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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. [ABSTRACT FROM AUTHOR]

This Research proposes an intelligent pruning method based on the improved Mask R-CNN (Mask Region-based Convolutional Neural Network) model to address the shortcomings of intelligent pruning technology for Sichuan pepper trees. Utilizing ResNeXt-50 as the backbone network, the algorithm optimizes the anchor boxes in the RPN (Region Proposal Network) layer to adapt to the complex morphology of pepper tree branches, thereby enhancing target detection and segmentation performance. Further reducing the quantization error of the RoI (Region of Interest) Align layer through bilinear interpolation, the algorithm innovatively introduces edge loss (Ledge) into the loss function to address the issue of blurred edge features caused by the overlap between retained and pruned branches. Experimental results demonstrate the outstanding performance of the improved Mask R-CNN model in segmenting and identifying pepper tree branches, achieving recognition accuracies of 92.2%, 96.3%, and 85.6% for Upright branches, Centripetal branches, and Competitive branches, respectively, while elevating the recognition accuracy of retained branches to 94.4%. Compared to the original Mask R-CNN, the enhanced model exhibits a 6.7% increase in the recognition rate of retained branches and a decrease of 0.12 in loss value, significantly enhancing recognition effectiveness. The research findings not only provide an effective tool for the precise pruning of pepper trees but also offer valuable insights for implementing intelligent pruning strategies for other fruit trees. [ABSTRACT FROM AUTHOR]

Sanguinarine (SAG) is the most abundant constituent of Macleaya cordata (Willd.) R. Br. (Popaceae). SAG has shown antimammary and colorectal metastatic effects in mice in vivo, suggesting its potential for cancer chemotherapy. To determine the antimetastatic effect and underlying molecular mechanisms of SAG on melanoma. CCK8 assay was used to determine the inhibition of SAG on the proliferation of A375 and A2058 cells. Network pharmacology analysis was applied to construct a compound-target network and select potential therapeutic targets of SAG against melanoma. Molecular docking simulation was conducted for further analysis of the selected targets. In vitro migration/invasion/western blot assay with 1, 1.5, 2 μM SAG and in vivo effect of 2, 4, 8 mg/kg SAG in xenotransplantation model in nude mice. The key targets of SAG treatment for melanoma were mainly enriched in PI3K-AKT pathway, and the binding energy of SAG to PI3K, AKT, and mTOR were −6.33, −6.31, and −6.07 kcal/mol, respectively. SAG treatment inhibited the proliferation, migration, and invasion ability of A375 and A2058 cells (p < 0.05) with IC50 values of 2.378 μM and 2.719 μM, respectively. It also decreased the phosphorylation levels of FAK, PI3K, AKT, mTOR and protein expression levels of MMP2 and ICAM-2. In the nude mouse xenograft model, 2, 4, 8 mg/kg SAG was shown to be effective in inhibiting tumour growth. Our research offered a theoretical foundation for the clinical antitumor properties of SAG, further suggesting its potential application in the clinic. [ABSTRACT FROM AUTHOR]

Self-adaptability is highly envisioned for artificial devices such as robots with chemical noses. For this goal, seeking catalysts with multiple and modulable reaction pathways is promising but generally hampered by inconsistent reaction conditions and negative internal interferences. Herein, we report an adaptable graphitic C6N6-based copper single-atom catalyst. It drives the basic oxidation of peroxidase substrates by a bound copper-oxo pathway, and undertakes a second gain reaction triggered by light via a free hydroxyl radical pathway. Such multiformity of reactive oxygen-related intermediates for the same oxidation reaction makes the reaction conditions capable to be the same. Moreover, the unique topological structure of CuSAC6N6 along with the specialized donor-π-acceptor linker promotes intramolecular charge separation and migration, thus inhibiting negative interferences of the above two reaction pathways. As a result, a sound basic activity and a superb gain of up to 3.6 times under household lights are observed, superior to that of the controls, including peroxidase-like catalysts, photocatalysts, or their mixtures. CuSAC6N6 is further applied to a glucose biosensor, which can intelligently switch sensitivity and linear detection range in vitro. Catalysts with multiple and modulable reaction pathways are promising but generally hampered by inconsistent reaction conditions and negative internal interferences. Herein, the authors report an adaptable graphitic C6N6-based copper singleatom catalyst [ABSTRACT FROM AUTHOR]

Numerous types of molecular mechanisms mediate the development of cancer. Non-coding RNAs (ncRNAs) are being increasingly recognized to play important role in mediating the development of diseases, including cancer. Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are the two most widely studied ncRNAs. Thus far, lncRNAs are known to have biological roles through a variety of mechanisms, including genetic imprinting, chromatin remodeling, cell cycle control, splicing regulation, mRNA decay and translational regulation, and miRNAs regulate gene expression through the degradation of mRNAs and lncRNAs. Although ncRNAs account for a major proportion of the total RNA, the mechanisms underlying the physiological or pathological processes mediated by various types of ncRNAs, and the specific interaction mechanisms between miRNAs and lncRNAs in various physiological and pathological processes, remain largely unknown. Thus, further research in this field is required. In general, the interaction mechanisms between miRNAs and lncRNAs in human cancer have become important research topics, and the study thereof has led to the recent development of related technologies. By providing examples and descriptions, and performing chart analysis, the present study aimed to review the interaction mechanisms and research approaches for these two types of ncRNAs, as well as their roles in the occurrence and development of cancer. These details have far-reaching significance for the utilization of these molecules in the diagnosis and treatment of cancer. [ABSTRACT FROM AUTHOR]