Mesoporous carbon spheres with programmable interiors as efficient nanoreactors for H2O2 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. Impressively, the pivotal achievement lies in the successful screening of a potent, selective, and durable two-electron oxygen reduction reaction catalyst for the direct synthesis of medical-grade hydrogen peroxide disinfectant. Serving as an exemplary demonstration of nanoreactor engineering in catalyst screening, this work highlights the immense potential of various well-designed carbon-based nanoreactors in extensive applications.

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. The second aim was to develop a visualization tool capable of emphasizing regions of ASR between follow-up scans in individual patients. MATERIALS AND METHODS: Lasso regression and Extreme Gradient Boosting (XGBoost) classifiers were employed for modeling radiomic features extracted from regions of interest delineated by two radiologists. A leave-one-out test (LOOT) was utilized for performance evaluation. A visualization method, creating difference heatmaps (diff-maps) between two follow-up scans, was developed to emphasize regions of growth and thereby highlighting potential ASR. RESULTS: A total of 55 patients, including 20 with and 35 without ASR, were included in the radiomic analysis. The best performing model was achieved by Lasso regression tested with the LOOT approach, reaching an area under the curve (AUC) of 0.97 and an accuracy of 92.73%. The XGBoost classifier demonstrated better performance when trained with all extracted radiomic features than without feature selection, achieving an AUC of 0.93 and an accuracy of 89.09%. The diff-maps correctly highlighted post-ablative liver tumor recurrence in all patients. CONCLUSIONS: Machine learning-based radiomic analysis and growth visualization proved effective in detecting ablation site recurrence on follow-up CT scans.

Nanoengineering of Porous 2D Structures with Tunable Fluid Transport Behavior for Exceptional H2O2 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. As proof of concept, the optimized mesoporous carbon nanosheet sample exhibited exceptional electrocatalytic performance, demonstrating high selectivity (>95%) and a notable diffusion-limiting disk current density of -3.1 mA cm-2 for H2O2 production. Impressively, the electrolysis process in the flow cell achieved a production rate of 14.39 mol gcatalyst-1 h-1 to yield a medical-grade disinfectant-worthy H2O2 solution. Our pore engineering research focuses on modulating oxygen reduction reaction activity and selectivity by affecting local fluid transport behavior, providing insights into the mesoscale catalytic mechanism.

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. A CT-based CNN was trained once on gallbladder only and once on gallbladder including a 2 cm adjacent liver parenchyma. The best-performing classifier was combined with the diagnostic results based on radiological visual analysis. RESULTS: A total of 127 patients were included in the study: 83 patients with benign gallbladder lesions and 44 with gallbladder cancer. The CNN trained on the gallbladder including adjacent liver parenchyma achieved the best performance with an AUC of 0.81 (95% CI 0.71-0.92), being >10% better than the CNN trained on only the gallbladder (p = 0.09). Combining the CNN with radiological visual interpretation did not improve the differentiation between GBC and benign gallbladder diseases. CONCLUSIONS: The CT-based CNN shows promising ability to differentiate gallbladder cancer from benign gallbladder lesions. In addition, the liver parenchyma adjacent to the gallbladder seems to provide additional information, thereby improving the CNN's performance for gallbladder lesion characterization. However, these findings should be confirmed in larger multicenter studies.

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. METHODS: Patients were retrospectively selected based on confirmed histopathological diagnosis and available contrast-enhanced portal venous phase CT-scan. The radiomic features were extracted from the entire gallbladder, then further analyzed by machine learning classifiers based on Lasso regression, Ridge regression, and XG Boosting. The results of the best-performing classifier were combined with radiological visual CT diagnosis and then compared with radiological visual CT assessment alone. RESULTS: In total, 127 patients were included: 83 patients with benign gallbladder lesions and 44 patients with GBC. Among all machine learning classifiers, XG boosting achieved the best AUC of 0.81 (95% CI 0.72-0.91) and the highest accuracy rate of 73% (95% CI 65-80%). When combining radiological visual interpretation and predictions of the XG boosting classifier, the highest diagnostic performance was achieved with an AUC of 0.98 (95% CI 0.96-1.00), a sensitivity of 91% (95% CI 86-100%), a specificity of 93% (95% CI 90-100%), and an accuracy of 92% (95% CI 90-100%). CONCLUSIONS: Machine learning analysis of CT-based radiomic features shows promising results in discriminating benign from malignant gallbladder disease. Combining CT-based radiomic analysis and radiological visual interpretation provided the most optimal strategy for GBC and benign gallbladder disease differentiation. KEY POINTS: Radiomic-based machine learning algorithms are able to differentiate benign gallbladder disease from gallbladder cancer. Combining machine learning algorithms with a radiological visual interpretation of gallbladder lesions at CT increases the specificity, compared to visual interpretation alone, from 73 to 93% and the accuracy from 85 to 92%. Combined use of machine learning algorithms and radiological visual assessment seems the most optimal strategy for GBC and benign gallbladder disease differentiation.

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. Through the use of a GO precursor with a high content of C-O groups, pure doping of the thiophene-sulfur configuration in the rGO can be achieved. Further electrochemical characterization reveals an increased electrocatalytic activity of the pure thiophene-sulfur-doped S-rGO in the oxygen reduction reaction, indicating the important role of thiophene-sulfur. The present work deepens the understanding of the functions of doped nonmetal elements in carbon materials in electrocatalysis and helps in the design of high performance electrocatalysts.

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

Background: 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. Methods: The study design is retrospective. Radiomic features were extracted from both liver and spleen on portal venous phase CT images of 252 consecutive patients with histologically proven liver fibrosis stages between 2006 and 2018. The radiomics analyses for liver fibrosis staging were done by hepatic and hepatic-splenic features, respectively. The most predictive radiomic features were automatically selected by machine learning models. Results: When using splenic-hepatic features in the CT-based radiomics analysis, the average accuracy rates for significant fibrosis, advanced fibrosis, and cirrhosis were 88%, 82%, and 86%, and area under the receiver operating characteristic curves (AUCs) were 0.92, 0.81, and 0.85. The AUC of hepatic-splenic-based radiomics analysis with the ensemble classifier was 7% larger than that of hepatic-based analysis (p < 0.05). The most important features selected by machine learning models included both hepatic and splenic features, and they were consistent with the location maps indicating the focus of deep learning when predicting liver fibrosis stage. Conclusions: Adding CT-based splenic radiomic features to hepatic radiomic features increases radiomics analysis performance for liver fibrosis staging. The most important features of the radiomics analysis were consistent with the information exploited by deep learning.

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. METHODS: The liver fibrosis staging network (LFS network) was developed at contrast-enhanced CT images in the portal venous phase in 252 patients with histologically proven liver fibrosis stage. To give a visual explanation of the diagnostic decisions made by the LFS network, Gradient-weighted Class Activation Mapping (Grad-cam) was used to produce location maps indicating where the LFS network focuses on when predicting liver fibrosis stage. RESULTS: The LFS network had areas under the receiver operating characteristic curve of 0.92, 0.89, and 0.88 for staging significant fibrosis (F2-F4), advanced fibrosis (F3-F4), and cirrhosis (F4), respectively, on the test set. The location maps indicated that the LFS network had more focus on the liver surface in patients without liver fibrosis (F0), while it focused more on the parenchyma of the liver and spleen in case of cirrhosis (F4). CONCLUSIONS: Deep learning methods are able to exploit CT-based information from the liver surface, liver parenchyma, and extrahepatic information to predict liver fibrosis stage. Therefore, we suggest using the entire upper abdomen on CT images when developing deep learning-based liver fibrosis staging algorithms. KEY POINTS: • Deep learning algorithms can stage liver fibrosis using contrast-enhanced CT images, but the algorithm is still used as a black box and lacks transparency. • Location maps produced by Gradient-weighted Class Activation Mapping can indicate the focus of the liver fibrosis staging network. • Deep learning methods use CT-based information from the liver surface, liver parenchyma, and extrahepatic information to predict liver fibrosis stage.

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. Combined with detailed spectroscopic and electrochemical characterizations, the structure-activity relationship is established by correlating the acidic/alkaline HER activity with the average oxidation state of single-atom Pt and the Pt-H/Pt-OH interaction. This study sheds light on the atomic-level mechanistic understanding of acidic and alkaline HER, and further provides guidelines for the rational design of high-performance single-atom catalysts.

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. The site-specific electrodeposition enables the formation of energetically favorable metal-support bonds, and then automatically terminates the sequential formation of metallic bonding. The self-terminating effect restricts the metal deposition to the atomic scale. The modulated ADMCs exhibit remarkable activity and stability in the hydrogen evolution reaction compared to state-of-the-art single-atom electrocatalysts. We demonstrate that this methodology could be extended to the synthesis of a variety of ADMCs (Pt, Pd, Rh, Cu, Pb, Bi, and Sn), showing its general scope for functional ADMCs manufacturing in heterogeneous catalysis.

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. Owing to the synergistic plasmonic photothermal effect (PPTT) of dual plasmonic PTAs, the plasmonic AuNR/GO nanohybrid exhibits remarkably higher photothermal conversion efficiency (PCE, 72.59%) than either individual AuNRs or GO under low laser power density (300 mW), leading to enhanced antibacterial/anticancer PTT. In addition, the synergistic plasmonic antibacterial/anticancer PTT induced by the plasmonic nanohybrid is also far superior to individual PTAs (AuNRs or GO), whereas the flow cytometric analysis of heat shock proteins (HSP 70) clearly dictates that the substantial killing of bacterial pathogens/tumor cells is solely due to the synergistic PPTT. Thus, the plasmonic AuNR/GO nanohybrid is a standalone PTA to perform simultaneous antibacterial/anticancer PTT under low power NIR laser activation for only 5 min, without any systemic side effects. The present study provides a clear demonstration about the potential therapeutic impact of plasmonic nanohybrids and thus will surely pave the way to design other hybrid nanoagents with enhanced PCE and integrate them with chemotherapeutic agents, leading to dual-modal chemo-/photothermal antibacterial/anticancer therapy under low power single laser excitation for a short duration.

In situ growing Bi2MoO6 on g-C3N4 nanosheets with enhanced photocatalytic hydrogen evolution and disinfection of bacteria under visible light irradiation.

Bi2MoO6/g-C3N4 heterojunctions were fabricated by an in situ solvothermal method using g-C3N4 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-C3N4 to g-C3N4 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 Bi2MoO6. Furthermore, intimate contact of Bi2MoO6 (BM) and g-C3N4 nanosheets (CNNs) in the BM/CNNs composites facilitates the transfer and separation of photogenetrated electron-hole pairs. 20%-BM/CNNs heterojunction exhibits the optimal photocatalytic hydrogen evolution as well as photocatalytic disinfection of bacteria. Furthermore, h+ was demonstrated as the dominant reactive species which could make the bacteria cells inactivated in the photocatalytic disinfection process. This study extends new chance of g-C3N4-based photocatalysts to the growing demand of clean new energy and drinking water.