Pentagalloyl glucose (PGG) is a natural hydrolyzable gallotannin abundant in various plants and herbs. It has a broad range of biological activities, specifically anticancer activities, and numerous molecular targets. Despite multiple studies available on the pharmacological action of PGG, the molecular mechanisms underlying the anticancer effects of PGG are unclear. Here, we have critically reviewed the natural sources of PGG, its anticancer properties, and underlying mechanisms of action. We found that multiple natural sources of PGG are available, and the existing production technology is sufficient to produce large quantities of the required product. Three plants (or their parts) with maximum PGG content were Rhus chinensis Mill, Bouea macrophylla seed, and Mangifera indica kernel. PGG acts on multiple molecular targets and signaling pathways associated with the hallmarks of cancer to inhibit growth, angiogenesis, and metastasis of several cancers. Moreover, PGG can enhance the efficacy of chemotherapy and radiotherapy by modulating various cancer-associated pathways. Therefore, PGG can be used for treating different human cancers; nevertheless, the data on the pharmacokinetics and safety profile of PGG are limited, and further studies are essential to define the clinical use of PGG in cancer therapies.

Since smallpox was eradicated in 1980, the monkeypox virus (MPXV) has emerged as the most threatening orthopoxvirus in the world. In this study, we conducted a comprehensive analysis of the currently published complete genome sequences of the monkeypox virus. The core/variable regions were identified through core-pan analysis of MPXV. Besides single-nucleotide polymorphisms, our study also revealed that specific genes, multi-copy genes, repeat sequences, and recombination fragments are primarily distributed in the variable region. This result suggests that variable regions are not only more susceptible to single-base mutations, but also to events such as gene loss or gain, as well as recombination. Taken together, our results demonstrate the genomic characteristics of the core/variable regions of MPXV, and contribute to our understanding of the evolution of MPXV.

The incidence and mortality of cardiac arrest (CA) is high. We developed interpretable machine learning models for early prediction of ICU mortality risk in patients diagnosed with CA. Data from the Medical Information Mart for Intensive Care (MIMIC-IV, version 2.2) was randomized to training set (0.7) and internal validation set (0.3), and data from eICU(version 2.0.1) was used as external validation set. Five models including Logistic Regression (LR), Random Forest (RF), K Nearest Neighbor (KNN), Decision Tree (DT), and Extreme Gradient Boost (XGBoost) were developed. The model with the largest area under the Receiver Operating Characteristic (ROC) curve (AUC) and good performance in other features was defined as the best model, and Shapley Additive Explanations (SHAP) was used to improve the interpretability of the optimal model. A total of 1088 patients from MIMIC-IV, and 3542 patients from eICU were included. Seven variables were selected to construct models by Least Absolute Shrinkage and Selection Operator (LASSO) regression. The RF model was the best predictive model with AUC and 95% CI at 0.83 (0.78–0.88) in internal validation set, and 0.71(0.68–0.74) in external validation set. SHAP analysis found that the variables that had a high impact on the risk of ICU death were minimal Glasgow Coma Scale (GCS), base excess, anion gap, and urine output. RF is the optimal model for predicting the risk of ICU death in CA patients. The development of this model is important for early identification and intervention of CA patients who are at risk of dying in the ICU.

Colorectal cancer (CRC) resistance to 5-fluorouracil (5-FU), primarily driven by cancer stem cells (CSCs) and epithelial-mesenchymal transition (EMT), remains a major clinical challenge, necessitating novel therapeutic strategies. This study aims to evaluate the therapeutic potential of pentagalloyl glucose (PGG), a bioactive compound derived from Bouea macrophylla seeds, in overcoming 5-FU resistance in CRC. Anti-tumor effects of PGG were investigated using two- and three-dimensional (2D and 3D) cell culture models and subcutaneous xenograft and metastatic mouse models. Transcriptome sequencing, western blotting, and pharmacological inhibitors were employed to elucidate the underlying molecular mechanisms. PGG demonstrated potent anti-CSC activity; suppressed EMT-driven invasion and metastasis; and induced apoptosis in 2D monolayers, 3D spheroid models, and xenograft tumor models. Mechanistically, PGG selectively inhibited the JAK1/JAK3-STAT3 signaling pathway, considerably reducing STAT3 phosphorylation. This disruption downregulated the expression of CSC markers (CD133 and CD44), EMT regulators (N-cadherin and vimentin), and anti-apoptotic proteins (Bcl-2), effectively sensitizing 5-FU-resistant CRC to therapy. PGG inhibit dual-target of CSCs and EMT via JAK1/JAK3-STAT3 signaling pathway in 5-FU-resistant CRC, providing a novel therapeutic approach to overcome chemoresistance.

Background: Protein serves as a core component of nutritional support for critically ill patients; however, no unified consensus or clinical standard has been established for protein supplementation to date. This gap highlights the urgent need for an updated systematic review and meta-analysis. Methods: Randomized controlled trials (RCTs) and meta-analyses that met our criteria were included in PubMed, Web of Science, Cochrane Library and Embase English databases up to April 1, 2026. We used meta-analysis, subgroup analysis and umbrella review methods to explore the effects of high-protein and conventional protein supplementation on critically ill patients. We also conducted Trial Sequential Analysis (TSA) to verify the effectiveness of the evidence. The assessment of bias risk was conducted according to the Revised Cochrane’s Risk of Bias tool (ROB 2.0) for RCTs and AMSTAR-2 tool for meta-analysis. Result: A total of 18 RCTs and 8 meta-analyses are included. In the meta-analysis, no significant association between high-protein supplementation and overall mortality is found (OR 0.99, 95% CI 0.95–1.28, p = 0.69; I ² = 0%). In the umbrella review, there are no significant differences in each outcome between high-protein supplementation and conventional protein supplementation. In addition, long term intervention for more than 14 days would slightly increase overall mortality of critically ill patients (OR 1.181, 95% CI 1.002 to 1.391, p = 0.047; I ² = 0%). Conclusion: Compared with high and conventional protein supplementation, high-protein supplementation has no significant impact on clinical outcomes of critically ill patients, and long-term intake might increase overall mortality of patients. Systematic review registration: Identifier: CRD420250652684.

Background: Sepsis is a life-threatening condition associated with high mortality rates, claiming millions of lives globally each year. To improve prognostic prediction in sepsis, this study aimed to establish a lipid metabolism-associated gene signature for risk stratification and immune function evaluation. Methods: U sing the sepsis dataset GSE65682 (Gene Expression Omnibus, GEO), lipid metabolism-associated genes were identified via GeneCards and intersection analysis. Hub genes selection integrated Univariate Cox, Least Absolute Shrinkage and Selection Operator (LASSO), and Multivariate Cox regression. Patients were stratified into high/low-risk groups by median risk scores. Prognostic performance was validated by Kaplan-Meier analysis and Receiver Operating Characteristic curves (ROC). Immune heterogeneity was analyzed using single-sample Gene Set Enrichment Analysis (ssGSEA), CIBERSORT, and correlation networks. Results: A 9-gene prognostic signature (Aryl Hydrocarbon Receptor Repressor, AHRR; Ceroid-Lipofuscinosis, Neuronal 8, CLN8; Fatty Acid Synthase, FASN; Lanosterol Synthase, LSS; Mediator Complex Subunit 29, MED29; Platelet-Activating Factor Acetylhydrolase IB Subunit Alpha, PAFAH1B1; Phosphatidylinositol-4-Phosphate 5-Kinase Type 1 Gamma, PIP5K1C; Tribbles Pseudokinase 3, TRIB3; UDP-Glucose Ceramide Glucosyltransferase, UGCG) demonstrated robust predictive value. High-risk patients showed poor survival (KM, p=6.75 × e⁻⁸;ROC AUC: 0.951) and enriched Chemokine Receptor (CCR) and parainflammation, while low-risk individuals exhibited elevated higher infiltration levels of Tumor-infiltrating lymphocytes(TIL), type_II_IFN_Response, Treg, and macrophages . Immune network analyses revealed coordinated interactions: activated NK cells synergized with M1 macrophages (r=0.44) but antagonized resting NK cells (r=-0.62). Immune checkpoints CD86/TNFSF4 were upregulated in low-risk patients, contrasting with CD200R1 suppression. Conclusion: This study successfully established a lipid metabolism-derived gene signature that provides a clinically actionable tool for prognostic stratification in sepsis patients, helping personalized clinical decision-making and facilitating early interventions.