Search Results (2,870)

Background/Objectives: Sea urchin gonads (“roe”) are a valuable seafood product and a chemically complex matrix whose composition varies with physiology and environment. We present a biphasic extraction and ¹H NMR workflow to build a reusable reference inventory of polar metabolites and apolar lipid features in Paracentrotus lividus. Methods: Gonads from 37 adults (23 males, 14 females) collected at two sites (Alicante and Jávea–Dénia, Spain; October 2024) were lyophilized, extracted with methanol/chloroform/water, and analyzed by 400 MHz ¹H NMR in buffered aqueous solution (polar) and CDCl₃ (apolar). Polar metabolite identification combined 1D patterns with database matching and ¹H–¹³C HSQC confirmation on representative samples, yielding 71 annotated resonances corresponding to 37 metabolites spanning amino acids, osmolytes/quaternary amines, carbohydrates/aminosugars, and nucleoside/purine-related compounds. Results: Polar fingerprints enabled supervised modelling: PLS-LDA separated sexes with low cross-validated error, and SPA/COSS ranking highlighted glycine, alanine, creatine and osmolyte-associated signals as key discriminants; pathway mapping supported the enrichment of amino-acid and one-carbon/purine networks. Apolar spectra were annotated at the motif level and used for lipid-index estimation, indicating substantial unsaturation but low docosahexaenoic acid (DHA) and modest sex effects. Conclusions: The curated peak lists and reporting framework facilitate reproducible NMR annotation and future comparative studies of P. lividus gonads. Full article

Transient receptor potential vanilloid 4 (TRPV4) is a polymodal cation channel that is widely expressed in sensory neurons, immune cells, and structural tissues, where it integrates mechanical, osmotic, and chemical stimuli to regulate both physiological responses and disease-associated signaling. Mast cells (MCs), key immune effector cells capable of rapid mediator release through degranulation, also express TRPV4. Increasing evidence supports TRPV4-MC signaling as an important neuroimmune interface, linking mechanical and inflammatory stimuli to tissue hypersensitivity and pain. In this review, we synthesize current evidence supporting a role for TRPV4 in MC-associated neuroimmune signaling across multiple disease contexts while distinguishing settings in which TRPV4 directly regulates MC activation from those in which MC responses arise through multicellular tissue interactions. Direct TRPV4-dependent MC activation has been described in conditions such as LL-37–driven rosacea and mechanically induced inflammation, whereas in disorders including asthma, visceral hypersensitivity, bladder pain syndromes, and osteoarthritis, TRPV4 activity in epithelial, neuronal, or stromal compartments more often influences MC function indirectly through ATP–purinergic signaling, cytokine release, and neuropeptide-mediated crosstalk. Across systems, TRPV4 emerges not as a single pathogenic switch but as part of a context-dependent signaling network whose functional consequences depend on cell type, tissue microenvironment, and disease stage. Altogether, these findings identify TRPV4 as a therapeutically actionable node within neuroimmune signaling pathways and support the development of tissue-specific and combination strategies targeting both TRPV4 activity and MC-mediated signaling in chronic inflammatory and pain disorders. Full article

Background: Exacerbations of chronic obstructive pulmonary disease (COPD) are a leading cause of morbidity, mortality, and healthcare burden worldwide. Early detection and timely intervention remain important challenges in COPD management, given the unpredictable nature of acute deterioration and limitations of traditional spirometry-based risk assessment. Methods: This narrative review synthesizes artificial intelligence (AI)-driven approaches for predicting and detecting chronic obstructive pulmonary disease (COPD) exacerbations across electronic health records, wearable sensors, imaging, environmental data, and patient-reported outcomes, emphasizing novel discoveries and emerging relationships rather than predictive performance. Results: Three major discoveries have been made. First, measurable physiological and behavioral deterioration may precede symptom recognition by approximately 7–14 days, thereby establishing a potential intervention window for anticipatory care. Second, machine learning (ML) models integrating pollutant exposure, medication adherence, and clinical characteristics have identified phenotypes with differential environmental sensitivity, including unexpected exposure–adherence interactions. Third, deep neural network analysis of full spirometry curves has revealed structural phenotypes beyond traditional Forced Expiratory Volume (FEV₁)-based measures and novel imaging biomarkers. The predictive performance ranges from the Area Under the Curve (AUC) 0.72–0.95, with a pooled meta-analytic AUC of approximately 0.77. Conclusions: AI has uncovered hidden patterns in the progression of COPD, supporting a shift from reactive to anticipatory management. Translation to routine care requires prospective validation, improved interpretability, workflow integration, and generalizability and equity. Full article

Cheliped regeneration in the E. sinensis is a tightly regulated physiological process, yet the molecular regulatory mechanisms underlying sexual dimorphism during regeneration remain unclear. In this study, we combined morphological observation with transcriptomic analysis to systematically investigate the regenerative stage characteristics and sex-related differences. The papilla stage 4 dpa was identified as a pivotal transitional stage, bridging initial wound healing and cellular dedifferentiation (2 dpa) with subsequent redifferentiation and morphogenesis (7 dpa). Morphological sex-based differences characterized by larger regenerating chelipeds in males became prominent by the late stage (28 dpa). Notably, the molecular foundation of sexual dimorphism was found to be established at 4 dpa, significantly preceding the emergence of phenotypic differences. This early divergence was driven by sex-dimorphic endocrine networks: males exhibited preferential expression of genes such as Fem-1c-like, Cyp2L1-like, CpAMP1A-like and Nedd4-like, while females showed enrichment in elevated aromatase activity. Weighted gene co-expression network analysis (WGCNA) identified the Hox gene Antp as a core hub regulator, exhibiting high co-expression with key epidermal-related genes such as Cht6, Cht2-like and more. Its suppressed expression at 2 dpa aligned with the requirements for dedifferentiation, whereas its peak at 4 dpa indicated a crucial role in orchestrating appendage patterning and exoskeleton assembly. RNA interference (RNAi) knockdown of Antp resulted in obscured differentiation between the propodus and carpus in both sexes and confirmed its regulatory control over downstream targets including Ubx, Bmp2-like, and CpAMP1A-like. This study suggests a putative hierarchical regulatory model in which systemic hormonal signals may integrate Antp and other sex-biased regulators to potentially facilitate structured limb regeneration. These findings offer tentative novel insights into the interplay between developmental plasticity and sex-based regulatory divergence in decapod crustaceans. Full article

Glucagon-like peptide-1 receptor agonists (GLP-1RAs) have transformed the management of type 2 diabetes and obesity, yet their actions extend beyond glycemic control and weight loss. This narrative review synthesizes current preclinical and clinical evidence examining the bidirectional relationship between glucagon-like peptide-1 (GLP-1) receptor agonists and circadian biology. A structured literature search was conducted in PubMed using combinations of the terms ‘GLP-1,’ ‘circadian,’ ‘chronobiology,’ ‘sleep,’ ‘obesity,’ and ‘type 2 diabetes’ through January 2026. Accumulating evidence indicates that GLP-1 physiology is closely coupled to circadian timing systems and sleep–wake regulation. In this narrative review, we synthesize emerging data that reframe GLP-1RAs as chronometabolic modulators, acting at the intersection of metabolism, circadian biology, and sleep. We review circadian control of GLP-1 secretion by intestinal L-cells, emphasizing the role of core clock genes and the vulnerability of incretin rhythms to circadian misalignment from shift work, nocturnal light exposure, and sleep loss. We then examine GLP-1 receptor signaling within central and peripheral clock networks, including feedback effects on hypothalamic and hepatic circadian regulation. Emerging data suggest that GLP-1 signaling is under circadian regulation and may, in turn, influence central and peripheral clock systems. Comparative discussion of semaglutide, liraglutide, and tirzepatide highlights agent-specific pharmacokinetics and emerging clinical data linking GLP-1RA therapy to sleep outcomes, particularly obstructive sleep apnea. Finally, we outline translational opportunities for chronotherapy and precision medicine, positioning GLP-1RAs as integrative tools for metabolic and sleep-related disease rather than purely weight-centric therapies. We propose that GLP-1 receptor agonists may function as chronometabolic modulators, with potential implications for personalized chronopharmacological strategies in metabolic disease. Full article

Scaevola taccada, as a key dominant plant in coastal ecosystems, plays an irreplaceable role in sand fixation, shoreline protection, and maintaining the ecological stability of coastal zones. To investigate the effects of drought stress on the Binghai plant Scaevola taccada seedlings, a natural drought treatment was applied. Physiological indicators were measured at 0, 10, 25, and 40 days of stress, and 5 days after rewatering. Transcriptome sequencing and long non-coding RNA (lncRNA) analysis were also conducted to reveal the drought response mechanisms and molecular regulatory networks. The results showed that: (1) Prolonged drought significantly inhibited growth, with relative height increase, leaf number, and relative water content declining by 46.8%, 37.2%, and 63.4%, respectively, at T40 compared to the control. (2) In terms of photosynthetic physiology, Rubisco activity, RCA activity, SPAD value, Fv/Fm, and qP all continuously declined with increasing stress, while NPQ increased, suggesting damage to the photosynthetic system but also the activation of energy dissipation mechanisms to alleviate photooxidative stress. (3) The antioxidant system played a crucial role in the drought response. Under drought stress, the activities of SOD, POD, and CAT, and MDA content, underwent significant changes, with antioxidant enzyme activities rebounding notably after rewatering. (4) Transcriptome analysis revealed that differentially expressed mRNAs and lncRNA-targeted genes were significantly enriched in the ‘photosynthesis’ and ‘carbon metabolism’ pathways. Key genes involved, including PSAD-1, PSAL, NPQ4, six LHCs, BAM3, BAM1, SSII-A, and FRK1, were identified as core components of the regulatory network. In summary, Scaevola taccada effectively responds to drought stress through multi-level mechanisms, including photosynthetic regulation, carbon metabolism regulation, antioxidant defense, and transcriptional reprogramming, demonstrating strong drought resistance and post-rewatering recovery potential. These findings provide scientific evidence for plant selection and application in ecological restoration projects in coastal areas in the context of global climate extremes. Full article

To characterize the transcriptional and physiological alterations induced by manganese stress in Coilia nasus, juveniles (mean weight 5.0 ± 0.2 g) were subjected to either manganese exposure (5.50 ± 0.03 mg/L) or control (0 mg/L) for a 12 h period. Subsequently, gill tissues were excised for evaluation of antioxidant parameters and RNA-Seq analysis. A total of 753 DEGs were identified in the manganese exposure group compared to controls, comprising 287 up-regulated and 466 down-regulated genes. GO and KEGG enrichment analysis of DEGs showed that most of the DEGs were involved in immune and metabolic pathways, which disturbed the biological processes related to immunity and metabolism at the molecular level. The acute manganese stress initiated a multi-level antioxidant response to cope with oxidative stress in Coilia nasus. This finding was further supported by the significant increase in MDA content and significant decrease in GSH content and GSH-Px activity under manganese exposure, while SOD and CAT activities were significantly increased. Simultaneously, the acute manganese stress triggered profound metabolic reprogramming to cope with energy pressure in Coilia nasus, which showed that manganese exposure significantly down-regulated energy metabolism-related genes (pfkm, pgam2, eno3, pkm, aqp9, apoa1, tkt, sds); furthermore, the overall energy metabolism network was widely inhibited, while lipid metabolism-related genes (fabp3, cpt1a) were significantly up-regulated to compensatorily activate fatty acid transport and β-oxidation pathways. In addition, the acute manganese stress initiated a complex immune response pattern to cope with cell damage in Coilia nasus, which showed that manganese exposure significantly enhanced the expression of inflammatory signaling genes (mapk1, stat1, tgfb3); furthermore, certain inflammatory pathways were activated, while the expressions of immune regulatory genes (traf6, il-10) were significantly decreased. In summary, these results indicated that manganese exposure could impair immune function, disrupt metabolism, and induce oxidative stress in Coilia nasus. Full article

Background/Objectives: Emotion classification based on eye-movement features has become a widely adopted approach due to the simplicity of data acquisition and the strong association between ocular responses and emotional states. However, several challenges remain with regard to existing emotion recognition methods, including the relatively weak correlation between eye-movement features and emotional labels and the fact that the key features are not prominently presented. Methods: To address abovelimitations, this study proposes an improved CNN-transformer combined with enhanced deep canonical correlation analysis network (ICTD). The proposed method first performs preprocessing and reconstruction of raw eye-movement signals to extract informative features. Subsequently, convolutional neural networks (CNNs) and transformer architectures are employed to capture local and global feature, respectively. In addition, an incremental feature feedforward network is incorporated to enhance the transformer, enabling the model to assign higher importance to salient feature information. Finally, the extracted representations are processed through deep canonical correlation analysis based on cosine similarity in order to generate classification outcomes. Results: Experiments conducted on the SEED-IV, SEED-V, and eSEE-d datasets demonstrate that the proposed ICTD framework consistently outperforms baseline approaches and attains optimal classification results. (1) On the eSEE-d dataset, the results of three-category arousal and valence classification reach 81.8% and 85.2%, respectively; (2) on the SEED-IV dataset, the emotion four-category classification result reaches 91.2%; (3) finally, on the SEED-V dataset, the emotion five-category classification result reaches 85.1%. Conclusions: The proposed ICTD framework effectively improves feature representation and classification performance, showing strong potential for practical emotion recognition and physiological signal analysis. Full article

Computed Tomography (CT) is a widely used imaging modality that employs X-rays and computational reconstruction to visualize internal anatomy. Although higher radiation doses produce higher-quality images, they also increase long-term cancer risk, motivating the use of low-dose protocols. However, low-dose CT data inherently suffer from elevated Poisson–Gaussian noise, necessitating effective denoising strategies. In myocardial CT perfusion (CTP) imaging, this challenge is compounded by residual cardiac motion, which misaligns consecutive time points and impairs accurate estimation of perfusion maps for diagnosing coronary artery disease. Traditional approaches typically treat these two problems, noise and motion, separately, denoising the reconstructed images first or applying the registration first. Such serial pipelines often degrade clinically significant features; e.g., denoising may destroy structural details essential for registration, while motion correction can distort subtle intensity cues needed for noise modelling. To overcome these limitations, we propose a unified deep learning framework that performs noise suppression and motion correction jointly for low-dose myocardial CTP. The method integrates two complementary components through a parallel ensemble strategy: (i) a modified Fast and Flexible Denoising Network (FFDNet) that incorporates noise-level maps to mitigate blended noise effectively, and (ii) a CNN-based registration model, extended with Time Enhancement Curve (TEC) correction and 4D physiological consistency constraints to estimate temporally coherent and anatomically plausible motion fields. By combining their outputs without iterative dependencies, the proposed framework produces motion-corrected and denoised CTP sequences in a single unified processing step, thereby better preserving myocardial structure and perfusion dynamics than conventional serial pipelines. The model has been evaluated using both reference-based (MSE, PSNR, SSIM, PCC, Noise Variance, TRE) and no-reference (NIQE, FID, KID, AUC) image quality metrics, supplemented by expert human assessment. Results demonstrate that jointly learning noise characteristics and motion patterns enables restoration of low-dose CTP images while minimizing feature corruption, thereby advancing the clinical utility of low-dose myocardial CTP imaging. Full article

Motor recovery after stroke, spinal cord injury, or traumatic brain injury reflects relearning rather than simple restitution, as surviving circuits retain plastic potential that can be re-engaged through temporally precise stimulation. This review synthesizes convergent findings demonstrating that Hebbian and spike-timing-dependent mechanisms govern reorganization across cortical, striatal, and spinal levels. Leveraging these timing rules to shape excitability during receptive network states enables durable changes in connectivity and behavior. This effect depends on temporal precision, physiological state, and reinforcement—not stimulus intensity alone—within plasticity windows regulated by metaplastic mechanisms that determine whether Hebbian processes are expressed. Together, these principles define a translational framework for neurorehabilitation, emphasizing biomarker-guided, adaptive, and scalable strategies aligned with intrinsic rules of experience-dependent reorganization. Full article

The dynamic interaction between plants and their root-associated microbiota represents a sophisticated and profound biological communication that regulates plant development and the formation of adaptation to the surrounding environment. These interactions function as critical regulators of multiple physiological processes, finally influencing soil fertility and agricultural productivity. Plants have evolved epigenetic networks that regulate beneficial plant–microbe interactions through regulating immune responses, gene regulation, and metabolite production to enhance stress tolerance and soil adaptation. These regulations collectively govern microbial colonization patterns while establishing reciprocal feedback loops through root exudate–microbe interactions. This review systematically updates contemporary advances in understanding how epigenetic modifications shape rhizosphere microbiome composition and function, and discusses their potential applications in enhancing the yield and quality of horticultural crops, as well as in mitigating continuous cropping obstacles. Full article

Ziziphus jujuba Mill. is a characteristic resource with both medicinal and edible values. At present, its lactic acid bacteria-fermented products are plagued by ambiguous variety selection and low added value. To clarify the variety-specific regulatory effects of Z. jujuba cultivars on metabolic profiles during lactic acid bacteria fermentation, this study analyzed the metabolic characteristics of fermented broths of Tan jujube, Jun jujube, and Ban jujube under a unified fermentation system using LC-MS untargeted metabolomics technology. Significantly differential metabolites were screened with the criteria of p < 0.05 and VIP > 1, and the metabolic regulatory mechanisms were further elucidated, combined with KEGG pathway enrichment analysis. The results showed that a total of 570 metabolites were identified in the three fermented broths. Tan jujube was enriched in linolenic acid, Ban jujube was rich in D-xylitol and dethiobiotin, and Jun jujube had prominent contents of S-adenosylmethionine and pyridoxine. All the aforementioned metabolites are involved in important physiological processes such as anti-inflammation and intestinal homeostasis maintenance. The differential metabolites were mainly enriched in 6 key pathways, including central carbon metabolism, ABC transporters, and phenylpropanoid biosynthesis, among which central carbon metabolism and ABC transporters were the core regulatory pathways. This study constructed an association network of Z. jujuba variety–differential metabolite–key pathway, systematically elucidated the metabolic differentiation mechanisms of fermented broths from different Z. jujuba cultivars, and provided a scientific basis for the precise selection of Z. jujuba varieties dedicated to fermentation and the targeted development of high-value-added functional fermented foods. Full article

Diet is a key modulator of the gut microbiota, thereby influencing host physiology. Microbial colonization begins early in life, influenced by maternal sources, mode of birth, diet, and environmental exposures, and stabilizes into an adult-like microbiome during early childhood. This maturation yields a microbial ecosystem dominated by Firmicutes and Bacteroidetes that contributes to host physiological homeostasis. Gut microorganisms function as an integrated metabolic system that transforms dietary substrates into bioactive metabolites, including short-chain fatty acids (SCFAs), amino acid-derived compounds, and microbial lipids. These metabolites regulate glucose and lipid metabolism, intestinal barrier integrity, and immune modulation. Although many metabolic functions are conserved, their activity is shaped by diet, microbial cross-feeding, and local intestinal conditions, enabling functional specialization within the gut. Disruption of this system, known as dysbiosis, is associated with alterations in microbial diversity and metabolic output that have been linked to metabolic diseases, including obesity and related disorders. Evidence from experimental models and observational studies suggests that these associations may involve interconnected inflammatory and metabolic mechanisms, such as impaired intestinal barrier function, low-grade inflammation, and altered dietary energy harvest; however, causal relationships in humans remain incompletely understood. Beyond peripheral effects, the gut microbiome influences host metabolism via the gut–brain axis, a bidirectional network that integrates neural, endocrine, immune, and metabolic signaling. Microbiota-derived metabolites and gut hormone modulation contribute to appetite regulation, energy balance, and glucose homeostasis, while central neuroendocrine signaling can reciprocally shape the intestinal microbial niche. Collectively, these findings highlight the gut microbiome as a central regulator of host metabolism, whose disruption may contribute to the development of metabolic disease. Full article

This review aims to explore the potential and mechanisms of selenium-biofortified probiotics as an innovative nutritional strategy for alleviating exercise-induced physiological stress. Exercise, particularly high-intensity or exhaustive exercise, triggers a cascade of physiological perturbations, including oxidative stress, inflammatory responses, gut barrier dysfunction, and muscle damage. Traditional single-nutrient strategies, such as inorganic selenium or probiotic supplementation, are often limited by low bioavailability or a narrow scope of action. Selenium-biofortified probiotics are produced via microbial biotransformation, which converts inorganic selenium into bioavailable organic forms like selenoamino acids or selenium nanoparticles that are loaded onto active probiotic carriers. This creates a synergistic entity combining the bioactivity of selenium with the gut-modulating functions of probiotics. Their core mechanism involves establishing a multi-layered defense system: by providing substrate for key selenoproteins like glutathione peroxidase, they directly enhance endogenous antioxidant defenses; by modulating immune cytokine networks, they downregulate excessive post-exercise inflammation; through probiotic colonization and metabolites, they maintain intestinal epithelial barrier integrity, countering exercise-induced intestinal hyperpermeability; and via the gut–muscle axis, they may regulate muscle metabolism and repair. Animal studies provide evidence for improved exercise endurance and reduced damage markers, but human clinical trials show inconsistent results, highlighting the influence of study design, dosage, and individual baseline status. Future research requires high-quality, long-term human trials to elucidate specific molecular pathways and develop personalized application protocols, advancing this synergistic strategy toward precision sports nutrition. Full article

Accurate detection of cardiac arrhythmias from electrocardiograms remains challenging for rare rhythm classes due to class imbalance and morphological variability. We present a hybrid deep learning framework combining per-lead convolutional encoders with a cross-lead transformer that models relationships across different lead signals through self-attention, accepting variable lead configurations. To address minority-class scarcity, a generative adversarial network synthesizes physiologically plausible beat segments for underrepresented arrhythmias. Attention-based visualizations localize influential waveform regions aligned with clinically meaningful structures. Post-training pruning and INT8 quantization enable efficient deployment with minimal performance loss. Extensive experiments on the MIT-BIH Arrhythmia Database across sixteen heartbeat classes from two-lead recordings yield exceptional results over ten independent runs: accuracy of 99.67%, F1-score of 99.66%, and AUC of 99.8%. External validation on the ECG5000 single-lead dataset and the St Petersburg INCART twelve-lead dataset confirms robust generalizability with F1-scores of 97.6% and 98% respectively. Our framework delivers accurate, interpretable, stable, and deployable arrhythmia detection across diverse clinical settings. Full article