LPG 18:0 is a general biomarker of asthma and inhibits the differentiation and function of regulatory T cells.

BACKGROUND: The diagnosis, severity assessment, and development of therapeutic strategies for asthma are crucial aspects of disease management. Since biomarkers are reliable tools in disease management, we aimed to identify and explore asthma-associated biomarkers and investigate their mechanisms. METHODS: Lipidomics was used to profile serum glycerophospholipids in asthmatic patients and controls. The absolute concentration of lysophosphatidylglycerol (LPG) 18:0 was quantified in various asthma subtypes. Mouse asthma models were used to confirm its potential as a biomarker and investigate its mechanisms in vivo. The effects of LPG 18:0 on CD4+ T cell differentiation, proliferation, and apoptosis were assessed in vitro by flow cytometry, while mitochondrial dysfunction was evaluated through mitochondrial membrane potential, reactive oxygen species, and ATP production measurements. The intracellular mechanism of LPG 18:0 in Tregs was investigated using small molecule inhibitors. RESULTS: The serum glycerophospholipid profile varied between asthmatic patients and control group, with LPG 18:0 levels being notably higher in asthmatic patients, correlating with asthma severity and control level. In vivo and in vitro studies revealed that LPG18:0 impaired naïve CD4+ T cell differentiation into Tregs and compromised their suppressive function. Further investigation demonstrated that LPG18:0 treatment reduced the FOXP3 protein level via SIRT1-mediated deacetylation during Treg differentiation. CONCLUSIONS: This study identifies that serum levels of LPG 18:0 are generally elevated in asthmatics and serve as a biomarker for asthma. LPG 18:0 impairs Treg function via the NAD+/SIRT1/FOXP3 pathway. Our research reveals the potential of LPG18:0 as a biomarker for asthma, elucidating its role in asthma diagnosis and treatment.

NK-92 cells activated by IL-2 inhibit the progression of endometriosis in vitro.

BACKGROUND: Interleukin (IL)-2 is a key cytokine capable of modulating the immune response by activating natural killer (NK) cells. This study was recruited to explore the therapeutic potential of IL-2-activated NK-92 cells in endometriosis in vitro. METHODS: Ectopic endometrial stromal cells (EESCs) were isolated and co-cultured with IL-2-activated NK-92 cells at varying effector-to-target (E:T) ratios (1:0 [Control], 1:1, 1:3, and 1:9). The viability, cytotoxicity, and cell surface antigen expression of IL-2-activated NK-92 cells were assessed. The viability, apoptosis, invasion, and migration ability of EESCs co-cultured with NK-92 cells at different ratios were evaluated. The apoptosis-related proteins, invasion and migration-related proteins as well as MEK/ERK pathway were examined via western blot. Each experiment was repeated three times. RESULTS: IL-2 activation enhanced NK-92 cytotoxicity in a concentration-dependent manner. Co-culturing EESCs with IL-2-activated NK-92 cells at E:T ratios of 1:1, 1:3, and 1:9 reduced EESC viability by 20%, 45%, and 70%, respectively, compared to the control group. Apoptosis rates in EESCs increased in correlation with the NK-92 cell proportion, with the highest rate observed at a 1:9 ratio. Moreover, EESC invasion and migration were significantly inhibited by IL-2-activated NK-92 cells, with a 60% reduction in invasion and a 50% decrease in migration at the 1:9 ratio. Besides, the MEK/ERK signalling pathway was down-regulated in EESCs by IL-2-activated NK-92 cells. CONCLUSION: IL-2-activated NK-92 cells exhibit potent cytotoxic effects against EESCs. They promote EESC apoptosis and inhibit viability, invasion, and migration through modulating the MEK/ERK signalling pathway.

Endometriosis is a common chronic systemic disease affecting approximately 190 million women worldwide. However, clinical treatments for endometriosis remain challenging due to the scarcity of high-quality scientific evidence and conflicting available guidelines. This research was designed to explore whether interleukin (IL)-2 affected the progression of endometriosis by modulating endometrial stromal cell apoptosis and natural killer (NK) cell-mediated cytotoxicity, thereby providing new therapeutic methods for endometriosis.

Machine learning-based models for prediction of the risk of stroke in coronary artery disease patients receiving coronary revascularization.

BACKGROUND: To construct several prediction models for the risk of stroke in coronary artery disease (CAD) patients receiving coronary revascularization based on machine learning methods. METHODS: In total, 5757 CAD patients receiving coronary revascularization admitted to ICU in Medical Information Mart for Intensive Care IV (MIMIC-IV) were included in this cohort study. All the data were randomly split into the training set (n = 4029) and testing set (n = 1728) at 7:3. Pearson correlation analysis and least absolute shrinkage and selection operator (LASSO) regression model were applied for feature screening. Variables with Pearson correlation coefficient<9 were included, and the regression coefficients were set to 0. Features more closely related to the outcome were selected from the 10-fold cross-validation, and features with non-0 Coefficent were retained and included in the final model. The predictive values of the models were evaluated by sensitivity, specificity, area under the curve (AUC), accuracy, and 95% confidence interval (CI). RESULTS: The Catboost model presented the best predictive performance with the AUC of 0.831 (95%CI: 0.811-0.851) in the training set, and 0.760 (95%CI: 0.722-0.798) in the testing set. The AUC of the logistic regression model was 0.789 (95%CI: 0.764-0.814) in the training set and 0.731 (95%CI: 0.686-0.776) in the testing set. The results of Delong test revealed that the predictive value of the Catboost model was significantly higher than the logistic regression model (P<0.05). Charlson Comorbidity Index (CCI) was the most important variable associated with the risk of stroke in CAD patients receiving coronary revascularization. CONCLUSION: The Catboost model was the optimal model for predicting the risk of stroke in CAD patients receiving coronary revascularization, which might provide a tool to quickly identify CAD patients who were at high risk of postoperative stroke.

Elucidating the Relationship between Neutrophil-Lymphocyte Ratio and Plaque Composition in Patients with Drug-Eluting Stent Restenosis by Virtual Histology-Intravascular Ultrasound.

(1) Background: In-stent Restenosis (ISR) is a major factor influencing the prognosis and revascularization of target lesions. The plaque composition is unclear; therefore, it is critical to investigate ISR composition to identify clinical intervention markers. (2) Methods: This study was conducted on 36 patients with drug-eluting stent restenosis. The patients were classified into a Low Neutrophil-Lymphocyte Ratio (L-NLR) and High Neutrophil-Lymphocyte Ratio (H-NLR) according to the median NLR level of 36 patients. Discrepancies in the current information such as baseline data, biochemical examination, cardiac ultrasound data, etc., were examined to identify the underlying risk factors, and a multifactorial linear regression analysis of plaque properties was conducted. (3) Results: NLR = 2.64 was utilized to classify 18 patients into the L-NLR group and 18 patients into the H-NLR group. There were statistically significant differences in age, a pre-percutaneous coronary intervention (PCI) SYNTAX II score, a C-reactive protein (CRP), interleukin (IL)-6, plaque loading, a fibro-lipid tissue area, calcified nubs, and virtual histology-thin fibrous cap atherosclerotic (VH-TCFA). The significant impacts of variations in age, neutrophil-lymphocyte ratio (NLR) levels, and IL-6 levels on the plaque stress and percentage of the fibro-lipid tissue in virtual histology-intravascular ultrasound (VH-IVUS) were identified through multifactorial linear regression. (4) Conclusions: The high NLR group demonstrated increased myocardial injury severity, consistent with higher SYNTAX II scores, a higher plaque burden, and higher proportions of vulnerable components. NLR proved to be a risk factor for both the plaque load and the proportion of the fibro-lipid tissue in ISR.