Mice Exposed to Chronic Intermittent Hypoxia Simulate Clinical Features of Deficiency of both Qi and Yin Syndrome in Traditional Chinese Medicine

Deficiency of both Qi and Yin Syndrome (DQYS) is one of the common syndromes in traditional Chinese medicine (TCM), mainly characterized by tiredness, emaciation, anorexia, fidget, palpitation and rapid pulse, and so forth. Currently, there is no available animal model which can reflect the clinical features of this syndrome. In the present paper, we observed the time-course changes of whole behavior, body weight, food intake, locomotive activity and electrocardiogram in mice exposed to chronic intermittent hypoxia for 6 weeks, and measured bleeding time at last according to the clinical features of DQYS and one key pathological factor. The results showed that the mice exposed to intermittent hypoxia for certain time presented lackluster hair, dull looking hair, resistance, attacking, body weight loss, food intake decline, locomotive activity decrease, heart rate quickening and T wave elevating, which were similar to the major clinical features of DQYS. Meanwhile, bleeding time shortening was also found, which was consistent with the clinical fact that DQYS often accompanied with blood stasis. The possible explanation was also outlined according to the available literature. Such findings suggested chronic intermittent hypoxia could induce similar symptoms and signs in mice accorded with the clinical features of DQYS, which provided a suitable animal model for evaluation of drugs for the treatment of this syndrome and further exploration of pathological process or correlation of the syndrome and related diseases

Multitasking Multiobjective Evolutionary Operational Indices Optimization of Beneficiation Processes

Yang C, Ding J, Jin Y, Wang C, Chai T. Multitasking Multiobjective Evolutionary Operational Indices Optimization of Beneficiation Processes. IEEE Transactions on Automation Science and Engineering. 2019;16(3):1046-1057.Operational indices optimization is crucial for the global optimization in beneficiation processes. This paper presents a multitasking multiobjective evolutionary method to solve operational indices optimization, which involves a formulated multiobjective multifactorial operational indices optimization (MO-MFO) problem and the proposed multiobjective MFO algorithm for solving the established MO-MFO problem. The MO-MFO problem includes multiple level of accurate models of operational indices optimization, which are generated on the basis of a data set collected from production. Among the formulated models, the most accurate one is considered to be the original functions of the solved problem, while the remained models are the helper tasks to accelerate the optimization of the most accurate model. For the MFO algorithm, the assistant models are alternatively in multitasking environment with the accurate model to transfer their knowledge to the accurate model during optimization in order to enhance the convergence of the accurate model. Meanwhile, the recently proposed two-stage assortative mating strategy for a multiobjective MFO algorithm is applied to transfer knowledge among multitasking tasks. The proposed multitasking framework for operational indices optimization has conducted on 10 different production conditions of beneficiation. Simulation results demonstrate its effectiveness in addressing the operational indices optimization of beneficiation problem. Note to Practitioners-Operational indices optimization is a typical approach to achieve global production optimization by efficiently coordinating all the indices to improve the production indices. In this paper, a multiobjective multitasking framework is developed to address the operational indices optimization, which includes a multitasking multiobjective operational indices optimization problem formulation and a multitasking multiobjective evolutionary optimization to solve the above-formulated optimization problem. The proposed approach can achieve a solution set for the decision-making. The simulation results on a real beneficiation process in China with 10 operational conditions show that the proposed approach is able to obtain a superior solution set, which is associated with a higher grade and yield of the product

The Establishment of a Mouse Model of Recurrent Primary Dysmenorrhea

Primary dysmenorrhea is one of the most common reasons for gynecologic visits, but due to the lack of suitable animal models, the pathologic mechanisms and related drug development are limited. Herein, we establish a new mouse model which can mimic the periodic occurrence of primary dysmenorrhea to solve this problem. Non-pregnant female mice were pretreated with estradiol benzoate for 3 consecutive days. After that, mice were injected with oxytocin to simulate menstrual pain on the 4th, 8th, 12th, and 16th days (four estrus cycles). Assessment of the cumulative writhing score, uterine tissue morphology, and uterine artery blood flow and biochemical analysis were performed at each time point. Oxytocin injection induced an equally severe writhing reaction and increased PGF2α accompanied with upregulated expression of COX-2 on the 4th and 8th days. In addition, decreased uterine artery blood flow but increased resistive index (RI) and pulsatility index (PI) were also observed. Furthermore, the metabolomics analysis results indicated that arachidonic acid metabolism; linoleic acid metabolism; glycerophospholipid metabolism; valine, leucine, and isoleucine biosynthesis; alpha-linolenic acid metabolism; and biosynthesis of unsaturated fatty acids might play important roles in the recurrence of primary dysmenorrhea. This new mouse model is able to mimic the clinical characteristics of primary dysmenorrhea for up to two estrous cycles

Multi-tasking Multi-objective Evolutionary Operational Indices Optimization of Beneficiation Processes

Operational indices optimization is crucial for the global optimization in beneficiation processes. This paper presents a multi-tasking multi-objective evolutionary method to solve operational indices optimization, which involves a formulated multi-objective multifactorial operational indices optimization problem (MO-MFO) and a proposed multi-objective multifactorial optimization algorithm for solving the established MO-MFO problem. The MO-MFO problem includes multiple level of accurate models of operational indices optimization, which are generated on the basis of a dataset collected from production. Among the formulated models, the most accurate one is considered to be the original functions of the solved problem, while the remained models are the helper tasks to accelerate the optimization of the most accurate model. For the multifactorial optimization algorithm, the assistant models are alternatively in multi-tasking environment with the accurate model to transfer their knowledge to the accurate model during optimization in order to enhance the convergence of the accurate model. Meanwhile, the recently proposed two-stage assortative mating strategy for a multi-objective multifactorial optimization algorithm is applied to transfer knowledge among multi-tasking tasks. The proposed multi-tasking framework for operational indices optimization has conducted on 10 different production Conditions of beneficiation. Simulation results demonstrate its effectiveness in addressing the operational indices optimization of beneficiation problem

Ribemansides A and B, TRPC6 Inhibitors from <i>Ribes manshuricum</i> That Suppress TGF-β1-Induced Fibrogenesis in HK‑2 Cells

Two new acylated β-hydroxynitrile glycosides, ribemansides A (<b>1</b>) and B (<b>2</b>), were isolated from the aerial parts of <i>Ribes manshuricum</i>. Their structures were elucidated by comprehensive spectroscopic analysis. Ribemansides A and B inhibited transforming growth factor β1 (TGF-β1)-induced expression of α-smooth muscle actin, fibronectin release, and changes in cell morphology in the human proximal tubular epithelial cell line (human kidney-2, HK-2). Further biological evaluation demonstrated that both <b>1</b> and <b>2</b> inhibit the activity of canonical transient receptor potential cation channel 6 (TRPC6), with IC₅₀ values of 24.5 and 25.6 μM, respectively. The antifibrogenic effect of these compounds appears to be mediated through TRPC6 inhibition, since the TRPC6 inhibitor, SAR7334, also suppressed TGF-β1-induced fibrogenesis in HK-2 cells