Search Results (6,767)

Background: Atherosclerosis is the pathological basis for lethal cardio-cerebral vascular diseases, such as coronary artery disease and stroke. Fructus Choerospondiatis (FC) has demonstrated cardiac protective effects in multiple ethnomedicine. Whether these protective effects are attributed to the prevention of vascular atherosclerosis, however, remains unknown. We aim to examine the anti-atherosclerotic effect of FC aqueous extract and elucidate the underlying mechanism. Methods: FC was separated into peel and pulp, and the aqueous extract was obtained separately by boiling in water to mimic decocting. Atherosclerosis model was established in ApoE^−/− mice fed with a high-fat diet, and histological analysis were utilized to evaluate the development of atherosclerosis. Various inflammatory models were constructed in mice to evaluate the anti-inflammatory effect of FC extract systemically, including acute local inflammation induced by traumatic injury (ear/foot swelling), acute systemic inflammation triggered by pathogenic infection (LPS- and POLY (I:C)-induced), as well as chronic inflammatory conditions associated with oxidative stress (D-galactose-induced), metabolic disorder (db/db mice), and aging. LC-MS and network pharmacology identified bioactive components and targets. Western blotting, ELISA, qPCR, and immunofluorescence were utilized to analyze the key genes involved in the mechanisms. Results: FC peel extract reduced serum IL-6 level, atherosclerotic plaque area, and macrophage content in the plaque, while pulp extract showed no protective effects. Peel extract exhibits anti-inflammatory effects in all models. The integrative application of LC-MS and network pharmacology identified ellagic acid as the major bioactive component and AKT as its target protein. Mechanistically, FC peel extract inhibits AKT phosphorylation, suppresses c-FOS expression and nuclear translocation, reduces IL-6 transcription and inflammation, and thus alleviates atherosclerosis. Conclusions: FC peel aqueous extract exerts anti-atherosclerotic effect by inhibiting inflammation through AKT/c-FOS/IL-6 axis. This study provides novel insights into the protective effects against atherosclerosis of FC peel and highlights its potential application in the prevention and treatment of coronary artery diseases. Full article

Intense training loads alter the skin microbiome and defence mechanisms in athletes, yet adaptation profiles remain insufficiently characterised. This study evaluated the relationships between skin bacterial microbiome structure, antimicrobial activity, dermcidin levels, and acne severity in male professional hockey players compared with amateur athletes and non-athletes. One hundred men (18–57 years) were examined and allocated to six subgroups by exercise intensity and acne status. Microbiota composition was assessed by culture-based methods and MALDI-TOF identification, antimicrobial activity measured spectrophotometrically, dermcidin quantified by ELISA, and sweat proteome characterised by HPLC-MS. Staphylococcus epidermidis and Micrococcus luteus predominated in all groups. Exercise intensity, rather than acne, was the main determinant of total bacterial colonisation, which increased approximately tenfold from non-athletes to professional hockey players. In non-athletes, higher antimicrobial activity correlated with greater acne severity, whereas in professionals this relationship was absent and dermcidin levels showed an inverse association with acne severity. Proteomic analysis identified 17 polypeptides; dermcidin and prolactin-inducible protein were dominant in all groups, and calprotectin (S100-A8/A9) was detected exclusively in healthy professionals. Full article

This study employed deep eutectic solvents (DES) combined with ultrasonic-assisted extraction technology to green and efficiently extract flavonoids from honeysuckle, and systematically evaluated its inhibitory mechanism on α-amylase (α-AMY). Through comparative screening of six DES systems and traditional solvents, DES-4 (Choline chloride–propylene glycol) was identified as the optimal extraction solvent. After single-factor and response surface optimization, the yield of honeysuckle flavonoids (HF) was significantly increased to 9.12 ± 0.08% under the conditions of ultrasonic power 300 W, solid–liquid ratio 1:32 (g/mL), and extraction time 60 min. HPLC-MS analysis revealed that luteolin (4.59 ± 0.09 mg/g) and quercetin (3.05 ± 0.02 mg/g) were the main active components, and they exhibited strong antioxidant activity. Enzyme kinetics and Lineweaver–Burk analysis indicated that the inhibition type of HF on α-AMY was reversible mixed inhibition. Fluorescence spectroscopy, thermodynamic analysis, and molecular docking results further revealed that HF primarily bound to α-AMY through hydrogen bonds and van der Waals forces (ΔH = −63.80 kJ/mol, ΔS = −0.19 J/mol·K), causing static fluorescence quenching and altering its hydrophobic microenvironment and spatial conformation. This study aims to provide new theoretical basis for the green and efficient extraction of HF and its development and application in functional foods and natural medicines. Full article

Rapid urban expansion has heightened the demand for accurate, scalable, and real-time methods to assess tree health and the provision of ecosystem services. Urban trees are the major contributors to air-quality improvement and climate change mitigation; however, their monitoring is mostly constrained to inherently subjective and inefficient manual inspections. In order to break this barrier, we put forward a lightweight multimodal deep-learning framework that fuses RGB imagery with environmental and biometric sensor data for a combined evaluation of tree-health condition as well as the estimation of the daily oxygen production and CO₂ absorption. The proposed architecture features an EfficientNet-B0 vision encoder upgraded with Mobile Inverted Bottleneck Convolutions (MBConv) and a squeeze-and-excitation attention mechanism, along with a small multilayer perceptron for sensor processing. A common multimodal representation facilitates a three-task learning set-up, thus allowing simultaneous classification and regression within a single model. Our experiments with a carefully curated dataset of segmented tree images accompanied by synchronized sensor measurements show that our method attains a health-classification accuracy of 92.03% while also lowering the regression error for O₂ (MAE = 1.28) and CO₂ (MAE = 1.70) in comparison with unimodal and multimodal baselines. The proposed architecture, with its 5.4 million parameters and an inference latency of 38 ms, can be readily deployed on edge devices and real-time monitoring platforms. Full article

The integration of solar photovoltaic (PV) systems into smart grids necessitates robust, real-time fault detection mechanisms, particularly in resource-constrained environments like the Solar–Hydrogen AIoT microgrid framework at a university. This study conducts a comparative analysis of four prominent Convolutional Neural Network (CNN) architectures VGG16, ResNet-50, DenseNet121, and EfficientNetB0 to determine the optimal model for low-latency, edge-based fault diagnosis. The models were trained and validated on a dataset of solar panel images featuring multiple fault types. Quantitatively, DenseNet121 achieved the highest classification accuracy at 86.00%, demonstrating superior generalization and feature extraction capabilities. However, when considering the stringent requirements of an AIoT system, computational efficiency became the decisive factor. EfficientNetB0 emerged as the most suitable architecture, delivering an acceptable accuracy of 80.00% while featuring the smallest model size (5.3 M parameters) and a fast inference time (approx. 26 ms/step). This efficiency-to-accuracy balance makes EfficientNetB0 ideal for deployment on edge computing nodes where memory and real-time processing are critical limitations. DenseNet121 achieved 86% accuracy, while EfficientNetB0 achieved 80% accuracy with lowest model size and fastest inference time. This research provides a validated methodology for implementing efficient deep learning solutions in sustainable, intelligent energy management systems. The novelty of this work lies in its deployment-focused comparison of CNN architectures tailored for real-time inference on resource-constrained Solar–Hydrogen AIoT systems. Full article

Neutral red (NR) is a phenazine dye that has been implicated in electron transfer processes in methanogenic archaea. NR has been previously observed to enhance methane production but its effects on Methanosarcina barkeri are unknown. This study aimed to investigate the effects of NR on M. barkeri DSM-804. M. barkeri cultures were grown in the presence of 10 and 250 µM NR for four weeks, and proteomic analysis was performed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). The results showed that methane production was significantly reduced in the presence of NR, at lower concentrations of both 10 and 250 µM NR treatments, compared to the control. Proteomic analysis revealed the downregulation of proteins related to substrate metabolism and methanogenesis, such as the heterodisulfide reductase subunits D (HDRD_METBF) and E (HDRE_METBF), suggesting that NR hindered essential metabolic processes. Proteomic analysis also revealed that M. barkeri lacked methanophenazine in its membrane, which is a component essential for electron transport via neutral red (NR) that supports enhanced growth and methane production. Further research is needed to explore the role of methanophenazine and understand the mechanisms underlying NR’s effects of NR on methanogenesis in M. barkeri. Full article

Adhesively bonded joints are extensively utilized in structural assemblies involving metals, composites, and hybrid materials due to their favorable mechanical and manufacturing characteristics. However, their performance under high-velocity impacts—common in aerospace, automotive, and defense applications—remains insufficiently understood. This work investigates the high-velocity performance and subsequent tensile response of adhesively bonded single-lap joints (SLJs) by integrating experimental testing with numerical simulations. High-velocity impacts were applied to SLJs fabricated from 4 mm aluminum adherends with overlap lengths of 15 mm and 25 mm, using a 1.25 g projectile at 288 m/s, followed by quasi-static tensile assessment. Experimental findings revealed substantial degradation in tensile strength for the 15 mm overlap configuration (reduced the load-bearing capacity by about 33% (from ~12 kN to ~8 kN)), while the 25 mm overlap retained its structural integrity. Finite element simulations conducted in ABAQUS 2021 employed the Johnson–Cook constitutive model for the adherends and a cohesive zone model for the adhesive layer, successfully replicating damage evolution and stress distributions. The results highlight the critical role of geometric parameters—particularly overlap length and adherend thickness—in determining the damage tolerance and residual load-bearing capacity of SLJs subjected to high-velocity impacts. These insights contribute to the development of more robust bonded joint designs for impact-prone environments. Full article

Harvesting of laver is an important link in the laver culture chain, and a new type of corrugated harvesting blade with a curved edge angle was designed to solve the problems of low cutting ratio in laver harvesting. The mechanical model of the corrugated blade cutting laver was established to elucidate the dynamic characteristics of laver cutting under single-point support. Based on the measured biomechanical characteristic parameters of Porphyra yezoensis, a rigid-flexible coupling model of laver harvesting was established based on ANSYS/LS-DYNA2022R2. The Box–Behnken design (BBD) test method was used to study the influence of the main structural parameters of the corrugated blade on the harvesting of laver, and the optimal structural parameter combinations of the corrugated blade were determined as follows: a slip angle of 21°, blade inclination angle of 106°, and curved edge angle of 15°; the slip-cutting mowing force of the laver was 11.18 N and the tensile force was 1.4 N. A bench test was completed, and the results showed that the corrugated blade could be used for harvesting laver. The results showed that the average loss rate of the harvesting equipment was 1.85% and the average net recovery rate was 98.75% when the corrugated blade rotational speed was 900 rpm and the boat speed was 0.71 m/s; compared to the traditional straight-blade hob-type harvesting machine, the cutting force on laver has increased by 45.26%, and the tensile force has decreased by 68.35%, which satisfied the requirements of laver harvesting. This study provides theoretical and simulation model references for the design, analysis, and optimization of laver harvesting equipment. Full article

Background/Objectives: Emerging evidence suggests that hippocampal neuroinflammation (HNF) drives cognitive decline via dysregulation of the microbiota-gut-brain axis. Corylus heterophylla Fisch. male flower extract (CFE), a flavonoid-rich by-product of hazelnut processing, presents a promising yet unexplored neuroprotective candidate. This study investigated the preventive effects and mechanisms of CFE against HNF-induced cognitive decline. Methods: In the present study, mice were pretreated with CFE (200 mg/kg) before the Lipopolysaccharide (LPS) administration. Cognitive function, inflammation, core pathology, neuroplasticity, gut microbiota and serum metabolites were assessed. The chemical composition of CFE was analyzed by UHPLC-MS and its direct immunomodulatory effects were investigated in BV2 cells. Results: Behavioral assessments demonstrated significant therapeutic efficacy. This was evidenced by the recovery from hippocampal damage, accompanied by reduced levels of core pathological markers (Aβ1–42, Tau, p-Tau (Ser404), GSK-3β), decreased expression of pro-inflammatory mediators including IL-33, elevated levels of neurotrophic factors (BDNF and MAP2), and attenuated abnormal activation of astrocytes and microglia. The 16S rRNA analysis confirmed that CFE ameliorated gut microbial dysbiosis. Notably, CFE significantly increased the relative abundance of Muribaculaceae and Lachnospiraceae, while significantly decreased Staphylococcus and Helicobacter. Metabolomics revealed enhanced levels of α-linolenic acid (ALA), serotonin (5-HT) and acetic acid, which correlated positively with Muribaculaceae and Lachnospiraceae. Phytochemical analysis identified luteolin and kaempferol as the predominant flavonoids in CFE. In BV2 cells, CFE, luteolin and kaempferol shifted microglial polarization from the M1 phenotype toward the M2 phenotype. Conclusions: CFE alleviated HNF-induced cognitive decline by regulating microbiota-gut-brain axis and microglial M1/M2 polarization. Full article

Background/Objectives: Understanding metabolic adaptations in long-livers provides critical insights into the biochemical mechanisms underlying extreme longevity. While many long-livers maintain metabolic stability, others exhibit significant metabolic alterations, potentially linked to age-related diseases. This study aims to identify distinct metabolic signatures in long-livers and their associations with clinical outcomes, particularly cardiovascular disease. Methods: We analyzed serum samples from 53 oldest long-livers (mean age 98.2 ± 2 years) using liquid chromatography–tandem mass spectrometry (LC-MS/MS) to identify metabolic alterations and gathered clinical data to link the detected metabolic changes with phenotypes. Results: Using Welch’s t-test with Benjamini–Hochberg FDR correction (q < 0.01, |log2FC| > 2), we identified 15 significantly altered metabolites distinguishing a subgroup of 6 long-livers from 47 metabolically stable individuals. This metabolically altered subgroup exhibited striking elevations in key metabolites, including L-serine (log2FC = 8.05, >250-fold increase, q = 1.26 × 10⁻⁸), D-galactose (log2FC = 6.86, 116-fold, q = 8.87 × 10⁻⁷), butyric acid (log2FC = 6.24, 75-fold, q = 9.79 × 10⁻⁵), and choline (log2FC = 6.11, ~69-fold, q = 5.45 × 10⁻⁷), with enrichment in the butyric acid metabolism pathway. Post hoc power analysis confirmed >80% power for all significant metabolites with very large effect sizes (Cohen’s d > 2.0). Conclusions: Our findings reveal substantial metabolic heterogeneity among long-livers, with a distinct subgroup exhibiting profound metabolic alterations and clinical features associated with cardiovascular and systemic disease. These results highlight that the butyric acid pathway may contribute to age-related disease survival in extreme aging. Full article

Background: Infections caused by Staphylococcus aureus are a health problem worsened by antibiotic resistance. New drugs, including those inhibiting virulence and resistance mechanisms, are needed. This study aimed to evaluate the anti-growth, anti-virulence, and anti-biofilm activities of Trametes villosa. (2) Methods: Fractions were obtained from the basidiomata of T. villosa. Anti-growth, anti-hemolysis, and anti-biofilm activities were tested against S. aureus strains using resazurin microtiter, blood cell lysis, and crystal violet assays, respectively. Cytotoxicity was evaluated in Vero and HaCaT cells using sulforhodamine B. The active fractions were subjected to GC-MS analysis and molecular docking with S. aureus quorum-sensing receptors. Results: The n-hexane and ethyl acetate (EtOAc) fractions exhibited anti-growth activity against all strains (MIC: 31.2–2000 µg/mL). These fractions also displayed anti-hemolysis (IC₅₀ = 33.8 ± 1.1–53.8 ± 5.1 µg/mL) and anti-biofilm formation activity (IC₅₀ = 106.6 ± 4.8–383.4 ± 31.4 µg/mL), while exhibiting low cytotoxicity in Vero and HaCat. GC-MS analysis revealed that both active fractions mainly contained alkanes, aldehydes, and fatty acids. Molecular docking revealed that isovanillic acid, identified in the EtOAc fraction, exhibited optimal interactions with S. aureus quorum-sensing receptors AgrA and SarA. (4) Conclusions: Our research highlights the potential of T. villosa as a source of bioactive compounds effective against S. aureus. Full article

Mass spectrometry imaging (MSI) visualizes the spatial distribution of biomolecules in tissues, whereas ion mobility–mass spectrometry (IM-MS) separates ions through the collision cross-section (CCS) with an inert gas, providing the structural characteristics of isomers. Recent advances have established an integrated workflow, ion mobility–mass spectrometry imaging (IM-MSI), that couples IM with MSI, uniting molecular discrimination with spatial mapping. This synergy has been widely applied in oncology and neuropsychiatric disorders, offering unprecedented insights into biomarker discovery and disease mechanisms. Here, we summarize the principles and classifications of IM-MSI, review their combined biomedical applications, and discuss data processing workflows and commonly used tools. Full article

Background: Nosocomial infections represent a significant clinical burden due to high morbidity, mortality and healthcare costs. Invasive fungal infections, particularly those caused by Candida species, are of growing concern due to increasing antifungal resistance, which limits therapeutic options and worsens patient outcomes. This study aimed to characterize the prevalence, species distribution, antifungal susceptibility profiles, and molecular mechanisms of resistance in clinical Candida isolates from hospitalized patients. Methods: A cross-sectional study was conducted involving 55 hospitalized patients, yielding 60 isolates from blood, secretions, fluids, and catheter tips. Species identification was performed using chromogenic media and confirmed by MALDI-TOF MS. Antifungal susceptibility testing followed CLSI M27-A4 broth microdilution guidelines for amphotericin B, fluconazole and 5-flucytosine. Gene expression of ERG2, ERG11 and MDR1 was evaluated by RT-qPCR after exposure to subinhibitory antifungal concentrations using the 2^−∆∆Ct method. Results:Candida albicans was the most frequent species, followed by Nakaseomyces glabratus, C. tropicalis and C. parapsilosis. Resistance varied among species, with elevated rates for fluconazole. ERG2 was notably overexpressed in amphotericin B-resistant isolates, while ERG11 and MDR1 showed species-dependent variation. Conclusions: Resistance mechanisms in Candida are species-specific and drug-dependent. Accurate species identification and understanding their molecular profiles are essential to guide targeted antifungal therapy and improve clinical outcomes. Full article

This study aimed to enhance the extraction efficiency and elucidate the mechanism of ultrasound-assisted extraction (UAE) of taxanes from Taxus chinensis by natural deep eutectic solvents (NADES). The processes of kinetics and thermodynamics were systematically investigated. These extractions adhered to a pseudo-second-order kinetic model (R² > 0.972), with intraparticle diffusion identified as the dominant mechanism. Key parameters such as temperature, ultrasonic power, and solid/liquid ratio significantly improved the effective diffusion coefficient (D_e) and mass transfer coefficient (K_T), reaching values of 6.21 × 10⁻⁹ m²/s and 4.14 × 10⁻³ m/s, respectively. A high Biot number (B_i > 59.21) confirmed that internal diffusion is the rate-determining step. Thermodynamic analysis indicated that the process is endothermic (ΔH > 0), irreversible (ΔS > 0), and spontaneous (ΔG < 0). These results elucidate the underlying mechanisms of UAE and establish a foundational framework for its industrial-scale implementation. Full article

CO₂ miscible flooding provides dual advantages in enhancing oil recovery and facilitating geological sequestration, and has become a key technical approach for developing low-permeability oil reservoirs and carbon emission reduction. The pore-scale flow mechanisms governing CO₂ behavior during miscible flooding are crucial for achieving efficient oil recovery and secure geological storage of CO₂. In this study, pore-scale two-phase flow simulations of CO₂ miscible flooding in porous media are performed using a coupled laminar-flow and diluted-species-transport framework. The model captures the effects of diffusion, concentration distribution, and pore structure on the behavior of CO₂ miscible displacement. The results indicate that: (1) during miscible flooding, CO₂ preferentially displaces oil in larger pore throats and subsequently invades smaller throats, significantly improving the mobilization of oil trapped in small pores; (2) increasing the injection velocity accelerates the displacement front and improves oil utilization in dead-end and trailing regions, but a “velocity saturation effect” is observed—when the inject velocity exceeds 0.02 m/s, the displacement pattern stabilizes and further gains in ultimate recovery become limited; (3) higher injected CO₂ concentration accelerates CO₂ accumulation within the pores, enlarges the miscible sweep area, promotes a more uniform concentration field, leads to a smoother displacement front, and reduces high-gradient regions, thereby suppressing local instabilities, and improves displacement efficiency, although its effect on overall recovery remains modest; (4) CO₂ dynamic viscosity strongly influences flow stability: low-viscosity conditions promote viscous fingering and severe local bypassing, whereas higher viscosity stabilizes flow but increases injection pressure drop and energy consumption, indicating a necessary trade-off between flow stability and operational efficiency. Full article