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BackgroundLiver transplantation (LT) is one of the main curative treatments for hepatocellular carcinoma (HCC). Milan criteria has long been applied to candidate LT patients with HCC. However, the application of Milan criteria failed to precisely predict patients at risk of recurrence. As a result, we aimed to establish and validate a deep learning model comparing with Milan criteria and better guide post-LT treatment.MethodsA total of 356 HCC patients who received LT with complete follow-up data were evaluated. The entire cohort was randomly divided into training set (n = 286) and validation set (n = 70). Multi-layer-perceptron model provided by pycox library was first used to construct the recurrence prediction model. Then tabular neural network (TabNet) that combines elements of deep learning and tabular data processing techniques was utilized to compare with Milan criteria and verify the performance of the model we proposed.ResultsPatients with larger tumor size over 7 cm, poorer differentiation of tumor grade and multiple tumor numbers were first classified as high risk of recurrence. We trained a classification model with TabNet and our proposed model performed better than the Milan criteria in terms of accuracy (0.95 vs. 0.86, p < 0.05). In addition, our model showed better performance results with improved AUC, NRI and hazard ratio, proving the robustness of the model.ConclusionA prognostic model had been proposed based on the use of TabNet on various parameters from HCC patients. The model performed well in post-LT recurrence prediction and the identification of high-risk subgroups.

BackgroundAnastomotic leakage (AL) is one of the most serious postoperative complications. This study aimed to investigate the predictive value of preoperative body composition for AL in patients with colorectal cancer (CRC).MethodsWe first reviewed data from 3,681 patients who underwent radical CRC resection 2013–2021 in our hospital, and 60 patients were diagnosed with AL after surgery. We designed a nested case-control study and two controls were randomly selected for each case according to the time and position of surgery. Body composition was measured at the level of the third lumbar vertebra based on computed tomography (CT) images. The risk factors of AL were analyzed by univariate and multivariate analysis. Nomogram was built using binary regression analysis and assessed for clinical usefulness, calibration, and discrimination.ResultsIn the multivariate analysis, gender, blood glucose, nutrition risk screening (NRS), skeletal muscle area (SMA) and visceral fat area (VFA) were independent risk factors for developing anastomotic leakage after surgery. The prognostic model had an area under the receiver operating characteristic curve of 0.848 (95% CI, 0.781–0.914). The calibration curve showed good consistency between the predicted and observed outcomes. Decision curve analysis indicated that patients with colorectal cancer can benefit from the prediction model.ConclusionsThe nomogram that combined with gender, blood glucose, NRS, SMA, and VFA had good predictive accuracy and reliability to AL. It may be conveniently for clinicians to predict AL preoperatively and be useful for guiding treatment decisions.

CARD11 functions as a key signaling scaffold that controls antigen-induced lymphocyte activation during the adaptive immune response. Somatic mutations in CARD11 are frequently found in Non-Hodgkin lymphoma, and at least three classes of germline CARD11 mutations have been described as the basis for primary immunodeficiency. In this review, we summarize our current understanding of how CARD11 signals, how its activity is regulated, and how mutations bypass normal regulation to cause disease.

RIG-I and MDA5 are cytoplasmic sensors that recognize different species of viral RNAs, leads to activation of the transcription factors IRF3 and NF-kappaB, which collaborate to induce type I interferons. In this study, we identified REUL, a RING-finger protein, as a specific RIG-I-interacting protein. REUL was associated with RIG-I, but not MDA5, through its PRY and SPRY domains. Overexpression of REUL potently potentiated RIG-I-, but not MDA5-mediated downstream signalling and antiviral activity. In contrast, the RING domain deletion mutant of REUL suppressed Sendai virus (SV)-induced, but not cytoplasmic polyI:C-induced activation of IFN-beta promoter. Knockdown of endogenous REUL by RNAi inhibited SV-triggered IFN-beta expression, and also increased VSV replication. Full-length RIG-I, but not the CARD domain deletion mutant of RIG-I, underwent ubiquitination induced by REUL. The Lys 154, 164, and 172 residues of the RIG-I CARD domain were critical for efficient REUL-mediated ubiquitination, as well as the ability of RIG-I to induce activation of IFN-beta promoter. These findings suggest that REUL is an E3 ubiquitin ligase of RIG-I and specifically stimulates RIG-I-mediated innate antiviral activity.