Mesoporous carbon spheres with programmable interiors as efficient nanoreactors for HO electrosynthesis.

The nanoreactor holds great promise as it emulates the natural processes of living organisms to facilitate chemical reactions, offering immense potential in catalytic energy conversion owing to its unique structural functionality. Here, we propose the utilization of precisely engineered carbon spheres as building blocks, integrating micromechanics and controllable synthesis to explore their catalytic functionalities in two-electron oxygen reduction reactions. After conducting rigorous experiments and simulations, we present compelling evidence for the enhanced mass transfer and microenvironment modulation effects offered by these mesoporous hollow carbon spheres, particularly when possessing a suitably sized hollow architecture.

Machine learning-based radiomic analysis and growth visualization for ablation site recurrence diagnosis in follow-up CT.

Objectives: Detecting ablation site recurrence (ASR) after thermal ablation remains a challenge for radiologists due to the similarity between tumor recurrence and post-ablative changes. Radiomic analysis and machine learning methods may show additional value in addressing this challenge. The present study primarily sought to determine the efficacy of radiomic analysis in detecting ASR on follow-up computed tomography (CT) scans.

Nanoengineering of Porous 2D Structures with Tunable Fluid Transport Behavior for Exceptional HO Electrosynthesis.

Precision nanoengineering of porous two-dimensional structures has emerged as a promising avenue for finely tuning catalytic reactions. However, understanding the pore-structure-dependent catalytic performance remains challenging, given the lack of comprehensive guidelines, appropriate material models, and precise synthesis strategies. Here, we propose the optimization of two-dimensional carbon materials through the utilization of mesopores with 5-10 nm diameter to facilitate fluid acceleration, guided by finite element simulations.

The Value of Deep Learning in Gallbladder Lesion Characterization.

Background: The similarity of gallbladder cancer and benign gallbladder lesions brings challenges to diagnosing gallbladder cancer (GBC). This study investigated whether a convolutional neural network (CNN) could adequately differentiate GBC from benign gallbladder diseases, and whether information from adjacent liver parenchyma could improve its performance.

Methods: Consecutive patients referred to our hospital with suspicious gallbladder lesions with histopathological diagnosis confirmation and available contrast-enhanced portal venous phase CT scans were retrospectively selected.

Optimal radiological gallbladder lesion characterization by combining visual assessment with CT-based radiomics.

Objectives: Differentiating benign gallbladder diseases from gallbladder cancer (GBC) remains a radiological challenge because they can appear very similar on imaging. This study aimed at investigating whether CT-based radiomic features of suspicious gallbladder lesions analyzed by machine learning algorithms could adequately discriminate benign gallbladder disease from GBC. In addition, the added value of machine learning models to radiological visual CT-scan interpretation was assessed.

Synthesis of Pure Thiophene-Sulfur-Doped Graphene for an Oxygen Reduction Reaction with High Performance.

Various S-bonding configurations existing in sulfur-doped reduced graphene oxide (S-rGO) show different electronic structures and physiochemical properties. Thus, understanding the properties of unique S-bonding configurations requires the construction of S-rGO with only single configuration. Here, we synthesized S-rGO with a pure thiophene-sulfur configuration through a simple and low-cost hydrothermal method by simply controlling the oxidation degree of the graphene oxide (GO) precursor.

Combining Hepatic and Splenic CT Radiomic Features Improves Radiomic Analysis Performance for Liver Fibrosis Staging.

The exact focus of computed tomography (CT)-based artificial intelligence techniques when staging liver fibrosis is still not exactly known. This study aimed to determine both the added value of splenic information to hepatic information, and the correlation between important radiomic features and information exploited by deep learning models for liver fibrosis staging by CT-based radiomics. The study design is retrospective.

Electronic metal-support interaction modulates single-atom platinum catalysis for hydrogen evolution reaction.

Tuning metal-support interaction has been considered as an effective approach to modulate the electronic structure and catalytic activity of supported metal catalysts. At the atomic level, the understanding of the structure-activity relationship still remains obscure in heterogeneous catalysis, such as the conversion of water (alkaline) or hydronium ions (acid) to hydrogen (hydrogen evolution reaction, HER). Here, we reveal that the fine control over the oxidation states of single-atom Pt catalysts through electronic metal-support interaction significantly modulates the catalytic activities in either acidic or alkaline HER.

Liver fibrosis staging by deep learning: a visual-based explanation of diagnostic decisions of the model.

Objectives: Deep learning has been proven to be able to stage liver fibrosis based on contrast-enhanced CT images. However, until now, the algorithm is used as a black box and lacks transparency. This study aimed to provide a visual-based explanation of the diagnostic decisions made by deep learning.

Site-specific electrodeposition enables self-terminating growth of atomically dispersed metal catalysts.

The growth of atomically dispersed metal catalysts (ADMCs) remains a great challenge owing to the thermodynamically driven atom aggregation. Here we report a surface-limited electrodeposition technique that uses site-specific substrates for the rapid and room-temperature synthesis of ADMCs. We obtained ADMCs by the underpotential deposition of a non-noble single-atom metal onto the chalcogen atoms of transition metal dichalcogenides and subsequent galvanic displacement with a more-noble single-atom metal.

Plasmonic Nanohybrid with High Photothermal Conversion Efficiency for Simultaneously Effective Antibacterial/Anticancer Photothermal Therapy.

Plasmonic metal/semiconductor nanohybrids hold great promise in photocatalysis and biosensor development; however, their potential phototherapeutic applications are yet fully unexplored. On the other hand, the demand of high laser power density to induce antibacterial photothermal therapeutic effects greatly restricts the practical applicability of the previously developed photothermal nanoagents (PTAs) for anticancer photothermal therapy (PTT). Here, we develop a plasmonic nanohybrid by integrating plasmonic noble metal gold nanorods (AuNRs) with a two-dimensional graphene oxide (2-D GO), capable to perform photothermal ablation of both bacterial pathogens as well as tumor cells, respectively, under low power single near-infrared (NIR) laser activation.

In situ growing BiMoO on g-CN nanosheets with enhanced photocatalytic hydrogen evolution and disinfection of bacteria under visible light irradiation.

BiMoO/g-CN heterojunctions were fabricated by an in situ solvothermal method using g-CN nanosheets. The photocatalytic activities of as-prepared samples were evaluated by hydrogen evolution from water splitting and disinfection of bacteria under visible light irradiation. The results indicate that exfoliating bulk g-CN to g-CN nanosheets greatly enlarges the specific surface area and shortens the diffusion distance for photogenerated charges, which could not only promote the photocatalytic performance but also benefit the sufficient interaction with BiMoO.