Microbial communities and mobile genetic elements determine the variations of antibiotic resistance genes for a continuous year in the urban river deciphered by metagenome assembly.

Antibiotic resistance genes (ARGs) have become emerging environmental contaminants influenced by intricate regulatory factors. However, there is a lack of comprehensive studies on the evolution and distribution of ARGs over a full year in urban rivers, which serve as significant reservoirs of ARGs due to dynamic human activities. In this study, we conducted a 12-month metagenomic assembly to explore the microbial communities, ARGs, mobile genetic elements (MGEs) coexisting with ARGs, ARGs hosts, and the impact of environmental factors. Bacitracin (32%-47%) and multidrug (13%-24%) were detected throughout the year, constituting over 60% of the total abundance, making them the primary ARGs types. The assembly mechanisms of microbial communities and ARGs were primarily driven by stochastic processes. Integrase, IntI1, recombinase, and transposase were identified as the main MGEs coexisting with ARGs. Procrustes analysis revealed a significant structural association, indicating that the composition of host communities likely plays crucial roles in the seasonal composition and distribution of ARGs. Human pathogenic bacteria (HPBs) were identified in the summer, autumn, and winter, with Escherichia coli, Klebsiella pneumoniae, Acinetobacter lwoffii, and Burkholderiales bacterium being the primary HPBs. Mantle tests and PLS-PM equation analysis indicated that microbial communities and MGEs are the most critical factors determining the distribution and composition of ARGs in the river. Environmental factors (including water properties and nutrients) and ARGs hosts influence the evolution and abundance of ARGs by directly regulating microbial communities and MGEs. This study provides critical insights into risk assessment and management of ARGs in urban rivers.

Blocking Metallothionein-2 Expression by Copper-Doped Carbon Dots Induces Cellular Antioxidant System Collapse for Antitumor Therapy.

The insufficient antioxidant reserves in tumor cells play a critical role in reactive oxygen species (ROS)-mediated therapeutics. Metallothionein-2 (MT-2), an intracellular cysteine-rich protein renowned for its potent antioxidant properties, is intricately involved in tumor development and correlates with a poor prognosis. Consequently, MT-2 emerges as a promising target for tumor therapy. Herein, we present the development of copper-doped carbon dots (Cu-CDs) to target MT-2 to compromise the delicate antioxidant reserves in tumor cells. These Cu-CDs with high tumor accumulation and prolonged body retention can effectively suppress tumor growth by inducing oxidative stress. Transcriptome sequencing unveils a significant decrease in MT-2 expression within the in vivo tumor samples. Further mechanical investigations demonstrate that the antitumor effect of Cu-CDs is intricately linked to apolipoprotein E (ApoE)-mediated downregulation of MT-2 expression and the collapse of the antioxidant system. The robust antitumor efficacy of Cu-CDs provides invaluable insights into developing MT-2-targeted nanomedicine for cancer therapies.

Coupled effects of redox-active substances and microbial communities on reactive oxygen species in rhizosphere sediments of submerged macrophytes.

Reactive oxygen species (ROS) play crucial roles in element cycling and pollutant dynamics, but their variations and mechanisms in the rhizosphere of submerged macrophytes are poorly investigated. This study investigated the light-dark cycle fluctuations and periodic variations in ROS, redox-active substances, and microbial communities in the rhizosphere of Vallisneria natans. The results showed sustained production and significant diurnal fluctuations in the O2•- and •OH from 27.6 ± 3.7 to 61.7 ± 3.0 µmol/kg FW and 131.0 ± 6.8 to 195.4 ± 8.7 µmol/kg FW, respectively, which simultaneously fluctuated with the redox-active substances. The ROS contents in the rhizosphere were higher than those observed in non-rhizosphere sediments over the V. natans growth period, exhibiting increasing-decreasing trends. According to the redundancy analysis results, water-soluble phenols, fungi, and bacteria were the main factors influencing ROS production in the rhizosphere, showing contribution rates of 74.0, 17.3, and 4.4 %, respectively. The results of partial least squares path modeling highlighted the coupled effects of redox-active substances and microbial metabolism. Our findings also demonstrated the degradation effect of ROS in rhizosphere sediments of submerged macrophytes. This study provides experimental evidence of ROS-related rhizosphere effects and further insights into submerged macrophytes-based ecological restoration.

Development and Validation of an Explainable Machine Learning Model for Predicting Myocardial Injury After Noncardiac Surgery in Two Centers in China: Retrospective Study.

Background: Myocardial injury after noncardiac surgery (MINS) is an easily overlooked complication but closely related to postoperative cardiovascular adverse outcomes; therefore, the early diagnosis and prediction are particularly important. Objective: We aimed to develop and validate an explainable machine learning (ML) model for predicting MINS among older patients undergoing noncardiac surgery. Methods: The retrospective cohort study included older patients who had noncardiac surgery from 1 northern center and 1 southern center in China. The data sets from center 1 were divided into a training set and an internal validation set. The data set from center 2 was used as an external validation set. Before modeling, the least absolute shrinkage and selection operator and recursive feature elimination methods were used to reduce dimensions of data and select key features from all variables. Prediction models were developed based on the extracted features using several ML algorithms, including category boosting, random forest, logistic regression, naïve Bayes, light gradient boosting machine, extreme gradient boosting, support vector machine, and decision tree. Prediction performance was assessed by the area under the receiver operating characteristic (AUROC) curve as the main evaluation metric to select the best algorithms. The model performance was verified by internal and external validation data sets with the best algorithm and compared to the Revised Cardiac Risk Index. The Shapley Additive Explanations (SHAP) method was applied to calculate values for each feature, representing the contribution to the predicted risk of complication, and generate personalized explanations. Results: A total of 19,463 eligible patients were included; among those, 12,464 patients in center 1 were included as the training set; 4754 patients in center 1 were included as the internal validation set; and 2245 in center 2 were included as the external validation set. The best-performing model for prediction was the CatBoost algorithm, achieving the highest AUROC of 0.805 (95% CI 0.778-0.831) in the training set, validating with an AUROC of 0.780 in the internal validation set and 0.70 in external validation set. Additionally, CatBoost demonstrated superior performance compared to the Revised Cardiac Risk Index (AUROC 0.636; P<.001). The SHAP values indicated the ranking of the level of importance of each variable, with preoperative serum creatinine concentration, red blood cell distribution width, and age accounting for the top three. The results from the SHAP method can predict events with positive values or nonevents with negative values, providing an explicit explanation of individualized risk predictions. Conclusions: The ML models can provide a personalized and fairly accurate risk prediction of MINS, and the explainable perspective can help identify potentially modifiable sources of risk at the patient level.

AI-Powered Microfluidics: Shaping the Future of Phenotypic Drug Discovery.

Phenotypic drug discovery (PDD), which involves harnessing biological systems directly to uncover effective drugs, has undergone a resurgence in recent years. The rapid advancement of artificial intelligence (AI) over the past few years presents numerous opportunities for augmenting phenotypic drug screening on microfluidic platforms, leveraging its predictive capabilities, data analysis, efficient data processing, etc. Microfluidics coupled with AI is poised to revolutionize the landscape of phenotypic drug discovery. By integrating advanced microfluidic platforms with AI algorithms, researchers can rapidly screen large libraries of compounds, identify novel drug candidates, and elucidate complex biological pathways with unprecedented speed and efficiency. This review provides an overview of recent advances and challenges in AI-based microfluidics and their applications in drug discovery. We discuss the synergistic combination of microfluidic systems for high-throughput screening and AI-driven analysis for phenotype characterization, drug-target interactions, and predictive modeling. In addition, we highlight the potential of AI-powered microfluidics to achieve an automated drug screening system. Overall, AI-powered microfluidics represents a promising approach to shaping the future of phenotypic drug discovery by enabling rapid, cost-effective, and accurate identification of therapeutically relevant compounds.

Preoperative N-terminal Pro-B-type Natriuretic Peptide and High-sensitivity Cardiac Troponin T and Outcomes after Major Noncardiac Surgery: A Prospective Cohort Study.

BACKGROUND: Patients undergoing noncardiac surgery have varying risk of cardiovascular complications. This study evaluated preoperative N-terminal pro-B-type natriuretic peptide (NT-proBNP) and high-sensitivity cardiac troponin T to enhance cardiovascular events prediction for major noncardiac surgery. METHODS: This prospective cohort study included adult patients with cardiovascular disease or risk factors undergoing elective major noncardiac surgery at four hospitals in China. Blood samples were collected within 30 days before surgery for NT-proBNP and high-sensitivity troponin T (hs-TnT) measurements. The primary outcome was a composite of any cardiovascular events within 30 days after surgery. Logistic regression models were used to assess associations, and the predictive performance was evaluated primarily using area under the receiver operating characteristics curve (AUC) and fraction of new predictive information. RESULTS: Between June 2019 and September 2021, a total of 2,833 patients were included, with 435 (15.4%) experiencing the primary outcome. In the logistic regression model that included clinical variables and both biomarkers, the odds ratio for the primary outcome was 1.68 (95% CI, 1.37 to 2.07) when comparing the 75th percentile to the 25th percentile of NT-proBNP distribution, and 1.91 (95% CI, 1.50 to 2.43) for hs-TnT. Each biomarker enhanced model discrimination beyond clinical predictors, with a change in AUC of 0.028 for NT-proBNP and 0.029 for high-sensitivity cardiac troponin T, and a fraction of new information of 0.164 and 0.149, respectively. The model combining both biomarkers demonstrated the best discrimination, with a change in AUC of 0.042 and a fraction of new information of 0.219. CONCLUSIONS: Preoperative NT-proBNP and hs-TnT both improved the prediction for cardiovascular events after noncardiac surgery in addition to clinical evaluation, with their combination providing maximal predictive information.

Frailty and psychiatric disorders: A bidirectional Mendelian randomization study.

BACKGROUND: The association between frailty and psychiatric disorders has been reported in observational studies. However, it is unclear whether frailty facilitates the appearance of psychiatric disorders or vice versa. Therefore, we conducted a bidirectional Mendelian randomization (MR) study to evaluate the causality. METHODS: Independent genetic variants associated with frailty index (FI) and psychiatric disorders were obtained from large genome-wide association studies (GWAS). The inverse variance weighted method was utilized as the primary method to estimate causal effects, followed by various sensitivity analyses. Multivariable analyses were performed to further adjust for potential confounders. RESULTS: The present MR study revealed that genetically predicted FI was significantly and positively associated with the risk of major depressive disorder (MDD) (odds ratio [OR] 1.79, 95 % confidence interval [CI] 1.48-2.15, P = 1.06 × 10-9), anxiety disorder (OR 1.61, 95 % CI 1.19-2.18, P = 0.002) and neuroticism (OR 1.38, 95 % CI 1.18-1.61, P = 3.73 × 10-5). In the reverse MR test, genetic liability to MDD (beta 0.232, 95 % CI 0.189-0.274, P = 1.00 × 10-26) and neuroticism (beta 0.128, 95 % CI 0.081-0.175, P = 8.61 × 10-8) were significantly associated with higher FI. Multivariable analyses results supported the causal association between FI and MDD and neuroticism. LIMITATIONS: Restriction to European populations, and sample selection bias. CONCLUSIONS: Our study suggested a bidirectional causal association between frailty and MDD neuroticism, and a positive correlation of genetically predicted frailty on the risk of anxiety disorder. Developing a deeper understanding of these associations is essential to effectively manage frailty and optimize mental health in older adults.

Inhibition of GBP5 activates autophagy to alleviate inflammatory response in LPS-induced lung injury in mice.

BACKGROUND: Pulmonary emphysema is a condition that causes damage to the lung tissue over time. GBP5, as part of the guanylate-binding protein family, is dysregulated in mouse pulmonary emphysema. However, the role of GBP5 in lung inflammation in ARDS remains unveiled. METHODS: To investigate whether GBP5 regulates lung inflammation and autophagy regulation, the study employed a mouse ARDS model and MLE-12 cell culture. Vector transfection was performed for the genetic manipulation of GBP5. Then, RT-qPCR, WB and IHC staining were conducted to assess its transcriptional and expression levels. Histological features of the lung tissue were observed through HE staining. Moreover, ELISA was conducted to evaluate the secretion of inflammatory cytokines, autophagy was assessed by immunofluorescent staining, and MPO activity was determined using a commercial kit. RESULTS: Our study revealed that GBP5 expression was altered in mouse ARDS and LPS-induced MLE-12 cell models. Moreover, the suppression of GBP5 reduced lung inflammation induced by LPS in mice. Conversely, overexpression of GBP5 diminished the inhibitory impact of LPS on ARDS during autophagy, leading to increased inflammation. In the cell line of MLE-12, GBP5 exacerbates LPS-induced inflammation by blocking autophagy. CONCLUSION: The study suggests that GBP5 facilitates lung inflammation and autophagy regulation. Thus, GBP5 could be a potential therapeutic approach for improving ARDS treatment outcomes, but further research is required to validate these findings.

Comparison of three concentrations of ropivacaine in posterior quadratus lumborum block: A randomized clinical trial.

Background: A conclusive evidence regarding the optimal concentration and volume of local anesthetic for quadratus lumborum block is lacking. Methods: In this single-center, prospective, randomized, controlled study, 60 patients scheduled for laparoscopic colorectal surgery were randomly assigned to 3 different combinations of volume and concentration of ropivacaine (3 mg/kg) - Group 0.25%, Group 0.375% and Group 0.5%. All subjects received ultrasound-guided posterior quadratus lumborum block prior to the induction. The primary outcome was the complete sensory block rate of surgical site measured at 30 min after quadratus lumborum block, after extubation, at 12, 24, and 48 h after operation. Secondary outcomes were the changes in hemodynamic parameters before and after incision (ΔSBP, ΔDBP and ΔHR), postoperative pain score, the sufentanil consumption after surgery, length of stay and adverse reactions. Results: The sensory block rate of surgical site at 5 time points differed significantly among the three groups (P < 0.001). Both Group 0.375% (P < 0.001) and Group 0.5% (P < 0.001) had a higher sensory block rate than Group 0.25%, but no significant difference was observed between the former two. Group 0.375% and Group 0.5% had lower postoperative pain scores, lower sufentanil consumption after surgery and shorter length of stay. No statistical difference was observed in ΔSBP, ΔDBP, ΔHR and the incidence of adverse reactions. Conclusions: 0.375% and 0.5% ropivacaine in posterior quadratus lumborum block provide better sensory block of surgical site when compared to 0.25% in laparoscopic colorectal surgery. Trial registration number: Chinese Clinical Trials Registry (ChiCTR2100043949).

Preoperative risk prediction models for acute kidney injury after noncardiac surgery: an independent external validation cohort study.

BACKGROUND: Numerous models have been developed to predict acute kidney injury (AKI) after noncardiac surgery, yet there is a lack of independent validation and comparison among them. METHODS: We conducted a systematic literature search to review published risk prediction models for AKI after noncardiac surgery. An independent external validation was performed using a retrospective surgical cohort at a large Chinese hospital from January 2019 to October 2022. The cohort included patients undergoing a wide range of noncardiac surgeries with perioperative creatinine measurements. Postoperative AKI was defined according to the Kidney Disease Improving Global Outcomes creatinine criteria. Model performance was assessed in terms of discrimination (area under the receiver operating characteristic curve, AUROC), calibration (calibration plot), and clinical utility (net benefit), before and after model recalibration through intercept and slope updates. A sensitivity analysis was conducted by including patients without postoperative creatinine measurements in the validation cohort and categorising them as non-AKI cases. RESULTS: Nine prediction models were evaluated, each with varying clinical and methodological characteristics, including the types of surgical cohorts used for model development, AKI definitions, and predictors. In the validation cohort involving 13,186 patients, 650 (4.9%) developed AKI. Three models demonstrated fair discrimination (AUROC between 0.71 and 0.75); other models had poor or failed discrimination. All models exhibited some miscalibration; five of the nine models were well-calibrated after intercept and slope updates. Decision curve analysis indicated that the three models with fair discrimination consistently provided a positive net benefit after recalibration. The results were confirmed in the sensitivity analysis. CONCLUSIONS: We identified three models with fair discrimination and potential clinical utility after recalibration for assessing the risk of acute kidney injury after noncardiac surgery.