Severe symptoms associated with sepsis syndrome (SS) are considered a severe threat, which not only increases therapeutic difficulty but also causes a prognostic mortality rate. However, at present, few related studies focused on the application of different score scales for disease and prognosis assessment in liver cirrhosis (LC) complicated with SS.

To determine the correlations of the model for end-stage liver disease (MELD), sequential organ failure assessment (SOFA), and modified early warning score (MEWS) points with the prognosis of patients with LC complicated with SS.

This retrospective analysis included 426 LC cases from February 2019 to April 2022. Of them, 225 cases that were complicated with SS were assigned to the LC + SS group, and 201 simple LC cases were included in the LC group. Intergroup differences in MELD, SOFA, and MEWS scores were compared, as well as their diagnostic value for LC + SS. The correlations of the three scores with the prognosis of patients with LC + SS were further analyzed, as well as the related risk factors affecting patients' outcomes, after the follow-up investigation.

MELD, SOFA, and MEWS scores were all higher in the LC + SS group vs the LC group, and their combined assessment for LC + SS revealed a diagnostic sensitivity and a specificity of 89.66% and 90.84%, respectively (P < 0.05). The LC + SS group reported 58 deaths, with an overall mortality rate of 25.78%. Deceased patients presented higher MELD, SOFA, and MEWS points than those who survived (P < 0.05). MELD, SOFA, and MEWS scores were determined by COX analysis as factors independently affecting the prognosis of patients with LC + SS (P < 0.05).

MELD, SOFA, and MEWS effectively diagnosed LC in patients complicated with SS, and they demonstrated great significance in assessing prognosis, which provides a reliable prognosis guarantee for patients with LC + SS. However, their assessment effects remain limited, which is worthy of further investigation by more in-depth and rigorous experimental analysis.

Machine learning (ML), a major branch of artificial intelligence, has not only demonstrated the potential to significantly improve numerous sectors of healthcare but has also made significant contributions to the field of solid organ transplantation. ML provides revolutionary opportunities in areas such as donor-recipient matching, post-transplant monitoring, and patient care by automatically analyzing large amounts of data, identifying patterns, and forecasting outcomes.

To conduct a comprehensive bibliometric analysis of publications on the use of ML in transplantation to understand current research trends and their implications.

On July 18, a thorough search strategy was used with the Web of Science database. ML and transplantation-related keywords were utilized. With the aid of the VOS viewer application, the identified articles were subjected to bibliometric variable analysis in order to determine publication counts, citation counts, contributing countries, and institutions, among other factors.

Of the 529 articles that were first identified, 427 were deemed relevant for bibliometric analysis. A surge in publications was observed over the last four years, especially after 2018, signifying growing interest in this area. With 209 publications, the United States emerged as the top contributor. Notably, the "Journal of Heart and Lung Transplantation" and the "American Journal of Transplantation" emerged as the leading journals, publishing the highest number of relevant articles. Frequent keyword searches revealed that patient survival, mortality, outcomes, allocation, and risk assessment were significant themes of focus.

The growing body of pertinent publications highlights ML's growing presence in the field of solid organ transplantation. This bibliometric analysis highlights the growing importance of ML in transplant research and highlights its exciting potential to change medical practices and enhance patient outcomes. Encouraging collaboration between significant contributors can potentially fast-track advancements in this interdisciplinary domain.

There is an unmet need for optimizing hepatic allograft allocation from nondirected living liver donors (ND-LLD).

Using OPTN living donor liver transplant (LDLT) data (1/1/2000-12/31/2019), we identified 6328 LDLTs (4621 right, 644 left, 1063 left-lateral grafts). Random forest survival models were constructed to predict 10-year graft survival for each of the 3 graft types.

Donor-to-recipient body surface area ratio was an important predictor in all 3 models. Other predictors in all 3 models were: malignant diagnosis, medical location at LDLT (inpatient/ICU), and moderate ascites. Biliary atresia was important in left and left-lateral graft models. Re-transplant was important in right graft models. C-index for 10-year graft survival predictions for the 3 models were: 0.70 (left-lateral); 0.63 (left); 0.61 (right). Similar C-indices were found for 1-, 3-, and 5-year graft survivals. Comparison of model predictions to actual 10-year graft survivals demonstrated that the predicted upper quartile survival group in each model had significantly better actual 10-year graft survival compared to the lower quartiles (p<0.005).

When applied in clinical context, our models assist with the identification and stratification of potential recipients for hepatic grafts from ND-LLD based on predicted graft survivals, while accounting for complex donor-recipient interactions. These analyses highlight the unmet need for granular data collection and machine learning modeling to identify potential recipients who have the best predicted transplant outcomes with ND-LLD grafts.

New methods such as machine learning could provide accurate predictions with little statistical assumptions. We seek to develop prediction model of pediatric surgical complications based on pediatric National Surgical Quality Improvement Program(NSQIP).

All 2012-2018 pediatric-NSQIP procedures were reviewed. Primary outcome was defined as 30-day post-operative morbidity/mortality. Morbidity was further classified as any, major and minor. Models were developed using 2012-2017 data. 2018 data was used as independent performance evaluation.

431,148 patients were included in the 2012-2017 training and 108,604 were included in the 2018 testing set. Our prediction models had high performance in mortality prediction at 0.94 AUC in testing set. Our models outperformed ACS-NSQIP Calculator in all categories for morbidity (0.90 AUC for major, 0.86 AUC for any, 0.69 AUC in minor complications).

We developed a high-performing pediatric surgical risk prediction model. This powerful tool could potentially be used to improve the surgical care quality.

Over the past two decades, understanding of hepatic macrophage biology has provided astounding details of their role in the progression and regression of liver diseases. The hepatic macrophages constitute resident macrophages, Kupffer cells, and circulating bone marrow monocyte-derived macrophages, which play a diverse role in liver injury and repair. Imbalance in the macrophage population leads to pathological consequences and is responsible for the initiation and progression of acute and chronic liver injuries. Further, distinct populations of hepatic macrophages and their high heterogeneity make their complex role enigmatic. The unique features of distinct phenotypes of macrophages have provided novel biomarkers for defining the stages of liver diseases. The distinct mechanisms of hepatic macrophages polarization and recruitment have been at the fore front of research. In addition, the secretome of hepatic macrophages and their immune regulation has provided clinically relevant therapeutic targets.

Herein we have highlighted the current understanding in the area of hepatic macrophages, and their role in the progression of liver injury.

It is essential to ascertain the physiological and pathological role of evolutionarily conserved distinct macrophage phenotypes in different liver diseases before viable approaches may see a clinical translation.

This study aimed to assess the differences between the United States and the United Kingdom in the characteristics and posttransplant survival of patients who received donation after circulatory death (DCD) liver allografts from donors aged >60 y.

Data were collected from the UK Transplant Registry and the United Network for Organ Sharing databases. Cohorts were dichotomized into donor age subgroups (donor >60 y [D >60]; donor ≤60 y [D ≤60]). Study period: January 1, 2001, to December 31, 2015.

1157 DCD LTs were performed in the United Kingdom versus 3394 in the United States. Only 13.8% of US DCD donors were aged >50 y, contrary to 44.3% in the United Kingdom. D >60 were 22.6% in the United Kingdom versus 2.4% in the United States. In the United Kingdom, 64.2% of D >60 clustered in 2 metropolitan centers. In the United States, there was marked inter-regional variation. A total of 78.3% of the US DCD allografts were used locally. One- and 5-y unadjusted DCD graft survival was higher in the United Kingdom versus the United States (87.3% versus 81.4%, and 78.0% versus 71.3%, respectively; P < 0.001). One- and 5-y D >60 graft survival was higher in the United Kingdom (87.3% versus 68.1%, and 77.9% versus 51.4%, United Kingdom versus United States, respectively; P < 0.001). In both groups, grafts from donors ≤30 y had the best survival. Survival was similar for donors aged 41 to 50 versus 51 to 60 in both cohorts.

Compared with the United Kingdom, older DCD LT utilization remained low in the United States, with worse D >60 survival. Nonetheless, present data indicate similar survivals for older donors aged ≤60, supporting an extension to the current US DCD age cutoff.

Advancements based on artificial intelligence have emerged in all areas of medicine. Many decisions in organ transplantation can now potentially be addressed in a more precise manner with the aid of artificial intelligence.

All elements of liver transplantation consist of a set of input variables and a set of output variables. Artificial intelligence identifies relationships between the input variables; that is, how they select the data groups to train patterns and how they can predict the potential outcomes of the output variables. The most widely used classifiers to address the different aspects of liver transplantation are artificial neural networks, decision tree classifiers, random forest, and naïve Bayes classification models. Artificial intelligence applications are being evaluated in liver transplantation, especially in organ allocation, donor-recipient matching, survival prediction analysis, and transplant oncology.

In the years to come, deep learning-based models will be used by liver transplant experts to support their decisions, especially in areas where securing equitability in the transplant process needs to be optimized.