Abstract: The use of microbial inoculants is a promising and sustainable alternative to agrochemicals. However, their field efficacy is inconsistent. This review critically evaluates the scientific basis for using microbial inoculants in modern agriculture, analyzing their complex interactions within agroecosystems. We demonstrate that the effectiveness of inoculants is governed by predictable ecological principles, rather than random processes. The formation of plant microbiomes follows distinct, deterministic patterns, with specific colonization patterns for each compartment and a strong influence from soil type and climate. Furthermore, this review demonstrates that, for plant-beneficial microorganisms used as bioinoculants, their antimicrobial metabolites function not merely as weapons, but as sophisticated ecosystem engineers that selectively reshape microbial communities. Compounds of plant growth-promoting microorganisms like cyclic lipopeptides, macrolactins, 2,4-DAPG, and gliotoxin demonstrate dose-dependent regulatory effects, enhancing specific soil functions while maintaining community stability. The transition from microbial monocultures to synergistic consortia proves essential, though success depends on matching inoculant composition to plant chemical signaling profiles. Practical recommendations include prioritizing native stress-tolerant strains, implementing soil-specific formulations, and developing metabolite-based preparations that function as ecological modulators rather than broad-spectrum suppressants. This ecological framework provides the scientific foundation for the next generation of predictable and effective microbial inoculants.

Abstract: The current use of artificial light during natural dark phase had been acquired contaminant dimensions, which is named “light pollution”. It is well known that the exposure to dim light at night (dLAN) during the postnatal period severely impair the immune system and related organs, but few reports have demonstrated the effect of dim light when exposed during foetal periods. That is why this report ask does dim light at night in two different stages of development (i.e., prenatal vs. postnatal exposure) generate a long-lasting dysregulation of circadian rhythms that modifies the circadian immune organization and responses of the spleen in the early adulthood? To answer this question, we exposed two groups of C57BL/6J male mice to dim night light at gestational and postnatal period and compared to control groups where the mice were exposured to light-dark conditions (12 h each, LD). Parametric and non-parametric activity/rest values were analyzed with circular statistics. Compared to their controls, we found differences in alpha, onset, offset, M10 and L5 startime in dLAN groups. We also assessed the transcript levels of clock genes and genes that mediate inflammation in spleen tissue and found a dampening daily variation in mRNA expression in both experimental groups. Finally, we use an ovalbumin (OVA) allergy challenge to test the B-cell response in the spleen and found a significant higher cell recruitment to the spleen and more anti-OVA IgE. Together, these results clearly show that dLAN, affects the peripheral molecular clocks and responses from the spleen and these effects are independent of period of life exposure of dim light at night.

Abstract: Wire Arc Additive Manufacturing (WAAM) is a cost-effective and scalable technique for producing large metallic components; however, coarse columnar microstructures, strong crystallographic texture, and significant residual stresses limit its widespread adoption. In recent years, hybrid WAAM processes integrating deformation-based techniques have been developed to address these limitations. This review provides a comprehensive analysis of deformation-assisted WAAM, encompassing interlayer rolling, friction stir processing (FSP), hammer peening, laser shock peening, and ultrasonic vibration-assisted approaches. These hybrid techniques introduce additional thermomechanical parameters—strain, strain rate, and applied stress—that significantly influence microstructure evolution. The governing physical metallurgy mechanisms are discussed in detail, including dislocation accumulation, recovery, static and dynamic recrystallization, and severe plastic deformation. Studies from 2022 to 2025 are critically reviewed, highlighting the effectiveness of hybrid WAAM in promoting columnar-to-equiaxed grain transformation, reducing anisotropy, mitigating defects, and improving mechanical properties across aluminum, titanium, steels, and nickel-based alloys. The integration of auxiliary processes such as in-situ machining and heat treatment is also discussed. This review establishes a process-structure-property framework for hybrid WAAM and provides guidance for the development of advanced additive manufacturing systems capable of delivering near-net-shape components with microstructures and properties approaching those of wrought or forged counterparts.

Abstract: By integrating high throughput eQTL and pQTL data generated using different platforms, in this study, the relationship between transcriptome and proteome, as well as, the efficacy of platforms in measuring transcript and protein levels in blood were investigated. eQTL data were obtained from the eQTLGen study that used Microarray and INTERVAL study that relied on RNASeq platform to measure transcripts. pQTL data were obtained from UK Biobank study that used Olink and deCODE study that used SomaScan platform to measure proteins. A total of 1,162 genes that were shared between the four platforms were selected and investigated.The outcome of Mendelian randomization analysis identified 211 genes that their transcript levels significantly (P<5e-8) predicted their protein levels across the panels. Similarly, genetic correlation analysis identified 67 genes that share significant correlation. %12(N=25) of genes identified through Mendelian randomization and 7% of those identified through genetic correlation showed negative associations. Cross-platform analysis revealed in INTERVAL-UKBB panel the effect size of SNPs on eQTLs and pQTLs show the highest correlation; while in eQTLGen-deCODE panel this value was the lowest. Co-localization analysis further confirmed these findings and indicated genes with strong evidence of colocalization in their eQTLs and pQTLs encode intracellular proteins while those with trivial evidence of colocalization encode secretory proteins that undergo glycosylationIntegrating both transcriptome and proteome for biomarker discovery and locus annotation is important, as overall genetics of transcriptome and proteome are not the same. RNASeq and Olink platforms provide more accurate measures of RNA and protein levels.

Abstract: Airborne LiDAR data acquired before and after the 2024 Noto Peninsula earthquake in Japan were used to estimate three-dimensional (3D) ground-surface displacements based on the Iterative Closest Point (ICP) algorithm. Digital elevation (terrain) models (DEMs) were generated from pre-earthquake point cloud data acquired by Ishikawa Prefecture and compared with post-earthquake DEMs developed by the Forestry Agency of Japan. Three-dimensional coseismic displacements were derived from the spatial correlation between pre- and post-event DEMs for 50 m × 50 m tiles. The results depend on tile size and are influenced by ground movements within and surrounding each tile. Therefore, moving-average windows of 250 m and 550 m were applied to the 50 m tiles to obtain continuous 3D displacement fields across the ground surface. A comparison between GNSS-measured displacements and the corresponding moving-average estimates for tiles containing triangulation points and continuously operating reference stations (CORSs) showed that the accuracy of the estimated displacements in all three components was within 0.2 m in terms of root mean square error (RMSE).

Abstract: Cryptographic security proofs are the invisible backbone of modern digital systems, yet they remain fragmented across multiple paradigms—game-based proofs, Universal Composability (UC), formal verification, and ad hoc insecurity arguments—each with its own language, assumptions, and limitations. This article introduces the \textbf{Market-Theoretic Security Framework (MTSF)}, a unified paradigm that reinterprets all security proofs as economic markets. In this view, the defender acts as a seller offering \emph{security goods} (such as confidentiality or unforgeability), while the adversary acts as a buyer bidding computational resources to break them. Security emerges naturally as \emph{market equilibrium}, where no efficient adversary can afford to win, while insecurity is characterized as \emph{market collapse}, where attacks succeed at negligible cost. For cryptographers, MTSF provides a rigorous and expressive framework that unifies four major proof paradigms into a single formal language. It introduces key technical innovations such as the \textbf{extended difference lemma} for handling multiple simultaneous failure events, \textbf{bidding-based reductions} that explicitly model adversarial strategies, a \textbf{dual methodology that treats proofs and disproofs symmetrically within the same structure}, and a \textbf{session pinging mechanism} for unbounded session verification. The framework seamlessly extends to classical and post-quantum primitives, real-world protocols (including TLS~1.3 and Signal), and even quantum-adversarial settings, while preserving quantitative security bounds and composability guarantees.MTSF offers an intuitive, accessible, and powerful meta model: security is like a marketplace where attackers try to ``buy'' a break, and defenders ensure the price is prohibitively high. Each proof becomes a sequence of small price adjustments, and each attack corresponds to a failed or successful bid. By combining mathematical rigor with economic intuition, MTSF transforms security proofs from opaque technical artifacts into transparent, auditable, and universally understandable arguments, enabling both experts and practitioners to reason about security with clarity and confidence.

Abstract: This position paper argues that recommender systems should now be designed towards agents. We use recommender systems towards agents (RSTA) to denote systems whose immediate consumer is an acting agent, an orchestration layer, or a multi-agent system; whose ranked objects are actionable interventions rather than human-viewable items; and whose success is measured by downstream trajectory utility under preference, cost, policy, and safety constraints. We advance three falsifiable claims: (1) priority-sensitive ranking can improve trajectory utility even when candidate sets are small, (2) service-side information can create value that local planning alone cannot fully recover, and (3) oversight actions such as verify, ask, defer, and escalate should be treated as recommendables rather than post-hoc filters. We sharpen the exclusion boundary against planning, routing, and human-facing recommendation; recast a WorkArena-style hardware-order task family as a full \RSTA worked example with an explicit candidate inventory, ranked intervention slate, and trajectory-level objective; and outline an agenda spanning candidate-set reconstruction, oversight-aware ranking, service-to-agent interfaces, multi-agent orchestration, interface-robust evaluation, and governance. The goal is not to relabel all agentic decision making. It is to identify a critical layer: when agents face massive action spaces or bounded compute, ranking dictates which trajectories they can reach---and which catastrophic failures they avoid.

Abstract: A broom graph is a linear graph with some pendant vertices attached to one of its ends. Using the formula for the commute time of the random walk between two vertices of the graph, which is given in terms of the effective resistance between the vertices, we find closed-form formulas for the hitting time index of some families of broom graphs, extending results found in the literature.

Abstract: Background: Climate projections for western Canada predict reduced water availability and more frequent heatwaves, underscoring the need to improve water-use efficiency and heat tolerance to sustain crop productivity and grain quality. Materials and Methods: A total of 198 historical and modern Canadian spring wheat cultivars were evaluated under water-deficient and high-temperature conditions. Measurements included whole-plant and leaf-level WUE, carbon isotope discrimination (δ¹³C) in flag leaves, and physiological traits such as leaf water potential, photosynthetically active radiation, and chlorophyll fluorescence parameters (F₀, FV/FM, FM, FV, φDo, and ETR) across six growth stages. Results: WUEWP showed a weak relationship with δ¹³C, indicating strong environmental and genetic in-fluences and limiting its reliability as a proxy across conditions. Spring wheat cultivars exhibited low genetic diversity for WUEWP and heat tolerance, suggesting limited adaptive capacity to increasing stress. Multivariate analyses (PCA and clustering) effectively captured trait variation and differentiated cultivars. Chlorophyll fluorescence traits sensitively reflected reductions in photosynthetic efficiency under drought and heat stress. Conclusion: Overall, the results indicate meaningful genotypic variation but limited genetic diversity and weak relationships among WUE, δ¹³C, and related traits, highlighting the need for new germplasm and integrated phenotyping to enhance selection efficiency and develop more climate-resilient spring wheat.

Abstract: Background: Nigeria bears a disproportionate burden of metabolic disease, with diabetes affecting an estimated 6.8-7.5% of adults and obesity prevalence among women reaching 15.7%. Despite the rich ethnobotanical tradition of using indigenous plants to manage these conditions, the molecular mechanisms by which Nigerian plant-derived phytochemicals modulate immunometabolic pathways remain poorly characterised. Objectives: This narrative review synthesises current evidence on the immunometabolic mechanisms of key phytochemicals derived from Nigerian medicinal plants, with specific focus on how these compounds regulate macrophage polarisation, inflammatory cytokine signalling, insulin sensitivity, and metabolic reprogramming in obesity and type 2 diabetes (T2D). Methods: A comprehensive narrative review was conducted using PubMed, Scopus, Google Scholar, Web of Science, and African Journals Online (AJOL). Studies reporting in vitro, in vivo, or in silico evidence for the immunometabolic activity of phytochemicals from Nigerian medicinal plants were included. Key immunometabolic targets - NF-kB, PPARgamma, AMPK, mTOR, HIF-1alpha, and macrophage M1/M2 polarisation markers - were used as mechanistic anchors for evidence synthesis. Results: Multiple phytochemicals abundant in Nigerian plants - including rutin, quercetin, luteolin, chlorogenic acid, vitexin, kolaviron, nimbolide, and 6-gingerol - demonstrate modulatory activity at immunometabolic nodes. These compounds converge on shared targets: suppression of NF-kB-driven M1 macrophage polarisation, activation of AMPK-mediated anti-inflammatory signalling, PPARgamma agonism to promote insulin sensitisation, and attenuation of HIF-1alpha-driven glycolytic reprogramming. Mechanistically, this represents a phytochemical-mediated shift from pro-inflammatory M1 immunometabolism toward an anti-inflammatory, insulin-sensitising M2 phenotype. Conclusions: Nigerian medicinal plants represent an underexplored reservoir of immunometabolic modulators. Their principal phytochemicals act on mechanistic targets directly relevant to the pathophysiology of obesity-linked T2D. Integrating ethnobotanical knowledge with network pharmacology and immunometabolic biology offers a compelling framework for rational drug discovery from Nigerian biodiversity. Future experimental validation using macrophage culture models and high-fat-diet animal systems is warranted.

Abstract: Rapid solidification by melt‑spinning produces aluminum alloys with extremely refined microstructures but also introduces strong structural gradients across the ribbon thickness. In this work, the microstructural evolution of a rapidly solidified Al‑Cu‑Li‑Mg‑Sc‑Zr alloy was investigated during model homogenization using in‑situ STEM heating experiments and correlated with bulk electrical‑resistivity measurements. The as‑cast ribbons exhibit two distinct solidification zones: a near‑contact region consisting of columnar cells containing fine Cu‑rich spherical precipitates, and a central region composed of larger eutectic cells enriched in Al₂Cu and Al₇Cu₂Fe phases. Stepwise in‑situ annealing between 200 °C and 550 °C reveals a sequence of transformations, including matrix depletion due to precipitation of strengthening phases, coarsening and dissolution of primary phases, and the formation of Al₃(Sc,Zr) dispersoids. Above 500 °C, rapid dissolution of primary phases followed by their coagulation into a limited number of stable grain‑boundary particles eliminates the original two‑zone structure and results in a fully homogenized ribbon. Ex‑situ annealing confirms that the resulting microstructure is uniform across the ribbon thickness and enables consistent precipitation strengthening during artificial aging. Microhardness measurements from both ribbon surfaces reveal identical peak‑aged hardness, validating the effectiveness of the short‑time homogenization strategy for rapidly solidified Al‑Cu‑Li‑Mg-based alloys.

Abstract: The demand for sustainable weed management and the limited discovery of new herbicide molecules have led to high interest in plant-derived bioherbicides, such as the water residues (WRs) from the hydrodistillation of aromatic plants, which contain biologically active secondary metabolites. Here, the phytotoxic potential of WRs of four aromatic plant species was investigated. Chemical composition of WRs was determined by SPME–GC–MS, and their effect was assessed on seed germination and seedling growth characteristics of Avena sterilis, Echinochloa crus-galli, and Zea mays. Five concentrations, i.e., 0, 10, 20, 50, and 100, with 100 representing pure WR were tested. Phenolic monoterpenes dominate WRs in oregano and thyme, and oxygenated monoterpenes in laurel and lavender. Germination and growth responses were dose-dependent and species-specific. Oregano and lavender WRs exhibited the strongest phytotoxicity, reducing weed germination by 82% and 79%, respectively. In contrast, laurel extracts showed weaker germination inhibition. Across all tested species, germination delays were observed, making WRs a promising candidate for weed control. The results also showed that WR affected root growth by up to 95% shoot by 70–80%. Maize exhibited greater tolerance than the weed species maintaining higher germination. Overall, WRs represent a promising tool for integrated weed management.

Abstract: Einstein's principle of the invariance of the speed of light in a vacuum is a core of modern physics, but the ISO 13690:2008 standard is only applicable to the visible light band, and traditional measurements have not verified the universality of vacuum permittivity and permeability across all bands. This study combs the limitations of historical precision measurements of the speed of light, derives the wavelength dependence of and in the interstellar medium based on quantum electrodynamics (QED) and Maxwell's equations, and proposes a micro-scale measurement scheme of "three-stage path splitting + two-dimensional compensation". Taking the 2026 Jupiter occultation as the carrier, a multi-band synchronous observation experiment is designed to predict the multi-band arrival sequence and time difference, providing theoretical and technical support for the refinement of light speed measurement and the expansion of the applicable boundary of relativity.This study provides a feasible precision metrology framework for high-accuracy light speed measurement in complex media, with potential engineering applications in astronomical observation and electromagnetic parameter calibration.

Abstract: Micro- and nanoplastics (MNPs) have now been detected in human blood, placenta, and arterial tissue, yet the oral cavity, which serves as the primary portal of environmental exposure, has received strikingly little mechanistic attention. This narrative review addresses that gap from an environmental microbiology perspective, synthesizing recent literature on periodontal disease, chronic low-grade inflammation, oral biofilms, dental materials, microbial–plastic interactions, and systemic chronic disease risk. Unlike prior reviews, we apply an explicit evidentiary framework that distinguishes what is directly demonstrated from what is biologically plausible but unproven, and we situate the periodontal environment specifically as a particle-retention and inflammatory-amplification niche. The strongest direct oral evidence shows that human dental calculus harbors at least 26 microplastic types, dominated by polyamide (41.4%), polyethylene (32.7%), and polyurethane (7.0%). Polyethylene isolated from calculus induces cytotoxicity, apoptosis, impaired migration, NF-κB activation, and upregulation of IL-1β and IL-6 in human gingival fibroblasts. The oral cavity is also a portal for environmental exposure and a local site of plastic generation: MNPs are released from chewing gum, oral care products, orthodontic appliances, and resin-based dental materials, while oral microorganisms have been documented to degrade methacrylate polymers. Across experimental systems, MNPs activate oxidative stress, inflammasome signaling, macrophage polarization, and barrier dysfunction — pathways that overlap extensively with the pathobiology of periodontitis. Environmental biofilm studies further indicate that plastic substrates can enhance extracellular polymeric substance production, quorum sensing, antibiotic resistance gene transfer, and pathogen persistence, suggesting a plausible but not yet proven oral plastisphere within plaque and calculus. We argue that periodontitis should be reconceptualized as a chronically inflamed particle-processing interface that may increase local MNP retention, cellular reactivity, and systemic inflammatory spillover, with implications for cardiovascular, metabolic, and other chronic disease risk pathways. Current evidence does not yet prove that environmental MNP exposure causes human periodontitis, and that evidentiary boundary is maintained throughout. A priority research agenda is proposed, centered on contamination-controlled subgingival biomonitoring stratified by periodontal status, spatially resolved multi-species biofilm models, polymer source attribution, and longitudinal clinical studies linking oral plastic burden to inflammatory and systemic outcomes.

Abstract: Contrastive learning–based models such as DrugCLIP have recently emerged as scalable tools for structure-based virtual screening by embedding protein structures and small molecules into a shared representation space. While these approaches demonstrate high throughput and competitive screening performance in ligand retrieval tasks, their ability to correctly identify biologically relevant ligand-binding pockets has not been systematically evaluated. Here, we construct a benchmarking dataset comprising 42 pharmacologically diverse human protein targets with experimentally validated drug-bound structures spanning multiple target families. Using this dataset, we evaluate the pocket recognition capability of DrugCLIP and compare its performance with a traditional structure-based workflow that integrates geometric pocket detection (Fpocket) with dynamics-informed pocket ranking (ESSA). DrugCLIP achieves perfect success rates for several well-studied target classes, including kinases (10/10), GPCRs (5/5), and nuclear receptors (5/5), but shows markedly reduced performance for ion channels (1/4) and transporters (2/5). Notably, pocket prediction accuracy does not strongly correlate with structural data availability, suggesting that intrinsic pocket characteristics rather than training data abundance primarily affect model performance. Across the benchmark, DrugCLIP does not outperform traditional pocket identification strategies (DrugCLIP vs. Fpocket+ESSA: 74% vs. 79%). Together, these results provide a quantitative evaluation of pocket recognition by contrastive learning–based models and highlight key limitations that should be considered when applying embedding-based approaches in prospective structure-based drug discovery.

Abstract: This paper critiques the established loss of simultaneity in special relativity which comes from Minkowski diagrams. Einstein's original thought experiment, with a train (observer M’), an embankment (observer M) and simultaneous lightnings, will become our test. For our purpose, lightnings will become photons. By applying the two postulates of special relativity (speed of light and principle of relativity), the paper shows that simultaneity should be observed by both observers. This would imply a superposition of some kind, as the photons meet simultaneously M and M’ while they are not at the same position. By using Lorentz invariance (therefore pure calculation), the conclusion of simultaneity for both observers will be confirmed. The superposition could be a quantum superposition. The conclusion is that Minkowski diagrams with their oblique coordinates are probably correct but, lacking the idea of superposition, fail to fully describe special relativity.

Abstract: One of the most promising approaches for replacing conventional power plants during the transition to clean energy is the conversion of existing coalfired power plants (CPPs) into nuclear power plants. This strategy offers numerous ecological and economic advantages. However, integrating a nuclear reactor with a steam turbine originally designed for a coal plant is far from trivial and involves significant technical challenges. The purpose of this work is to analyze and evaluate various options for coupling a HighTemperature GasCooled Small Modular Reactor (HTGR SMR) with a potentially suitable subcritical steam turbine from an existing CPP, thereby creating several repowering configurations. The main difficulties stem from the fact that the turbine was designed to operate with live steam at lower flow rates, temperatures, and pressures than those typically provided by an HTGR SMR. In addition, the feedwater temperature and pressure requirements for the HTGR SMR steam generator differ substantially from those in a CPP, leading at best to additional efficiency losses. Moreover, the overall thermal cycle layouts of the two systems are fundamentally different. Despite these challenges, technically feasible combinations can be achieved. However, determining which option is the most economically viable depends on numerous additional factors, including the specific characteristics of the individual CPP and the regulatory framework of the country in which it operates.

Abstract: Ticks are important arthropod vectors that transmit various pathogens to humans, livestock, and wildlife, thereby contributing significantly to the global burden of vector-borne diseases. The tick microbiome, consisting of bacteria, viruses, protozoa, and other microorganisms, plays a crucial role in pathogen transmission dynamics and the emergence of new zoonotic diseases. This review examines the characteristics of tick vectors, the composition and dynamics of tick-associated microbiomes, and their implications for zoonotic disease transmission. We analyze current knowledge of tick-borne pathogens, including Borrelia burgdorferi, Rickettsia species, Anaplasma species, and Coxiella species, and highlight the potential for microbiome constituents to serve as reservoirs for emerging pathogens. The complex interactions between tick hosts, their microbiomes, and vertebrate hosts create opportunities for pathogen evolution and interspecies transmission. Recent advances in molecular techniques have revealed previously unknown microbial diversity within tick populations, suggesting that many potential zoonotic pathogens remain undiscovered. We discuss future research directions, including field screening methodologies for pathogen detection, microbiome-based risk assessment approaches, and the development of novel prevention strategies, including tick vaccines.

Abstract: With the advent of the neural network era, traditional machine learning methods have increasingly been overshadowed. Nevertheless, continuing research on the role of geometry for learning in data science is crucial to envision and understand new principles behind the design of efficient machine learning. Linear classifiers are favored in certain tasks due to their reduced susceptibility to overfitting and their ability to provide interpretable decision boundaries. However, achieving both scalability and high predictive performance in linear classification remains a persistent challenge. Here, we propose a theoretical framework named geometric discriminant analysis (GDA). GDA includes the family of linear classifiers that can be expressed as a function of a centroid discriminant basis (CDB0) - the connection line between two centroids - adjusted by geometric corrections under different constraints. We demonstrate that linear discriminant analysis (LDA) is a subcase of the GDA theoretical framework, and we show its convergence to CDB0 under certain conditions. Then, based on the GDA framework, we propose an efficient linear classifier named centroid discriminant analysis (CDA) which is defined as a special case of GDA under a 2D plane geometric constraint. CDA training is initialized starting from CDB0 and involves the iterative calculation of new adjusted centroid discriminant lines whose optimal rotations on the associated 2D planes are searched via Bayesian optimization. CDA has good scalability (quadratic time complexity) which is lower than LDA and support vector machine (SVM) (cubic complexity). Results on 27 real datasets across classification tasks of standard images, medical images and chemical properties, offer empirical evidence that CDA outperforms other linear methods such as LDA, SVM and logistic regression (LR) in terms of scalability, performance and stability. Furthermore, we show that linear CDA can be generalized to nonlinear CDA via kernel method, demonstrating improvements on the linear version with tests on three challenging datasets of images and chemical data. GDA's general validity as a new theoretical framework may inspire the design of new classifiers under the definition of different geometric constraints, paving the way towards a deeper understanding of the role of geometry in learning from data.

Abstract: Glutamine is the most abundant amino acid in human plasma and tissues and plays essential roles in cellular metabolism, biosynthesis, and redox homeostasis. Beyond these canonical functions, glutamine availability and utilization have emerged as key regulators of multiple cellular stress responses, including the integrated stress response, endoplasmic reticulum stress, metabolic checkpoint signaling, and autophagy. During viral infection, host glutamine metabolism is frequently reprogrammed to meet the energetic and biosynthetic demands of viral replication, thereby inducing or reshaping glutamine-linked stress pathways. Increasing evidence indicates that these stress responses are not merely secondary consequences of infection but actively influence key stages of the viral life cycle, including viral entry, genome replication, protein synthesis, and host antiviral responses. In this review, we summarize current advances in understanding how glutamine metabolism regulates cellular stress responses in the context of both viral and non-viral infections, and how these pathways, in turn, modulate viral pathogenesis and host defense. We discuss the context-dependent roles of glutamine-linked stress signaling in either promoting viral replication or restricting infection, depending on viral species, host cell type, and metabolic conditions. Finally, we highlight emerging concepts and unresolved questions, including the potential of targeting glutamine metabolism and associated stress pathways as host-directed antiviral strategies. A deeper understanding of the interplay between glutamine metabolism, cellular stress responses, and viral infection may provide new insights into disease mechanisms and inform the development of novel therapeutic approaches.