Ultrasensitive SERS Detection of Five ß-Blockers Achieved Using Chemometrics with a Two-Dimensional Substrate Formed by Large-Sized Ag@SiO2 Nanoparticles.

We report on a surface-enhanced Raman scattering (SERS) platform for the detection of five beta-blockers (ß-blockers): atenolol, esmolol, labetalol, sotalol, and propranolol. Key to this platform was a two-dimensional substrate formed by self-assembling large Ag@SiO2 nanoparticles (Ag@SiO2 NPs) on a silicon wafer. The close arrangement of these large nanoparticles on the surface generated a strong and uniform electromagnetic field, which enhanced SERS signal intensity for the detection of small amounts of the target molecules. The intensities of characteristic peaks of the five ß-blocker drugs increased linearly with the increase of their concentrations in the range of 10-5 to 10-8 mol/L. The detection limits were 10-10 mol/L for propranolol, 10-9 mol/L for atenolol, labetalol, and sotalol, and 10-8 mol/L for esmolol. Determination of these five ß-blocker drugs added to human urine samples, using a portable Raman spectroscopy instrument, showed quantitative recovery (93-101%). Principal component analysis (PCA) and hierarchical cluster analysis (HCA) of SERS spectral data improved the differentiation among these five ß-blockers. This study highlights the potential of the developed SERS platform for rapid, on-site detection of illicit drugs and for antidoping screening.

Towards more precise automatic analysis: a systematic review of deep learning-based multi-organ segmentation.

Accurate segmentation of multiple organs in the head, neck, chest, and abdomen from medical images is an essential step in computer-aided diagnosis, surgical navigation, and radiation therapy. In the past few years, with a data-driven feature extraction approach and end-to-end training, automatic deep learning-based multi-organ segmentation methods have far outperformed traditional methods and become a new research topic. This review systematically summarizes the latest research in this field. We searched Google Scholar for papers published from January 1, 2016 to December 31, 2023, using keywords "multi-organ segmentation" and "deep learning", resulting in 327 papers. We followed the PRISMA guidelines for paper selection, and 195 studies were deemed to be within the scope of this review. We summarized the two main aspects involved in multi-organ segmentation: datasets and methods. Regarding datasets, we provided an overview of existing public datasets and conducted an in-depth analysis. Concerning methods, we categorized existing approaches into three major classes: fully supervised, weakly supervised and semi-supervised, based on whether they require complete label information. We summarized the achievements of these methods in terms of segmentation accuracy. In the discussion and conclusion section, we outlined and summarized the current trends in multi-organ segmentation.

Enantioselective Copper-Catalyzed Intermolecular Cyanobenzoyldifluoromethylation of Alkenes: Access to Chiral ß-Difluoroacyl Nitriles.

A novel asymmetric copper-catalyzed intermolecular cyanobenzoyldifluoromethylation of alkenes with iododifluoromethyl ketones and TMSCN has been reported, which provides a particularly valuable route to access chiral ß-difluoroacyl nitriles with excellent enantioselectivities. The method permits the efficient cyanation of varied ß-difluoroacyl-benzylic radicals in mild conditions with high functional group tolerance. The reaction proceeds through a radical pathway. In order to get insight into the stereochemical outcome, computational mechanistic studies were conducted.

Auditing road safety achievement using MEREC-ARAS-QBKM model: an empirical study for APEC member economies.

Progress monitoring and action recalibrations are advocated as promising methods for improving road safety, which significantly relates to economic stability and social development. To achieve this, an auditing framework that can evaluate road safety and aid in policymaking is urgently required. To this end, this study developed a systemic decision model that integrates the method based on the removal effects of criteria (MEREC), additive ratio assessment (ARAS), and quantile-based k-means clustering (QBKM), termed MEREC-ARAS-QBKM, with the aim of auditing road safety achievements and providing corresponding policy suggestions with substantial reliability. In particular, the performance of the traditional k-means clustering model was improved by implanting quantiles to determine the initial clustering, which overcomes the uncertainty of k-means clustering owing to the variety of initial cluster centers. Multiple comparisons of empirical results based on a case study of the Asia-Pacific Economic Cooperation (APEC) member economies verified the robustness of the proposed model, demonstrating its applicability, practicability, and reliability in handling real-world multi-criteria decision-making problems in the field of road safety. The empirical findings show that road safety developments among the APEC countries are of class differentiation, suggesting an urgent regional benchmarking. Overall, the proposed methodology empowers decision-makers and policymakers in APEC to swiftly formulate effective action plans, countermeasures, and investment schemes, ultimately contributing to the enhancement of road safety performance and socio-economic benefit across APEC members.

Segmentation of Portal Vein in Multiphase CTA Image Based on Unsupervised Domain Transfer and Pseudo Label.

BACKGROUND: Clinically, physicians diagnose portal vein diseases on abdominal CT angiography (CTA) images scanned in the hepatic arterial phase (H-phase), portal vein phase (P-phase) and equilibrium phase (E-phase) simultaneously. However, existing studies typically segment the portal vein on P-phase images without considering other phase images. METHOD: We propose a method for segmenting portal veins on multiphase images based on unsupervised domain transfer and pseudo labels by using annotated P-phase images. Firstly, unsupervised domain transfer is performed to make the H-phase and E-phase images of the same patient approach the P-phase image in style, reducing the image differences caused by contrast media. Secondly, the H-phase (or E-phase) image and its style transferred image are input into the segmentation module together with the P-phase image. Under the constraints of pseudo labels, accurate prediction results are obtained. RESULTS: This method was evaluated on the multiphase CTA images of 169 patients. The portal vein segmented from the H-phase and E-phase images achieved DSC values of 0.76 and 0.86 and Jaccard values of 0.61 and 0.76, respectively. CONCLUSION: The method can automatically segment the portal vein on H-phase and E-phase images when only the portal vein on the P-phase CTA image is annotated, which greatly assists in clinical diagnosis.