The current supply of acceptable donor livers is not sufficient to meet the demands of listed patients awaiting transplantation resulting in thousands of deaths each year. Increased utilization of marginal livers may help alleviate this supply/demand mismatch by expanding the donor liver pool. The current status of liver transplantation using marginal donor grafts and efforts to optimize usage are discussed with attention to elderly donors, steatotic livers, donors after circulatory death, and split liver grafts.

We have generated human hepatocyte spheroids with uniform size and shape by co-culturing 1∶1 mixtures of primary human hepatocytes (hHeps) from partial hepatectomy specimens and human adipose-derived stem cells (hADSCs) in concave microwells. The hADSCs in spheroids could compensate for the low viability and improve the functional maintenance of hHeps. Co-cultured spheroids aggregated and formed compact spheroidal shapes more rapidly, and with a significantly higher viability than mono-cultured spheroids. The liver-specific functions of co-cultured spheroids were greater, although they contained half the number of hepatocytes as mono-cultured spheroids. Albumin secretion by co-cultured spheroids was 10% higher on day 7, whereas urea secretion was similar, compared with mono-cultured spheroids. A quantitative cytochrome P450 assay showed that the enzymatic activity of co-cultured spheroids cultured for 9 days was 28% higher than that of mono-cultured spheroids. These effects may be due to the transdifferentiation potential and paracrine healing effects of hADSCs on hHeps. These co-cultured spheroids may be useful for creating artificial three-dimensional hepatic tissue constructs and for cell therapy with limited numbers of human hepatocytes.

Liver transplantation is a highly complex and challenging field of clinical practice. Although it was originally developed in western countries, it has been further advanced in Asian countries through the use of living donor liver transplantation. This method of transplantation is the only available option in many countries in the Asia-Pacific region due to the lack of deceased organ donation. As a result of this clinical situation, there is a growing need for guidelines that are specific to the Asia-Pacific region. These guidelines provide comprehensive recommendations for evidence-based management throughout the entire process of liver transplantation, covering both deceased and living donor liver transplantation. In addition, the development of these guidelines has been a collaborative effort between medical professionals from various countries in the region. This has allowed for the inclusion of diverse perspectives and experiences, leading to a more comprehensive and effective set of guidelines.

Previous studies have indicated that the model for end-stage liver disease (MELD) score may fail to predict post-transplantation patient survival. Similarly, other scores (donor MELD score, balance of risk score) that have been developed to predict transplant outcomes have not gained widespread use. These scores are typically derived using linear statistical models. This study aimed to compare the performance of traditional statistical models with machine learning approaches for predicting survival following liver transplantation. Data were obtained from 785 deceased donor liver transplant recipients enrolled in the Korean Organ Transplant Registry (2014-2019). Five machine learning methods (random forest, artificial neural networks, decision tree, naïve Bayes, and support vector machine) and four traditional statistical models (Cox regression, MELD score, donor MELD score and balance of risk score) were compared to predict survival. Among the machine learning methods, the random forest yielded the highest area under the receiver operating characteristic curve (AUC-ROC) values (1-month = 0.80; 3-month = 0.85; and 12-month = 0.81) for predicting survival. The AUC-ROC values of the Cox regression analysis were 0.75, 0.86, and 0.77 for 1-month, 3-month, and 12-month post-transplant survival, respectively. However, the AUC-ROC values of the MELD, donor MELD, and balance of risk scores were all below 0.70. Based on the variable importance of the random forest analysis in this study, the major predictors associated with survival were cold ischemia time, donor ICU stay, recipient weight, recipient BMI, recipient age, recipient INR, and recipient albumin level. As with the Cox regression analysis, donor ICU stay, donor bilirubin level, BAR score, and recipient albumin levels were also important factors associated with post-transplant survival in the RF model. The coefficients of these variables were also statistically significant in the Cox model (p < 0.05). The SHAP ranges for selected predictors for the 12-month survival were (-0.02,0.10) for recipient albumin, (-0.05,0.07) for donor bilirubin and (-0.02,0.25) for recipient height. Surprisingly, although not statistically significant in the Cox model, recipient weight, recipient BMI, recipient age, or recipient INR were important factors in our random forest model for predicting post-transplantation survival. Machine learning algorithms such as the random forest were superior to conventional Cox regression and previously reported survival scores for predicting 1-month, 3-month, and 12-month survival following liver transplantation. Therefore, artificial intelligence may have significant potential in aiding clinical decision-making during liver transplantation, including matching donors and recipients.

Cytokines are involved in early host defense against pathogen infections. In particular, tumor necrosis factor (TNF) and interferon-gamma (IFN-γ) have critical functions in non-cytopathic elimination of hepatitis B virus (HBV) in hepatocytes. However, the molecular mechanisms and mediator molecules are largely unknown. Here we show that interleukin-32 (IL-32) is induced by TNF and IFN-γ in hepatocytes, and inhibits the replication of HBV by acting intracellularly to suppress HBV transcription and replication. The gamma isoform of IL-32 (IL-32γ) inhibits viral enhancer activities by downregulating liver-enriched transcription factors. Our data are validated in both an in vivo HBV mouse model and primary human hepatocytes. This study thus suggests that IL-32γ functions as intracellular effector in hepatocytes for suppressing HBV replication to implicate a possible mechanism of non-cytopathic viral clearance.