Metabolic dysfunction-associated steatotic liver disease (MASLD) and chronic kidney disease (CKD) have shown a significant increase in comorbidity on a global scale due to the prevalence of metabolic syndrome. In 2023, a number of academic societies formally proposed the concept of MASLD, superseding the previous terminology of “non-alcoholic fatty liver disease” and “metabolic dysfunction-associated fatty liver disease”. The diagnostic criteria have been revised to place greater emphasis on the association between hepatic steatosis and cardiometabolic risk factors. MASLD constitutes an independent risk factor for CKD, with this risk potentially increasing in line with the severity of fatty degeneration and the progression of hepatic fibrosis. CKD may represent a potential risk factor for the progression of fibrosis in patients with MASLD. The interaction between the two conditions may accelerate the occurrence of cardiovascular events and increase the risk of all-cause mortality. MASLD and CKD may share core pathophysiological mechanisms, including genetic variants, insulin resistance, lipid metabolism disorders, chronic inflammation, oxidative stress, and gut microbiota dysbiosis. However, the bidirectional causal relationship between the two conditions and the molecular dialogue between organs remains unclear. Furthermore, there are significant gaps in clinical prediction tools and targeted treatment strategies for comorbidities. This paper reviews common pathophysiological mechanisms in MASLD and CKD, the epidemiological and clinical evidence linking MASLD to the risk of CKD, biomarkers and clinical prediction models for coexisting conditions, and potential therapeutic strategies. Our aim is to provide a theoretical basis for early identification, mechanism exploration, and clinical treatment of comorbidities.

The long-term clinical outcomes of patients with hepatitis B virus (HBV)-related cirrhosis receiving nucleos(t)ide analog (NA) therapy according to virological response patterns remain inadequately defined. This study aimed to investigate the association between virological response patterns and clinical outcomes in a large, long-term, real-world cohort.

This retrospective–prospective cohort study enrolled patients with HBV-related cirrhosis receiving NA therapy from 2009 to 2019. According to the serum HBV DNA levels during the initial two years of antiviral treatment, patients were categorized as having a complete (CVR) or partial virological response (PVR). Patients with CVR were further stratified according to their dynamic HBV DNA changes during follow-up into maintained virological response (MVR) or virological breakthrough (VBT) patterns. The primary clinical outcomes included hepatocellular carcinoma (HCC), acute-on-chronic liver failure, and liver-related death. Secondary endpoints included recompensation and progression to decompensation. Cox proportional hazards regression was used to assess the association between virological response patterns and clinical endpoints.

In total, 1,869 patients were enrolled. During a median follow-up of seven years, the MVR, VBT, and PVR rates were 65.4%, 26.5%, and 8.1%, respectively. The cumulative serum hepatitis B surface antigen (HBsAg) clearance rate was 9.8%. Moreover, 34.9% of patients with HBsAg < 100 IU/mL at baseline experienced HBsAg clearance. Compared with patients with VBT and PVR, those with MVR had a lower five- and ten-year cumulative incidence of HCC in both the compensated (five-year: 10.1% vs. 17.0%; ten-year: 14.2% vs. 33.6%; P < 0.001) and decompensated cirrhosis subgroups (five-year: 19.5% vs. 36.7%; ten-year: 25.7% vs. 49.7%; P < 0.001). Similarly, patients with MVR also had a lower cumulative incidence of liver-related death. Additionally, a higher hepatic recompensation rate was observed in patients with MVR than in those with VBT (34.1% vs. 22.5%, P < 0.001). Importantly, patients achieving HBsAg clearance and undetectable serum HBV DNA levels (“functional cure” during ongoing NA therapy) had the lowest five- and ten-year cumulative incidence of HCC (3.9% and 8.7%, respectively).

Patients with long-term MVR exhibited a lower incidence of HCC and liver-related death in both compensated and decompensated HBV-related cirrhosis subgroups, especially those achieving “functional cure.” However, more than 30% of patients experienced PVR or VBT during long-term NA antiviral therapy. These findings highlight the importance of long-term, rigorous monitoring after initial CVR to optimize outcomes and support clinical decision-making.

Traditional Chinese medicine (TCM) is widely used in cancer care in China as an integral part of treatment. This study aimed to understand the motivations of cancer patients in China for adopting TCM in their treatment and to examine their communication with oncologists. Gaining insights into these factors can enhance culturally sensitive, patient-centered oncology care.

A consecutive sample of 287 outpatients with cancer was recruited. Sociodemographic and clinical data, TCM usage, primary reasons for adopting TCM, and communication about TCM with oncologists were collected. Descriptive statistics, binary logistic regression, and thematic analysis were used to analyze the data.

Patients’ primary reasons for choosing TCM fell into five main categories: (1) belief in the benefits of TCM itself, (2) recommendations from others (family, friends, or oncologists), (3) belief in the benefits of combining TCM with Western medicine (WM), (4) previous positive experiences with TCM, and (5) dissatisfaction with or intolerance to WM. Among the 103 patients who consulted external TCM providers, 65% disclosed this to their oncologists. A longer time since diagnosis was associated with a higher likelihood of disclosure, while employed patients were less likely to inform their oncologists. Oncologists’ responses varied, with 55% neither approving nor disapproving of external TCM prescriptions.

The primary reasons for TCM use were perceived benefits and recommendations from oncologists and family members. However, communication about TCM with oncologists remains inconsistent. Enhancing patient-provider communication through education and fostering the integration of TCM and WM can improve holistic cancer care.

Acute liver failure (ALF) is a severe hepatic injury associated with high short-term mortality. Our previous study found that 1,5-anhydroglucitol (1,5AG) levels correlate with clinical outcomes in patients with liver failure. This study aimed to explore the potential effects and mechanisms of 1,5AG in ALF.

An experimental model of ALF was established using LPS and D-GalN. 1,5AG was administered to mice by gavage before modeling. Empagliflozin was then administered to reduce 1,5AG levels in mice. Peroxisome proliferator-activated receptor alpha (PPARα) agonists were also used to explore the role of 1,5AG in mice with liver failure.

1,5AG pretreatment significantly increased ALT and AST levels, aggravated histological damage and hepatocyte apoptosis, and increased mortality in ALF mice. Transcriptomic analysis and western blot validation revealed that 1,5AG significantly inhibited the PPARα signaling pathway and its downstream target, fibroblast growth factor 21. Empagliflozin treatment reduced 1,5AG levels, alleviated liver injury and hepatocyte apoptosis, and promoted the PPARα signaling pathway in ALF. PPARα agonists effectively reversed the effects of 1,5AG on ALF, thereby alleviating liver damage, pathological injury, and hepatocyte apoptosis.

1,5AG exacerbated liver injury in ALF mice by inhibiting the hepatic PPARα pathway, thereby promoting hepatocyte apoptosis.

Liver transplant rejection significantly affects patient prognosis. Myeloid-derived suppressor cells (MDSCs), known for their potent immunoregulatory functions, represent a promising target for managing liver transplant rejection. This study aimed to systematically characterize the diversity of MDSC subsets and their context-dependent functions, particularly within the context of transplant tolerance.

We analyzed clinical and murine liver transplants using single-cell RNA sequencing, bulk RNA sequencing, flow cytometry, multiplex immunohistochemistry, and co-culture assays to phenotype MDSC subsets.

Single-cell RNA sequencing analysis of human and murine samples revealed MDSC involvement in transplant rejection. In mice, MDSC scores followed a normal distribution during the first week post-transplant and correlated with clinical flow cytometry data at one month. A distinct LDLR+ monocytic MDSC (M-MDSC) subset was identified and confirmed through spatial mapping by multiplex immunohistochemistry. Flow cytometry demonstrated dynamic changes in LDLR+ M-MDSCs across tissues (liver, spleen, peripheral blood, bone marrow, and lymph nodes), with a peak during acute rejection. Co-culture experiments showed that LDLR−/− M-MDSCs exhibited reduced Arg-1/iNOS expression and an impaired capacity to induce inhibitory receptors (TIGIT, PD1, CTLA-4) or suppress effector molecules (GZMB, IFN-γ, IL-2) in CD8+ T cells.

These findings highlight the critical role of MDSCs in liver transplant rejection. LDLR+ M-MDSCs exhibited enhanced immunosuppressive properties, underscoring their potential clinical relevance in mitigating rejection and promoting immune tolerance.

Predicting the malignant transformation of rectal precancerous lesions remains challenging because conventional Whole Slide Images (WSIs) capture morphological information but lack molecular insight. Multiomics data provide complementary biological signals that often precede visible morphological changes. This study aimed to develop an artificial intelligence (AI)-based multimodal framework integrating WSI and multiomics data for accurate early prediction of malignant transformation.

WSI patches (512×512 px at 20× magnification) and matched multiomics profiles were used for 450 rectal tissue samples from the publicly available The Cancer Genome Atlas dataset. A multimodal architecture was designed, employing a Vision Transformer (ViT-B/16) for WSI feature extraction and a Variational Autoencoder for multiomics representation learning. Features were fused via a cross-attention mechanism to capture inter-modality dependencies. Baseline models, including a convolutional neural network-only image model and an omics-only multilayer perceptron, were trained for comparison. Five-fold cross-validation was applied, with binary cross-entropy loss, the AdamW optimizer, early stopping, and hyperparameter tuning to ensure reproducibility.

The multimodal Vision Transformer–Variational Autoencoder fusion model outperformed unimodal baselines, achieving an accuracy of 0.892 ± 0.012 and an area under the receiver operating characteristic curve of 0.927 ± 0.009, corresponding to a 7–10% improvement over WSI-only and omics-only models. Cross-attention–based fusion improved prediction stability and classification performance, while interpretability analyses (Grad-CAM and SHAP) highlighted biologically meaningful histopathological regions and molecular feature contributions.

This study presents a robust and scalable AI-based framework for integrating WSI and multiomics data in rectal precancerous lesions. The model improves predictive precision compared with unimodal baselines and offers preliminary interpretability insights through attention mechanisms. These findings support the potential of multimodal AI for early cancer risk assessment and precision pathology.