Objective: To investigate the anti-obesity effects of the pomegranate leaf extract (PLE) in a mouse model of high-fat diet induced obesity and hyperlipidemia. Design: For the anti-obesity experiment, male and female ICR mice were fed with a high-fat diet to induce obesity. When the weight of the high-fat diet group was 20% higher than the normal diet group, the animals were treated with 400 or 800 mg/kg/ day of PLE for 5 weeks. Body weight and daily food intake were measured regularly during the experimental period. The various adipose pads were weighed and serum total cholesterol (TC), triglyceride (TG), glucose and high-density lipoprotein cholesterol (HDL-C) were measured after 5 weeks, treatment with PLE. In the fat absorption experiment, both the normal and obese mice were given 0.5 ml lipid emulsion and PLE at a dose of 800 mg/kg at the same time. Serial serum TG levels were measured at times 1, 2, 3, 4 and 6 h after the treatment. TGs in fecal excretions were measured after the mice were orally given a lipid emulsion. Effects of PLE and its isolated compounds (ellagic acid and tannic acid) on pancreatic lipase activity were examined in vitro.Results: The PLE-treated groups showed a significant decrease in body weight, energy intake and various adipose pad weight percents and serum, TC, TG, glucose levels and TC/HDL-C ratio after 5 weeks treatment. Furthermore, PLE significantly attenuated the raising of the serum TG level and inhibited the intestinal fat absorption in mice given a fat emulsion orally. PLE showed a significant difference in decreasing the appetite of obese mice fed a high-fat diet, but showed no effect in mice fed a normal diet. Conclusion: PLE can inhibit the development of obesity and hyperlipidemia in high-fat diet induced obese mice. The effects appear to be partly mediated by inhibiting the pancreatic lipase activity and suppressing energy intake. PLE may be a novel appetite suppressant that only affects obesity owing to a high-fat diet.

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Single cell trapping increasingly serves as a key manipulation technique in single cell analysis for many cutting-edge cell studies. Due to their inherent advantages, microfluidic devices have been widely used to enable single cell immobilization. To further improve the single cell trapping efficiency, this paper reports on a passive hydrodynamic microfluidic device based on the "least flow resistance path" principle with geometry optimized in line with corresponding cell types. Different from serpentine structure, the core trapping structure of the micro-device consists of a series of concatenated T and inverse T junction pairs which function as bypassing channels and trapping constrictions. This new device enhances the single cell trapping efficiency from three aspects: (1) there is no need to deploy very long or complicated channels to adjust flow resistance, thus saving space for each trapping unit; (2) the trapping works in a "deterministic" manner, thus saving a great deal of cell samples; and (3) the compact configuration allows shorter flowing path of cells in multiple channels, thus increasing the speed and throughput of cell trapping. The mathematical model of the design was proposed and optimization of associated key geometric parameters was conducted based on computational fluid dynamics (CFD) simulation. As a proof demonstration, two types of PDMS microfluidic devices were fabricated to trap HeLa and HEK-293T cells with relatively significant differences in cell sizes. Experimental results showed 100% cell trapping and 90% single cell trapping over 4 × 100 trap sites for these two cell types, respectively. The space saving is estimated to be 2-fold and the cell trapping speed enhancement to be 3-fold compared to previously reported devices. This device can be used for trapping various types of cells and expanded to trap cells in the order of tens of thousands on 1-cm(2) scale area, as a promising tool to pattern large-scale single cells on specific substrates and facilitate on-chip cellular assay at the single cell level.

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Cytoplasmic viscosity ( μ c ) is a key biomechanical parameter for evaluating the status of cellular cytoskeletons. Previous studies focused on white blood cells, but the data of cytoplasmic viscosity for tumour cells were missing. Tumour cells (H1299, A549 and drug-treated H1299 with compromised cytoskeletons) were aspirated continuously through a micropipette at a pressure of −10 or −5 kPa where aspiration lengths as a function of time were obtained and translated to cytoplasmic viscosity based on a theoretical Newtonian fluid model. Quartile coefficients of dispersion were quantified to evaluate the distributions of cytoplasmic viscosity within the same cell type while neural network-based pattern recognitions were used to classify different cell types based on cytoplasmic viscosity. The single-cell cytoplasmic viscosity with three quartiles and the quartile coefficient of dispersion were quantified as 16.7 Pa s, 42.1 Pa s, 110.3 Pa s and 74% for H1299 cells at −10 kPa ( n cell = 652); 144.8 Pa s, 489.8 Pa s, 1390.7 Pa s, and 81% for A549 cells at −10 kPa ( n cell = 785); 7.1 Pa s, 13.7 Pa s, 31.5 Pa s, and 63% for CD-treated H1299 cells at −10 kPa ( n cell = 651); and 16.9 Pa s, 48.2 Pa s, 150.2 Pa s, and 80% for H1299 cells at −5 kPa ( n cell = 600), respectively. Neural network-based pattern recognition produced successful classification rates of 76.7% for H1299 versus A549, 67.0% for H1299 versus drug-treated H1299 and 50.3% for H1299 at −5 and −10 kPa. Variations of cytoplasmic viscosity were observed within the same cell type and among different cell types, suggesting the potential role of cytoplasmic viscosity in cell status evaluation and cell type classification.

Prediction is an important problem in different science domains. In this paper, we focus on trend prediction in complex networks, i.e. to identify the most popular nodes in the future. Due to the preferential attachment mechanism in real systems, nodes’ recent degree and cumulative degree have been successfully applied to design trend prediction methods. Here we took into account more detailed information about the network evolution and proposed a temporal-based predictor (TBP). The TBP predicts the future trend by the node strength in the weighted network with the link weight equal to its exponential aging. Three data sets with time information are used to test the performance of the new method. We find that TBP have high general accuracy in predicting the future most popular nodes. More importantly, it can identify many potential objects with low popularity in the past but high popularity in the future. The effect of the decay speed in the exponential aging on the results is discussed in detail.

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