Assembly of Silver Nanoparticles into Hollow Spheres Using Eu(III) Compound based on Trifluorothenoyl-Acetone

The preparation of luminescent silver hollow spheres using Eu(III) compound based on trifluorothenoyl-acetone is described. The structure and size of silver hollow spheres were determined by TEM images. The result shows the formation of hollow structure and average size of the silver hollow spheres (0.9 μm). The silver hollow spheres were further characterized by UV absorption spectrum, SNOM and SEM images, suggesting them to be formed by self-assemble of some isolated silver nanoparticles. The luminescent properties of them were also investigated and they are shown to be high emission strength; moreover, they offer the distinct advantage of a lower packing density compared with other commercial luminescent products

HAI and NAI titer correlates of inactivated and live attenuated influenza vaccine efficacy

Abstract Background High hemagglutination inhibition (HAI) and neuraminidase inhibition (NAI) titers are generally associated with reduced influenza risk. While repeated influenza vaccination reduces seroresponse, vaccine effectiveness is not always reduced. Methods During the 2007-2008 influenza season, a randomized, placebo-controlled trial (FLUVACS) evaluated the efficacies of live-attenuated (LAIV) and inactivated influenza vaccines (IIV) among healthy adults aged 18-49 in Michigan; IIV vaccine efficacy (VE) and LAIV VE against influenza disease were estimated at 68% and 36%. Using the principal stratification/VE moderation framework, we analyzed data from this trial to assess how each VE varied by HAI or NAI responses to vaccination observed for vaccinated individuals and predicted counterfactually for placebo recipients. We also assessed how each VE varied with pre-vaccination/baseline variables including HAI titer, NAI titer, and vaccination history. Results IIV VE appeared to increase with Day 30 post-vaccination HAI titer, albeit not significantly (p=0.20 and estimated VE 14.4%, 70.5%, and 85.5% at titer below the assay lower quantification limit, 512, and 4096 (maximum)). Moreover, IIV VE increased significantly with Day 30 post-vaccination NAI titer (p=0.040), with estimated VE zero at titer 10 and 92.2% at highest titer 640. There was no evidence that fold-change in post-vaccination HAI or NAI titer associated with IIV VE (p=0.76, 0.38). For LAIV, there was no evidence that VE associated with post-vaccination or fold-rise HAI or NAI titers (p-values >0.40). For IIV, VE increased with increasing baseline NAI titer in those previously vaccinated, but VE decreased with increasing baseline NAI titer in those previously unvaccinated. In contrast, for LAIV, VE did not depend on previous vaccination or baseline HAI or NAI titer. Conclusions: Future efficacy trials should measure baseline and post-vaccination antibody titers in both vaccine and control/placebo recipients, enabling analyses to better elucidate correlates of vaccine- and natural-protection. Trial registration: ClinicalTrials.gov NCT00538512. October 1, 2007.https://deepblue.lib.umich.edu/bitstream/2027.42/149182/1/12879_2019_Article_4049.pd

The Formation and Stabilization of a Novel G-Quadruplex in the 5′-Flanking Region of the Relaxin Gene

It has been reported that binding of STAT3 protein to the 5′-flanking region of the relaxin gene may result in downregulation of the relaxin expression. There is a Guanine(G)-rich segment located in about 3.8 Kb upstream of the relaxin gene and very close to the STAT3's binding site. In our study, NMR spectroscopy revealed the formation of G-quadruplex by this G-rich strand, and the result was confirmed by ESI mass spectrometry and CD spectroscopy. The theoretical structure of RLX G-quadruplex was constructed and refined by molecular modeling. When this relaxin G-quadruplex was stabilized by berberine(ΔTm = 10°C), a natural alkaloid from a Chinese herb, the gene expression could be up-regulated in a dose-dependent manner which was proved by luciferase assay. This result is different from the general G-quadruplex function that inhibiting the telomere replication or down-regulating many oncogenes expression. Therefore, our study reported a novel G-quadruplex in the relaxin gene and complemented the regulation mechanism about gene expression by G-quadruplexes

Cardiomyocyte overexpression of miR-27b induces cardiac hypertrophy and dysfunction in mice

Recent studies have begun to reveal critical roles of microRNAs (miRNAs) in the pathogenesis of cardiac hypertrophy and dysfunction. In this study, we tested whether a transforming growth factor-β (TGF-β)-regulated miRNA played a pivotal role in the development of cardiac hypertrophy and heart failure (HF). We observed that miR-27b was upregulated in hearts of cardiomyocyte-specific Smad4 knockout mice, which developed cardiac hypertrophy. In vitro experiments showed that the miR-27b expression could be inhibited by TGF-β1 and that its overexpression promoted hypertrophic cell growth, while the miR-27b suppression led to inhibition of the hypertrophic cell growth caused by phenylephrine (PE) treatment. Furthermore, the analysis of transgenic mice with cardiomyocyte-specific overexpression of miR-27b revealed that miR-27b overexpression was sufficient to induce cardiac hypertrophy and dysfunction. We validated the peroxisome proliferator-activated receptor-γ (PPAR-γ) as a direct target of miR-27b in cardiomyocyte. Consistently, the miR-27b transgenic mice displayed significantly lower levels of PPAR-γ than the control mice. Furthermore, in vivo silencing of miR-27b using a specific antagomir in a pressure-overload-induced mouse model of HF increased cardiac PPAR-γ expression, attenuated cardiac hypertrophy and dysfunction. The results of our study demonstrate that TGF-β1-regulated miR-27b is involved in the regulation of cardiac hypertrophy, and validate miR-27b as an efficient therapeutic target for cardiac diseases

Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges

Radiomics leverages existing image datasets to provide non-visible data extraction via image post-processing, with the aim of identifying prognostic, and predictive imaging features at a sub-region of interest level. However, the application of radiomics is hampered by several challenges such as lack of image acquisition/analysis method standardization, impeding generalizability. As of yet, radiomics remains intriguing, but not clinically validated. We aimed to test the feasibility of a non-custom-constructed platform for disseminating existing large, standardized databases across institutions for promoting radiomics studies. Hence, University of Texas MD Anderson Cancer Center organized two public radiomics challenges in head and neck radiation oncology domain. This was done in conjunction with MICCAI 2016 satellite symposium using Kaggle-in-Class, a machine-learning and predictive analytics platform. We drew on clinical data matched to radiomics data derived from diagnostic contrast-enhanced computed tomography (CECT) images in a dataset of 315 patients with oropharyngeal cancer. Contestants were tasked to develop models for (i) classifying patients according to their human papillomavirus status, or (ii) predicting local tumor recurrence, following radiotherapy. Data were split into training, and test sets. Seventeen teams from various professional domains participated in one or both of the challenges. This review paper was based on the contestants' feedback; provided by 8 contestants only (47%). Six contestants (75%) incorporated extracted radiomics features into their predictive model building, either alone (n = 5; 62.5%), as was the case with the winner of the “HPV” challenge, or in conjunction with matched clinical attributes (n = 2; 25%). Only 23% of contestants, notably, including the winner of the “local recurrence” challenge, built their model relying solely on clinical data. In addition to the value of the integration of machine learning into clinical decision-making, our experience sheds light on challenges in sharing and directing existing datasets toward clinical applications of radiomics, including hyper-dimensionality of the clinical/imaging data attributes. Our experience may help guide researchers to create a framework for sharing and reuse of already published data that we believe will ultimately accelerate the pace of clinical applications of radiomics; both in challenge or clinical settings

Threshold models improve estimates of molt parameters in datasets with small sample sizes

The timing of events in birds\u27 annual cycles is important to understanding life history evolution and response to global climate change. Molt timing is often measured as an index of the sum of grown feather proportion or mass within the primary flight feathers. The distribution of these molt data over time has proven difficult to model with standard linear models. The parameters of interest are at change points in model fit over time, and so least-squares regression models that assume molt is linear violate the assumption of even variance. This has led to the introduction of other nonparametric models to estimate molt parameters. Hinge models directly estimate changes in model fit and have been used in many systems to find change points in data distributions. Here, we apply a hinge model to molt timing, through the introduction of a double-hinge (DH) threshold model. We then examine its performance in comparison to current models using simulated and empirical data. Our results suggest that the Underhill-Zucchini (UZ) and Pimm models perform well under many circumstances and appear to outperform the DH model in datasets with high variance. The DH model outperforms the UZ model at low sample sizes of birds in active molt and shorter molt durations and provides more realistic confidence intervals at smaller sample sizes. The DH model provides a novel addition to the toolkit for estimating molt phenology, expanding the conditions under which molt can accurately be estimated. LAY SUMMARY Modeling molt timing has been difficult for researchers, especially when sample sizes of birds in active molt are small. Many species of birds in museum and banding databases are represented by relatively small sample sizes of birds in active molt. We apply a threshold model approach by developing a novel double-hinge threshold model that estimates onset and termination of molt. We test this new model and existing models by comparing performance on simulated datasets and find that the threshold model performs well with small sample sizes of birds in active molt. We also demonstrate the threshold model on empirical datasets and make this model available in an update to the existing R package cnhgpt