Search Results (13,063)

Cancer is still one of the major causes of death globally. While there are many cancer therapies, they often come with significant adverse effects and limitations. A lot of studies have shown that using natural products can improve the efficacy of conventional therapies and decrease their toxicity. Natural products and their derivatives encompassing unmodified molecules, semi-synthetic analogs, and synthetic structures featuring natural pharmacophores, represent a major foundation for approved anticancer agents. Phenolic compounds are a large family of naturally occurring compounds found in plants and have attracted a lot of attention for their potential anticancer properties. Preclinical studies have shown that phenolic compounds have anticancer activity through different mechanisms, mainly by modulating inflammation, which plays a critical role in cancer development. Inflammatory processes in cancer involve complex molecular mechanisms regulated by both oncogenic and tumor-suppressing transcription factors. This review focuses on how phenolic compounds regulate transcription factors, such as nuclear factor kappa-light-chain-enhancer of activated B-cells (NF-κB), nuclear factor erythroid 2-related factor 2 (Nrf2), and signal transducer and activator of transcription 3 (STAT3), which are critical in cancer-related gene expression, ultimately leading to the inhibition of cancer cell proliferation, invasion, and metastasis. Furthermore, phenolic compounds have been shown to synergize with conventional anticancer drugs, enhancing their efficacy and reducing side effects. This review investigates the role of phenolic compounds in regulating transcription factors in cancer animal models, highlighting both the therapeutic potential and limitations of these compounds in cancer prevention and treatment. Full article

Passive Ultra-High Frequency (UHF) Radio Frequency Identification (RFID) systems deployed in metallic commercial aircraft cabins suffer from severe multipath fading, non-stationary channel dynamics, and operator gait-induced signal jitter. Addressing these challenges without physical airframe modifications or regulatory recertification remains a critical operational bottleneck. This paper presents an edge-native, software-defined framework that integrates micro-electromechanical system (MEMS) inertial measurements with an IMU-assisted Adaptive Kalman Filter (AKF) and a distilled surrogate decision tree. The proposed algorithm extracts localized motion energy ($E_{\mathrm{IMU}}$) to dynamically scale the measurement noise covariance ($R_k$) prior to physical-layer signal corruption, thereby eliminating phase lag and power hunting. For deterministic edge execution on COTS handheld devices, surrogate model distillation compresses a parent Random Forest ensemble into an $8.2\mathrm{KB}$ 13-leaf decision tree (depth 5) yielding $0.12\mathrm{ms}$ inference latency. Empirical validation across 17 operational sessions in Airbus A320, Boeing 737, and Airbus A321 cabins (10,720 valid reads) demonstrates a $99.45\%$ mean RSSI jitter reduction ($95\%\mathrm{CI}:[99.21\%,99.63\%]$) and a $7.30\times$ suppression of transmit power oscillations. Statistically, asset detection completeness is fully preserved ($0.791$ vs. $0.795$ baseline, $z=0.281,p=0.779$). Operating entirely within standard handheld software runtimes, this approach bypasses Supplemental Type Certificate (STC) requirements while ensuring robust aerospace asset visibility. Full article

Chemical examination of Penicillium steckii HJT-A-10, an endophytic fungus derived from Rhodiola tibetica, led to the isolation of ten metabolites. Structural elucidation by spectroscopic methods revealed that two of these were previously unreported compounds (1 and 2), though the absolute stereochemistry of 2 remained provisional; the other eight isolates (3–10) were identified as known compounds. All ten metabolites were tested for their capacity to inhibit nitric oxide (NO) production in RAW 264.7 macrophages stimulated with lipopolysaccharide (LPS). Compounds 1–4 and 7–10 exhibited marked inhibitory effects. Among the two new compounds, 2 displayed stronger activity, with an IC₅₀ value of 19.01 µM. To explore the potential mechanisms of action, network pharmacology and molecular docking analyses were subsequently conducted to predict the relevant targets and signaling pathways. Full article

Background: Non-alcoholic fatty liver disease (NAFLD), now largely encompassed by the metabolic dysfunction-associated steatotic liver disease (MASLD) framework, is a leading cause of chronic liver disease worldwide and is closely linked to obesity, insulin resistance, type 2 diabetes (T2D), and cardiometabolic dysfunction. Although hepatic lipid accumulation and metabolic injury remain central to its pathogenesis, increasing evidence implicates the intestinal microbiota as a modulator of disease onset and progression through the gut–liver axis. Objectives: This narrative review summarizes current evidence linking gut microbiota to NAFLD/MASLD, with emphasis on functional microbial alterations relevant to the gut–liver axis, disease severity, microbiota-related biomarkers, and microbiota-targeted interventions. Methods: The literature search for this narrative review was mainly performed using PubMed/MEDLINE (which produced 200 results since 2023). The search addressed the relationship between gut microbiota, NAFLD/MASLD, and gut–liver axis dysfunction. Results: Early studies mainly focused on disease-associated bacterial taxa; however, no consistent microbial signature has been reproduced across populations, disease stages, and methodological platforms. This limitation supports a shift from a taxonomic view of dysbiosis toward a functional interpretation. Different microbial communities may converge on shared pathogenic outputs, including impaired epithelial barrier integrity, microbial translocation, lipopolysaccharide-driven Toll-like receptor 4 activation, bile acid remodelling, altered short-chain fatty acid production, endogenous ethanol generation, choline and trimethylamine-N-oxide metabolism, and tryptophan-derived indole signalling. These mechanisms may influence hepatic steatosis, inflammatory activation, and fibrogenesis. Conclusions: Current data support the microbiota as a biologically plausible disease modifier, but not yet as a standalone diagnostic or therapeutic target; clinical translation will require longitudinal, multi-omics, and function-oriented studies. Full article

Objective: This study established a rat model of lipopolysaccharide (LPS)-induced acute lung injury (ALI) to evaluate pathological damage, collagen deposition, inflammatory cytokine levels, and key gene/protein expression following Hedyotis diffusa water extract (HDWE) intervention. Combined with ultra-high-performance liquid chromatography-quadrupole Orbitrap high-resolution mass spectrometry (UHPLC-Q-Orbitrap HRMS), transcriptomic analysis, and molecular simulation, this study identified the bioactive components of HDWE, evaluated their potential interactions with ALI-related targets, and explored the multi-omics-based protective mechanisms of HDWE. Methods: Thirty-six Sprague–Dawley (SD) rats were randomly divided into six groups: Control group, ALI group, DXMS group, HDWE-L group (100 mg/kg), HDWE-M group (200 mg/kg), and HDWE-H group (300 mg/kg). Hematoxylin and eosin (H&E) and Masson’s trichrome staining were used to evaluate lung pathological changes and collagen deposition. Enzyme-linked immunosorbent assay (ELISA) was used to measure serum tumor necrosis factor-α $\mathrm{T}\mathrm{N}\mathrm{F}-\mathsf{\alpha }$ interleukin-1β $\mathrm{I}\mathrm{L}-1\mathsf{\beta }$, erleukin-6 (IL-6), and interleukin-10 (IL-10) levels. Transcriptomic analysis identified differentially expressed genes (DEGs), followed by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), receiver operating characteristic (ROC), and immune infiltration analyses. Quantitative real-time polymerase chain reaction (qRT-PCR) detected the mRNA expression levels of SPHK1, RELA, and NFKBIA. Immunohistochemistry evaluated the expression of eight hub targets, including endothelin-1 (EDN1), sphingosine kinase 1 (SPHK1), intercellular adhesion molecule 1 (ICAM1), interleukin-17 (IL-17), prostaglandin-endoperoxide synthase 2 (PTGS2/COX-2), NF-κB p65 (encoded by RELA), WT1-associated protein (WTAP), and myeloperoxidase (MPO). UHPLC-Q-Orbitrap HRMS characterized HDWE constituents. Molecular docking analysis was performed between 22 compounds and eight hub targets, followed by 100 ns molecular dynamics simulations and molecular mechanics-Poisson–Boltzmann surface area (MM/PBSA) binding free energy calculations for five core targets. Compared with the control group, the ALI group showed increased levels of TNF-α (86%), IL-1β (107%), and IL-6 (66%), accompanied by a 43% reduction in IL-10 and a 300% increase in lung collagen deposition. All HDWE doses alleviated inflammatory responses, with medium-dose HDWE showing the most pronounced effects. Specifically, medium-dose HDWE increased IL-10 levels by 52% and reduced IL-6, TNF-α, and IL-1β levels by 18%, 22%, and 11%, respectively. Transcriptomic analysis identified 2512 DEGs between the control group and ALI groups, 832 exclusive DEGs between the ALI group and HDWE-M groups, and 876 overlapping DEGs enriched in TNF, IL-17, and NF-κB signaling pathways. The eight-hub-gene diagnostic model achieved an area under the curve (AUC) of 0.969. RELA, SPHK1, and four other hub genes showed positive correlations with Th1, Th17, and neutrophil infiltration. In the ALI group, SPHK1, RELA, and NFKBIA mRNA expression levels were 1.30-, 0.96-, and 0.71-fold of those in the control group, respectively. Compared with the ALI group, high-dose HDWE treatment and low-dose HDWE treatment reduced SPHK1 expression to 0.62- and 0.57-fold, respectively, and increased NFKBIA expression to 1.68- and 1.58-fold, respectively. High-dose HDWE treatment reduced RELA expression to 0.43-fold. The expression levels of inflammation-related proteins were increased in the ALI group and were reduced after HDWE treatment. Twenty-two HDWE components were identified, 16 of which met the docking criteria. Asperulosidic acid exhibited favorable predicted binding affinities with all eight targets, with calculated binding free energies of −14.74, −14.92, −17.58, −23.04, and −16.10 kcal/mol for MPO, IL-17, NF-κB p65, PTGS2/COX-2, and SPHK1, respectively. Conclusions: This study provides systematic in vivo pharmacodynamic and in silico component-target evidence regarding the protective effects of HDWE against LPS-induced ALI. HDWE treatment increased NFKBIA expression and reduced SPHK1, RELA, and multiple inflammatory protein levels, suggesting that HDWE may regulate the IL-17/NF-κB-associated inflammatory network, although direct causal relationships require further validation. Asperulosidic acid may represent a key bioactive component with broad target-binding potential. This study was limited by the use of an LPS-induced rat ALI model without gene knockout or target inhibitor validation; therefore, further functional experiments are required to confirm the proposed regulatory mechanisms. Full article

Energy management in battery-powered embedded Artificial Intelligence of Things (AIoT) nodes requires coordinated control of sensing, computation, and wireless communication. This structured review combines a documented search of Scopus, Web of Science Core Collection, and IEEE Xplore with screening of 548 unique records and full-text source-to-construct coding of 56 database-derived sources. The synthesis shows that acquisition, Tiny Machine Learning (TinyML) inference, communication, protocol-state overhead, adaptive sensing, and energy harvesting are commonly optimized separately, while field-validated joint control remains limited. The review develops a full-cycle energy model and cross-layer framework linking sensing, processing, payload representation, radio policy, and energy availability. Dimensionless break-even analysis shows that the admissible local-processing budget is bounded by fixed communication overhead and non-machine-learning edge costs. A targeted Long-Term Evolution for Machines (LTE-M) experiment comprising 40 complete transactions showed that payload length alone was insufficient to characterize communication energy; a state-aware model incorporating measured signaling duration achieved $R^2=0.980$ and predicted a temporally separated second session with 1.66% relative root mean square error (RMSE). Precision agriculture and precision apiculture illustrate modality- and network-dependent trade-offs. The findings support full-cycle benchmarking and context-aware orchestration rather than isolated optimization of inference or communication. Full article

Sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. Its development and progression involve multiple interconnected mechanisms, including uncontrolled inflammation, immunosuppression, metabolic reprogramming, intestinal barrier disruption, and multi-organ injury. Short-chain fatty acids (SCFAs), primarily acetate, propionate, and butyrate, are important metabolites produced by the anaerobic fermentation of dietary fiber and indigestible carbohydrates by gut microbiota. During sepsis, antibiotic exposure, intestinal hypoperfusion, insufficient nutritional substrates, and microbial dysbiosis may deplete SCFA-producing bacteria and lower SCFA levels, thereby aggravating intestinal barrier dysfunction, endotoxin translocation, and systemic inflammatory responses. SCFAs can influence sepsis-associated intestinal, pulmonary, cardiac, hepatic, renal, and cerebral injury by activating receptors such as free fatty acid receptor 2 (FFAR2)/G protein-coupled receptor 43 (GPR43), free fatty acid receptor 3 (FFAR3)/G protein-coupled receptor 41 (GPR41), and G protein-coupled receptor 109A (GPR109A); inhibiting histone deacetylases; and regulating immune-cell metabolism, inflammasome activation, oxidative stress, mitochondrial function, and modes of cell death. In recent years, strategies such as direct SCFA supplementation, promotion of endogenous SCFA production, restoration of SCFA-producing microbial communities, and targeting of SCFA receptors and downstream signaling pathways have shown therapeutic potential. However, their clinical translation remains limited by uncertainties regarding dose, timing, route of administration, patient stratification, and safety. This review systematically summarizes the mechanisms of action and therapeutic advances of SCFAs in sepsis, aiming to provide a reference for microbiome-based interventions and metabolism-targeted therapies in sepsis. Full article

Background/Objectives: Stress is increasingly recognized as an important modulator of gut microbiome composition and function through the bidirectional microbiota–gut–brain axis. Dietary, psychobiotic, and other lifestyle-oriented interventions have therefore attracted growing interest as potential strategies for simultaneously influencing stress-related outcomes and the gut microbiome. This systematic review aimed to evaluate randomized controlled trials (RCTs) investigating the effects of lifestyle-oriented and psychobiotic interventions on both stress-related outcomes and the gut microbiome in adults. Methods: A systematic literature search was conducted in PubMed, Scopus, and Cochrane CENTRAL for English-language RCTs published between 2016 and April 2026, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Eligible studies included adults (≥18 years), evaluated lifestyle-oriented or psychobiotic interventions, and reported both gut microbiome-related and stress-related psychological or biological outcomes. Risk of bias was assessed using the Cochrane Risk of Bias 2 (RoB 2) tool. Owing to substantial methodological and clinical heterogeneity, findings were synthesized narratively. Results: Twenty-nine RCTs met the eligibility criteria. Interventions included psychobiotic supplementation, dietary interventions and functional foods, and other lifestyle-oriented approaches. Across intervention categories, microbiome responses were heterogeneous, and no single taxonomic or diversity signature consistently characterized trials reporting favorable stress-related outcomes. Nevertheless, recurrent changes involving Bifidobacterium/Bifidobacteriaceae, Lactobacillus/Lactobacillaceae, Faecalibacterium, Roseburia, and members of the Lachnospiraceae/Ruminococcaceae families were identified across several interventions, together with changes in SCFA-related microbial features. Changes in global microbial diversity were inconsistent, and psychological and microbiome responses did not invariably occur in parallel. Overall, the randomized evidence indicates recurrent taxonomic and functional signals rather than a uniform microbiome response across stress-targeting interventions. Substantial heterogeneity in populations, intervention characteristics, study duration, outcome measures, and microbiome assessment methods limited direct comparisons across trials. Conclusions: Current randomized evidence does not identify a uniform microbiome signature associated with successful stress-targeting interventions but suggests partial convergence involving selected bacterial taxa and functional microbial features. The lack of consistent parallel changes in psychological and microbiome outcomes precludes conclusions regarding whether microbiome alterations mediate improvements in stress-related outcomes. Larger, longer-term RCTs incorporating standardized microbiome methodologies and functional microbial analyses are needed to determine the reproducibility and mechanistic relevance of these signals. Full article

Floods degrade road networks at the same time as demand for emergency medical services (EMSs) rises, yet national EMS command systems rarely receive any information on flood-induced road barriers. This paper presents Ambulance STARS (SaTellite-assisted Ambulance Routing System), a service-oriented framework that links satellite observation with ambulance dispatch. A cloud-based flood detection service derives flood extent from Sentinel-1 SAR amplitude change detection executed in a cloud-based Earth observation data and compute backend and translates it into road passability layers. A routing engine then maintains an in-memory road graph whose travel times are calibrated with empirical ambulance speed models built from four years (2020–2023) of GPS records of an EMS fleet in southern Poland, with separate speeds for driving with and without emergency signals (61.8 and 37.2 km/h, respectively). An API gateway with single-file tile delivery, a replicated relational data tier, and an observability stack complete the architecture, and a web client offers dispatchers live routing and multi-unit incident simulation. The framework was tested on the September 2024 flood in the Municipality of Nysa, Poland. The SAR module delineated 665 ha of inundation and marked 8.5 km of the 656.7 km routing network as impassable (508 barrier points), and the same procedure applied to a reference optical mask of 18 September yielded 17.3 km and 1006 points. Because the SAR and optical acquisitions captured different phases of the flood wave, agreement on the rare impassable-road class was low, and the two products were, therefore, used to bracket operational uncertainty rather than to define a single ground truth. Applied without local retuning to Lewin Brzeski, the same flood detection workflow showed consistent performance against the CEMS reference product. The routing module produced statutory 8/15/20 min accessibility maps in 12–34 s under warm-cache benchmark conditions. With SAR-derived barriers, the share of the network reachable within 15 min fell from 88% to 80%, and 2 villages with 938 inhabitants lost road access to EMS entirely. With barriers derived from the optical mask, the 15 min share fell to 39.8% and seventeen settlements lost road access entirely, underlining how strongly the barrier source shapes the operational picture. Post-acquisition processing completes in under one minute under warm-cache conditions with road data preloaded, and satellite-derived road passability is fast enough to support near-real-time decision-making, subject to the constellation revisit time and to integration with EMS command systems. Full article

Automatic modulation classification (AMC) suffers from performance degradation under low signal-to-noise ratio (SNR) conditions, where modulation characteristics are affected by noise and signals belonging to the same modulation family exhibit similar feature representations. To address these challenges, this paper proposes a Transformer-based spatiotemporal fusion network that jointly exploits local spatial waveform characteristics and temporal dependency information while leveraging the global context modeling capability of the Transformer to integrate complementary multi-dimensional features. The proposed architecture improves feature representation capability under different SNR conditions. Experimental results demonstrate that the proposed method achieves improved classification performance compared with comparative approaches under different SNR conditions. In particular, it achieves an overall classification accuracy of 82.45% over the SNR range from $-10$ to 18 dB, and an average accuracy of 95.55% at SNRs of 2 dB and above, showing improved classification performance in the low-to-medium SNR transition region. Full article

Introductory and intermediate electrical-engineering education commonly models fundamental circuit and device parameters, including inductance, capacitance, resistance, permeability, permittivity, conductivity, characteristic impedance, transformer turns ratio, machine constants, amplifier gain, resonant frequency, propagation velocity, and mutual inductance, as numerical constants. While this approximation is valid within the intended small-signal operating regime, it becomes methodologically incomplete when these coefficients exhibit measurable state dependence. This paper presents a comparative audit of thirteen such coefficients by systematically contrasting their textbook formulations with the established engineering literature and interpreting the results through two complementary frameworks: the JCGM GUM-6:2020 measurement-model methodology for omitted effects, and the Heckmann–Nye/Gasik multidomain coupling architecture for multi-axis physical interactions. The analysis demonstrates that mainstream engineering practice routinely exploits state-dependent coefficients without invoking new physical laws, and that relaxing the constant-coefficient assumption naturally introduces physically meaningful terms, including the inductive contribution $I\mathrm{d}L/\mathrm{d}t$, the capacitive counterpart $V\mathrm{d}C/\mathrm{d}t$, and the mutual-inductance term $i_2\mathrm{d}M/\mathrm{d}t$. The principal scientific contribution is the development and experimental validation of a unified theoretical and engineering framework for high-power resonant transformers and orthogonal Metglas AMCC-1000 paraformers. The proposed approach combines a new physics-based modal theory of octave ($2:1$) parametric excitation with simultaneous optimization of magnetic-core resonance, electrical resonance, nonlinear inductance modulation, resonant conductor lengths selected as integer multiples of the operating resonant wavelength, multi-stranded high-frequency Litz-wire windings, resonant capacitor synthesis, and the nonlinear magnetic characteristics of the AMCC-1000 amorphous core. The modal analysis demonstrates how coupled resonant eigenmodes and engineered state-dependent inductance can be used to satisfy the conditions for stable octave parametric excitation. Experimental results obtained from both the symmetric two-leg resonant transformer and the orthogonal paraformer are in close agreement with analytical predictions and numerical simulations, thereby validating both the proposed electromagnetic design methodology and the underlying modal theory. Full article

Accurate discrimination among static eccentricity fault (SEFs), dynamic eccentricity fault (DEFs), and mixed eccentricity fault (MEFs) in permanent-magnet-synchronous motors remains difficult because these conditions produce similar stator-current patterns. This paper proposes an STFT–CNN–LSTM-based Eccentricity Fault Diagnosis System (STFT-CL-EFDS) for classifying Normal, SEF, DEF, and MEF conditions using only three-phase stator currents. Six signal transformations were initially compared, after which the three leading representations—STFT, DWT, and CWT—were evaluated with seven neural-network architectures. Sensitivity and ablation analyses selected a 500-sample observation window and a compact log-magnitude STFT representation over the nominal 0–1 kHz band. Following full-schedule retraining, the proposed model achieved 99.57% accuracy and a 99.57% weighted F1-score, with recalls of 100.00%, 98.66%, 100.00%, and 99.53% for Normal, SEF, DEF, and MEF, respectively. It exceeded the strongest machine-learning benchmark, STFT–MLP, by 8.42 percentage points in accuracy and 8.52 percentage points in weighted F1-score. On an independent unseen test bench, the proposed model provided the most balanced response across the three fault types and ultimately converged to the correct class in every case, although temporary DEF–MEF confusion remained. These results demonstrate the effectiveness of STFT-CL-EFDS for current-only multiclass PMSM eccentricity diagnosis. The main contributions of this study are as follows: (1) a systematic comparison of six signal-transformation methods, namely Fast Fourier Transform (FFT), STFT, Discrete Wavelet Transform (DWT), Continuous Wavelet Transform (CWT), Hilbert–Huang Transform (HHT), and Variational Mode Decomposition (VMD), to determine their suitability for eccentricity fault diagnosis; (2) a comparative evaluation of seven neural-network architectures, including CNN, LSTM, CNN–LSTM, DNN, TCN, ModernTCN, and TimesNet, using the three best-performing transformation methods, namely STFT, CWT, and DWT; and (3) the development of a unified current-only STFT-CL-EFDS that combines STFT-based time–frequency representation with convolutional feature extraction and temporal-sequence learning for the classification of Normal, SEF, DEF, and MEF conditions. Full article

Fatty liver hemorrhagic syndrome (FLHS) is a prevalent metabolic disease in laying hens, causing severe economic losses. A previous study showed that conjugated linoleic acids (CLA) alleviated estrogen-induced FLHS in chickens, but the mechanism remains unclear. In the present study, we found that CLA improved serum lipid homeostasis, reduced hepatic lipid accumulation, and enhanced antioxidant activity in FLHS chickens. Transcriptome analysis identified differentially expressed genes enriched in inflammatory response, lipid homeostasis, carbohydrate metabolism, and mitogen-activated protein kinase (MAPK)/peroxisome proliferator-activated receptor (PPAR)/insulin signaling pathways. Metabolome analysis detected differentially abundant metabolites enriched in bile secretion, thyroid hormone synthesis, the insulin signaling pathway, and glycerophospholipid metabolism. Integrated analyses revealed that CLA reshaped the hepatic metabolic profile by upregulating protective metabolites (such as ubiquinol) and downregulating pro-inflammatory/lipogenic metabolites (such as 15-hydroperoxyeicosa-8Z,11Z,13E-trienoate), which synergized with key gene regulation (fatty acid synthase, jun proto-oncogene, and fatty acid desaturase 2) and core pathway activity (MAPK/nuclear factor kappa-B inhibition, PPARα activation). The multi-omics study using an estradiol-induced rooster FLHS model elucidated the molecular regulatory network of CLA against hepatic steatosis and liver injury, and provided preliminary mechanistic clues for developing CLA functional additives to prevent and treat FLHS in commercial laying hens. Full article

Bioactive polyphenols have emerged as multi-target regulators of cellular processes involved in the pathogenesis of skin diseases. Skin disorders, including inflammatory conditions, photoaging, and skin cancers, are characterized by complex pathogenic mechanisms associated with oxidative stress, mitochondrial dysfunction, dysregulated signaling pathways, and metabolic imbalance. Excessive production of reactive oxygen species (ROS) and persistent inflammatory signaling contribute to disease progression and cellular adaptation under stress conditions. Unlike conventional agents that typically target a single pathway, polyphenols act on interconnected signaling and metabolic networks. These compounds regulate key signaling pathways, including phosphoinositide 3-kinase/protein kinase B (PI3K/Akt), mitogen-activated protein kinase/extracellular signal-regulated kinase (MAPK/ERK), AMP-activated protein kinase (AMPK), nuclear factor-κB (NF-κB), and nuclear factor erythroid 2-related factor 2 (Nrf2), thereby regulating cell survival, proliferation, inflammatory responses, antioxidant defense, and metabolic adaptation. Polyphenols also influence mitochondrial function by maintaining redox homeostasis, regulating energy metabolism, and affecting apoptosis-related signaling pathways. This review provides a mechanistic overview of the effects of polyphenols on oxidative stress, mitochondrial function, and cell survival signaling in skin diseases. In addition, the therapeutic implications and current limitations of polyphenol-based approaches are discussed, with particular emphasis on the translational gap between experimental findings and physiological relevance. Factors such as concentration, bioavailability, and cellular microenvironment are highlighted as major determinants of polyphenol activity and key challenges for clinical translation. Finally, the need for further in vivo and clinical investigations is emphasized to support the development of effective polyphenol-based therapeutic strategies for skin diseases. Full article

Background: Plant flavonoids are candidate alternatives to ionophores for modulating rumen fermentation, yet the intact glycoside rutin (quercetin-3-O-rutinoside) has received little attention. The objective of this study was to determine whether rutin changes the in vitro ruminal fermentation of total mixed rations (TMR) differing in forage-to-concentrate ratio; monensin was included at a single dose as a reference additive rather than a direct comparator. Methods: In a 2 × 4 factorial batch-culture experiment, a roughage-based (60:40) and a concentrate-based (40:60) TMR were incubated for 24 h without additive (control) or with rutin at 2.5% or 5% of substrate dry matter or monensin at 30 ppm. Results: Rutin did not change the gas production kinetics, ruminal pH, the volatile fatty acid (VFA) profile, or the stoichiometrically estimated methane and carbon dioxide at either dose (p > 0.05), and no dose response was found. Substrate type likewise left the VFA profile and the estimated gases unchanged (p > 0.05). Ammonia nitrogen (NH₃-N) responded to the additive group (p = 0.027) and to the substrate × additive interaction (p = 0.022), but Tukey comparisons separated no individual means, so this response is preliminary. Conclusions: Neither rutin nor monensin markedly altered fermentation of these silage-, alfalfa- and cereal-based rations in the 24 h batch system; the substrate-dependent NH₃-N signal warrants confirmation, and direct methane measurement is required before any anti-methanogenic potential can be claimed. Full article