AxonFinder: Automated segmentation of tumor innervating neuronal fibers.

Neurosignaling is increasingly recognized as a critical factor in cancer progression, where neuronal innervation of primary tumors contributes to the disease's advancement. This study focuses on segmenting individual axons within the prostate tumor microenvironment, which have been challenging to detect and analyze due to their irregular morphologies. We present a novel deep learning-based approach for the automated segmentation of axons, AxonFinder, leveraging a U-Net model with a ResNet-101 encoder, based on a multiplexed imaging approach.

Electron-Rich Heptacyclic S,N Heteroacene Enabling C-Shaped A-D-A-type Electron Acceptors With Photoelectric Response beyond 1000 Nm for Highly Sensitive Near-Infrared Photodetectors.

A highly electron-rich S,N heteroacene building block is developed and condensed with FIC and Cl-IC acceptors to furnish CT-F and CT-Cl, which exhibit near-infrared (NIR) absorption beyond 1000 nm. The C-shaped CT-F and CT-Cl self-assemble into a highly ordered 3D intermolecular packing network via multiple π-π interactions in the single crystal structures. The CT-F-based organic photovoltaic (OPV) achieved an impressive efficiency of 14.

Plasma Hepatic Transaminases and Incidence of Metabolic Syndrome Before Midlife in Military Adults: A CHIEF Cohort Study.

Background: Plasma AST and ALT may reflect the nonalcoholic fatty liver disease (NAFLD) severity and have been associated with the risk of MetS in middle- or old-aged individuals.

Aims: This study aimed to examine the associations of plasma hepatic aspartate and alanine transaminases (AST and ALT) levels with incident metabolic syndrome (MetS) in young adults, which have not been verified before.

Objective: The goal of this study was to identify the association between plasma hepatic transaminases and the incidence of new-onset MetS among young adults.

Identifying core symptom clusters based on symptom distress levels in patients with maintenance hemodialysis: a cross-sectional network analysis.

Background: To explore the symptom clusters of patients undergoing maintenance hemodialysis and construct a symptom network to identify the core symptoms and core symptom clusters, to provide reference for precise symptom management.

Methods: Conveniently selected 354 patients with maintenance hemodialysis were surveyed cross-sectionally using the general information questionnaire, the Dialysis Symptom Index and the Kidney Disease Questionnaire. Symptom clusters were extracted using exploratory factor analysis, and core symptom clusters were identified using hierarchical regression and network analysis.

Methanol-Enhanced Low-Cell-Voltage Hydrogen Generation at Industrial-Grade Current Density by Triadic Active Sites of Pt-Pd-(Ni,Co)(OH).

Methanol (ME) is a liquid hydrogen carrier, ideal for on-site-on-demand H generation, avoiding its costly and risky distribution issues, but this "ME-to-H" electric conversion suffers from high voltage (energy consumption) and competitive oxygen evolution reaction. Herein, we demonstrate that a synergistic cofunctional PtPd/(Ni,Co)(OH) catalyst with Pt single atoms (Pt) and Pd nanoclusters (Pd) anchored on OH-vacancy(V)-rich (Ni,Co)(OH) nanoparticles create synergistic triadic active sites, allowing for methanol-enhanced low-voltage H generation. For MOR, OH* is preferentially adsorbed on Pd and then interacts with the intermediates (such as *CHO or *CHOOH) adsorbed favorably on neighboring Pt with the assistance of hydrogen bonding from the surface hydrogen of (Ni,Co)(OH).