Boosting N2 Conversion into NH3 over Ru Catalysts via Modulating the Ru-Promoter Interface.

Promoters are indispensable components of Ru-based catalysts to promote N2 activation in ammonia (NH3) synthesis. The rational addition and regulation of promoters play a critical role in affecting the NH3 synthesis rate. In this work, we report a simple method by altering the loading sequence of Ba and Ru species to modulate the Ru-promoter interface, thus significantly boosting the NH3 synthesis rate. The Ba-Ru/GC BM catalyst via the prior loading of Ba rather than Ru over graphitic carbon (GC) exhibits a high NH3 synthesis rate of 18.7 mmol gcat-1 h-1 at 400 °C and 1 MPa, which is 2.5 times that of the Ru-Ba/GC BM catalyst via the conventional prior loading of Ru rather than Ba on GC. Our studies reveal that the prior loading of Ba benefits the high dispersion of the basic Ba promoter over an electron-withdrawing GC support, and then Ba species serve as structural promoters to stabilize Ru with small particle sizes, which exposes more active sites for N2 activation. Additionally, the intimate Ba and Ru interface enables facile electron donation from Ba to Ru sites, thus accelerating N2 dissociation to realize efficient NH3 synthesis. This work provides a simple approach to modulating the Ru-promoter interface and maximizing promoter utilization to enhance NH3 synthesis performance.

Development of a radiomics model to diagnose pheochromocytoma preoperatively: a multicenter study with prospective validation.

BACKGROUND: Preoperative diagnosis of pheochromocytoma (PHEO) accurately impacts preoperative preparation and surgical outcome in PHEO patients. Highly reliable model to diagnose PHEO is lacking. We aimed to develop a magnetic resonance imaging (MRI)-based radiomic-clinical model to distinguish PHEO from adrenal lesions. METHODS: In total, 305 patients with 309 adrenal lesions were included and divided into different sets. The least absolute shrinkage and selection operator (LASSO) regression model was used for data dimension reduction, feature selection, and radiomics signature building. In addition, a nomogram incorporating the obtained radiomics signature and selected clinical predictors was developed by using multivariable logistic regression analysis. The performance of the radiomic-clinical model was assessed with respect to its discrimination, calibration, and clinical usefulness. RESULTS: Seven radiomics features were selected among the 1301 features obtained as they could differentiate PHEOs from other adrenal lesions in the training (area under the curve [AUC], 0.887), internal validation (AUC, 0.880), and external validation cohorts (AUC, 0.807). Predictors contained in the individualized prediction nomogram included the radiomics signature and symptom number (symptoms include headache, palpitation, and diaphoresis). The training set yielded an AUC of 0.893 for the nomogram, which was confirmed in the internal and external validation sets with AUCs of 0.906 and 0.844, respectively. Decision curve analyses indicated the nomogram was clinically useful. In addition, 25 patients with 25 lesions were recruited for prospective validation, which yielded an AUC of 0.917 for the nomogram. CONCLUSION: We propose a radiomic-based nomogram incorporating clinically useful signatures as an easy-to-use, predictive and individualized tool for PHEO diagnosis.

Coexpression of TLR9 and VEGF-C is associated with lymphatic metastasis in prostate cancer.

Prostate cancer (PCa) is one of the most frequent cancers in men, and its biomolecular targets have been extensively studied. This study aimed to analyze the expression of toll-like receptor 9 (TLR9) and vascular endothelial growth factor C (VEGF-C) and the clinical value of the coexpression of TLR9 and VEGF-C in PCa. We retrospectively evaluated 55 patients with clinically localized, intermediate-risk, or high-risk PCa who underwent laparoscopic radical prostatectomy (LRP) and extended pelvic lymph node dissection (ePLND) without neoadjuvant hormonal therapy at a single institution from June 2013 to December 2016. In all 55 patients, the median number of lymph nodes (LNs) resected was 23 (range: 18-31), and a total of 1269 LNs were removed, of which 78 LNs were positive. Seventeen patients had positive LNs, with a positive rate of 30.9%. In addition, the immunohistochemical results in the above patients revealed that high TLR9 expression was correlated with higher Gleason score (GS) (P = 0.049), increased LN metastasis (P = 0.004), and more perineural invasion (PNI) (P = 0.033). Moreover, VEGF-C expression was associated with GS (P = 0.040), pathological stage (pT stage) (P = 0.022), LN metastasis (P = 0.003), and PNI (P = 0.001). Furthermore, a significant positive correlation between TLR9 and VEGF-C was found (P < 0.001), and the TLR9/VEGF-C phenotype was associated with LN metastasis (P = 0.047). Collectively, we propose that TLR9 stimulation may promote LN metastasis in PCa cells through the upregulation of VEGF-C expression, thereby affecting the prognosis of PCa patients. Therefore, these markers may serve as valuable targets for the treatment of PCa.

Autophagy-Related Long Non-coding RNA Is a Prognostic Indicator for Bladder Cancer.

Bladder cancer (BC) is one of the most common malignant urinary system tumors, and its prognosis is poor. In recent years, autophagy has been closely linked to the development of BC. Therefore, we investigated the potential prognostic role of autophagy-related long non-coding RNA (lncRNA) in patients with BC. We obtained the lncRNA information and autophagy genes, respectively, from The Cancer Genome Atlas (TCGA) data set and the human autophagy database (HADb) and performed a co-expression analysis to identify autophagy gene-associated lncRNAs. Then, we divided the data into training group and testing group. In the training group, 15 autophagy-related lncRNAs were found to have a prognostic value (AC026369.3, USP30-as1, AC007991.2, AC104785.1, AC010503.4, AC037198.1, AC010331.1, AF131215.6, AC084357.2, THUMPD3-AS1, U62317.4, MAN1B1-DTt, AC024060.1, AL662844.4, and AC005229.4). The patients were divided into low-risk group and high-risk group based on the prognostic lncRNAs. The overall survival (OS) time for the high-risk group was shorter than that for the low-risk group [risk ratio (hazard ratio, HR) = 1.08, 95% CI: 1.06-1.10; p < 0.0001]. Using our model, the defined risk value can predict the prognosis of a patient. Next, the model was assessed in the TCGA testing group to further validate these results. A total of 203 patients with BC were recruited to verify the lncRNA characteristics. We divided these patients into high-risk group and low-risk group. The results of testing data set show that the survival time of high-risk patients is shorter than that of low-risk patients. In the training group, the area under the curve (AUC) was more than 0.7, indicating a high level of accuracy. The AUC for a risk model was greater than that for each clinical feature alone, indicating that the risk value of a model was the best indicator for predicting the prognosis. Further training data analysis showed that the gene set was significantly enriched in cancer-related pathways, including actin cytoskeleton regulation and gap junctions. In conclusion, our 15 autophagy-related lncRNAs have a prognostic potential for BC, and may play key roles in the biology of BC.