Trolle Gauthier (thumbshadow84)

Polymer nanostructures have drawn tremendous attention due to their wide applications in nanotechnology. However, the morphology of the polymer nanostructures is fragile under harsh conditions such as high-power irradiation and organic-solution environments during the fabrication or the measurement processes, significantly limiting their potential applications. In this work, we propose and demonstrate a simple approach to improve the stability of polymer nanostructures by coating a conformal ultrathin oxide film via atomic-layer deposition. Due to the refractory and dense coating of the oxide layer, the stability of polymer structures is enhanced by the prohibition of deformation occurrences from thermally induced reflow and organic solution. As a proof of concept, poly(methyl methacrylate) (PMMA) nanostructures coated with a sub-10-nm TiO2layer are demonstrated, and the structures exhibit high temperature stability at 180 °C and good resistance to soluble damage from organic solutions. Subsequently, the mechanism of the improved thermal stability is analyzed via mechanical simulations. Such an effective approach is proposed to significantly broaden the application of polymer nanostructures as functional elements for optical structures/devices that require excellent thermal and chemical stability.Background and objective.Early detection of hepatocellular carcinoma (HCC) is crucial for clinical management. Current studies have reported large HCC detections using automatic algorithms, but there is a lack of research on automatic detection of small HCCs (sHCCs). This study is to investigate the feasibility of automatic detection of sHCC (≤2 cm) based on pattern matching and deep learning (PM-DL) model.Materials and methods. A retrospective study included 5376 image sets from 56 cirrhosis patients (28 sHCC patients with 32 pathologically confirmed lesions and 28 non-HCC cirrhosis patients) in the training-validation cohort to build and validate the model through five-fold cross-validation. In addition, an external test cohort including 6144 image sets from 64 cirrhosis patients (32 sHCC patients with 38 lesions and 32 non-HCC cirrhosis patients) was applied to further verify the generalization ability of the model. The proposed PM-DL model consisted of three main steps 3D co-registration and liver segmentation, screening of suspicious lesions on diffusion-weighted imaging images based on pattern matching algorithm, and identification/segmentation of sHCC lesions on dynamic contrast-enhanced images with convolutional neural network.Results.The PM-DL model achieved a sensitivity of 89.74% and a positive predictive value of 85.00% in the external test cohort for per-lesion analysis. No significant difference was observed in volumes (P= 0.13) and the largest sizes (P= 0.89) between manually delineated and segmented lesions. The DICE coefficient reached 0.77 ± 0.16. Similar performances were identified in the validation cohort. Moreover, the PM-DL model outperformed Liver Imaging Reporting and Data System (LI-RADS) in sensitivity (probable HCCs LR-5 or LR-4,P= 0.18; definite HCCs LR-5,P less then 0.001), with a similar high specificity for per-patient analysis.Conclusion. The PM-DL model may be feasible for accurate automatic detection of sHCC in cirrhotic liver.Objective.Intracortical brain interfaces are an ever evolving technology with growing potential for clinical and research applications. selleck kinase inhibitor The chronic tissue response to these devices traditionally has been characterized by glial scarring, inflammation, oxidative stress, neuronal loss, and blood-brain barrier disruptions. The full complexity of the tissue response to implanted devices is still under investigation.Approach.In this study, we have utilized RNA-sequencing to identify the spatiotemporal gene expression patterns in interfacial (within 100µm) and distal (500µm from implant) brain tissue around implanted silicon microelectrode arrays. Naïve, u