Phosphonate poly(vinylbenzyl chloride)-Modified Sulfonated poly(aryl ether nitrile) for Blend Proton Exchange Membranes: Enhanced Mechanical and Electrochemical Properties.

Blend proton exchange membranes (BPEMs) were prepared by blending sulfonated poly(aryl ether nitrile) (SPAEN) with phosphorylated poly(vinylbenzyl chloride) (PPVBC) and named as SPM-x%, where x refers to the proportion of PPVBC to the weight of SPAEN. The chemical complexation interaction between the phosphoric acid and sulfonic acid groups in the PPVBC-SPAEN system resulted in BPEMs with reduced water uptake and enhanced mechanical properties compared to SPAEN proton exchange membranes. Furthermore, the flame retardancy of the PPVBC improved the thermal stability of the BPEMs. Despite a decrease in ion exchange capacity, the proton conductivity of the BPEMs in the through-plane direction was significantly enhanced due to the introduction of phosphoric acid groups, especially in low relative humidity (RH) environments. The measured proton conductivity of SPM-8% was 147, 98, and 28 mS cm-1 under 95%, 70%, and 50% RH, respectively, which is higher than that of the unmodified SPAEN membrane and other SPM-x% membranes. Additionally, the morphology and anisotropy of the membrane proton conductivities were analyzed and discussed. Overall, the results indicated that PPVBC doping can effectively enhance the mechanical and electrochemical properties of SPAEN membranes.

A deep learning model using hyperspectral image for EUS-FNA cytology diagnosis in pancreatic ductal adenocarcinoma.

BACKGROUND AND AIMS: Endoscopic ultrasonography-guided fine-needle aspiration/biopsy (EUS-FNA/B) is considered to be a first-line procedure for the pathological diagnosis of pancreatic cancer owing to its high accuracy and low complication rate. The number of new cases of pancreatic ductal adenocarcinoma (PDAC) is increasing, and its accurate pathological diagnosis poses a challenge for cytopathologists. Our aim was to develop a hyperspectral imaging (HSI)-based convolution neural network (CNN) algorithm to aid in the diagnosis of pancreatic EUS-FNA cytology specimens. METHODS: HSI images were captured of pancreatic EUS-FNA cytological specimens from benign pancreatic tissues (n = 33) and PDAC (n = 39) prepared using a liquid-based cytology method. A CNN was established to test the diagnostic performance, and Attribution Guided Factorization Visualization (AGF-Visualization) was used to visualize the regions of important classification features identified by the model. RESULTS: A total of 1913 HSI images were obtained. Our ResNet18-SimSiam model achieved an accuracy of 0.9204, sensitivity of 0.9310 and specificity of 0.9123 (area under the curve of 0.9625) when trained on HSI images for the differentiation of PDAC cytological specimens from benign pancreatic cells. AGF-Visualization confirmed that the diagnoses were based on the features of tumor cell nuclei. CONCLUSIONS: An HSI-based model was developed to diagnose cytological PDAC specimens obtained using EUS-guided sampling. Under the supervision of experienced cytopathologists, we performed multi-staged consecutive in-depth learning of the model. Its superior diagnostic performance could be of value for cytologists when diagnosing PDAC.

A Membrane-Embedded Macromolecular Catalyst with Substrate Selectivity in Live Cells.

Substrate selectivity is one of the most attractive features of natural enzymes from their "bind-to-catalyze" working flow and is thus a goal for the development of synthetic enzyme mimics that mediate abiotic transformations. However, despite the recent success in the preparation of substrate-selective enzyme mimics based on single-chain nanoparticles, examples extending such selectivity into living systems have been absent. In this article, we report the in cellulo substrate selectivity of an enzyme-mimicking macromolecular catalyst based on a cationic dense-shell nanoparticle (DSNP) scaffold. With a systematic study on DSNP's structure-activity relationship, we demonstrate that the DSNP has excellent membrane affinity that is governed by several contributing factors, namely, charge density, type of charge, and particle size, and the best-performing phosphonium-rich DSNP can be used as a membrane-embedded catalyst (MEC) for efficient on-membrane synthesis. Importantly, the DSNP catalyst retains its selectivity toward lipophilic and anionic substrates when working as an MEC for on-membrane ligation. The usefulness of such substrate selectivity and on-membrane catalysis strategy was exemplified with several molecules of interest with low cell permeability and anionic nature, which were successfully transported into eukaryotic cells by after their formation directly on the cell membrane.