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Objective To investigate the independent risk factors among preoperative systemic inflammatory indicators associated with lymph node metastasis (LNM) in patients with colorectal cancer (CRC) and to construct and validate a related risk prediction model. Methods Clinical data of 241 patients with CRC who received surgery at Affiliated Xinhua Hospital of Dalian University from January 2012 to December 2017 were retrospective analyzed. Variable selection was performed using univariate analysis combined with Least Absolute Shrinkage and Selection Operator (LASSO) regression and 10-fold cross-validation. After constructing the best logistic regression model, multivariate analysis was conducted to determine the independent risk factors for preoperative LNM in CRC, and a nomogram was developed. The model was internally validated using the Bootstrap method and its predictive performance and clinical utility were evaluated through receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA). Results Univariate analysis and LASSO regression with cross-validation identified smoking history, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), fibrinogen-to-albumin ratio (FAR), and fecal occult blood (FOB) as variables with non-zero coefficients. Multivariate analysis using these factors showed that smoking history (OR=2.669, 95%CI: 1.158-6.150, P=0.021), high NLR(OR=1.895, 95%CI: 1.379-2.605, P＜0.001), low LMR (OR=0.907, 95%CI: 0.823-0.999, P=0.048), high FAR (OR=1.145, 95%CI: 1.062-1.235, P＜0.001), and positive FOB (OR=2.289, 95%CI: 1.132-4.630, P=0.021) were independent risk factors for LNM in CRC (P＜0.05). The ROC curve, calibration curve, and DCA curve indicated that the nomogram constructed in this study provided benefits to patients. Conclusion The risk predictive model constructed in this study demonstrated good predictive performance and clinical utility for preoperatively identifying LNM in CRC patients.

The production of ammonia (NH3) from nitrogen sources involves competitive adsorption of different intermediates and multiple electron and proton transfers, presenting grand challenges in catalyst design. In nature nitrogenases reduce dinitrogen to NH3 using two component proteins, in which electrons and protons are delivered from Fe protein to the active site in MoFe protein for transfer to the bound N2. We draw inspiration from this structural enzymology, and design a two‐component metal–sulfur–carbon (M−S−C) catalyst composed of sulfur‐doped carbon‐supported ruthenium (Ru) single atoms (SAs) and nanoparticles (NPs) for the electrochemical reduction of nitrate (NO3−) to NH3. The catalyst demonstrates a remarkable NH3 yield rate of ~37 mg L−1 h−1 and a Faradaic efficiency of ~97 % for over 200 hours, outperforming those consisting solely of SAs or NPs, and even surpassing most reported electrocatalysts. Our experimental and theoretical investigations reveal the critical role of Ru SAs with the coordination of S in promoting the formation of the HONO intermediate and the subsequent reduction reaction over the NP‐surface nearby. Such process results in a more energetically accessible pathway for NO3− reduction on Ru NPs co‐existing with SAs. This study proves a better understanding of how M−S−Cs act as a synthetic nitrogenase mimic during ammonia synthesis, and contributes to the future mechanism‐based catalyst design. [ABSTRACT FROM AUTHOR]