Anti-inflammatory activity and chemical composition of the essential oils from Senecio flammeus

Many species from Senecio genus have been used in traditional medicine, and their pharmacological activities have been demonstrated. This study investigated the chemical composition and anti-inflammatory activities of essential oils from Senecio flammeus. A total of 48 components representing 98.41 % of the total oils were identified. The main compounds in the oils were α-farnesene (11.26 %), caryophyllene (8.69 %), n-hexadecanoic acid (7.23 %), and α-pinene (6.36 %). The anti-inflammatory activity of the essential oils was evaluated in rodents (10–90 mg/kg bw) in classical models of inflammation [carrageenan-induced paw edema, 12-O-tetradecanoyl-phorbol-13-acetate (TPA)-induced ear edema, and cotton pellet-induced granuloma]. The essential oils at doses of 10, 30, and 90 mg/kg bw significantly reduced carrageenan-induced paw edema by 17.42 % (P < 0.05), 52.90 % (P < 0.05), and 66.45 % (P < 0.05) 4 h after carrageenan injection, respectively, and significantly reduced myeloperoxidase activity (P < 0.05). The essential oils (10, 30, and 90 mg/kg) also produced asignificant dose-dependent response to reduce TPA-induced ear edema by 20.27 % (P < 0.05), 33.06 % (P < 0.05), and 53.90 % (P < 0.05), respectively. The essential oils produced significant dose-response anti-inflammatory activity against cotton pellet-induced granuloma that peaked at the highest dose of 90 mg/kg (49.08 % wet weight and 47.29 % dry weight). Results demonstrate that the essential oils of S. flammeus were effective in the treatment of both acute and chronic inflammatory conditions, there by supporting the traditional use of this herb

Anti-inflammatory effect of selagin-7-O-(6''-O-acetyl-)-ß-D-glycoside isolated from Cancrinia discoidea on lipopolysaccharide-induced mouse macrophage RAW 264.7 cells

Selagin-7-O-(6''-O-acetyl-)-β-D-glycoside, a new flavone glycoside isolated from Cancrinia discoidea, is known to exhibit anti-inflammatory activity in vivo. This study aimed to investigate the protection of this flavone glycoside on inflammation in lipopolysaccharide (LPS)-stimulated RAW 264.7 cells. The effects of selagin-7-O-(6''-O-acetyl-)-β-D-glycoside on inflammatory cytokines and signaling pathways were analyzed by enzyme-linked immunosorbent assay, reverse transcription-polymerase chain reaction, and western blot. Results show that selagin-7-O-(6''-O-acetyl-)-β-D-glycoside protected LPS-induced macrophage RAW 264.7 cells from injury. The flavone glycoside markedly inhibited the LPS-induced production of tumor necrosis factor-α, interleukin-1β, and interleukin-6 and increased interleukin-10 release in a concentration-dependent manner. Furthermore, treatment with the flavone glycoside decreased nitric oxide and prostaglandin E2 in LPS-challenged RAW 264.7 cells. These decreases were associated with the down-regulation of inducible nitric oxide synthase (iNOS), cyclooxygenase (COX-2), and nuclear factor kappa B (NF-κB) activity. These findings suggest that the anti-inflammatory effects of selagin-7-O-(6''-O-acetyl-)-β-D-glycoside were associated with the adjustment of in flammatory cytokines, and attributed to the down-regulation of NF-κB and consequent suppression of the expression of iNOS and COX-2

Structured Generation and Exploration of Design Space with Large Language Models for Human-AI Co-Creation

Thanks to their generative capabilities, large language models (LLMs) have become an invaluable tool for creative processes. These models have the capacity to produce hundreds and thousands of visual and textual outputs, offering abundant inspiration for creative endeavors. But are we harnessing their full potential? We argue that current interaction paradigms fall short, guiding users towards rapid convergence on a limited set of ideas, rather than empowering them to explore the vast latent design space in generative models. To address this limitation, we propose a framework that facilitates the structured generation of design space in which users can seamlessly explore, evaluate, and synthesize a multitude of responses. We demonstrate the feasibility and usefulness of this framework through the design and development of an interactive system, Luminate, and a user study with 8 professional writers. Our work advances how we interact with LLMs for creative tasks, introducing a way to harness the creative potential of LLMs

Universal Anomaly of Dynamics at Phase Transition Points Induced by Pancharatnam-Berry Phase

Recently, dynamical anomalies more than critical slowing down are often observed near both the continuous and first-order phase transition points. We propose that the universal anomalies could originate from the geometric phase effects. A Pancharatnam-Berry phase is accumulated continuously in quantum states with the variation of tuning parameters. Phase transitions are supposed to induce a abrupt shift of the geometric phase. In our multi-level quantum model, the quantum interference induced by the geometric phase could prolong or shorten the relaxation times of excited states at phase transition points, which agrees with the experiments, models under sudden quenches and our semi-classical model. Furthermore, we find that by setting a phase shift of \text{\ensuremath{\pi}}, the excited state could be decoupled from the ground state by quantum cancellation so that the relaxation time even could diverge to infinity. Our work introduces the geometric phase to the study of conventional phase transitions and quantum phase transition, and could substantially extend the dephasing time of qubits for quantum computing.Comment: 6 pages, 3 figures, minor revisio

Modeling and control of operator functional state in a unified framework of fuzzy inference petri nets

Background and objective: In human-machine (HM) hybrid control systems, human operator and machine cooperate to achieve the control objectives. To enhance the overall HM system performance, the discrete manual control task-load by the operator must be dynamically allocated in accordance with continuous-time fluctuation of psychophysiological functional status of the operator, so-called operator functional state (OFS). The behavior of the HM system is hybrid in nature due to the co-existence of discrete task-load (control) variable and continuous operator performance (system output) variable. Methods: Petri net is an effective tool for modeling discrete event systems, but for hybrid system involving discrete dynamics, generally Petri net model has to be extended. Instead of using different tools to represent continuous and discrete components of a hybrid system, this paper proposed a method of fuzzy inference Petri nets (FIPN) to represent the HM hybrid system comprising a Mamdani-type fuzzy model of OFS and a logical switching controller in a unified framework, in which the task-load level is dynamically reallocated between the operator and machine based on the model-predicted OFS. Furthermore, this paper used a multi-model approach to predict the operator performance based on three electroencephalographic (EEG) input variables (features) via the Wang-Mendel (WM) fuzzy modeling method. The membership function parameters of fuzzy OFS model for each experimental participant were optimized using artificial bee colony (ABC) evolutionary algorithm. Three performance indices, RMSE, MRE, and EPR, were computed to evaluate the overall modeling accuracy. Results: Experiment data from six participants are analyzed. The results show that the proposed method (FIPN with adaptive task allocation) yields lower breakdown rate (from 14.8% to 3.27%) and higher human performance (from 90.30% to 91.99%). Conclusion: The simulation results of the FIPN-based adaptive HM (AHM) system on six experimental participants demonstrate that the FIPN framework provides an effective way to model and regulate/optimize the OFS in HM hybrid systems composed of continuous-time OFS model and discrete-event switching controller