Tumour lymphangiogenesis plays an important role in promoting the growth and lymphatic metastasis of tumours. The process is associated with cell proliferation, migration and tube‐like structure formation in lymphatic endothelial cells (LEC), but no antilymphangiogenic agent is currently used in clinical practice. Fucoxanthin is a material found in brown algae that holds promise in the context of drug development. Fucoxanthin is a carotenoid with variety of pharmacological functions, including antitumour and anti‐inflammatory effects. The ability of fucoxanthin to inhibit lymphangiogenesis remains unclear. The results of experiments performed as part of this study show that fucoxanthin, extracted from Undaria pinnatifida (Wakame), inhibits proliferation, migration and formation of tube‐like structures in human LEC (HLEC). In this study, fucoxanthin also suppressed the malignant phenotype in human breast cancer MDA‐MB‐231 cells and decreased tumour‐induced lymphangiogenesis when used in combination with a conditional medium culture system. Fucoxanthin significantly decreased levels of vascular endothelial growth factor (VEGF)‐C, VEGF receptor‐3, nuclear factor kappa B, phospho‐Akt and phospho‐PI3K in HLEC. Fucoxanthin also decreased micro‐lymphatic vascular density (micro‐LVD) in a MDA‐MB‐231 nude mouse model of breast cancer. These findings suggest that fucoxanthin inhibits tumour‐induced lymphangiogenesis in vitro and in vivo, highlighting its potential use as an antilymphangiogenic agent for antitumour metastatic comprehensive therapy in patients with breast cancer.

Background: Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) are life-threatening disorders with a high risk of mortality. Astaxanthin (AST) is a supernatural antioxidant that has been extensively studied due to its role in immunomodulation, oxidative stress, and lipid peroxidation. However, the association between ferroptosis and AST is not well understood. The purpose of this study is to explore the regulatory role of AST on ferroptosis in lipopolysaccharide (LPS)-induced ALI. Methods: We established an MLE-12 cell injury model and a mouse ALI model by treating with LPS. The levels of IL-6, TNF-α, and IL-1β in the mouse serum were measured using an enzyme-linked immunosorbent assay. Moreover, immunohistochemical, immunofluorescence, western blot, and quantitative real-time polymerase chain reaction analyses were conducted to examine the effects of AST and ferrostatin-1. Results: We discovered that AST pretreatment greatly alleviated LPS-induced lung injury and inhibited ferroptosis, which was demonstrated by a decrease in the accumulation of malondialdehyde and Fe²⁺ and an increase in the levels of glutathione and glutathione peroxidase 4 in the lung tissues of ALI mice and MLE-12 cells. Additionally, we found that AST also evidently suppressed ferritinophagy by upregulation of ferritin and downregulation of nuclear receptor co-activator 4 (NCOA4) in MLE-12 cells. Conclusions: AST pretreatment could lead to a relief of LPS-induced ALI, perhaps via suppressing ferroptosis, and could also reduce unstable iron accumulation by inhibiting NCOA4-mediated ferritin phagocytosis from mitigating lipid peroxidation and ferroptosis in lung epithelial cells.

Background: Urosepsis is a subset of sepsis with a high mortality rate. Currently, the ranking of urosepsis in sepsis etiology is on the rise. Our goal is to use machine learning (ML) methods to construct and validate an interpretable prognosis prediction model for patients with urosepsis.Method: Data were collected from the Intensive Care Medical Information Mart IV database version 3.1 and divided into a training cohort and a validation cohort in a 7:3 ratio. Random Forest (RF), Lasso, Boruta, and eXtreme Gradient Boosting (XGBoost) were used to identify the most influential variables in the model development dataset, and the optimal variables were selected based on achieving the λ_1se value. Model development includes seven machine learning methods and ten cross validations. Accuracy and Decision Curve Analysis (DCA) were used to evaluate the performance of the model in order to select the optimal model. Internal validation of the model included area under the ROC curve (AUC), sensitivity, specificity, Matthews correlation coefficient, and F1-score. Finally, SHapley Additive exPlans (SHAP) was used to explain ML models.Result: A total of 1389 patients with urosepsis were included. Optimal predictors were selected through statistical regularization, yielding a parsimonious set of 9 variables for model development. The performance of XGBoost model is the best and the accuracy of XGBoost was 0.818, with an AUC of 0.904 (95% CI: 0.886-0.923). The internal validation accuracy was 0.797, AUC was 0.869 (95% CI: 0.834-0.904), sensitivity was 0.797, specificity was 0.752, Matthews correlation coefficient was 0.597, and F1-score was 0.791. This indicates that the predictive model performs well in internal validation. SHAP-based summary graphs and diagrams were used to globally explain the XGBoost model.Conclusion: ML demonstrates strong prognostic capability in urosepsis, with the SHAP method providing clinically intuitive explanations of model predictions. This enables clinicians to identify critical prognostic factors and personalize treatments. While our model achieved high predictive accuracy, its retrospective derivation from a single-center database necessitates external validation in diverse populations, which should be addressed through future prospective multicenter studies to establish clinical generalizability.

Background: Activating transcription factor 3 (ATF3) is a nuclear protein that is widely expressed in a variety of cells. It is a stress‐inducible transcription gene and a member of the activating transcription factor/cAMP responsive element‐binding protein (ATF/CREB) family.Methods: The comprehensive literature review was conducted by searching PubMed and Google Scholar. Search terms used were “ATF3”, “ATF3 and (ALI or ARDS)”, “ATF3 and COPD”, “ATF3 and PF”, and “ATF3 and Posttranslational modifications”.Results: Recent studies have shown that ATF3 plays a critical role in many inflammatory pulmonary diseases, including acute lung injury (ALI)/acute respiratory distress syndrome (ARDS), chronic obstructive pulmonary disease (COPD), and pulmonary fibrosis (PF). ATF3 participates in many signaling pathways and complex pathophysiological processes, such as inflammation, immunity, endoplasmic reticulum stress, and cell proliferation. However, the role of ATF3 in current studies is controversial, and there are reports showing that ATF3 plays different roles in different pulmonary diseases.Conclusions: In this review, we first summarized the structure, function, and mechanism of ATF3 in various inflammatory pulmonary diseases. The impact of ATF3 on disease pathogenesis and the clinical implications was particularly focused on, with an overall aim to identify new targets for treating inflammatory pulmonary diseases.

Background: Septic patients with persistent lymphopenia may be in an immunosuppressed state. Therefore, we evaluated and compared the clinical characteristics and outcomes of septic patients with persistent lymphopenia (≥2d) and those with nonpersistent lymphopenia. Methods: A retrospective cohort study was designed. A total of 1306 patients with sepsis who were attended to the First Affiliated Hospital of Dalian Medical University from March 2016 to August 2022 were included. The primary clinical outcome was 90d mortality. The secondary clinical outcomes were the length of stay, hospital mortality, 28d mortality, the incidence of secondary infection, and differences in clinical characteristics. Results: Among 1306 patients with sepsis, 913 (69.9%) patients developed persistent lymphopenia. Compared with patients with nonpersistent lymphopenia, patients with persistent lymphocytopenia were admitted to intensive care unit (75.7% vs 52.7%, P  <   .05), treated with mechanical ventilation (67.6% vs 39.2%, P  < .05), positive rate of microbial culture pathogens (86.7% vs 71.2%, P  <   .05), SOFA [8.0 (6.0-10.0) vs 6.0 (4.0-8.0), P  < .05], length of stay [17.0d (12.0-27.0) vs 13.0d (10.0-21.0), P  < .05], hospital mortality (37.7% vs 24.2%, P  < .05), 28d mortality (38.0% vs 22.9%, P  < .05), and 90d mortality (51.2% vs 31.3%, P  < .05) were higher. As the duration of lymphocytopenia increased, so did the mortality rate in hospital. In addition, the onset time of persistent lymphopenia was not associated with SOFA. But we found that the frequency of persistent lymphopenia during hospitalization was positively associated with SOFA. Conclusion: Septic patients with persistent lymphopenia have higher mortality, worse conditions, increased risk of secondary infection, and poor prognosis regardless of shock.