Refractive-index nonlinearities of intersubband transitions in GaN/AlN quantum-well waveguides

The refractive-index nonlinearities of intersubband transitions in GaN/AlN quantum-well waveguides are investigated. A large spectral broadening of TM-polarized near-infrared pulses is observed after propagation through these devices due to intersubband self-phase modulation. From the measured data, a nonlinear refractive index n 2 of 1.8ϫ 10 −12 cm 2 / W is estimated at the operating wavelength of 1550 nm. A detailed theoretical model of the intersubband refractive index as a function of wavelength and optical intensity is then presented. This model assumes an inhomogeneously broadened transition line and is based on experimentally determined material and device parameters. The results of this study are finally used to discuss the prospects of nitride quantum wells for all-optical switching via cross-phase modulation

Reversion of pH-Induced Physiological Drug Resistance: A Novel Function of Copolymeric Nanoparticles

The extracellular pH of cancer cells is lower than the intracellular pH. Weakly basic anticancer drugs will be protonated extracellularly and display a decreased intracellular concentration. In this study, we show that copolymeric nanoparticles (NPs) are able to overcome this “pH-induced physiological drug resistance” (PIPDR) by delivering drugs to the cancer cells via endocytosis rather than passive diffussion.As a model nanoparticle, Tetradrine (Tet, Pka 7.80) was incorporated into mPEG-PCL. The effectiveness of free Tet and Tet-NPs were compared at different extracellular pHs (pH values 6.8 and 7.4, respectively) by MTT assay, morphological observation and apoptotic analysis in vitro and on a murine model by tumor volume measurement, PET-CT scanning and side effect evaluation in vivo.<0.05) when the extracellular pH decreased from 7.4 to 6.8. Meanwhile, the cytotoxicity of Tet-NPs was not significantly influenced by reduced pH. In vivo experiment also revealed that Tet-NPs reversed PIPDR more effectively than other existing methods and with much less side effects.The reversion of PIPDR is a new discovered mechanism of copolymeric NPs. This study emphasized the importance of cancer microenvironmental factors in anticancer drug resistance and revealed the superiority of nanoscale drug carrier from a different aspect

Validating Antimetastatic Effects of Natural Products in an Engineered Microfluidic Platform Mimicking Tumor Microenvironment

Development of new, antimetastatic drugs from natural products has been substantially constrained by the lack of a reliable in vitro screening system. Such a system should ideally mimic the native, three-dimensional (3D) tumor microenvironment involving different cell types and allow quantitative analysis of cell behavior critical for metastasis. These requirements are largely unmet in the current model systems, leading to poor predictability of the in vitro collected data for in vivo trials, as well as prevailing inconsistency among different in vitro tests. In the present study, we report application of a 3D, microfluidic device for validation of the antimetastatic effects of 12 natural compounds. This system supports co-culture of endothelial and cancer cells in their native 3D morphology as in the tumor microenvironment and provides real-time monitoring of the cells treated with each compound. We found that three compounds, namely sanguinarine, nitidine, and resveratrol, exhibited significant antimetastatic or antiangiogenic effects. Each compound was further examined for its respective activity with separate conventional biological assays, and the outcomes were in agreement with the findings collected from the microfluidic system. In summary, we recommend use of this biomimetic model system as a new engineering tool for high-throughput evaluation of more diverse natural compounds with varying anticancer potentials

Recombinant Human Endostatin Endostar Inhibits Tumor Growth and Metastasis in a Mouse Xenograft Model of Colon Cancer

To investigate the effects of recombinant human endostatin Endostar on metastasis and angiogenesis and lymphangiogenesis of colorectal cancer cells in a mouse xenograft model. Colon cancer cells SW620 were injected subcutaneously into the left hind flank of nude mice to establish mouse xenograft models. The mice were treated with normal saline or Endostar subcutaneously every other day. The growth and lymph node metastasis of tumor cells, angiogenesis and lymphangiogenesis in tumor tissue were detected. Apoptosis and cell cycle distribution were studied by flow cytometry. The expression of VEGF-A, -C, or -D in SW620 cells was determined by immunoblotting assays. Endostar inhibited tumor growth and the rate of lymph node metastasis (P < 0.01). The density of blood vessels in or around the tumor area was 12.27 ± 1.21 and 22.25 ± 2.69 per field in Endostar-treated mice and controls (P < 0.05), respectively. Endostar also decreased the density of lymphatic vessels in tumor tissues (7.84 ± 0.81 vs. 13.83 ± 1.08, P < 0.05). Endostar suppresses angiogenesis and lymphangiogenesis in the lymph nodes with metastases, simultaneously. The expression of VEGF-A, -C and -D in SW620 cells treated with Endostar was substantially lower than that of controls. Endostar inhibited growth and lymph node metastasis of colon cancer cells by inhibiting angiogenesis and lymphangiogenesis in a mouse xenograft model of colon cancer

Particular distribution and expression pattern of endoglin (CD105) in the liver of patients with hepatocellular carcinoma

<p>Abstract</p> <p>Background</p> <p>Endoglin (CD105) has been considered a prognostic marker for hepatocellular carcinoma (HCC), and widely used as an appropriate targeting for antiangenesis therapy in some cancers. Our aim was to evaluate the distribution and expression of CD105 in the liver of patients with HCC, and to discuss whether CD105 may be used as an appropriate targeting for antiangenesis therapy in HCC.</p> <p>Methods</p> <p>Three parts of liver tissues from each of 64 patients with HCC were collected: tumor tissues (TT), adjacent non-tumor (AT) liver tissues within 2 cm, and tumor free tissues (TF) 5 cm far from the tumor edge. Liver samples from 8 patients without liver diseases served as healthy controls (HC). The distribution and expression of CD105 in tissues were evaluated by immunohistochemistry, Western blotting analysis, and real-time PCR. HIF-1alpha and VEGF₁₆₅protein levels in tissues were analyzed by Immunohistochemistry and Western blotting analysis or ELISA.</p> <p>Results</p> <p>CD105 was positively stained mostly in a subset of microvessels 'endothelial sprouts' in TT of all patients while CD105 showed diffuse positive staining, predominantly on hepatic sinus endothelial cells in the surrounding of draining veins in TF and AT. The mean score of MVD-CD105 (mean ± SD/0.74 mm²) was 19.00 ± 9.08 in HC, 153.12 ± 53.26 in TF, 191.12 ± 59.17 in AT, and 85.43 ± 44.71 in TT, respectively. Using a paired <it>t </it>test, the expression of CD105 in AT and TF was higher than in TT at protein (MVD, <it>p </it>= 0.012 and <it>p </it>= 0.007, respectively) and mRNA levels (<it>p </it>< 0.001 and <it>p </it>= 0.009, respectively). Moreover, distribution and expression of CD105 protein were consistent with those of HIF-1alpha and VEGF₁₆₅protein in liver of patients with HCC. The level of <it>CD105 </it>mRNA correlated with VEGF₁₆₅level in TF (r = 0.790, <it>p </it>= 0.002), AT (r = 0.723, <it>p </it>< 0.001), and TT (r = 0.473, <it>p </it>= 0.048), respectively.</p> <p>Conclusion</p> <p>It is demonstrated that CD105 was not only present in neovessels in tumor tissues, but also more abundant in hepatic sinus endothelium in non-tumor tissues with cirrhosis. Therefore, CD105 may not be an appropriate targeting for antiangenesis therapy in HCC, especially with cirrhosis.</p

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages