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Insect meal inclusion in aquaculture feed has received increased interest as a sustainable alternative to fishmeal and recent evidence has shown additional effects on modulating the immune response. However, lack of effects of insect meal on fish immunity in a few studies have put these beneficial effects into question. The objective of this meta-analysis was to summarize the effects of fishmeal replacement with insect meal on the innate immune response of several fish species based on differential gene expression via qPCR. A systematic literature search was conducted using online databases including Web of Science and ScienceDirect that found 197 studies as of August 2023, however only 20 studies met the criteria of high-quality studies focused on the immune response of fish and were included in this meta-analysis. The most studied insect meal was from black soldier fly, followed by yellow mealworm, and the most commonly analyzed tissue was the liver, followed by the intestine. The effect of fishmeal replacement with insect meal on fish immune responses were examined using a mixed model, with study as the random effect. This meta-analysis found a non-linear (quadratic) relationship (P = 0.017) between immune gene expression fold change and the level of fishmeal replacement with insect meal. Based on the fitted quadratic curve, a 40 % replacement of fishmeal with insect meal resulted in the highest upregulated immune response in fish. Fish taxonomic family was also found to have an effect (P = 0.004) on immune gene expression, where the taxonomic family Moronidae was more affected, with an average fold change of 2.783 (± 0.325). Other variables examined, including the insect type, tissue analyzed, dietary crude protein level, did not affect the immune response (P > 0.05). This meta-analysis also found that dietary lipid had a significant correlation with immune gene expression, and should be taken into consideration in future studies and meta-analyses. These findings are impactful since they provide evidence for the optimal dietary replacement level of insect meals required to significantly affect the innate immune response across several fish species.

This research provides a rolling planning method for distribution networks, which takes into account shared energy storage capacity configuration and grid topology optimization due to the stochastic unpredictability of the planning scenarios and the interaction between multi-agents. First, the grid topology and the investment capacity of the shared energy storage are optimized by the gird company with the aim of minimizing the overall cost within the planning time scale, and a distribution network planning model that precisely satisfies the reliability requirements is built. Second, the operational utility model of prosumers is suggested based on the planning strategy of the grid company. The cooperation of grid company corporations with prosumers is established in the evolutionary game model. The differential evolution algorithm is used to realize the dynamic solution of the planning strategy by creating the replicator dynamic equation of the grid company. The IEEE-33 bus system is taken as an example for simulation analysis for demonstrating the usefulness of the suggested evolution game-based planning model, which can reduce network planning costs and system operational costs while optimizing prosumers’ load curves.

BackgroundAs the leading cause of chronic kidney disease, diabetic kidney disease (DKD) is an enormous burden for all healthcare systems around the world. However, its early diagnosis has no effective methods.MethodsFirst, gene expression data in GEO database were extracted, and the differential genes of diabetic tubulopathy were obtained. Immune-related genesets were generated by WGCNA and immune cell infiltration analyses. Then, differentially expressed immune-related cuproptosis genes (DEICGs) were derived by the intersection of differential genes and genes related to cuproptosis and immune. To investigate the functions of DEICGs, volcano plots and GO term enrichment analysis was performed. Machine learning and protein-protein interaction (PPI) network analysis helped to finally screen out hub genes. The diagnostic efficacy of them was evaluated by GSEA analysis, receiver operating characteristic (ROC) curve, single-cell RNA sequencing and the Nephroseq website. The expression of hub genes at the animal level by STZ -induced and db/db DKD mouse models was further verified.ResultsFinally, three hub genes, including FSTL1, CX3CR1 and AGR2 that were up-regulated in both the test set GSE30122 and the validation set GSE30529, were screened. The areas under the curve (AUCs) of ROC curves of hub genes were 0.911, 0.935 and 0.922, respectively, and 0.946 when taking as a whole. Correlation analysis showed that the expression level of three hub genes demonstrated their negative relationship with GFR, while those of FSTL1 displayed a positive correlation with the level of serum creatinine. GSEA was enriched in inflammatory and immune-related pathways. Single-nucleus RNA sequencing indicated the main distribution of FSTL1 in podocyte and mesangial cells, the high expression of CX3CR1 in leukocytes and the main localization of AGR2 in the loop of Henle. In mouse models, all three hub genes were increased in both STZ-induced and db/db DKD models.ConclusionMachine learning was combined with WGCNA, immune cell infiltration and PPI analyses to identify three hub genes associated with cuproptosis, immunity and diabetic nephropathy, which all have great potential as diagnostic markers for DKD and even predict disease progression.

BackgroundMechanical ventilation (MV) is vital for critically ill ICU patients but carries significant mortality risks. This study aims to develop a predictive model to estimate hospital mortality among MV patients, utilizing comprehensive health data to assist ICU physicians with early-stage alerts.MethodsWe developed a Machine Learning (ML) framework to predict hospital mortality in ICU patients receiving MV. Using the MIMIC-III database, we identified 25,202 eligible patients through ICD-9 codes. We employed backward elimination and the Lasso method, selecting 32 features based on clinical insights and literature. Data preprocessing included eliminating columns with over 90% missing data and using mean imputation for the remaining missing values. To address class imbalance, we used the Synthetic Minority Over-sampling Technique (SMOTE). We evaluated several ML models, including CatBoost, XGBoost, Decision Tree, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Logistic Regression, using a 70/30 train-test split. The CatBoost model was chosen for its superior performance in terms of accuracy, precision, recall, F1-score, AUROC metrics, and calibration plots.ResultsThe study involved a cohort of 25,202 patients on MV. The CatBoost model attained an AUROC of 0.862, an increase from an initial AUROC of 0.821, which was the best reported in the literature. It also demonstrated an accuracy of 0.789, an F1-score of 0.747, and better calibration, outperforming other models. These improvements are due to systematic feature selection and the robust gradient boosting architecture of CatBoost.ConclusionThe preprocessing methodology significantly reduced the number of relevant features, simplifying computational processes, and identified critical features previously overlooked. Integrating these features and tuning the parameters, our model demonstrated strong generalization to unseen data. This highlights the potential of ML as a crucial tool in ICUs, enhancing resource allocation and providing more personalized interventions for MV patients.

Plasma electrolytic oxidation (PEO) is normally carried out under conditions with electrolyte cooling. However, the effect of the temperature of the electrolytes on the PEO behavior and properties of the resulting coatings is seldom investigated. In this study, PEO of pure Al was carried out in a dilute aluminate electrolyte with the electrolyte temperature being controlled under low (~10–30 °C), medium (~40–50 °C) and high (~70–80 °C) temperature ranges, respectively. The morphology, microstructure, composition and phase component of the coatings fabricated under the different temperature ranges were analyzed by scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS) and X-ray diffraction (XRD). The corrosion resistances of the coatings were evaluated by electrochemical methods. The hardness of the coatings and substrate following the PEO treatment in the different electrolyte temperature ranges were also tested. It was found that a higher electrolyte temperature resulted in a higher growth rate and rougher coatings. Moreover, the α-Al2O3 content was reduced as the electrolyte temperature increased. The highest corrosion resistance was registered for the coating obtained under the lowest temperature range, whereas the corrosion resistance of the coating obtained under the highest temperature range was the worst. The higher growth rate under high electrolyte temperatures was associated with the increased kinetics of the PEO reactions. However, the temperature of the electrolyte should be controlled under a suitable range to ensure reasonable coating properties.