Pharmacokinetics of ligustrazine ethosome patch in rats and anti-myocardial ischemia and anti-ischemic reperfusion injury effect

The objective of this study was to investigate the pharmacokinetics of the ligustrazine ethosome patch and antimyocardial ischemia and anti-ischemic reperfusion injury effect. Male Sprague Dawley rats were divided randomly into 3 groups: Group A (intragastric ligustrazine), Group B (transdermal ligustrazine ethosome patch), and Group C (conventional transdermal ligustrazine patch). After treatment, samples of blood and of various tissues such as heart, liver, spleen, lung, kidney, brain, and muscle samples were taken at different time points. Drug concentration was measured with HPLC, and the drug concentration–time curve was plotted. Pharmacokinetic software 3p97 was applied to calculate pharmacokinetic parameters and the area under the drug concentration–time curve (AUC) in various tissues. The rat model of acute myocardial ischemia was constructed with intravenous injection of pituitrin and the model of myocardial ischemia-perfusion injury was constructed by tying off the left anterior descending coronary artery of rats to observe the effect of ligustrazine ethosome patches on ischemic myocardium and ischemia-reperfusion injury. Results showed that AUC was highest in the transdermal drug delivery group of ligustrazine ethosome patch. There were significant differences in whole blood viscosity, plasma viscosity, hematocrit, red blood cell aggregation index, and deformation index between ligustrazine the ethosome patch group and ischemic control group (P < 0.01). Moreover, ligustrazine ethosome patches could reduce the scope of myocardial infarction induced by long-term ischemia. Ligustrazine ethosome patches have a sustained-release property. They can maintain stable and sustained blood drug concentration, increase bioavailability, and reduce administration times. The drug patch can decrease hemorheological indices of myocardial ischemia in rats, as well as protect acute ischemic myocardium and ischemia-reperfusion injured myocardium

Taming Gradient Variance in Federated Learning with Networked Control Variates

Federated learning, a decentralized approach to machine learning, faces significant challenges such as extensive communication overheads, slow convergence, and unstable improvements. These challenges primarily stem from the gradient variance due to heterogeneous client data distributions. To address this, we introduce a novel Networked Control Variates (FedNCV) framework for Federated Learning. We adopt the REINFORCE Leave-One-Out (RLOO) as a fundamental control variate unit in the FedNCV framework, implemented at both client and server levels. At the client level, the RLOO control variate is employed to optimize local gradient updates, mitigating the variance introduced by data samples. Once relayed to the server, the RLOO-based estimator further provides an unbiased and low-variance aggregated gradient, leading to robust global updates. This dual-side application is formalized as a linear combination of composite control variates. We provide a mathematical expression capturing this integration of double control variates within FedNCV and present three theoretical results with corresponding proofs. This unique dual structure equips FedNCV to address data heterogeneity and scalability issues, thus potentially paving the way for large-scale applications. Moreover, we tested FedNCV on six diverse datasets under a Dirichlet distribution with {\alpha} = 0.1, and benchmarked its performance against six SOTA methods, demonstrating its superiority.Comment: 14 page

Trinity: Syncretizing Multi-/Long-tail/Long-term Interests All in One

Interest modeling in recommender system has been a constant topic for improving user experience, and typical interest modeling tasks (e.g. multi-interest, long-tail interest and long-term interest) have been investigated in many existing works. However, most of them only consider one interest in isolation, while neglecting their interrelationships. In this paper, we argue that these tasks suffer from a common "interest amnesia" problem, and a solution exists to mitigate it simultaneously. We figure that long-term cues can be the cornerstone since they reveal multi-interest and clarify long-tail interest. Inspired by the observation, we propose a novel and unified framework in the retrieval stage, "Trinity", to solve interest amnesia problem and improve multiple interest modeling tasks. We construct a real-time clustering system that enables us to project items into enumerable clusters, and calculate statistical interest histograms over these clusters. Based on these histograms, Trinity recognizes underdelivered themes and remains stable when facing emerging hot topics. Trinity is more appropriate for large-scale industry scenarios because of its modest computational overheads. Its derived retrievers have been deployed on the recommender system of Douyin, significantly improving user experience and retention. We believe that such practical experience can be well generalized to other scenarios

Graph Learning and Its Applications: A Holistic Survey

Graph learning is a prevalent domain that endeavors to learn the intricate relationships among nodes and the topological structure of graphs. These relationships endow graphs with uniqueness compared to conventional tabular data, as nodes rely on non-Euclidean space and encompass rich information to exploit. Over the years, graph learning has transcended from graph theory to graph data mining. With the advent of representation learning, it has attained remarkable performance in diverse scenarios, including text, image, chemistry, and biology. Owing to its extensive application prospects, graph learning attracts copious attention from the academic community. Despite numerous works proposed to tackle different problems in graph learning, there is a demand to survey previous valuable works. While some researchers have perceived this phenomenon and accomplished impressive surveys on graph learning, they failed to connect related objectives, methods, and applications in a more coherent way. As a result, they did not encompass current ample scenarios and challenging problems due to the rapid expansion of graph learning. Different from previous surveys on graph learning, we provide a holistic review that analyzes current works from the perspective of graph structure, and discusses the latest applications, trends, and challenges in graph learning. Specifically, we commence by proposing a taxonomy from the perspective of the composition of graph data and then summarize the methods employed in graph learning. We then provide a detailed elucidation of mainstream applications. Finally, based on the current trend of techniques, we propose future directions.Comment: 20 pages, 7 figures, 3 table

The Aroma Composition of Baby Ginger Paocai

The purpose of this study was to analyze the volatile compounds in baby ginger paocai and the fresh baby ginger and identify the key aroma components that contribute to the flavor of baby ginger paocai. A total of 86 volatile compounds from the two baby ginger samples were quantified; these compounds were extracted by headspace solid-phase microextraction (HS-SPME) and analyzed by gas chromatography–mass spectrometry (GC-MS). The aroma composition of baby ginger paocai was different from that of fresh baby ginger. Baby ginger paocai was characterized by the presence of aroma-active compounds which varied in concentration from 0.03 to 28.14%. Geranyl acetate was the aroma component with the highest relative content in baby ginger paocai. β-myrcene, eucalyptol, trans-β-ocimene, Z-ocimene, linalool, decanal, cis-citral, geraniol, geranyl acetate, curcumene, and β-bisabolene contributed to the overall aroma of the product of baby ginger paocai which had gone through a moderate fermentation process

A Fractional Creep Constitutive Model for Frozen Soil in Consideration of the Strengthening and Weakening Effects

The triaxial creep tests of frozen silty clay mixed with sands were performed under different pressures, and the test results demonstrated that, under the low confining pressure, when the shear stress is lower than the long-term strength, the test specimen exhibits an attenuation creep because the strengthening effect is greater than the weakening effect. When the shear stress is higher than the long-term strength, the test specimen exhibits a nonattenuation creep due to the level of the strengthening and weakening effects change in different stages. As the confining pressure increases, the test specimens only exhibit an attenuation creep because of the enhancing strengthening effect. Both the hardening parameter and the damage variable were introduced to describe the strengthening and weakening effects, respectively, and a new creep constitutive model for frozen soil considering these effects was put forward based on the theory of elastoviscoplastic and the fractional derivative. Finally, the model parameters were analyzed and their determination method was also provided to reveal the trend of parameters according to the triaxial test results. The calculated results of the constitutive model show that the proposed model can describe the whole creep process of frozen soil well

Effects of brain–Computer interface combined with mindfulness therapy on rehabilitation of hemiplegic patients with stroke: a randomized controlled trial

AimTo explore the effects of brain–computer interface training combined with mindfulness therapy on Hemiplegic Patients with Stroke.BackgroundThe prevention and treatment of stroke still faces great challenges. Maximizing the improvement of patients’ ability to perform activities of daily living, limb motor function, and reducing anxiety, depression, and other social and psychological problems to improve patients’ overall quality of life is the focus and difficulty of clinical rehabilitation work.MethodsPatients were recruited from December 2021 to November 2022, and assigned to either the intervention or control group following a simple randomization procedure (computer-generated random numbers). Both groups received conventional rehabilitation treatment, while patients in the intervention group additionally received brain–computer interface training and mindfulness therapy. The continuous treatment duration was 5 days per week for 8 weeks. Limb motor function, activities of daily living, mindfulness attention awareness level, sleep quality, and quality of life of the patients were measured (in T0, T1, and T2). Generalized estimated equation (GEE) were used to evaluate the effects. The trial was registered with the Chinese Clinical Trial Registry (ChiCTR2300070382).ResultsA total of 128 participants were randomized and 64 each were assigned to the intervention and control groups (of these, eight patients were lost to follow-up). At 6 months, compared with the control group, intervention group showed statistically significant improvements in limb motor function, mindful attention awareness, activities of daily living, sleep quality, and quality of life.ConclusionBrain–computer interface combined with mindfulness therapy training can improve limb motor function, activities of daily living, mindful attention awareness, sleep quality, and quality of life in hemiplegic patients with stroke.ImpactThis study provides valuable insights into post-stroke care. It may help improve the effect of rehabilitation nursing to improve the comprehensive ability and quality of life of patients after stroke.Clinical review registrationhttps://www.chictr.org.cn/, identifier ChiCTR2300070382