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Hepatitis B surface antigen (HBsAg) persists after liver transplantation in almost all patients receiving HBsAg-positive grafts. Chronic hepatitis B virus (HBV) infection is one of the main causes of hepatocellular carcinoma (HCC). We aimed to investigate possible interactions between HBsAg-positive donors, HCC, HBV-related transplant indication, and long-term outcomes. This retrospective study enrolled 1176 patients from two centers between January 2015 and May 2019, of which 135 (11.5%) were HBsAg-positive and 1041 (88.5%) were HBsAg-negative donors. Cox regression models were fitted to study the association between variables and patient and graft survival. In univariate and multivariate analyses, the donor HBsAg status was not significantly associated with patient and graft survival in the entire cohort, but there was a significant interaction between HBsAg-positive donors and HCC, independent of HBV-related transplant indication. The cumulative incidence of patient and graft survival was significantly lower in the subgroup of HCC recipients receiving HBsAg-positive grafts, but no significant difference was found in recipients with benign liver disease. In a subgroup analysis of HCC recipients, HBsAg-positive donors were significantly associated with an increased risk of HCC recurrence (hazard ratio: 1.73; 95% confidence interval: 1.20-2.48; p = 0.003) and similar results were obtained after propensity score matching analysis. We showed excellent outcomes of using HBsAg-positive grafts in patients with benign liver disease, regardless of HBV-related transplant indications. However, positive grafts should be used with caution in recipients with HCC, which are associated with an increased risk of HCC recurrence.

A new double-layer, pH-sensitive, composite hydrogel sustained-release system based on polysaccharides and synthetic polymers with combined functions of different inner/outer hydrogels was prepared. The polysaccharides inner core based on sodium alginate (SA) and carboxymethyl cellulose (CMC), was formed by physical crosslinking with pH-sensitive property. The synthetic polymer out-layer with enhanced stability was introduced by chemical crosslinking to eliminate the expansion of inner core and the diffusion of inner content. The physicochemical structure of the double-layer hydrogels was characterized. The drug-release results demonstrated that the sustained-release effect of the hydrogels for different model drugs could be regulated by changing the composition or thickness of the hydrogel layer. The significant sustained-release effect for BSA and indomethacin indicated that the bilayer hydrogel can be developed into a novel sustained delivery system for bioactive substance or drugs with potential applications in drugs and functional foods.

Highlights • The predictive model used preoperatively accessible clinical parameters and radiographic features developed and validated by us to predict micro vascular invasion (MVI), based on a large sample, two Liver Transplantation (LT) centers observed 5 years among Hepatocellular Carcinoma (HCC) patients who underwent LT. • This is the first study to report preoperative clinical variables and radiographic features for preoperative prediction of MVI among HCC patients undergoing LT. • Prediction of the presence of MVI can help surgical decision-making and improve surgical management for HCC to further distinguish clinical outcomes., Purpose The prediction of microvascular invasion (MVI) has increasingly been recognized to reflect prognosis involving local invasion and distant metastasis of hepatocellular carcinoma (HCC). The aim of this study was to assess a predictive model using preoperatively accessible clinical parameters and radiographic features developed and validated to predict MVI. This predictive model can distinguish clinical outcomes after liver transplantation (LT) for HCC patients. Methods In total, 455 HCC patients who underwent LT between January 1, 2015, and December 31, 2019, were retrospectively enrolled in two centers in China as a training cohort (ZFA center; n = 244) and a test cohort (SLA center; n = 211). Univariate and multivariate backward logistic regression analysis were used to select the significant clinical variables which were incorporated into the predictive nomogram associated with MVI. Receiver operating characteristic (ROC) curves based on clinical parameters were plotted to predict MVI in the training and test sets. Results Univariate and multivariate backward logistic regression analysis identified four independent preoperative risk factors for MVI: α-fetoprotein (AFP) level (p

Profound organ shortages worldwide have led to the increased utilization of marginal organs from older individuals. However, the effectiveness of liver transplantation (LT) with organs from elderly donors for patients with hepatocellular carcinoma (HCC) remains controversial. The objective of the current study was to assess the overall survival (OS) and disease-free survival (DFS) of patients with HCC following LT using grafts from deceased donors over 60 years old.Patients with HCC who underwent LT between 2015 and 2018 were identified in the China Liver Transplant Registry database. The overall survival and disease-free survival of older liver donors (OLDs) were compared with those of younger liver donors (YLDs) after propensity score matching.From January 2015 to December 2018, a total of 4971 HCC patients were enrolled in the study according to the screening criteria. The absolute and relative utilization of liver grafts from elderly patients over 60 years for HCC patients increased every year, from 65 (9.3%) in 2015 to 268 (14.5%) in 2018. Disease-free survival (DFS) was significantly lower in HCC patients with elderly donors (both P 0.05) after propensity score matching. The OLD group had worse DFS than YLD group if patients had tumors beyond the Milan criteria (P 0.05).The use of older donors for LT has been growing quickly in the last few years in China. Grafts from older donors can be safely used in HCC recipients with similar OS and comparable perioperative complications. However, further investigation into whether older donor has an impact on recurrence is warranted, especially among those with tumors beyond the Milan criteria.

Background: We aimed to investigate whether D-dimer and fibrinogen levels could predict prognosis of patients with hepatocellular carcinoma (HCC) following liver transplantation. Methods: From January 2015 to January 2020, we conducted a study on patients with hepatitis B-related liver cancer. Two hundred seventy (270) liver transplant recipients were recruited. Considering D-dimer and plasma fibrinogen levels, a model was established to predict liver cancer recurrence following liver transplantation. Subsequent verification was performed on a validation cohort of 295 recipients from two other hospitals. Results: Elevated D-dimer and plasma fibrinogen levels demonstrated independent correlation between overall survival and tumour-free survival among patients with HCC who underwent liver transplantation. Those who had preoperative fibrinogen ≥2.27 g/L had significantly reduced overall survival and tumour-free survival than those who had preoperative fibrinogen

Deep Neural Networks (DNNs) usually work in an end-to-end manner. This makes the trained DNNs easy to use, but they remain an ambiguous decision process for every test case. Unfortunately, the interpretability of decisions is crucial in some scenarios, such as medical or financial data mining and decision-making. In this paper, we propose a Tree-Network-Tree (TNT) learning framework for explainable decision-making, where the knowledge is alternately transferred between the tree model and DNNs. Specifically, the proposed TNT learning framework exerts the advantages of different models at different stages: (1) a novel James&ndash, Stein Decision Tree (JSDT) is proposed to generate better knowledge representations for DNNs, especially when the input data are in low-frequency or low-quality, (2) the DNNs output high-performing prediction result from the knowledge embedding inputs and behave as a teacher model for the following tree model, and (3) a novel distillable Gradient Boosted Decision Tree (dGBDT) is proposed to learn interpretable trees from the soft labels and make a comparable prediction as DNNs do. Extensive experiments on various machine learning tasks demonstrated the effectiveness of the proposed method.

Acute kidney injury (AKI) is a common complication after liver transplantation (LT) and is an indicator of poor prognosis. The establishment of a more accurate preoperative prediction model of AKI could help to improve the prognosis of LT. Machine learning algorithms provide a potentially effective approach.A total of 493 patients with donation after cardiac death LT (DCDLT) were enrolled. AKI was defined according to the clinical practice guidelines of kidney disease: improving global outcomes (KDIGO). The clinical data of patients with AKI (AKI group) and without AKI (non-AKI group) were compared. With logistic regression analysis as a conventional model, four predictive machine learning models were developed using the following algorithms: random forest, support vector machine, classical decision tree, and conditional inference tree. The predictive power of these models was then evaluated using the area under the receiver operating characteristic curve (AUC).The incidence of AKI was 35.7% (176/493) during the follow-up period. Compared with the non-AKI group, the AKI group showed a remarkably lower survival rate (P 0.001). The random forest model demonstrated the highest prediction accuracy of 0.79 with AUC of 0.850 [95% confidence interval (CI): 0.794-0.905], which was significantly higher than the AUCs of the other machine learning algorithms and logistic regression models (P 0.001).The random forest model based on machine learning algorithms for predicting AKI occurring after DCDLT demonstrated stronger predictive power than other models in our study. This suggests that machine learning methods may provide feasible tools for forecasting AKI after DCDLT.

While cloud computing is touted as a promising information technology advancement, predictions of its value are inconclusive. This research investigates the impact of cloud computing within-firm and across-firm. Drawing on the resource-based view and sociotechnical theory, technical attributes and social attributes of cloud computing are identified to impact firm performance via primary and support use. Results from data collected from 513 firms show varying effects of technical and social attributes in primary use and support use, which help create value and better performance. Such effects also are found to differ between firms in service and manufacturing sectors.

Orthogonal circulant matrix transform precoding (OCT) combined with peak-clipping based PAPR reduction technique is firstly proposed for ACO-OFDM system and experimentally demonstrated in an IM/DD system with a cost-efficient directly modulated laser.