Time from symptom onset to surgery ≤72 h, preoperative PaO2/FiO2 ≤300, white blood cell count >15 000/μl and deep hypothermic circulatory arrest time >25 min were found to be independently associated with hypoxaemia after surgery for acute type A aortic dissection.

Objective: Prediction of acute renal failure (ARF) and paraplegia after thoracoabdominal aortic aneurysm repair (TAAAR) is helpful for decision-making during the postoperative phase. To find a more efficient method for making a prediction, we performed tests on the efficacy of different machine learning predicting models.Methods: Perioperative TAAAR data were retrospectively collected from Beijing Anzhen Hospital and Shanghai DeltaHealth Hospital. Operations were conducted under normothermia using a four-branched graft. Four commonly used machine learning classification models (ie, logistic regression, linear and Gaussian kernel support vector machine, and random forest) were chosen to predict ARF and paraplegia separately. The efficacy of the models was validated by five-fold crossvalidation.Results: From 2009 to 2017, 212 TAAARs were performed. ARF was identified in 27 patients, and paraplegia was found in 18 patients. Five-fold cross-validation showed that among the four classification models, random forest was the most precise model for predicting ARF, with an average area under the curve (AUC) of 0.89 ± 0.08. Linear support vector machine was the most precise model for predicting paraplegia, with an average AUC of 0.89 ± 0.18. The prediction program has been uploaded to GitHub for open access. Conclusion:Machine learning models can precisely predict ARF and paraplegia during early stages after surgery. This program allows cardiac surgeons to address complications earlier and may help improve the clinical outcomes of TAAAR.

DC-SIGN is previously focused on its physiologic and pathophysiologic roles in immune cells. Little is known about whether DC-SIGN is expressed in malignant epithelial cells and how DC-SIGN participates in tumor progression. Here we showed that DC-SIGN expression was increased in metastatic colorectal cancer (CRC) cell lines and patient tissues. The overall survival in CRC patients with positive DC-SIGN was remarkably reduced. Gain of DC-SIGN function facilitated the CRC metastases both in vitro and in vivo, and this effect was reversed by miR-185. DC-SIGN and Lyn interacted physically, and Lyn maintained the stability of DC-SIGN in cells. DC-SIGN activation recruited Lyn and p85 to form the DC-SIGN-Lyn-p85 complex, which promoted CRC metastasis by increasing PI3K/Akt/β-catenin signaling in tyrosine kinase Lyndependent manner. Furthermore, activation of DC-SIGN promoted the transcription of MMP-9 and VEGF by increasing PI3K/Akt/β-catenin signaling, and induced TCF1/LEF1-mediated suppression of miR-185. Our findings reveal the presence of the DC-SIGN-TCF1/LEF1-miR-185 loop in cancer cells with metastatic traits, implying that it may represent a new pathogenic mechanism of CRC metastasis. This character of the loop promises to provide new targets for blocking CRC invasive and metastatic activity.