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Abstract To select the core target (RAB13) in sepsis patients’ peripheral blood and investigate its molecular functions and possible mechanisms. The peripheral blood of sepsis patients (n = 21) and healthy individuals (n = 9) within 24 h after admission were collected for RNA-seq, and differential gene screening was performed by iDEP online analysis software (P

Sepsis-Associated Liver Injury (SALI) is an independent risk factor for death from sepsis. The aim of this study was to develop an interpretable machine learning model for early prediction of 28-day mortality in patients with SALI. Data from the Medical Information Mart for Intensive Care (MIMIC-IV, v2.2, MIMIC-III, v1.4) were used in this study. The study cohort from MIMIC-IV was randomized to the training set (0.7) and the internal validation set (0.3), with MIMIC-III (2001 to 2008) as external validation. The features with more than 20% missing values were deleted and the remaining features were multiple interpolated. Lasso-CV that lasso linear model with iterative fitting along a regularization path in which the best model is selected by cross-validation was used to select important features for model development. Eight machine learning models including Random Forest (RF), Logistic Regression, Decision Tree, Extreme Gradient Boost (XGBoost), K Nearest Neighbor, Support Vector Machine, Generalized Linear Models in which the best model is selected by cross-validation (CV_glmnet), and Linear Discriminant Analysis (LDA) were developed. Shapley additive interpretation (SHAP) was used to improve the interpretability of the optimal model. At last, a total of 1043 patients were included, of whom 710 were from MIMIC-IV and 333 from MIMIC-III. Twenty-four clinically relevant parameters were selected for model construction. For the prediction of 28-day mortality of SALI in the internal validation set, the area under the curve (AUC (95% CI)) of RF was 0.79 (95% CI: 0.73-0.86), and which performed the best. Compared with the traditional disease severity scores including Oxford Acute Severity of Illness Score (OASIS), Sequential Organ Failure Assessment (SOFA), Simplified Acute Physiology Score II (SAPS II), Logistic Organ Dysfunction Score (LODS), Systemic Inflammatory Response Syndrome (SIRS), and Acute Physiology Score III (APS III), RF also had the best performance. SHAP analysis found that Urine output, Charlson Comorbidity Index (CCI), minimal Glasgow Coma Scale (GCS_min), blood urea nitrogen (BUN) and admission_age were the five most important features affecting RF model. Therefore, RF has good predictive ability for 28-day mortality prediction in SALI. Urine output, CCI, GCS_min, BUN and age at admission(admission_age) within 24 h after intensive care unit(ICU) admission contribute significantly to model prediction.

Background: To explore the effect of continuous renal replacement therapy (CRRT) in patients with sepsis-associated acute kidney injury (SA-AKI) with the Acute Kidney Injury Network Classification III and its effect on inflammatory mediators and coagulation function. Methods: We evaluated 90 patients who were diagnosed with sepsis and treated at our hospital. Forty patients received CRRT (group A) and the remainder received routine therapy (group B). We compared the renal function indices, represented by blood urea nitrogen (BUN) and serum creatinine (Scr), the urinary levels of kidney injury molecule 1, and the curative effect indices between the two groups. The incidence of major adverse cardiovascular events was compared between both groups. Further, the therapeutic effect (total effective rate) was evaluated and compared. Results: After treatment, the levels of BUN and Scr in group A were significantly lower than those in group B (p

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.

CD247, which is also known as CD3ζ, CD3H, CD3Q, CD3Z, IMD25, T3Z and TCRZ, encodes CD3ζ protein, which is expressed primarily in natural killer (NK) and T cells. Since the discovery of the ζ peptide in 1986, it has been continuously investigated. In this paper, we review the composition, molecular mechanisms and regulatory factors of CD247 expression in T cells; and review the autoimmune diseases, tumours and inflammatory diseases associated with CD247, providing a detailed and comprehensive reference for further research on the mechanism of CD247 and related diseases. [ABSTRACT FROM AUTHOR]