Dynamic simulation of a peristaltic micropump considering coupled fluid flow and structural motion

This paper presents lumped-parameter simulation of dynamic characteristics of peristaltic micropumps. The pump consists of three pumping cells connected in series, each of which is equipped with a compliant diaphragm that is electrostatically actuated in a peristaltic sequence to mobilize the fluid. Diaphragm motion in each pumping cell is first represented by an effective spring subjected to hydrodynamic and electrostatic forces. These cell representations are then used to construct a system-level model for the entire pump, which accounts for both cell- and pump-level interactions of fluid flow and diaphragm vibration. As the model is based on first principles, it can be evaluated directly from the device's geometry, material properties and operating parameters without using any experimentally identified parameters. Applied to an existing pump, the model correctly predicts trends observed in experiments. The model is then used to perform a systematic analysis of the impact of geometry, materials and pump loading on device performance, demonstrating its utility as an efficient tool for peristaltic micropump design

Surface micromachined electrostatically actuated micro peristaltic pump

An electrostatically actuated micro peristaltic pump is reported. The micro pump is entirely surface micromachined using a multilayer parylene technology. Taking advantage of the multilayer technology, the micro pump design enables the pumped fluid to be isolated from the electric field. Electrostatic actuation of the parylene membrane using both DC and AC voltages was demonstrated and applied to fluid pumping based on a 3-phase peristaltic sequence. A maximum flow rate of 1.7 nL min^–1 and an estimated pumping pressure of 1.6 kPa were achieved at 20 Hz phase frequency. A dynamic analysis was also performed with a lumped-parameter model for the peristaltic pump. The analysis results allow a quantitative understanding of the peristaltic pumping operation, and correctly predict the trends exhibited by the experimental data. The small footprint of the micro pump is well suited for large-scale integration of microfluidics. Moreover, because the same platform technology has also been used to fabricate other devices (e.g. valves, electrospray ionization nozzles, filters and flow sensors), the integration of these different devices can potentially lead to versatile and functional micro total analysis systems (µTAS)

Attenuation of hedgehog/GLI signaling by NT1721 extends survival in pancreatic cancer.

BackgroundPancreatic cancer is one of the most lethal malignancies due to frequent late diagnosis, aggressive tumor growth and metastasis formation. Continuously raising incidence rates of pancreatic cancer and a lack of significant improvement in survival rates over the past 30 years highlight the need for new therapeutic agents. Thus, new therapeutic agents and strategies are urgently needed to improve the outcome for patients with pancreatic cancer. Here, we evaluated the anti-tumor activity of a new natural product-based epidithiodiketopiperazine, NT1721, against pancreatic cancer.MethodsWe characterized the anticancer efficacy of NT1721 in multiple pancreatic cancer cell lines in vitro and in two orthotopic models. We also compared the effects of NT1721 to clinically used hedgehog inhibitors and the standard-of-care drug, gemcitabine. The effect of NT1721 on hedgehog/GLI signaling was assessed by determining the expression of GLI and GLI target genes both in vitro and in vivo.ResultsNT1721 displayed IC50 values in the submicromolar range in multiple pancreatic cancer cell lines, while largely sparing normal pancreatic epithelial cells. NT1721 attenuated hedgehog/GLI signaling through downregulation of GLI1/2 transcription factors and their downstream target genes, which reduced cell proliferation and invasion in vitro and significantly decreased tumor growth and liver metastasis in two preclinical orthotopic mouse models of pancreatic cancer. Importantly, treatment with NT1721 significantly improved survival times of mice with pancreatic cancer compared to the standard-of-care drug, gemcitabine.ConclusionsFavorable therapeutics properties, i.e. 10-fold lower IC50 values than clinically used hedgehog inhibitors (vismodegib, erismodegib), a 90% reduction in liver metastasis and significantly better survival times compared to the standard-of-care drug, gemcitabine, provide a rational for testing NT1721 in the clinic either as a single agent or possibly in combination with gemcitabine or other therapeutic agents in PDAC patients overexpressing GLI1/2. This could potentially result in promising new treatment options for patients suffering from this devastating disease

Expression and Clinical Significance of Antiapoptotic Gene (Survivin) in NB4 and Acute Promyelocytic Leukemia Cells

To study survivin gene expression in APL cells and to explore its correlation with clinical manifestations. PML/RARα and survivin mRNA expression were analysed using RT-PCR. By treatment of ATRA, the survivin mRNA expression in NB4 cells gradually decreased with time and was almost undetectable in the 72th hour. Survivin was expressed in 67% of the 36 APL cases (de novo and relapse patients) with PML/RARα fusion gene expression. However, in 22 cases of remission stage patients without PML/RARα fusion gene expression, survivin was expressed in 36%. The survivin mRNA expression positive rate in de novo and relapse groups, and PML/RARα fusion gene L-type positive groups, was obviously higher than those in remission period groups and was significantly lower than those in acute leukemia groups. In 36 cases of de novo and relapse APL patients, all cases could obtain complete remission, irrespective of the survivin expression. APL patients expressed with survivin mRNA had DIC and serious infection (one patient died). The clinical symptom included slight skin or mucosa bleeding, fever, and asthenic for patients without the survivin mRNA expression. Later, two cases of APL patients with the survivin mRNA expression were treated by ATRA, induction differentiation sign in their peripheral blood and bone marrow figure was not obvious. It was concluded that the survive gene expression was lower in APL than those in any other types of leukemia, thus closely associated with clinical manifestation

Confirming the 115.5-day periodicity in the X-ray light curve of ULX NGC 5408 X-1

The Swift/XRT light curve of the ultraluminous X-ray (ULX) source NGC 5408 X-1 was re-analyzed with two new numerical approaches, Weighted Wavelet $Z$-transform (WWZ) and CLEANest, that are different from previous studies. Both techniques detected a prominent periodicity with a time scale of $115.5\pm1.5$ days, in excellent agreement with the detection of the same periodicity first reported by Strohmayer (2009). Monte Carlo simulation was employed to test the statisiticak confidence of the 115.5-day periodicity, yielding a statistical significance of $> 99.98$ (or $>3.8\sigma$). The robust detection of the 115.5-day quasi-periodic oscillations (QPOs), if it is due to the orbital motion of the binary, would infer a mass of a few thousand $M_\odot$ for the central black hole, implying an intermediate-mass black hole in NGC 5408 X-1.Comment: 6 pages, 2 figures, submitted to Research in Astronomy and Astrophysics (RAA

WR-ONE2SET: Towards Well-Calibrated Keyphrase Generation

Keyphrase generation aims to automatically generate short phrases summarizing an input document. The recently emerged ONE2SET paradigm (Ye et al., 2021) generates keyphrases as a set and has achieved competitive performance. Nevertheless, we observe serious calibration errors outputted by ONE2SET, especially in the over-estimation of $\varnothing$ token (means "no corresponding keyphrase"). In this paper, we deeply analyze this limitation and identify two main reasons behind: 1) the parallel generation has to introduce excessive $\varnothing$ as padding tokens into training instances; and 2) the training mechanism assigning target to each slot is unstable and further aggravates the $\varnothing$ token over-estimation. To make the model well-calibrated, we propose WR-ONE2SET which extends ONE2SET with an adaptive instance-level cost Weighting strategy and a target Re-assignment mechanism. The former dynamically penalizes the over-estimated slots for different instances thus smoothing the uneven training distribution. The latter refines the original inappropriate assignment and reduces the supervisory signals of over-estimated slots. Experimental results on commonly-used datasets demonstrate the effectiveness and generality of our proposed paradigm.Comment: EMNLP202