Search Results (14,425)

Internet of things long range (IoT/LoRa) devices emit radiofrequency electromagnetic fields (RF-EMF), ensuring long-range, low-power communication, and their use in precision agriculture continuously expands. Thus, the interest in the impact of low-intensity but long-term EMF exposure on plants has increased. In this study, maize plants were exposed to 868 MHz, 10 mW EMF for the first 28 days of their development with soil-buried antennas. Plants were divided into three groups: Control, Sham-exposed, and EMF-exposed. Biological effects were followed on morphological, physiological, and biochemical levels every week. The plant height values were fitted to a Gompertz function modeling the growth. The results showed slightly faster early development of EMF-exposed plants in about 21 days. The relative dry-leaf biomass from EMF-affected plants was a bit higher than in the Control and Sham groups until day 21. Chlorophyll fluorescence analysis (JIP-test) indicated photosynthetic stability. Antioxidant enzyme activity, antioxidant capacity, content of malondialdehyde, hydrogen peroxide, and reducing sugars were measured, and principal component analysis was done for all parameters. Overall, the developmental stage accounts for most of the observed variations in the data rather than EMF exposure. The results suggest that under the tested conditions, IoT/LoRa-emitted EMF did not provoke adverse effects in maize and acted as a modest modulator of physiological functions. Full article

Rapid and non-destructive estimation of maize (Zea mays L.) leaf flavonoid (Flav) content is important for crop stress monitoring and precision agriculture. This study aimed to improve Flav estimation by integrating unmanned aerial vehicle (UAV)-based multispectral data, texture features, and phenological parameters across six key growth stages in the Guanzhong Plain, China. Maize Flav content was measured in situ using a Dualex Scientific+ meter, while canopy reflectance was acquired with a DJI M300 RTK UAV equipped with an MS600 Pro multispectral camera. A comprehensive feature set, including spectral bands, vegetation indices, texture features, texture indices, and logistic curve-derived phenological parameters, was constructed. Three feature selection methods, competitive adaptive reweighted sampling (CARS), the genetic algorithm (GA), and the successive projections algorithm (SPA), together with three regression models, partial least squares regression (PLSR), extreme gradient boosting (XGBoost), and convolutional neural network (CNN), were evaluated for Flav estimation. The results showed that integrating spectral, texture, and phenological information significantly improved model performance compared with spectral variables alone. CNN and XGBoost generally outperformed PLSR. Across the six growth stages, the stage-specific optimal models achieved coefficient of determination (R²) values ranging from 0.7749 to 0.8686 and residual prediction deviation (RPD) values ranging from 2.0046 to 2.6019, indicating high to outstanding predictive ability. The highest accuracy was obtained at R3 using the CARS-X_II-CNN model, with R² = 0.8686, root mean square error of validation (RMSEV) = 0.0382, and RPD = 2.6019. Texture features and phenological metrics, especially the start of season derived from the normalized difference vegetation index (NDVI_SOS) and the rate of senescence derived from the enhanced vegetation index (EVI_ROS), contributed substantially to model accuracy. In addition, maize Flav showed a unimodal response to nitrogen supply, with moderate nitrogen levels associated with higher Flav content. This study demonstrates the potential of UAV-based multisource feature integration and machine learning for accurate maize Flav estimation, and provides a useful framework for digital crop phenotyping and stress diagnosis. Full article

Tailings remediation alleviates ecosystem degradation and protects species. To conserve terrestrial biodiversity and address sustainability challenges while achieving economic growth, numerous researchers have devoted efforts to monitoring ecological functions and optimizing community structures. This study investigates the microbial characteristics and functional diversity across ecological succession stages of tailings. Selecting three typical restoration stages, including biological crust, moss, and grassland stages, we adopt 16S rRNA and ITS gene amplification, Illumina high-throughput sequencing, spectroscopy, and network correlation analysis to explore the responses of soil multifunctionality index, microbial communities, and carbon metabolism during tailings restoration. The experimental results indicate that the functional diversity index increases with ecological succession and is significantly correlated with the bacterial genera Rubrobacter and Arenimicrobium, whereas no significant correlation is observed with dominant fungi. The network interactions among bacterial communities are gradually strengthened along the succession process. In terms of carbon metabolic functions, the relative abundances of galactose, starch, and sucrose metabolism pathways increase obviously with restoration progression, while inositol phosphate metabolism, peroxisome metabolism, retinol metabolism, glyoxylate and dicarboxylate metabolism, and xenobiotics metabolism exhibit no significant variations. These findings provide novel empirical evidence for explaining microbe-mediated ecological succession in tailing ecosystems and highlight the necessity of multi-perspective analysis for ecological restoration. Policy and practical implications emphasize that the application of specific microorganisms and their interspecific interactions to promote iron tailings ecological restoration should fully consider the spatiotemporal heterogeneity of tailings areas. This study deepens the understanding of differential microbial responses at different tailings restoration stages and provides actionable insights for balancing mining economic development and terrestrial ecosystem conservation. Full article

As sustainable finance continues to advance, an important question is how scientifically designed and well-targeted policies can curb corporate ESG greenwashing and improve the quality of firms’ ESG and sustainability disclosure. From the perspective of fiscal–financial policy coordination, we exploit the green loan interest subsidy policy (GLIS) as a quasi-natural experiment and develop an analytical framework around four policy components: commercial banks’ information screening, local governments’ green screening, the subsidy instrument’s leverage and certification effects, and firms’ internal green governance. Within this framework, we examine whether the GLIS can restrain corporate ESG greenwashing. Using Chinese listed firms from 2009 to 2022 as the sample and identifying the effect through a multi-period difference-in-differences (DID) model, we find that the GLIS significantly curbs corporate ESG greenwashing. In exploring the underlying channels, we find that the GLIS curbs corporate ESG greenwashing by strengthening commercial banks’ information screening, enhancing local governments’ green screening, easing firms’ external financing constraints, and reinforcing firms’ internal green governance. Further analysis indicates that the inhibitory effect of the GLIS on corporate ESG greenwashing is more pronounced among non-state-owned firms, firms in the growth stage, firms in heavily polluting industries, and firms located in regions with weaker resource endowments. In addition, the stronger a firm’s digital technology R&D capability and corporate governance capability, the greater the restraining effect of the GLIS on its ESG greenwashing. By systematically evaluating the governance effect of fiscal–financial policy coordination on corporate ESG greenwashing, our study provides useful insights for governments seeking to improve green finance policies and optimize the coordination of green policy instruments. Full article

Umbilical cord mesenchymal stromal cells (UC-MSCs) are a key element of regenerative medicine due to their ability to secrete growth factors that stimulate proliferation and angiogenesis, and modulate the inflammatory response. Despite their widespread use, the influence of the perinatal microenvironment on their biological properties remains poorly understood. The aim of this study was to assess the influence of pH and blood gas parameters in umbilical cord blood on the global transcriptomic profile of UC-MSCs and to analyze the correlation between the metabolic status of the newborn and the expression of key trophic factors: EGF, FGF2, FGFR1, FGFR3, GDNF, HGF, IGF1, NES, NGF, and PGF. Methods: The study was conducted in two stages. In the first phase, transcriptomic screening was performed using Affymetrix HuGene 2.0 ST microarray on cells isolated from three environmental groups defined by cord blood pH: acidic (pH < 7.35), physiological (7.35–7.39), and alkaline (pH ≥ 7.4). In the second phase, the results were validated using qPCR on an expanded study group (N = 50). Gene expression levels (RQ) were related to blood gas parameters (pH, pCO₂, pO₂, cHCO₃) and the presence of clinical features of threatened neonatal asphyxia. Results: Microarray analysis revealed that environmental pH acts as a molecular phenotypic switch. Under low pH conditions (<7.35), a shift in cell profile from proliferative to structural–migratory was observed. Significant overexpression of genes responsible for extracellular matrix (ECM) organization and adhesion (e.g., COMP, DCN, LUM, FMOD) was observed, while pathways related to cell cycle and cell division (↓CDK1, AURKA, TOP2A) were downregulated. qPCR validation confirmed these observations, demonstrating a strong positive correlation between blood pH and the expression of regenerative mediators: FGFR1 (r = 0.28), EGF (r = 0.30), NGF (r = 0.39), and IGF1 (r = 0.30). A negative correlation was also found between carbon dioxide pressure (pCO₂) and the expression of NGF, FGFR1, and EGF. A significant clinical finding was that in newborns diagnosed with threatened asphyxia, EGF, FGFR1, and NGF gene expression was significantly reduced, indicating impaired trophic potential of the cells in response to metabolic stress. Conclusions: These results indicate that cord blood gas parameters are critical regulators of the genetic activity of UC-MSCs. Metabolic and respiratory acidosis not only inhibit the cells’ proliferative potential but also force them into a matrix remodeling mode, permanently modifying their transcriptomic profile. This suggests that the neonatal acid–base status may serve as an objective indicator of the “biological quality” of isolated stromal cells, which has significant implications for their future applications in cell therapies. Full article

Sustainable land-use regulation in historical and cultural cities requires balancing heritage conservation, development demand, cropland retention, and urban–rural spatial restructuring. However, the stage-wise reorganization of urban–rural construction land under these coupled pressures remains insufficiently understood. Taking Jingzhou District, China, as a case study, this study uses land-use data from 2000, 2005, 2010, 2015, and 2020 and integrates stage-wise random-forest analysis, consistency-based interaction-network mining, and multi-scenario simulation within the intPLUS framework. Population, GDP, and areal-water distance layers were matched to the corresponding stage-terminal snapshots where applicable, whereas 2020 POI data were used as contemporary spatial-context proxies. From 2000 to 2020, urban industrial land (UIL) expanded from 16.63 to 46.42 km², increasing by approximately 179.1%, whereas rural settlements (RS) increased more moderately from 56.59 to 60.27 km², increasing by approximately 6.5%. The stage-wise RF and interaction-network results show that UIL and RS followed different spatial association structures, with stronger UIL self-reinforcement and stronger RS self-continuity in the later stage. Historical validation showed overall accuracy values of approximately 91% and Kappa values around 0.80, but FoM values remained relatively low, ranging from 0.098 to 0.176. Class-specific mapping accuracy was higher for RS (81.90–82.37%) than for UIL (55.20–66.93%), indicating a weaker performance in locating UIL change. Therefore, the 2035 simulations should be interpreted as parameter-conditioned regulatory comparisons rather than deterministic pixel-level forecasts. The scenario results indicate that the conservation-oriented limited growth was associated with the restricted UIL expansion and better cropland retention under the prescribed demand and constraint settings, while the RS reduction occurred only under explicit village-consolidation and construction-land quota reallocation assumptions. By distinguishing UIL and RS, this study provides differentiated regulation-oriented evidence for sustainable land-use governance in historical and cultural cities. Full article

In this study, Fe₇₀Ni₃₀ metal was deposited onto continuous glass fiber composites via magnetron sputtering, followed by surface coating with SiO₂. The effects of key process parameters-including Fe₇₀Ni₃₀ sputtering duration (2, 5, 10, 20, and 30 min) and SiO₂ surface coating-on the electromagnetic properties and microwave absorption performance of the materials were systematically investigated. Scanning electron microscopy (SEM) characterization revealed that as sputtering time increased, the metal coating evolved from discrete small particles into a continuous film. Cross-sectional SEM analysis further demonstrated the formation of a bilayer structure after SiO₂ introduction. X-ray diffraction (XRD) patterns confirmed the presence of diffraction peaks corresponding to the Fe₇₀Ni₃₀ alloy solid solution. Electromagnetic parameter measurements indicated that the influence of sputtering time on electromagnetic properties was primarily pronounced during the metal layer growth stage; once a continuous film was formed, the variation in electromagnetic parameters diminished. Concurrently, the SiO₂ coating exhibited a significant regulatory effect on dielectric parameters. Reflection coefficient calculations showed that the optimal absorption thickness for the single-layer material ranged from 2.5 to 3.0 mm, with the absorption peak shifting toward lower frequencies as thickness increased. However, the effective absorption bandwidth (EAB) was only 3–5 GHz, failing to meet wideband requirements. In contrast, the three-layer composite structure (total thickness: 3.8 mm) optimized via genetic algorithm achieved impedance gradient and loss synergy, expanding the EBW (R < −10 dB) from 4.8 GHz (single layer) to 10 GHz (8–18.0 GHz)-a substantial improvement over the single-layer configuration. This work provides experimental evidence and technical support for the structural design and process optimization of lightweight, high-efficiency, wideband microwave-absorbing materials. Full article

The impact of arbuscular mycorrhiza fungi (AMF) on root–shoot scaling strategies under zinc and phosphorus deficiency remains poorly understood in maize. The aims of this study were (i) To quantify the effects of zinc/phosphorus deficiency on AMF colonization, (ii) to quantify biomass accumulation in different plant parts in the presence of AMF, and (iii) to characterize how AMF alter root–shoot allometric scaling under zinc/phosphorus deficiency. We conducted a pot experiment arranged in RCBD split plot with 6 replications. SUWAN 5819 maize seeds were grown for 22 days under five Hoagland’s solution-based nutrient regimes (+Zn+P, −Zn−P, +Zn−P, −Zn+P, and deionized water), with and without AMF. AMF colonization was highest (49.6%) under −Zn+P contrary to hypothesis 1 which predicted highest colonization under dual deficiency, while the deionized water treatment had the lowest colonization (30.1%). Phosphorus was the dominant factor affecting biomass accumulation with a 2–4-fold reduction in organ dry weights for phosphorus-deficient treatments compared to phosphorus-sufficient treatments. AMF colonization significantly reduced dry weights in +Zn+P by 8.6%, 19.0%, and 47.5% in the leaf, stem, and roots, respectively, consistent with mycorrhiza-induced growth depression (MGD). Nutrient deficiency resulted in root biomass accumulation, consistent with the optimal partitioning theory. AMF increased shoot mass fraction from 50% to 63% in +Zn+P, and from 41% to 52.5% in −Zn−P, suggesting AMF role in modulating biomass accumulation. Root–shoot scaling slopes derived from LMM revealed that zinc deficiency caused negative scaling trajectory, and AMF was associated with positive root–shoot scaling trajectory in the −Zn+P treatment, though the scaling relationship was not confirmed by SMA analysis. These findings highlight nutrient specific AMF-mediated growth dynamics in early vegetative stage. Full article

Tetranychus urticae is a highly destructive, polyphagous mite that has developed resistance to multiple acaricides, necessitating the evaluation of new compounds. Isocycloseram is a novel insecticide with potential to control this mite; the effects of its sublethal concentrations are still uninvestigated. In this study, an age-stage, two-sex life table model was used to evaluate the sublethal effects of isocycloseram concentrations (LC₁₀ and LC₃₀) on population growth, reproduction, and development of the two-spotted spider mite. The results showed that the LC₁₀ and LC₃₀ values were 0.012 mg/L and 0.022 mg/L, respectively. Sublethal concentrations of LC₁₀ significantly affected population growth by reducing fertility, while LC₃₀ significantly prolonged the immature stage and reduced average fecundity by 37%. With the LC₃₀ treatment, the net reproductive rate R₀ decreased by 43%, and the intrinsic rate of increase r decreased significantly, from 0.152 day⁻¹ to 0.117 day⁻¹. The doubling time DT was extended by 30%, from 4.55 days to 5.92 days. This study covers the importance of life table analysis for investigating sublethal effects and for ensuring that, when isocycloseram is incorporated into integrated pest management, both its direct toxicity and its effects on population dynamics are considered. Full article

Achieving a favorable synergy of strength, ductility, and toughness is a critical challenge for expanding the engineering applications of titanium alloys. In this work, a medium-strength and high-toughness novel Ti-Al-Mo-V-Cr-Sn-Zr (named Ti62F) titanium alloy in the form of a Φ400 mm bar was adopted to systematically investigate the regulation behavior of double annealing on its microstructure and mechanical properties, and quantitative correlations between microstructural parameters and macroscopic properties were established. Increasing the cooling rate during the first annealing stage (air cooling, force air cooling and water quenching) significantly refined the secondary α (α_s) phase and reduced the volume fraction and size of the primary α (α_p) phase, leading to an increase in the ultimate tensile strength of the alloy from 1077 MPa to 1229 MPa. However, the impact-absorbed energy decreased from 51.5 J to 23.3 J. When the second annealing temperature was varied within the range of 625–675 °C, the ultimate tensile strength fluctuated slightly and the impact toughness increased moderately. Equiaxed α_p phase and relatively thick α_s can induce multiple crack deflections, prolong the crack propagation path and enhance energy absorption. Dislocations are mainly piled up at α/β phase boundaries, triggering void nucleation and growth, which dominate the ductility and toughness levels. Tensile twinning acts only as an auxiliary deformation mechanism and contributes limitedly to toughness. After heat treatment under the optimized schedule of 880 °C/2 h/AC + 650 °C/4 h/AC, the Ti62F alloy exhibits a superior strength–toughness balance compared with conventional medium-strength titanium alloys such as TA15, TC4, and TC4-DT. The findings can provide a heat treatment basis for microstructural regulation of large-size Ti62F bars and their engineering applications in aerospace structural components. Full article

Accurate wheat spike detection is essential for crop phenotyping and yield estimation, but real-world field conditions—such as dense spike overlap, environmental domain shifts, and degradation-induced failures like motion blur—pose significant challenges. Achieving robust perception under these circumstances while maintaining a strict accuracy-efficiency trade-off for edge devices remains a pressing research problem. To overcome these limitations, we propose GG-YOLO, a unified lightweight detection framework specifically tailored for complex agricultural environments. Rather than a simple recombination of existing lightweight modules, GG-YOLO integrates three original structural adaptations: First, a Dual-path Attentive Ghost Mechanism (DAGM) introduces gradient-guided attention modulation to enhance feature discrimination and explicitly resolve feature confusion in dense, overlapping regions. Second, a C3Ghost module combines multi-branch aggregation with linear feature generation, mitigating parameter redundancy in the prediction head by approximately 31% compared to the standard YOLOv8s without sacrificing semantic capacity. Third, DSample, a dynamic upsampling operator featuring an original dual-mode adaptive mechanism, robustly recovers fine-grained spatial details during multi-scale feature pyramid fusion. Extensive cross-dataset experiments on the GlobalWheat2020 and HNKJXYwheat datasets validate the model’s exceptional resilience to domain shifts and varying growth stages. GG-YOLO achieves a precision of 94.35%, a recall of 91.93%, and a state-of-the-art mAP@50 of 96.47%. Furthermore, the model contains only 7.89 M parameters and requires 20.4 GFLOPs, reaching an inference speed of 165 FPS on a desktop GPU and a validated real-time speed of 64 FPS on an NVIDIA Jetson edge computing platform. These results demonstrate that GG-YOLO establishes a superior accuracy-efficiency frontier, making it highly reliable for real-time field deployment in precision agriculture. Full article

Qinghai Lake, the largest endorheic saline lake in China, has undergone a pronounced hydrological regime shift from a multi-decadal decline to a rapid post-2004 recovery, reflecting strong hydroclimatic non-stationarity in the northeastern Tibetan Plateau (TP). This paper supplements the current water level and lake area status of Qinghai Lake to provide basic background for future prediction. Reliable forecasting of such climate sensitive lake systems remains difficult because conventional statistical models often fail to capture non-linear fluctuations, whereas standalone deep learning models may overlook long-term deterministic evolution. To address this challenge, we developed a serial decomposition GeoAI framework that integrates autoregressive integrated moving average (ARIMA), one-dimensional convolutional neural networks (1D-CNNs), and long short-term memory (LSTM) networks for non-stationary water level forecasting. Using annual water level observations from 1960 to 2025, the ARIMA component was first used to extract the low-frequency deterministic trend, after which the CNN-LSTM module reconstructed the nonlinear residual variability. The model was trained on the 1960–2012 period and validated over 2013–2025, which represents the most dynamic expansion stage of Qinghai Lake. The hybrid framework outperformed the benchmark models, achieving a Root Mean Square Error (RMSE) of 0.2033 m, Mean Absolute Error (MAE) of 0.1727 m, and Mean Squared Error (MSE) of 0.0413 m² during validation. The decomposition strategy effectively reduced phase lag and amplitude attenuation, improving both predictive accuracy and process interpretability. Multi-step forecasting for 2026–2056 suggests that Qinghai Lake will continue to rise, reaching approximately 3204.08 m by 2056, although the growth rate is projected to slow as negative hydrological feedback strengthen. By explicitly separating deterministic climate scale signals from nonlinear short-term variability, the proposed framework provides a robust and transferable geoinformation based tool for forecasting water level dynamics and supporting adaptive management in climate sensitive, data scarce lake basins. Full article

Major depressive disorder (MDD) is a complex and heterogeneous psychiatric disorder with a high prevalence. Neuroinflammation may define biologically distinct patient subgroups with different mechanisms, clinical phenotypes, and treatment responses. This narrative review integrates current evidence around three linked questions: how neuroinflammatory processes contribute to depression, how biomarkers can identify clinically relevant inflammatory phenotypes, and how these findings can inform anti-inflammatory treatment strategies. The major mechanisms discussed include microglial activation and neuroimmune signaling, hypothalamic–pituitary–adrenal axis dysregulation and glucocorticoid receptor resistance, kynurenine pathway alterations, and cytokine-driven impairment of neurogenesis and synaptic plasticity. These pathways interact with stress responses, neurotransmitter systems, and neuronal function, while their expression may vary according to sex, age, hormonal status, disease stage, and treatment exposure. These interconnected pathways may contribute to depressive symptoms by disrupting neurotransmitter systems and impairing neural plasticity. In addition, this review discusses several candidate biomarkers, including C-reactive protein (CRP), interleukin-1β (IL-1β), interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), brain-derived neurotrophic factor (BDNF) and transforming growth factor-β1 (TGF-β), which may support patient stratification, treatment prediction, and assessment of target engagement. Clinical trials of anti-inflammatory agents have shown inconsistent and generally modest effects in unselected MDD populations. By integrating mechanistic evidence with biomarker-guided therapeutic implications, this review aims to clarify how neuroinflammatory research may inform more precise and individualized treatment strategies for depression. Full article

The dormancy of lilies is an important physiological process involving vernalisation and the differentiation and maturation of flower buds. We have cloned an aquaporin, SbP1P1;2, from the Lilium ‘Siberia’. Subcellular localisation analysis indicates that it is a protein that is localised to the plasma membrane in Nicotiana benthamiana. VIGS-mediated transient silencing revealed that silencing the SbPIP1;2 gene inhibited the development of lily flower buds, while those in the control group differentiated earlier to the anther primordia stage. Notably, the ABA levels in the control group had dropped significantly by day 63, suggesting that dormancy ended earlier than in the treatment group. The test plants’ phenotype is characterised primarily by the fact that silencing the SbPIP1;2 gene inhibits both flower bud development and root growth. The dormancy-to-sleep transition phase (PS vs. TS) was also the period during which the largest number of differentially expressed genes was observed. KEGG enrichment analysis indicates that starch and sucrose metabolic pathways are most active from the onset to the completion of dormancy release and that significant differences occur in several key genes within these pathways. These include alpha-trehalose-phosphate synthase (TPS), sucrose phosphate synthase (SPS), trehalase (TREH), fructokinase-1 (E2.7.1.1), beta-glucosidase (bglB), glycogen synthase (glgA), glucose-6-phosphate isomerase (GPI), and ectonucleotide pyrophosphatase/phosphodiesterase family members 1 and 3 (ENPP1/3). The discovery that aquaporins promote dormancy breaking in lilies is a highly successful case study for aquaporin research in flowers. Full article

Adolescence is a developmental stage marked by dynamic interactions between diet, the gut microbiome and endocrine maturation, creating a physiological environment in which early metabolic disturbances can rapidly translate into long-term cardiovascular vulnerability. This narrative review summarises the latest research on the diet–microbiome–hormone axis in adolescents, focusing on the metabolic pathways through which microbial metabolites influence host physiology. Short-chain fatty acids (SCFAs), microbially transformed bile acids and postbiotic signalling molecules regulate enteroendocrine communication, insulin sensitivity, vascular function and inflammatory tone, thereby linking dietary exposures to early cardiometabolic alterations. Dysbiosis, driven by ultra-processed dietary patterns, low fibre intake and reduced microbial diversity, promotes metabolic endotoxemia, neuroendocrine imbalance and endothelial impairment, all of which are recognised as early indicators of cardiovascular disease. A distinctive contribution of this review is the integration of PF into the adolescent cardiometabolic framework. This emerging biotechnological process enables the controlled production of structurally defined bioactive compounds, including angiotensin-converting enzyme (ACE) inhibitory peptides, targeted prebiotic oligosaccharides, fermentable substrates that promote SCFA formation, microbially derived eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), phytosterols and purified postbiotics. These compounds modulate several regulatory pathways, such as the renin–angiotensin–aldosterone system, lipid and bile acid metabolism, gut barrier stability, inflammatory signalling and endocrine axes involving glucagon-like peptide-1 (GLP-1), peptide YY (PYY), leptin, insulin sensitivity and growth hormone/insulin-like growth factor-1 (GH/IGF-1) dynamics. By situating precision fermentation within the broader context of adolescent metabolic susceptibility, this review highlights its potential to support microbiome resilience, stabilise hormonal regulation and mitigate early cardiovascular risk. However, further adolescent-specific clinical trials and long-term safety assessments are required to translate these advances into effective public health strategies. Full article