Highly Dispersed Pd-CeOx Nanoparticles in Zeolite Nanosheets for Efficient CO2-Mediated Hydrogen Storage and Release.

Formic acid (FA) dehydrogenation and CO2 hydrogenation to FA/formate represent promising methodologies for the efficient and clean storage and release of hydrogen, forming a CO2-neutral energy cycle. Here, we report the synthesis of highly dispersed and stable bimetallic Pd-based nanoparticles, immobilized on self-pillared silicalite-1 (SP-S-1) zeolite nanosheets using an incipient wetness co-impregnation technique. Owing to the highly accessible active sites, effective mass transfer, exceptional hydrophilicity, and the synergistic effect of the bimetallic species, the optimized PdCe0.2/SP-S-1 catalyst demonstrated unparalleled catalytic performance in both FA dehydrogenation and CO2 hydrogenation to formate. Remarkably, it achieved a hydrogen generation rate of 5974 molH2 molPd-1 h-1 and a formate production rate of 536 molformate molPd-1 h-1 at 50 °C, surpassing most previously reported heterogeneous catalysts under similar conditions. Density functional theory calculations reveal that the interfacial effect between Pd and cerium oxide clusters substantially reduces the activation barriers for both reactions, thereby increasing the catalytic performance. Our research not only showcases a compelling application of zeolite nanosheet-supported bimetallic nanocatalysts in CO2-mediated hydrogen storage and release but also contributes valuable insights towards the development of safe, efficient, and sustainable hydrogen technologies.

Metabolic signatures of tear extracellular vesicles caused by herpes simplex keratitis.

PURPOSE: Herpes simplex keratitis (HSK), caused by type 1 herpes simplex virus (HSV) reactivation, is a severe infectious disease that leads to vision loss. HSV can trigger metabolic reprogramming in the host cell and change the extracellular vesicles (EV) cargos; however, little is known about the EV metabolic signatures during ocular HSV infection. Here, we aimed to depict the EV-associated metabolic landscape in HSK patients' tears. METHODS: We collected 82 samples from 41 participants with unilateral HSK (contralateral unaffected tears were set as negative control), including subtype cohorts of 13 epithelial, 20 stromal, and 8 endothelial HSK. We isolated tear EVs via our previously established platform and conducted metabolic analysis using LC-MS/MS. The metabolic signatures for recognizing HSK and subtypes were assessed through differential analysis and machine learning algorithms. RESULTS: Hypopsia and increased extracellular CD63 levels were observed in affected eyes. We identified 339 metabolites based on sEVs isolated from tears. Differential analysis revealed alterations in energy and amino acid metabolism, as well as the infectious microenvironment. Furthermore, we observed dysregulated metabolite such as methyldopa, which is associated with inappropriate neovascularization and corneal sensation loss, contributing to the HSK severity particularly in the stromal subtype. Moreover, machine learning classification also suggested a set of EV metabolic signatures that have potential for pan-keratitis detection. CONCLUSIONS: Our findings demonstrate that tear EV metabolites can serve as valuable indicators for comprehending the underlying pathological mechanisms. This knowledge is expected to facilitate the development of liquid biopsy means and therapeutic target discovery.

A nomogram based on clinicopathological and ultrasound characteristics to predict central neck lymph node metastases in papillary thyroid cancer.

Purpose: Papillary thyroid cancer (PTC) has grown rapidly in prevalence over the past few decades, and central neck lymph node metastasis (CNLNM) is associated with poor prognoses. However, whether to carry out preventive central neck lymph node dissection (CNLND) is still controversial. We aimed to construct a prediction model of CNLNM to facilitate making clinical surgical regimens. Methods: A total of 691 patients with PTC between November 2018 and December 2021 were included in our study. Univariate and multivariate analyses were performed on basic information and clinicopathological characteristics, as well as ultrasound characteristics (American College of Radiology (ACR) scores). The prediction model was constructed and performed using a nomogram, and then discriminability, calibrations, and clinical applicability were evaluated. Results: Five variables, namely, male, age >55 years, clinical lymph node positivity, tumor size ≥1 cm, and ACR scores ≥6, were independent predictors of CNLNM in the multivariate analysis, which were eventually included to construct a nomogram model. The area under the curve (AUC) of the model was 0.717, demonstrating great discriminability. A calibration curve was developed to validate the calibration of the present model by bootstrap resampling, which indicated that the predicted and actual values were in good agreement and had no differentiation from the ideal model. The decision curve analysis (DCA) indicated that the prediction model has good clinical applicability. Conclusions: Our non-invasive prediction model combines ACR scores with clinicopathological features presented through nomogram and has shown good performance and application prospects for the prediction of CNLNM in PTCs.