Selective CO2 -to-Syngas Conversion Enabled by Bimetallic Gold/Zinc Sites in Partially Reduced Gold/Zinc Oxide Arrays.

Electrocatalytic CO2 -to-syngas (gaseous mixture of CO and H2 ) is a promising way to curb excessive CO2 emission and the greenhouse gas effect. Herein, we present a bimetallic AuZn@ZnO (AuZn/ZnO) catalyst with high efficiency and durability for the electrocatalytic reduction of CO2 and H2 O, which enables a high Faradaic efficiency of 66.4 % for CO and 26.5 % for H2 and 3 h stability of CO2 -to-syngas at -0.9 V vs. the reversible hydrogen electrode (RHE). The CO/H2 ratios show a wide range from 0.25 to 2.50 over a narrow potential window (-0.7 V to -1.1 V vs. RHE). In situ attenuated total reflection surface-enhanced infrared absorption spectroscopy combined with density functional theory calculations reveals that the bimetallic synergistic effect between Au and Zn sites lowers the activation energy barrier of CO2 molecules and facilitates electronic transfer, further highlighting the potential to control CO/H2 ratios for efficient syngas production using the coexisting Au sites and Zn sites.

Stabilizing Undercoordinated Zn Active Sites through Confinement in CeO2 Nanotubes for Efficient Electrochemical CO2 Reduction.

Zn-based catalysts hold great potential to replace the noble metal-based ones for CO2 reduction reaction (CO2 RR). Undercoordinated Zn (Znδ+ ) sites may serve as the active sites for enhanced CO production by optimizing the binding energy of *COOH intermediates. However, there is relatively less exploration into the dynamic evolution and stability of Znδ+ sites during CO2 reduction process. Herein, we present ZnO, Znδ+ /ZnO and Zn as catalysts by varying the applied reduction potential. Theoretical studies reveal that Znδ+ sites could suppress HER and HCOOH production to induce CO generation. And Znδ+ /ZnO presents the highest CO selectivity (FECO 70.9 % at -1.48 V vs. RHE) compared to Zn and ZnO. Furthermore, we propose a CeO2 nanotube with confinement effect and Ce3+ /Ce4+ redox to stabilize Znδ+ species. The hollow core-shell structure of the Znδ+ /ZnO/CeO2 catalyst enables to extremely expose electrochemically active area while maintaining the Znδ+ sites with long-time stability. Certainly, the target catalyst affords a FECO of 76.9 % at -1.08 V vs. RHE and no significant decay of CO selectivity in excess of 18 h.

Performance of Molecular Lymphosonography for Detection and Quantification of Metastatic Involvement in Sentinel Lymph Nodes.

OBJECTIVES: To assess the performance of molecular lymphosonography with dual-targeted microbubbles in detecting and quantifying the metastatic involvement in sentinel lymph nodes (SLNs) using a swine melanoma model. METHODS: Targeted microbubbles were labeled with P-selectin and αV ß3 -integrin antibodies. Control microbubbles were labeled with immunoglobulin G antibodies. First lymphosonography with Sonazoid (GE Healthcare, Oslo, Norway) was used to identify SLNs. Then dual-targeted and control microbubbles were injected intravenously to detect and quantify metastatic disease in the SLNs. Distant non-SLNs were imaged as benign controls. All evaluated lymph nodes (LNs) were surgically removed, and metastatic involvement was characterized by a histopathologic analysis. Two radiologists blinded to histopathologic results assessed the baseline B-mode images of LNs, and the results were compared to the histologic reference standard. The mean intensities of targeted and control microbubbles within the examined LNs were measured and compared to the LN histologic results. RESULTS: Thirty-five SLNs and 34 non-SLNs from 13 Sinclair swine were included in this study. Twenty-one SLNs (62%) were malignant, whereas 100% of non-SLNs were benign. The sensitivity of B-mode imaging for metastatic LN diagnosis for both readers was relatively high (90% and 71%), but the specificity was very poor (50% and 58%). The sensitivity and specificity of molecular lymphosonography for metastatic LN detection were 91% and 67%, respectively. The mean intensities from dual-targeted microbubbles correlated well with the degree of metastatic LN involvement (r = 0.6; P < 0.001). CONCLUSIONS: Molecular lymphosonography can increase the specificity of metastatic LN detection and provide a measure to quantify the degree of metastatic involvement.

Association of interleukin 10 rs1800896 polymorphism with susceptibility to breast cancer: a meta-analysis.

OBJECTIVE: To evaluate the correlation between interleukin 10 (IL-10) -1082A/G polymorphism (rs1800896) and breast cancers by performing a meta-analysis. METHODS: The Embase and Medline databases were searched through 1 September 2018 to identify qualified articles. Odds ratios (OR) and corresponding 95% confidence intervals (CIs) were applied to evaluate associations. RESULTS: In total, 14 case-control studies, including 5320 cases and 5727 controls, were analyzed. We detected significant associations between the IL10 -1082 G/G genotype and risk of breast cancer (AA + AG vs. GG: OR = 0.88, 95% CI = 0.80-0.97). Subgroup analyses confirmed a significant association in Caucasian populations (OR = 0.89, 95% CI = 0.80-0.99), in population-based case-control studies (OR = 0.87, 95% CI = 0.78-0.96), and in studies with ≥500 subjects (OR = 0.88, 95% CI = 0.79-0.99) under the recessive model (AA + AG vs. GG). No associations were found in Asian populations. CONCLUSIONS: The IL10 -1082A/G polymorphism is associated with an increased risk of breast cancer. The association between IL10 -1082 G/G genotype and increased risk of breast cancer is more significant in Caucasians, in population-based studies, and in larger studies.

Machine Learning by Ultrasonography for Genetic Risk Stratification of Thyroid Nodules.

Importance: Thyroid nodules are common incidental findings. Ultrasonography and molecular testing can be used to assess risk of malignant neoplasm. Objective: To examine whether a model developed through automated machine learning can stratify thyroid nodules as high or low genetic risk by ultrasonography imaging alone compared with stratification by molecular testing for high- and low-risk mutations. Design, Setting, and Participants: This diagnostic study was conducted at a single tertiary care urban academic institution and included patients (n = 121) who underwent ultrasonography and molecular testing for thyroid nodules from January 1, 2017, through August 1, 2018. Nodules were classified as high risk or low risk on the basis of results of an institutional molecular testing panel for thyroid risk genes. All thyroid nodules that underwent genetic sequencing for cytological results with Bethesda System categories III and IV were reviewed. Patients without diagnostic ultrasonographic images within 6 months of fine-needle aspiration or who received definitive treatment at an outside medical center were excluded. Main Outcomes and Measures: Thyroid nodules were categorized by the model as high risk or low risk using ultrasonographic images. Results were compared using genetic testing. Results: Among the 134 lesions identified in 121 patients (mean [SD] age, 55.7 [14.2] years; 102 women [84.3%]), 683 diagnostic ultrasonographic images were selected. Of the 683 images, 556 (81.4%) were used for training the model, 74 (10.8%) for validation, and 53 (7.8%) for testing. Most nodules had no mutation (75 [56.0%]), whereas 43 nodules (32.1%) had a high-risk mutation and 16 (11.9%) had an unknown or a low-risk mutation (χ2 = 39.060; P < .001). In total, 228 images (33.4%) were of nodules classified as genetically high risk (n = 43), and 455 (66.6%) were of low-risk nodules (n = 91). The model performed with a sensitivity of 45% (95% CI, 23.1%-68.5%), a specificity of 97% (95% CI, 84.2%-99.9%), a positive predictive value of 90% (95% CI, 55.2%-98.5%), a negative predictive value of 74.4% (95% CI, 66.1%-81.3%), and an overall accuracy of 77.4% (95% CI, 63.8%-97.7%). Conclusions and Relevance: The study found that the model developed through automated machine learning could produce high specificity for identifying nodules with high-risk mutations on molecular testing. This finding shows promise for the diagnostic applications of machine learning interpretation of sonographic imaging of indeterminate thyroid nodules.