47 research outputs found
Nanoparticles Penetrate into the Multicellular Spheroid-on-Chip: Effect of Surface Charge, Protein Corona, and Exterior Flow
Nanoparticles
(NPs) are widely studied as tumor targeted vehicles.
The penetration of NPs into the tumor is considered as a major barrier
for delivery of NPs into tumor cell and a big challenge to translate
NPs from lab to the clinic. The objective of this study is to know
how the surface charge of NPs, the protein corona surrounding the
NPs, and the fluid flow around the tumor surface affect the penetration
and accumulation of NPs into the tumor, through in vitro penetration
study based on a spheroid-on-chip system. Surface decorated polystyrene
(PS) NPs (100 nm) carrying positive and negative surface charge were
loaded to the multicellular spheroids under static and flow conditions,
in the presence or absence of serum proteins. NP penetration was investigated
by confocal laser microscopy scanning followed with quantitative image
analysis. The results reveal that negatively charged NPs are attached
more on the spheroid surface and easier to penetrate into the spheroids.
Protein corona, which is formed surrounding the NPs in the presence
of serum protein, changes the surface properties of the NPs, weakens
the NP–cell affinity, and, therefore, results in lower NP concentration
on the spheroid surface but might facilitate deeper penetration. The
exterior fluid flow enhances the interstitial flow into the spheroid,
which benefits the penetration but also strips the NPs (especially
the NPs with protein corona) on the spheroid surface, which decreases
the penetration flux significantly. The maximal penetration was obtained
by applying negatively charged NPs without protein corona under the
flow condition. We hope the present study will help to understand
the spatiotemporal performance of drug delivery NPs and inform the
rational design of NPs with highly defined drug accumulation localized
at a target site
Locations of sampling sites in the Hongfeng and Baihua Reservoirs on the Yunnan-Guizhou Plateau, Southwest China.
<p>Locations of sampling sites in the Hongfeng and Baihua Reservoirs on the Yunnan-Guizhou Plateau, Southwest China.</p
Bacterial/virus affection status Subgroup analysis of <i>IL1RN</i> VNTR polymorphism to cancer risk.
a<p>Number of comparisons.</p>b<p>P value of Q-test for heterogeneity test.</p>c<p>Random-effects model was used when a P value <0.05 for heterogeneity test; otherwise, fixed-effects model was used.</p
Begg’s funnel plot for publication bias test.
<p>CT versus CC. Each circle denotes an independent study for the indicated association. Log[OR], natural logarithm of OR. Horizontal line stands for mean effect size.</p
Comparison of heavy metal concentrations in sediments.
<p>(HFT: sites at inlets of main tributaries in the Hongfeng Reservoir, namely sites 1–5; HFR: representative sites within Hongfeng Reservoir, namely sites 6–13; BHT: sites at inlets of main tributaries in the Baihua Reservoir (except site 14), namely sites 15–22; BHR: representative sites within the Baihua Reservoir, namely sites 23–26).</p
Map of Hongfeng and Baihua Reservoirs on the Yunnan-Guizhou Plateau, Southwest China.
<p>Map of Hongfeng and Baihua Reservoirs on the Yunnan-Guizhou Plateau, Southwest China.</p
IL1 Receptor Antagonist Gene <em>IL1-RN</em> Variable Number of Tandem Repeats Polymorphism and Cancer Risk: A Literature Review and Meta-Analysis
<div><p>IL1 receptor antagonist (IL1RA) and IL1beta (IL1β), members of the pro-inflammatory cytokine interleukin-1 (IL1) family, play a potential role against infection and in the pathogenesis of cancers. The variable number of tandem repeats (VNTR) polymorphism in the second intron of the IL1 receptor antagonist gene (<em>IL1-RN</em>) and a polymorphism in exon 5 of <em>IL1B</em> (<em>IL1B</em>+3954C>T, rs1143634) have been suggested in predisposition to cancer risk. However, studies have shown inconsistent results. To validate any association, a meta-analysis was performed with 14,854 cases and 19,337 controls from 71 published case–control studies for <em>IL1-RN</em> VNTR and 33 eligible studies contained 7,847 cases and 8917 controls for <em>IL1B</em> +3954. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated from comparisons to assess the strength of the association. There was significant association between the <em>IL1-RN</em> VNTR polymorphism and the risk of cancer for any overall comparison. Furthermore, cancer type stratification analysis revealed that there were significantly increased risks of gastric cancer, bladder cancer and other cancer groups. Infection status analysis indicated that the <em>H. pylori</em> or HBV/HCV infection and <em>IL1-RN</em> VNTR genotypes were independent factors for developing gastric or hepatocellular cancers. In addition, a borderline significant association was observed between <em>IL1B</em>+3954 polymorphism and the increased cancer risk. Although some modest bias could not be eliminated, this meta-analysis suggested that the <em>IL1-RN</em> VNTR polymorphisms may contribute to genetic susceptibility to gastric cancer. More studies are needed to further evaluate the role of the <em>IL1B</em>+3954 polymorphism in the etiology of cancer.</p> </div
Stratification analyses of genetic susceptibility of <i>IL1B+</i>3954 polymorphism to cancer risk.
a<p>Number of studies.</p>b<p>P value of Q-test for heterogeneity test.</p>c<p>Random-effects model was used when a P value <0.05 for heterogeneity test; otherwise, fixed-effects model was used.</p><p><i>I</i><sup>2</sup> 0–25, no heterogeneity; 25–50, modest heterogeneity; 50 high heterogeneity.</p
Evaluation of the de-noising results of RS1 and RS2 series obtained by different wavelets.
<p>*: In <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0110733#pone-0110733-t006" target="_blank">Table 6</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0110733#pone-0110733-t007" target="_blank">7</a>, <i>MSE</i> and <i>R<sub>xy</sub></i> are used to compare original series and the de-noised series.</p><p>Evaluation of the de-noising results of RS1 and RS2 series obtained by different wavelets.</p
Energy distributions of RS1 (a) and RS2 (b) series obtained by different wavelets.
<p>Energy distributions of RS1 (a) and RS2 (b) series obtained by different wavelets.</p