurological conditions.

The rapid degradation and limited tumor accumulation of temozolomide (TMZ) remain important challenges in glioblastoma chemotherapy, motivating the development of nanocarrier systems that can improve TMZ retention and delivery. This study employed density functional theory (DFT) to evaluate the adsorption potential of two-dimensional graphitic carbon nitride (gCN) and its Al/Ga-doped variants (gCN-Al and gCN-Ga) as nanocarriers for TMZ delivery. A comprehensive analysis, including the electronic structure, natural bond orbital, quantum theory of atoms-in-molecules, and noncovalent interaction analyses, revealed that TMZ adsorbs onto the nanocarriers via spontaneous, physisorptive interactions, primarily by hydrogen bonding and van der Waals forces. The adsorption strength follows the order gCN-Ga > gCN-Al > pristine gCN, with gCN-Ga exhibiting the most favorable adsorption energy (-1.24 eV). Doping introduces new electronic states that narrow the HOMO-LUMO gap and enhance charge transfer, rationalizing the improved adsorption. The absence of imaginary frequency confirmed that each optimized geometry corresponds to a true minimum on the potential energy surface. Thermodynamic property analyses revealed the spontaneous and exothermic nature of the drug-nanocarrier complex formation. Recovery-time estimates suggest that TMZ desorption is thermally accessible, with Ga doping producing the longest predicted residence time. These results suggest that Al/Ga doping can modulate TMZ-gCN interactions at the molecular level and may provide a useful computational basis for future experimental evaluation of gCN-based TMZ delivery platforms.

Inner speech (IS), or imagined speech without overt articulation, is a promising target for brain-computer interfaces (BCIs) aimed at restoring communication in individuals with severe speech impairments, such as locked-in syndrome. Foundation models (FMs), typically trained using self-supervised learning (SSL) on large-scale datasets, offer new opportunities for learning transferable and robust representations from neural signals. This mini review provides an overview of FM-based approaches for IS decoding using non-invasive neuroimaging modalities, including functional magnetic resonance imaging, electroencephalography, magnetoencephalography, and functional near-infrared spectroscopy, highlighting architectural trends, pretraining strategies, and model adaptation techniques. We discuss how recent models move beyond task-specific classification toward scalable representation learning and semantic-level decoding. Despite these advances, several challenges remain, including the weak, noisy, and non-stationary nature of neural signals, variability in data acquisition, and limitations in dataset scale, standardization, computational resources, interpretability, and evaluation metrics. Ethical and privacy considerations are also critical. Overall, FMs provide a promising paradigm for non-invasive IS decoding, addressing neurophysiological, methodological, and ethical challenges is essential for developing scalable and reliable BCI systems.

Precision nutrition requires tools that move beyond static dietary biomarkers toward approaches that capture the dynamic nature of human metabolism. Fluxomics, the study of metabolic fluxes, offers valuable opportunities to enrich nutritional research by revealing temporal patterns of dietary responses and clarifying mechanisms that underlie inter-individual variability. In this review, we examine how fluxomics, when combined with metabolomics and broader multiomics approaches, can help identify responder and nonresponder phenotypes, refine metabotype-based dietary recommendations, and strengthen causal inference in nutrition studies. We outline the methodological foundations of fluxomics, including stable isotope tracer administration, high-resolution MS and NMR, and computational frameworks that translate isotopomer data into quantitative flux maps. We further examine recent advances in postprandial profiling, isotopic tracing, and longitudinal repeated-measure designs that are expanding the translational potential of fluxomics in nutrition research. Artificial intelligence is positioned as a supportive analytical layer that facilitates the integration, scalability and interpretation of complex datasets in fluxomics-informed precision nutrition. Finally, we discuss current challenges related to standardization, reproducibility, scalability, and ethical governance. Addressing these barriers through well-designed longitudinal interventions in diverse populations will be essential to advance fluxomics from experimental settings toward more targeted and equitable precision nutrition strategies.