Ying Cheng

Donor-derived infection (DDI), particularly when involving multidrug-resistant organisms (MDROs), represents a formidable barrier in liver transplantation, contributing significantly to high graft discard rates amid the global organ shortage. Conventional static cold storage (SCS) remains inadequate for infected grafts, as it lacks the capacity for dynamic functional assessment or targeted therapeutic intervention, frequently necessitating the exclusion of these high-risk organs. Machine perfusion (MP) has emerged as a transformative strategy, preserving liver grafts under physiological conditions while effectively mitigating ischemia-reperfusion injury (IRI). Beyond its preservation benefits, the integration of antibiotic therapy into MP platforms facilitates a conceptual shift toward active ex vivo pharmacologic decontamination. By leveraging continuous vascular flow, MP enhances intrahepatic antibiotic delivery efficiency and distribution uniformity, creating a potent anti-infective barrier that suppresses microbial proliferation and reduces pathogen-induced inflammation, all while avoiding the systemic toxicity associated with conventional drug administration. Despite this promise, clinical implementation faces hurdles, including the absence of standardized perfusion protocols, delayed diagnostic turnaround times, and concerns regarding potential hepatotoxicity from high-dose antibiotics. Future progress hinges on the integration of rapid diagnostic technologies like metagenomic sequencing and the development of novel anti-infective agents. Ultimately, precision antimicrobial management via MP offers a viable pathway to transform infection-exposed livers from marginal to safely transplantable resources, thereby expanding the donor pool and improving recipient outcomes.

Background Hepatocellular carcinoma (HCC) currently poses a formidable threat to human life and health, and an observable increase in the number of deaths is evident year by year. Currently, surgical resection stands as the foremost treatment modality; however, recurrence remains a persistent challenge, posing a significant barrier to the long-term prognosis for individuals diagnosed with HCC. Studies indicated that the risk of HCC may be influenced by inflammatory proteins and immune cells, but the associations between inflammatory proteins, immune cells, and HCC remained unclear. Methods The investigation integrated data from 731 types of circulating immune cells and 91 inflammatory proteins, alongside a cohort involving 456,348 participants (comprising 456,220 controls and 128 cases) sourced from genome-wide association studies (GWAS). The principal objective of our research was to assess the potential causal association between inflammatory proteins and HCC by bidirectional univariate MR (UVMR) analysis. Furthermore, the total genetic prediction effect of immune cells-mediating inflammatory proteins on the likelihood of developing HCC was investigated by a two-step multivariable MR (MVMR). Results Our results indicated that 2-positive inflammatory proteins (IL-17A and TNF-β) suggest a potential causal relationship on HCC, and HCC could affect FGF-21 by bidirectional UVMR analysis. Additionally, four immune cell types (CD25 on IgD+ CD38dim B cells, CD4 on CD39+ secreting CD4 regulatory T cells, CD25 on B cells, and CD25 on IgD+ B cells) exhibited an inverse relationship with the risk of HCC. Moreover, two inflammatory proteins demonstrated dual effects on HCC risk through modulation—either decreasing or increasing—the aforementioned four immune cell types, each with varying proportions of mediation effects as analyzed through two-step mediation MR analysis. Conclusion This study revealed the potential causality between inflammatory proteins, immune cells, and HCC risk by MR analyses, which may potentially offer a deeper comprehension for the risk of HCC and the interaction between inflammatory proteins, immune cells and HCC and may help to seek new biomarkers for predicting the likelihood of developing HCC.

Delayed graft function (DGF) is a severe complication following kidney transplantation, and currently, there is a lack of accurate prediction tools tailored for the Chinese population. This study integrates data from 1,093 kidney transplant cases across four medical centers in China (2016–2024) to develop and validate a machine learning-based model for DGF prediction. By comparing nine machine learning algorithms, we found that the LightGBM model performed best in external validation (AUC = 0.80, accuracy = 0.73). SHAP analysis identified donor GFR, donor hemoglobin, and recipient plasma BNP levels as the primary predictive factors, while also highlighting novel predictors such as donor microscopic hematuria and APTT. Cox regression analysis showed that preoperative dialysis duration in recipients (HR = 1.006, 95% CI: 1.001–1.012) was an independent predictor of DGF recovery. In the follow-up study, we observed that while the DGF mortality group exhibited the most significant kidney function impairment (serum creatinine β = 200.57, eGFR β = -39.91), the prognosis of the DGF survival group was comparable to that of the non-DGF survival group. Additionally, the duration of DGF (16.66 ± 13.73 vs. 15.44 ± 14.62 days) and the number of dialysis treatments (8.13 ± 7.39 vs. 7.78 ± 7.22 sessions) were not significantly associated with prognosis. Based on these findings, we developed an online prediction platform (www.kidney-dgf-match.cn) to support clinical decision-making. This study not only establishes the first high-precision DGF prediction model for the Chinese population but also reveals the potential for favorable outcomes in DGF patients with proper management, offering new insights for optimizing post-transplant management strategies.

Background: Hepatocellular carcinoma (HCC) ranks among the most prevalent and lethal malignancies worldwide. Histone modifications (HMs) play a pivotal role in the initiation and progression of HCC. However, our understanding of HMs in HCC remains limited due to the disease’s heterogeneity and the complexity of HMs.Methods: We integrated multi-omics data from multiple cohorts, including single-cell RNA sequencing, bulk RNA sequencing, and clinical information. Weighted gene co-expression network analysis (WGCNA) and consensus clustering were employed to identify histone-related genes. We developed a histone modification-related signature (HMRS) using 117 machine learning methods. Comprehensive analyses of molecular characteristics, immune landscape, and drug sensitivity associated with the HMRS were performed.Results: Through integrative analysis, we defined 110 histone-related genes and identified 45 HCC-HM-related genes (HCC-HMRgenes). The HMRS demonstrated robust prognostic value across multiple cohorts. Patients with high HMRS scores exhibited distinct genomic alterations, including higher tumor heterogeneity and TP53 mutations. The high-risk group showed enrichment in cell cycle, DNA repair, and metabolic pathways. Immune landscape analysis revealed significant differences in immune cell infiltration and pathway activities between high- and low-risk groups. Drug sensitivity prediction suggested potential therapeutic strategies for different risk groups.Conclusion: Our study provides a comprehensive understanding of HMs in HCC and establishes a robust prognostic signature. The HMRS not only stratifies patients into distinct risk groups but also offers insights into underlying molecular mechanisms, immune characteristics, and potential therapeutic strategies, paving the way for personalized medicine in HCC.

The oral delivery of nano-drug delivery systems (Nano-DDS) remains a challenge. Taking inspirations from viruses, here we construct core–shell mesoporous silica nanoparticles (NPs, ~80 nm) with virus-like nanospikes (VSN) to simulate viral morphology, and further modified VSN with L-alanine (CVSN) to enable chiral recognition for functional bionics. By comparing with the solid silica NPs, mesoporous silica NPs and VSN, we demonstrate the delivery advantages of CVSN on overcoming intestinal sequential barriers in both animals and human via multiple biological processes. Subsequently, we encapsulate indomethacin (IMC) into the nanopores of NPs to mimic gene package, wherein the payloads are isolated from bio-environments and exist in an amorphous form to increase their stability and solubility, while the chiral nanospikes multi-sited anchor and chiral recognize on the intestinal mucosa to enhance the penetrability and ultimately improve the oral adsorption of IMC. Encouragingly, we also prove the versatility of CVSN as oral Nano-DDS.

Aim: This study investigated whether ischemic postconditioning (IPO) improved the outcome of organs from donors after cardiac death and had a synergistic effect with hypothermic machine perfusion (HMP) in a pig liver transplantation model.Methods: A donor after cardiac death (DCD) model was developed in 48 healthy Bama miniature pigs randomly divided into four groups: simple cold storage group (SCS group), IPO group, HMP group, HMP–IPO group. The levels of serum alanine aminotransferase (ALT), total bilirubin, histopathological findings, apoptotic activity of hepatocytes, international normalized ratio (INR), tumor necrosis factor‐α (TNF‐α), and Malondialdehyde (MDA) were compared.Results: All recipients in the SCS group died within 6 h after transplantation. The livers of the recipients in the IPO had 50% survival on day 5. HMP allowed 83.3% survival and HMP–IPO allowed 100% survival. After reperfusion, the recipients in the IPO and HMP–IPO group had lower ALT and total bilirubin levels, less Suzuki score, less apoptosis, and less injury to hepatocytes and biliary ducts and attenuated inflammatory response and oxidative load.Conclusions: IPO improved the outcome of organs from donors after cardiac death and had a synergistic effect with HMP in the pig liver transplantation model.