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  Novel Co(OH) 2 /MXene-Ti 3 C 2 nanocomposites with oxidase (OXD)-mimic, peroxidase (POD)-mimic, and catalase (CAT)-mimic activities were prepared by a simple two-step method. The Co(OH) 2 /MXene-Ti 3 C 2 nanocomposites with triple-enzyme mimic activities were embedded into sodium alginate (SA) gels for the first time to fabricate a target-responsive hydrogel-assisted assay. The catalytic mechanism and steady-state kinetics of Co(OH) 2 /MXene-Ti 3 C 2 nanocomposites were investigated. Subsequently, hypoxanthine (Hx) was catalyzed by xanthine oxidase (XOD) to form H 2 O 2 , which reacts with 3,3′,5,5′-tetramethyl-benzidine (TMB) in the presence of Co(OH) 2 /MXene-Ti 3 C 2 nanocomposites to form a blue oxide (ox-TMB) in the hydrogel. The visible color change of the hydrogel with the increase of Hx concentration can be recognized through a smartphone App to transfer the red (R), green (G), and blue (B) values for the quantitative determination of  Hx, with a detection range from 5 to 250 μM, and detection limit of 0.2 μM. The method was applied to the determination of Hx content in different aquatic products. The spiked recoveries of the aquatic products were from 94.1 to 106.4%, and the relative standard deviations (RSD) were less than 5.4%. Our results show that the Co(OH) 2 /MXene-Ti 3 C 2 nanocomposites hydrogel-assisted colorimetric biosensor is cost-effective, sensitive, and selective and has excellent application prospects for in-the-field determination of Hx. Graphical abstract

Containers and microservices have become the most popular method for hosting IoT applications in cloud servers. However, one major security issue of this method is that if a container image contains software with security vulnerabilities, the associated microservices also become vulnerable at run-time. Existing works attempted to reduce this risk with vulnerability-scanning tools. They, however, demand an up-to-date database and may not work with unpublished vulnerabilities. In this paper, we propose a novel system to strengthen container security from unknown attack using the mimic defense framework. Specifically, we constructed a resource pool with variant images and observe the inconsistency in execution results, from which we can identify potential vulnerabilities. To avoid continuous attack, we created a graph-based scheduling strategy to maximize the randomness and heterogeneity of the images used to replace the current images. We implemented a prototype using Kubernetes. Experimental results show that our framework makes hackers have to send 54.9% more random requests to complete the attack and increases the defence success rate by around 8.16% over the baseline framework to avoid the continuous unknown attacks.

Background Acute kidney injury (AKI) is the most common and serious complication of sepsis, accompanied by high mortality and disease burden. The early prediction of AKI is critical for timely intervention and ultimately improves prognosis. This study aims to establish and validate predictive models based on novel machine learning (ML) algorithms for AKI in critically ill patients with sepsis. Methods Data of patients with sepsis were extracted from the Medical Information Mart for Intensive Care III (MIMIC- III) database. Feature selection was performed using a Boruta algorithm. ML algorithms such as logistic regression (LR), k -nearest neighbors (KNN), support vector machine (SVM), decision tree, random forest, Extreme Gradient Boosting (XGBoost), and artificial neural network (ANN) were applied for model construction by utilizing tenfold cross-validation. The performances of these models were assessed in terms of discrimination, calibration, and clinical application. Moreover, the discrimination of ML-based models was compared with those of Sequential Organ Failure Assessment (SOFA) and the customized Simplified Acute Physiology Score (SAPS) II model. Results A total of 3176 critically ill patients with sepsis were included for analysis, of which 2397 cases (75.5%) developed AKI during hospitalization. A total of 36 variables were selected for model construction. The models of LR, KNN, SVM, decision tree, random forest, ANN, XGBoost, SOFA and SAPS II score were established and obtained area under the receiver operating characteristic curves of 0.7365, 0.6637, 0.7353, 0.7492, 0.7787, 0.7547, 0.821, 0.6457 and 0.7015, respectively. The XGBoost model had the best predictive performance in terms of discrimination, calibration, and clinical application among all models. Conclusion The ML models can be reliable tools for predicting AKI in septic patients. The XGBoost model has the best predictive performance, which can be used to assist clinicians in identifying high-risk patients and implementing early interventions to reduce mortality.

[This corrects the article DOI: 10.3389/fphar.2025.1613681.].

Electrocatalytic generation of H₂ is challenging in neutral pH water, where high catalytic currents for the hydrogen evolution reaction (HER) are particularly sensitive to the proton source and solution characteristics. A tris(hydroxymethyl)aminomethane (TRIS) solution at pH 7 with a [2Fe-2S]-metallopolymer electrocatalyst gave catalytic current densities around two orders of magnitude greater than either a more conventional sodium phosphate solution or a potassium chloride (KCl) electrolyte solution. For a planar polycrystalline Pt disk electrode, a TRIS solution at pH 7 increased the catalytic current densities for H₂ generation by 50 mA/cm² at current densities over 100 mA/cm² compared to a sodium phosphate solution. As a special feature of this study, TRIS is acting not only as the primary source of protons and the buffer of the pH, but the protonated TRIS ([TRIS-H]⁺) is also the sole cation of the electrolyte. A species that is simultaneously the proton source, buffer, and sole electrolyte is termed a protic buffer electrolyte (PBE). The structure–activity relationships of the TRIS PBE that increase the HER rate of the metallopolymer and platinum catalysts are discussed. These results suggest that appropriately designed PBEs can improve HER rates of any homogeneous or heterogeneous electrocatalyst system. General guidelines for selecting a PBE to improve the catalytic current density of HER systems are offered.

Background Sepsis is a significant cause of mortality in-hospital, especially in ICU patients. Early prediction of sepsis is essential, as prompt and appropriate treatment can improve survival outcomes. Machine learning methods are flexible prediction algorithms with potential advantages over conventional regression and scoring system. The aims of this study were to develop a machine learning approach using XGboost to predict the 30-days mortality for MIMIC-III Patients with sepsis-3 and to determine whether such model performs better than traditional prediction models. Methods Using the MIMIC-III v1.4, we identified patients with sepsis-3. The data was split into two groups based on death or survival within 30 days and variables, selected based on clinical significance and availability by stepwise analysis, were displayed and compared between groups. Three predictive models including conventional logistic regression model, SAPS-II score prediction model and XGBoost algorithm model were constructed by R software. Then, the performances of the three models were tested and compared by AUCs of the receiver operating characteristic curves and decision curve analysis. At last, nomogram and clinical impact curve were used to validate the model. Results A total of 4559 sepsis-3 patients are included in the study, in which, 889 patients were death and 3670 survival within 30 days, respectively. According to the results of AUCs (0.819 [95% CI 0.800–0.838], 0.797 [95% CI 0.781–0.813] and 0.857 [95% CI 0.839–0.876]) and decision curve analysis for the three models, the XGboost model performs best. The risk nomogram and clinical impact curve verify that the XGboost model possesses significant predictive value. Conclusions Using machine learning technique by XGboost, more significant prediction model can be built. This XGboost model may prove clinically useful and assist clinicians in tailoring precise management and therapy for the patients with sepsis-3.

Background Acute kidney injury (AKI) is a common complication in critically ill patients with sepsis and is often associated with a poor prognosis. We aimed to construct and validate an interpretable prognostic prediction model for patients with sepsis-associated AKI (S-AKI) using machine learning (ML) methods. Methods Data on the training cohort were collected from the Medical Information Mart for Intensive Care IV database version 2.2 to build the model, and data of patients were extracted from Hangzhou First People's Hospital Affiliated to Zhejiang University School of Medicine for external validation of model. Predictors of mortality were identified using Recursive Feature Elimination (RFE). Then, random forest, extreme gradient boosting (XGBoost), multilayer perceptron classifier, support vector classifier, and logistic regression were used to establish a prognosis prediction model for 7, 14, and 28 days after intensive care unit (ICU) admission, respectively. Prediction performance was assessed using the receiver operating characteristic (ROC) curve and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) were used to interpret the ML models. Results In total, 2599 patients with S-AKI were included in the analysis. Forty variables were selected for the model development. According to the areas under the ROC curve (AUC) and DCA results for the training cohort, XGBoost model exhibited excellent performance with F1 Score of 0.847, 0.715, 0.765 and AUC (95% CI) of 0.91 (0.90, 0.92), 0.78 (0.76, 0.80), and 0.83 (0.81, 0.85) in 7 days, 14 days and 28 days group, respectively. It also demonstrated excellent discrimination in the external validation cohort. Its AUC (95% CI) was 0.81 (0.79, 0.83), 0.75 (0.73, 0.77), 0.79 (0.77, 0.81) in 7 days, 14 days and 28 days group, respectively. SHAP-based summary plot and force plot were used to interpret the XGBoost model globally and locally. Conclusions ML is a reliable tool for predicting the prognosis of patients with S-AKI. SHAP methods were used to explain intrinsic information of the XGBoost model, which may prove clinically useful and help clinicians tailor precise management.

Information regarding the prevalence and distribution of herbicide-resistant waterhemp [Amaranthus tuberculatus (Moq.) Sauer] in Minnesota is limited. Whole-plant bioassays were conducted in the greenhouse on 90 A. tuberculatus populations collected from 47 counties in Minnesota. Eight postemergence herbicides, 2,4-D, atrazine, dicamba, fomesafen, glufosinate, glyphosate, imazamox, and mesotrione, were applied at 1× and 3× the labeled doses. Based on their responses, populations were classified into highly resistant (≥40 % survival at 3× the labeled dose), moderately resistant (<40% survival at 3× the labeled dose but ≥40% survival at 1× the labeled dose), less sensitive (10% to 39% survival at 1× the labeled dose), and susceptible (<10% survival at 1× the labeled dose) categories. All 90 populations were resistant to imazamox, while 89% were resistant to glyphosate. Atrazine, fomesafen, and mesotrione resistance was observed in 47%, 31%, and 22% of all populations, respectively. Ten percent of the populations were resistant to 2,4-D, and 2 of 90 populations exhibited >40% survival following dicamba application at the labeled dose. No population was confirmed to be resistant to glufosinate. However, 22% of all populations were classified as less sensitive to glufosinate. Eighty-two populations were found to be multiple-herbicide resistant. Among these, 15 populations exhibited resistance to four different herbicide sites of action (SOAs); 7 and 4 populations were resistant to five and six SOAs, respectively. All six-way-resistant populations were from southwest Minnesota. Two populations, one from Lincoln County and the other from Lyon County, were resistant to 2,4-D, atrazine, dicamba, fomesafen, glyphosate, imazamox, and mesotrione, leaving only glufosinate as a postemergence control option for these populations in corn (Zea mays L.) and soybean [Glycine max (L.) Merr.]. Diversified management tactics, including nonchemical control measures along with herbicide applications from effective SOAs, should be implemented to slow down the evolution and spread of herbicide-resistant A. tuberculatus populations.

Although accumulating researches were done for investigating the relationship between triglyceride‐glucose index (TyG index) and different diseases, none of the researches have been made in sepsis yet. In this study, we aimed to explore the relationship between TyG index and clinical outcomes in sepsis based on a large critical care public database. Sepsis patients in Medical Information Mart for Intensive Care IV (MIMIC‐IV) Database were included. The exposure was TyG index, which was calculated by the equation: ln (TG (mg/dL) × FBG (mg/dL)/2). The outcomes were in‐hospital mortality and 1‐year mortality. The relationship between TyG index and outcomes was performed by Cox regression analysis. Smooth fitting curves were constructed by using generalized additive model. Kaplan–Meier analyses for cumulative hazard of 1‐year mortality in different groups were done. 1103 sepsis patients were included with a median TyG index of 9.78. The mortalities of in‐hospital and 1‐year were 37.53% (n = 414) and 42.25% (n = 466), respectively. After adjusting confounders, there was a significantly negative relationship between TyG index and mortalities of in‐hospital and 1‐year. With the per unit increment in TyG index, the risk of in‐hospital and 1‐year mortality both decreased by 21% (HR = 0.79, 95% CI: 0.66–0.94, p = 0.0086 and HR = 0.79, 95% CI: 0.66–0.94, p = 0.0080, respectively). A negative relationship between TyG index and clinical outcomes in sepsis was found.

[This corrects the article DOI: 10.3389/fnhum.2025.1737110.].