Grimesmccarthy3312

Facilitators were more common with live visual communication increasing teamwork and efficiency, the ease of access to neurologist, increased flexibility, and high overall satisfaction and usability. Future research should focus on eliminating these barriers and supporting the distributed cognition of caregivers.The automated and accurate carotid plaque segmentation in B-mode ultrasound (US) is an essential part of stroke risk stratification. Previous segmented methods used AtheroEdge™ 2.0 (AtheroPoint™, Roseville, CA) for the common carotid artery (CCA). This study focuses on automated plaque segmentation in the internal carotid artery (ICA) using solo deep learning (SDL) and hybrid deep learning (HDL) models. The methodology consists of a novel design of 10 types of SDL/HDL models (AtheroEdge™ 3.0 systems (AtheroPoint™, Roseville, CA) with a depth of four layers each. Five of the models use cross-entropy (CE)-loss, and the other five models use Dice similarity coefficient (DSC)-loss functions derived from UNet, UNet+, SegNet, SegNet-UNet, and SegNet-UNet+. The K10 protocol (TrainTest90%10%) was applied for all 10 models for training and predicting (segmenting) the plaque region, which was then quantified to compute the plaque area in mm2. Further, the data augmentation effect was analyzed. The database consisted of 970 ICA B-mode US scans taken from 99 moderate to high-risk patients. Using the difference area threshold of 10 mm2 between ground truth (GT) and artificial intelligence (AI), the area under the curve (AUC) values were 0.91, 0.911, 0.908, 0.905, and 0.898, all with a p-value of less then 0.001 (for CE-loss models) and 0.883, 0.889, 0.905, 0.889, and 0.907, all with a p-value of less then 0.001 (for DSC-loss models). The correlations between the AI-based plaque area and GT plaque area were 0.98, 0.96, 0.97, 0.98, and 0.97, all with a p-value of less then 0.001 (for CE-loss models) and 0.98, 0.98, 0.97, 0.98, and 0.98 (for DSC-loss models). Overall, the online system performs plaque segmentation in less than 1 s. We validate our hypothesis that HDL and SDL models demonstrate comparable performance. SegNet-UNet was the best-performing hybrid architecture.MicroRNAs (miRNAs) are significant regulators in various biological processes. They may become promising biomarkers or therapeutic targets, which provide a new perspective in diagnosis and treatment of multiple diseases. Since the experimental methods are always costly and resource-consuming, prediction of disease-related miRNAs using computational methods is in great need. selleck chemicals In this study, we developed MDA-CF to identify underlying miRNA-disease associations based on a cascade forest model. In this method, multi-source information was integrated to represent miRNAs and diseases comprehensively, and the autoencoder was utilized for dimension reduction to obtain the optimal feature space. The cascade forest model was then employed for miRNA-disease association prediction. As a result, the average AUC of MDA-CF was 0.9464 on HMDD v3.2 in five-fold cross-validation. Compared with previous computational methods, MDA-CF performed better on HMDD v2.0 with an average AUC of 0.9258. Moreover, MDA-CF was implemented to investigate colon neoplasm, breast neoplasm, and gastric neoplasm, and 100%, 86%, 88% of the top 50 potential miRNAs were validated by authoritative databases. In conclusion, MDA-CF appears to be a reliable method to uncover disease-associated miRNAs. The source code of MDA-CF is available at https//github.com/a1622108/MDA-CF.

A novel Generative Adversarial Networks (GAN) based bidirectional cross-modality unsupervised domain adaptation (GBCUDA) framework is developed for cardiac image segmentation, which can effectively tackle the problem of network's segmentation performance degradation when adapting to the target domain without ground truth labels.

GBCUDA uses GAN for image alignment, applies adversarial learning to extract image features, and gradually enhances the domain invariance of extracted features. The shared encoder performs an end-to-end learning task in which features that differ between the two domains complement each other. The self-attention mechanism is incorporated to the GAN network, which can generate details based on the prompts of all feature positions. Furthermore, spectrum normalization is implemented to stabilize the training of GAN, and knowledge distillation loss is introduced to process high-level feature-maps in order to better complete the cross-mode segmentation task.

The effectiveness of our proposed unsupervised domain adaptation framework is tested over the Multi-Modality Whole Heart Segmentation (MM-WHS) Challenge 2017 dataset. The proposed method is able to improve the average Dice from 74.1% to 81.5% for the four cardiac substructures, and reduce the average symmetric surface distance (ASD) from 7.0 to 5.8 over CT images. For MRI images, our proposed framework trained on CT images gives the average Dice of 59.2% and reduces the average ASD from 5.7 to 4.9.

The evaluation results demonstrate our method's effectiveness on domain adaptation and the superiority to the current state-of-the-art domain adaptation methods.

The evaluation results demonstrate our method's effectiveness on domain adaptation and the superiority to the current state-of-the-art domain adaptation methods.Biofilm tolerance to antibiotics has led to the search for new alternatives in treating biofilms. The use of metallic nanoparticles has been a suggested strategy against biofilms, but their potential environmental toxicity and high cost of synthesizing have limited their applications. In this study, we investigate the potential of polysaccharidic phytoglycogen nanoparticles extracted from corn, in treating cyanobacterial biofilms, which are the source of toxins and pollution in aquatic environments. Our results revealed that the surface of cyanobacterial cells was dominated by the negatively charged functional groups such as carboxylic and phosphoric groups. The native phytoglycogen (PhX) nanoparticles were dominated with non-charged groups, such as hydroxyl groups, and the cationized phytoglycogen (PhXC) nanoparticles showed positively charged surfaces due to the presence of quaternary ammonium cations. Our results indicated that, as opposed to PhX, PhXC strongly inhibited biofilm formation when dispersed in the culture medium. PhXC also eradicated the already grown cyanobacterial biofilms. The antibiofilm properties of PhXC were attributed to its strong electrostatic interactions with the cyanobacterial cells, which could inhibit cell/cell and cell/substrate interactions and nutrient exchange with the media. This class of antibacterial polysaccharide nanoparticles may provide a novel cost-effective and environment-friendly strategy for treating biofilm formation by a broad spectrum of bacteria.In this paper, a one-dimensional shallow convolutional neural network structure combined with elastic nets (1D-SCNN-EN) was firstly proposed to predict the glucose concentration of blood by Raman spectroscopy. A total of 106 different blood glucose spectra were obtained by Fourier transform (FT) Raman spectroscopy. The one-dimensional shallow convolutional neural network, with elastic nets added to the full connected layer, was presented to capture multiple deep features and reduce the complexity of the model. The 1D-SCNN-EN model has a better performance than conventional approaches (partial least squares and support vector machine). The root mean squared error of calibration (RMSEC), the root mean squared error of prediction (RMSEP), the determination coefficient of prediction (RP2), and the residual predictive deviation of prediction (RPD) were 0.10262, 0.11210, 0.99403, and 12.94601, respectively. The experiment results showed that the 1D-SCNN-EN model has a higher prediction accuracy and stronger robustness than the other regression models. The overall studies indicated that the 1D-SCNN-EN model looked promising for predict the glucose concentration of blood by Raman spectroscopy when the sample size is small.Endometriomas are typically an advanced form of endometriosis that leads to the formation of scar tissue, adhesions, and an inflammatory reaction. There is no certain serum marker for the diagnosis of endometriosis. This study aims to research the correlation between the amount of peaks corresponding to proteins and lipids with the volume of endometrioma and determine the chemical structure of blood serum collected from women suffering from endometriosis patients with endometrioma and healthy subjects using Fourier Transform Infrared (FTIR) spectroscopy. FTIR spectroscopy is used as a non-invasive diagnostic technique for the discrimination of endometriosis women with endometrioma and control blood sera. The FTIR spectra of 100 serum samples acquired from 50 patients and 50 healthy individuals were used for this study. For this purpose, multivariate analyses such as Principal Component Analysis (PCA), Partial Last Square analysis (PLS) with Variables Importance in Projection (VIP), and probability models, were performed. Our results showed that FTIR range 1500 cm-1 and 1700 cm-1 and around 2700 cm-1 - 3000 cm-1, regions may be used for the diagnosis of endometriosis. Also, we find that proteins and lipids fraction increase with the volume of endometrioma. Moreover, PLS and VIP analysis suggested that lipids could be helpful in the diagnosis of endometriosis women with endometrioma.Curcumin is a natural product that is frequently utilized in cancer prevention and treatment. The significant benefit of vegetable-derived nutraceuticals in combination with widespread cytostatic medication such as ponatinib is to reduce toxicity and side effects. In this paper, we focus the study on analytical quantification of ponatinib and curcumin through highly sensitive synchronous spectrofluorometric method. Applying this method at Δλ = 160 nm, each of ponatinib and curcumin could be measured at 303 and 412 nm without interference from each others. The diverse experimental factors impacting the performance of the method were studied and optimized. The method exhibited a reasonable linearity in the ranges of 5.0-60.0 and 10.0-200.0 ng/mL for ponatinib and curcumin, respectively with detection limits of 1.48 and 1.22 ng/mL and quantitation limits of 4.49 and 3.68 ng/mL, respectively. The anticipated method was employed for the assessment and evaluation of the studied drugs in the spiked human plasma samples. The mean % recoveries in plasma samples (n = 6) for each of ponatinib and curcumin were 99.84 ± 1.86 and 100.06 ± 2.72, accordingly. The developed method was validated in conformity with the requirements of International Council of Harmonization (ICH).Hydrogen sulfide (H2S) is an important gasotransmitter in biological system, and plays a crucial role in varied physiological and pathological processes. Exogenous H2S is widely employed as a positive control in H2S related biological study. Herein, we develop a reactive oxygen species (ROS) triggered donor HSD545 that delivers H2S and simultaneously generates a fluorophore to real-time monitoring the process of H2S release in vitro and in vivo. The donor exhibits low cytotoxicity and strong cytoprotection against ROS-induce oxidative stress.