Carboxylic acid cellulose nanocrystals (CNC-COOHs) that have been covalently functionalized (via peptide coupling chemistry) with a range of different hydrophobic groups have been investigated as nanoparticle surfactants to stabilize styrene-in-water nanoemulsions. It is shown that the size and stability of these nanoemulsions depend on both the amount of surface carboxylic acid groups as well as the amount and type of hydrophobic alkyl groups on the CNC surface. Two different biosources for the CNCs, microcrystalline cellulose (MCC) and Miscanthus x. Giganteus (MxG), were investigated to see the effect that the CNC aspect ratio has on these nanoemulsions. Stable oil-in-water (o/w) Pickering emulsions with particle diameters of only a few hundred nanometers can be accessed using these hydrophobic functionalized CNCs, and the resulting emulsions can be polymerized to access nanometer sized latexes. The hydrophobic/hydrophilic balance of the functionalized CNCs was found to be critical to lower the interfacial tension between oil and water, which allowed access to stable emulsions with droplet diameters <1 μm. The ability to stabilized nanosized emulsions and latexes extends the potential of CNCs as green surfactants for numerous technological applications, such as food, cosmetics, drug delivery systems, and coatings.

BackgroundFetal heart rate (FHR) monitoring is a screening tool used by obstetricians to evaluate the fetal state. Because of the complexity and non-linearity, a visual interpretation of FHR signals using common guidelines usually results in significant subjective inter-observer and intra-observer variability. Objective: Therefore, computer aided diagnosis (CAD) systems based on advanced artificial intelligence (AI) technology have recently been developed to assist obstetricians in making objective medical decisions.MethodsIn this work, we present an 8-layer deep convolutional neural network (CNN) framework to automatically predict fetal acidemia. After signal preprocessing, the input 2-dimensional (2D) images are obtained using the continuous wavelet transform (CWT), which provides a better way to observe and capture the hidden characteristic information of the FHR signals in both the time and frequency domains. Unlike the conventional machine learning (ML) approaches, this work does not require the execution of complex feature engineering, i.e., feature extraction and selection. In fact, 2D CNN model can self-learn useful features from the input data with the prerequisite of not losing informative features, representing the tremendous advantage of deep learning (DL) over ML.ResultsBased on the test open-access database (CTU-UHB), after comprehensive experimentation, we achieved better classification performance using the optimal CNN configuration compared to other state-of-the-art methods: the averaged ten-fold cross-validation of the accuracy, sensitivity, specificity, quality index defined as the geometric mean of the sensitivity and specificity, and the area under the curve yielded results of 98.34, 98.22, 94.87, 96.53 and 97.82%, respectivelyConclusionsOnce the proposed CNN model is successfully trained, the corresponding CAD system can be served as an effective tool to predict fetal asphyxia objectively and accurately.

As a member of the Forkhead box protein family, Forkhead box Q1 (FOXQ1) is a transcription factor that functions to regulate cell differentiation. Recently, an increasing number of studies have demonstrated that FOXQ1 is significantly associated with the pathogenesis of tumors. This review aims to predominantly discuss the relationship between FOXQ1 and various types of tumor. The FOXQ1 gene is located at human chromosome 6p25.3 and encodes a functional 403 amino acid protein, which has many physiological functions, including promoting epithelial differentiation, inhibiting smooth muscle differentiation, activating T cells and autoimmunity, and controlling mucin gene expression and granule content in stomach surface mucous cells. There are several modes of regulation of FOXQ1 expression that have been demonstrated in normal and tumor cells, such as microRNA and the Wnt signaling pathway. The activation of FOXQ1 affects downstream genes promoting the initiation, proliferation and invasion, in addition to the metastasis of tumor cells. Amongst these, the regulation of invasion and metastasis by FOXQ1 is the most extensively studied. The detailed mechanism involves angiogenesis, tumor re-initiation, alterations in the tumor microenvironment and epithelial-mesenchymal transition. In a number of studies, the expression of FOXQ1 has been reported to be upregulated in breast, colorectal, pancreatic, bladder and ovarian cancer, and glioma, amongst other tumor types. Together, these studies contribute to cancer diagnostics, prognostics and therapeutics. In conclusion, the application prospect of FOXQ1 in tumors is hopeful.

It has been well confirmed ox-LDL plays key roles in the development of atherosclerosis via binding to LOX-1 and inducing apoptosis in vascular endothelial cells. Recent studies have shown ox-LDL can suppress microRNA has-let-7g, which in turn inhibits the ox-LDL induced apoptosis. However, details need to be uncovered. To determine the anti-atherosclerosis effect of microRNA has-let-7g, and to evaluate the possibility of CASP3 as an anti-atherosclerotic drug target by has-let-7g, the present study determined the role of hsa-let-7g miRNA in ox-LDL induced apoptosis in the vascular endothelial cells. We found that miRNA has-let-7g was suppressed during the ox-LDL-induced apoptosis in EAhy926 endothelial cells. In addition, overexpression of has-let-7g negatively regulated apoptosis in the endothelial cells by targeting caspase-3 expression. Therefore, miRNA let-7g may play important role in endothelial apoptosis and atherosclerosis.

For both the acquisition of mobile electrocardiogram (ECG) devices and early warning and diagnosis of clinical work, high-quality ECG signals is particularly important. We describe an effective system which could be deployed as a stand-alone signal quality assessment algorithm for vetting the quality of ECG signals. The proposed ECG quality assessment method is based on the simple heuristic fusion and fuzzy comprehensive evaluation of the SQIs. This method includes two modules, i.e., the quantification and extraction of Signal Quality Indexes (SQIs) for different features, intelligent assessment and classification. First, simple heuristic fusion is executed to extract SQIs and determine the following SQIs: R peak detection match qSQI, QRS wave power spectrum distribution pSQI, kurtosis kSQI, and baseline relative power basSQI. Then, combined with Cauchy distribution, rectangular distribution and trapezoidal distribution, the membership function of SQIs was quantified, and the fuzzy vector was established. The bounded operator was selected for fuzzy synthesis, and the weighted membership function was used to perform the assessment and classification. The performance of the proposed method was tested on the database from Physionet ECG database, with an accuracy (Acc) of 97.67%, sensitivity (Se) of 96.33% and specificity (Sp) of 98.33% on the training set. Testing against the test datasets resulted in scores of 94.67, 90.33, and 93.00%, respectively. There's no gold standard exists for determining the quality of ECGs. However, the proposed algorithm discriminates between high- and poor-quality ECGs, which could aid in ECG acquisition for mobile ECG devices, early clinical diagnosis and early warning.

Background Racial/ethnic minority groups remain underrepresented in clinical trials. Many strategies to increase minority recruitment focus on minority communities, and emphasize common diseases such as hypertension. Scant literature focuses on minority recruitment to trials of less common conditions, often conducted in specialty clinics, and dependent on physician referrals. We identified trust/mistrust of specialist physician investigators and institutions conducting medical research and consequent participant reluctance to participate in clinical trials as key-shared barriers across racial/ethnic groups. We developed a trust-based continuous quality improvement (CQI) intervention to build trust between specialist physician investigators and community minority-serving physicians and ultimately potential trial participants. To avoid the inherent biases of non-randomized studies, we evaluated the intervention in the national Randomized Recruitment Intervention Trial (RECRUIT). This report presents the design of RECRUIT. Specialty clinic follow-up continues through April 2017. Methods We hypothesized that specialist physician investigators and coordinators trained in the trust-based CQI intervention would enroll a greater proportion of minority participants in their specialty clinics than specialist physician investigators in control specialty clinics. Specialty clinic was the unit of randomization. Using CQI, the specialist physician investigators and coordinators tailored recruitment approaches to their specialty clinic characteristics and populations. Primary analyses were adjusted for clustering by specialty clinic within parent trial and matching covariates. Results RECRUIT was implemented in four multi-site clinical trials (parent trials) supported by three NIH Institutes and included 50 associated specialty clinics from these parent trials. Using current data, we have 88% power or greater to detect a 0.15 or greater difference from the currently observed control proportion adjusting for clustering. We detected no differences in baseline matching criteria between intervention and control specialty clinics (all p-values >0.17). Conclusions RECRUIT was the first multi-site randomized control trial to examine the effectiveness of a trust-based CQI intervention to increase minority recruitment into clinical trials. RECRUIT’s innovations included its focus on building trust between specialist investigators and minority-serving physicians, the use of CQI to tailor the intervention to each specialty clinic’s specific racial/ethnic populations and barriers to minority recruitment, and the use of specialty clinics from more than one parent multi-site trial to increase generalizability. The effectiveness of the RECRUIT intervention will be determined after the completion of trial data collection and planned analyses.

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