Radioresistance is an important challenge for clinical treatments. The main causes of radioresistance include hypoxia in the tumor microenvironment, the antioxidant system within cancer cells, and ...the upregulation of DNA repair proteins. Here, a multiple radiosensitization strategy of high‐Z‐element‐based radiation enhancement is designed, attenuating hypoxia and microRNA therapy. The novel 2D graphdiyne (GDY) can firmly anchor and disperse CeO2 nanoparticles to form GDY–CeO2 nanocomposites, which exhibit superior catalase‐mimic activity in decomposing H2O2 to O2 to significantly alleviate tumor hypoxia, promote radiation‐induced DNA damage, and ultimately inhibit tumor growth in vivo. The miR181a‐2‐3p (miR181a) serum levels in patients are predictive of the response to preoperative radiotherapy in locally advanced esophageal squamous cell carcinoma (ESCC) and facilitate personalized treatment. Moreover, miR181a can act as a radiosensitizer by directly targeting RAD17 and regulating the Chk2 pathway. Subsequently, the GDY–CeO2 nanocomposites with miR181a are conjugated with the iRGD‐grafted polyoxyethylene glycol (short for nano‐miR181a), which can increase the stability, efficiently deliver miR181a to tumor, and exhibit low toxicity. Notably, nano‐miR181a can overcome radioresistance and enhance therapeutic efficacy both in a subcutaneous tumor model and human‐patient‐derived xenograft models. Overall, this GDY–CeO2 nanozyme and miR181a‐based multisensitized radiotherapy strategy provides a promising therapeutic approach for ESCC.
A radiotherapy sensitization system that integrates graphdiyne–CeO2‐based nanozymes and miR181a‐2‐3p (miR181a) delivery with excellent catalase‐mimic activity and radiosensitization is developed for the efficient radiotherapy of esophageal squamous cell carcinoma (ESCC). Since miR181a is secreted and predicts response to preoperative radiotherapy in human locally advanced ESCC, this strategy may offer opportunities for the clinical translation of therapies and facilitate patient‐personalized treatment.
Acute kidney injury (AKI) is a common and potential life-threatening disease in patients admitted to hospital, affecting 10%-15% of all hospitalizations and around 50% of patients in the intensive ...care unit. Severe, recurrent, and uncontrolled AKI may progress to chronic kidney disease or end-stage renal disease. AKI thus requires more efficient, specific therapies, rather than just supportive therapy. Mesenchymal stem cells (MSCs) are considered to be promising cells for cellular therapy because of their ease of harvesting, low immunogenicity, and ability to expand
. Recent research indicated that the main therapeutic effects of MSCs were mediated by MSC-derived extracellular vesicles (MSC-EVs). Furthermore, compared with MSCs, MSC-EVs have lower immunogenicity, easier storage, no tumorigenesis, and the potential to be artificially modified. We reviewed the therapeutic mechanism of MSCs and MSC-EVs in AKI, and considered recent research on how to improve the efficacy of MSC-EVs in AKI. We also summarized and analyzed the potential and limitations of EVs for the treatment of AKI to provide ideas for future clinical trials and the clinical application of MSC-EVs in AKI.
As the earliest commercial cathode material for lithium-ion batteries, lithium cobalt oxide (LiCoO
2
) shows various advantages, including high theoretical capacity, excellent rate capability, ...compressed electrode density, etc. Until now, it still plays an important role in the lithium-ion battery market. Due to these advantages, further increasing the charging cutoff voltage of LiCoO
2
to guarantee higher energy density is an irresistible development trend of LiCoO
2
cathode materials in the future. However, using high charging cutoff voltage may induce a lot of negative effects, especially the rapid decay of cycle capacity. These are mainly caused by rapid destruction of crystal structure and aggravation of interface side reaction at high voltage during the cycle. Therefore, how to maintain a stable crystal structure of LiCoO
2
to ensure the excellent long cycle performance at high voltage is a hot research issue in the further application of LiCoO
2
. In this review, we summarized the failure causes and extensive solutions of LiCoO
2
at high voltage and promoted some new modification strategies. Moreover, the development trend of solving the failure problem of high-voltage LiCoO
2
in the future such as defect engineering and high-temperature shock technique is also discussed.
Graphical abstract
Patients receiving surgical treatment of acute type A Aortic Dissection (aTAAD) are common to suffer organ dysfunction in the intensive care unit due to overwhelming inflammation. Previous studies ...have revealed that glucocorticoids may reduce complications in certain patient groups, but evidence between postoperative glucocorticoids administration and improvement in organ dysfunction after aTAAD surgery are lacking.
This study will be an investigator-initiated, prospective, single-blind, randomized, single-center study. Subjects with confirmed diagnosis of aTAAD undergoing surgical treatment will be enrolled and 1:1 randomly assigned to receive either glucocorticoids or normal treatment. All patients in the glucocorticoids group will be given methylprednisolone intravenously for 3 days after enrollment. The primary endpoint will be the amplitude of variation of Sequential Organ Failure Assessment score on post-operative day 4 compared to baseline.
The trial will explore the rationale for postoperative application of glucocorticoids in patients after aTAAD surgery.
This study has been registered on ClinicalTrials.gov (NCT04734418).
DNA hybridization can finely regulate the intrinsic glucose oxidase like catalytic activity of AuNPs owing to the marked difference in adsorption of single‐ and double‐stranded DNA on its surface. A ...sensing strategy for DNA and microRNA is presented; in a different approach, this DNA‐regulated AuNP catalysis was coupled with AuNP‐mediated seed growth, which was monitored in real time and at a single‐nanoparticle level.
Background:
Extubation failure (EF) can lead to an increased chance of ventilator-associated pneumonia, longer hospital stays, and a higher mortality rate. This study aimed to develop and validate an ...accurate machine-learning model to predict EF in intensive care units (ICUs).
Methods:
Patients who underwent extubation in the Medical Information Mart for Intensive Care (MIMIC)-IV database were included. EF was defined as the need for ventilatory support (non-invasive ventilation or reintubation) or death within 48 h following extubation. A machine-learning model called Categorical Boosting (CatBoost) was developed based on 89 clinical and laboratory variables. SHapley Additive exPlanations (SHAP) values were calculated to evaluate feature importance and the recursive feature elimination (RFE) algorithm was used to select key features. Hyperparameter optimization was conducted using an automated machine-learning toolkit (Neural Network Intelligence). The final model was trained based on key features and compared with 10 other models. The model was then prospectively validated in patients enrolled in the Cardiac Surgical ICU of Zhongshan Hospital, Fudan University. In addition, a web-based tool was developed to help clinicians use our model.
Results:
Of 16,189 patients included in the MIMIC-IV cohort, 2,756 (17.0%) had EF. Nineteen key features were selected using the RFE algorithm, including age, body mass index, stroke, heart rate, respiratory rate, mean arterial pressure, peripheral oxygen saturation, temperature, pH, central venous pressure, tidal volume, positive end-expiratory pressure, mean airway pressure, pressure support ventilation (PSV) level, mechanical ventilation (MV) durations, spontaneous breathing trial success times, urine output, crystalloid amount, and antibiotic types. After hyperparameter optimization, our model had the greatest area under the receiver operating characteristic (AUROC: 0.835) in internal validation. Significant differences in mortality, reintubation rates, and NIV rates were shown between patients with a high predicted risk and those with a low predicted risk. In the prospective validation, the superiority of our model was also observed (AUROC: 0.803). According to the SHAP values, MV duration and PSV level were the most important features for prediction.
Conclusions:
In conclusion, this study developed and prospectively validated a CatBoost model, which better predicted EF in ICUs than other models.
Sepsis is an abnormal immune response after infection, wherein the lung is the most susceptible organ to fail, leading to acute lung injury. To overcome the limitations of current therapeutic ...strategies and develop more specific treatment, the inflammatory process, in which T cell-derived extracellular vesicles (EVs) play a central role, should be explored deeply.
Liquid chromatography-tandem mass spectrometry was performed for serum EV protein profiling. The serum diacylglycerol kinase kappa (DGKK) and endotoxin contents of patients with sepsis-induced lung injury were measured. Apoptosis, oxidative stress, and inflammation in A549 cells, bronchoalveolar lavage fluid, and lung tissues of mice were measured by flow cytometry, biochemical analysis, enzyme-linked immunosorbent assay, quantitative real-time polymerase chain reaction, and western blot.
DGKK, the key regulator of the diacylglycerol (DAG)/protein kinase C (PKC) pathway, exhibited elevated expression in serum EVs of patients with sepsis-induced lung injury and showed strong correlation with sepsis severity and disease progression. DGKK was expressed in CD4
T cells under regulation of the NF-κB pathway and delivered by EVs to target cells, including alveolar epithelial cells. EVs produced by CD4
T lymphocytes exerted toxic effects on A549 cells to induce apoptotic cell death, oxidative cell damage, and inflammation. In mice with sepsis induced by cecal ligation and puncture, EVs derived from CD4
T cells also promoted tissue damage, oxidative stress, and inflammation in the lungs. These toxic effects of T cell-derived EVs were attenuated by the inhibition of PKC and NOX4, the downstream effectors of DGKK and DAG.
This approach established the mechanism that T-cell-derived EVs carrying DGKK triggered alveolar epithelial cell apoptosis, oxidative stress, inflammation, and tissue damage in sepsis-induced lung injury through the DAG/PKC/NOX4 pathway. Thus, T-cell-derived EVs and the elevated distribution of DGKK should be further investigated to develop therapeutic strategies for sepsis-induced lung injury.
Abstract
Background
Noninvasive ventilation (NIV) has been widely used in critically ill patients after extubation. However, NIV failure is associated with poor outcomes. This study aimed to ...determine early predictors of NIV failure and to construct an accurate machine-learning model to identify patients at risks of NIV failure after extubation in intensive care units (ICUs).
Methods
Patients who underwent NIV after extubation in the eICU Collaborative Research Database (eICU-CRD) were included. NIV failure was defined as need for invasive ventilatory support (reintubation or tracheotomy) or death after NIV initiation. A total of 93 clinical and laboratory variables were assessed, and the recursive feature elimination algorithm was used to select key features. Hyperparameter optimization was conducted with an automated machine-learning toolkit called Neural Network Intelligence. A machine-learning model called Categorical Boosting (CatBoost) was developed and compared with nine other models. The model was then prospectively validated among patients enrolled in the Cardiac Surgical ICU of Zhongshan Hospital, Fudan University.
Results
Of 929 patients included in the eICU-CRD cohort, 248 (26.7%) had NIV failure. The time from extubation to NIV, age, Glasgow Coma Scale (GCS) score, heart rate, respiratory rate, mean blood pressure (MBP), saturation of pulse oxygen (SpO
2
), temperature, glucose, pH, pressure of oxygen in blood (PaO
2
), urine output, input volume, ventilation duration, and mean airway pressure were selected. After hyperparameter optimization, our model showed the greatest accuracy in predicting NIV failure (AUROC: 0.872 95% CI 0.82–0.92) among all predictive methods in an internal validation. In the prospective validation cohort, our model was also superior (AUROC: 0.846 95% CI 0.80–0.89). The sensitivity and specificity in the prediction group is 89% and 75%, while in the validation group they are 90% and 70%. MV duration and respiratory rate were the most important features. Additionally, we developed a web-based tool to help clinicians use our model.
Conclusions
This study developed and prospectively validated the CatBoost model, which can be used to identify patients who are at risk of NIV failure. Thus, those patients might benefit from early triage and more intensive monitoring.
Trial registration
: NCT03704324. Registered 1 September 2018,
https://register.clinicaltrials.gov
.
Background
Evaluation of fluid responsiveness during veno-arterial extracorporeal membrane oxygenation (VA-ECMO) support is crucial. The aim of this study was to investigate whether changes in left ...ventricular outflow tract velocity–time integral (ΔVTI), induced by a Trendelenburg maneuver, could predict fluid responsiveness during VA-ECMO.
Methods
This prospective study was conducted in patients with VA-ECMO support. The protocol included four sequential steps: (1) baseline-1, a supine position with a 15° upward bed angulation; (2) Trendelenburg maneuver, 15° downward bed angulation; (3) baseline-2, the same position as baseline-1, and (4) fluid challenge, administration of 500 mL gelatin over 15 min without postural change. Hemodynamic parameters were recorded at each step. Fluid responsiveness was defined as ΔVTI of 15% or more, after volume expansion.
Results
From June 2018 to December 2019, 22 patients with VA-ECMO were included, and a total of 39 measurements were performed. Of these, 22 measurements (56%) met fluid responsiveness. The
R
2
of the linear regression was 0.76, between ΔVTIs induced by Trendelenburg maneuver and the fluid challenge. The area under the receiver operating characteristic curve of ΔVTI induced by Trendelenburg maneuver to predict fluid responsiveness was 0.93 95% confidence interval (CI) 0.81–0.98, with a sensitivity of 82% (95% CI 60–95%), and specificity of 88% (95% CI 64–99%), at a best threshold of 10% (95% CI 6–12%).
Conclusions
Changes in VTI induced by the Trendelenburg maneuver could effectively predict fluid responsiveness in VA-ECMO patients.
Trial registration
ClinicalTrials.gov, NCT 03553459 (the TEMPLE study). Registered on May 30, 2018
Sepsis-induced coagulopathy (SIC) denotes an increased mortality rate and poorer prognosis in septic patients.
Our study aimed to develop and validate machine-learning models to dynamically predict ...the risk of SIC in critically ill patients with sepsis.
Machine-learning models were developed and validated based on two public databases named Medical Information Mart for Intensive Care (MIMIC)-IV and the eICU Collaborative Research Database (eICU-CRD). Dynamic prediction of SIC involved an evaluation of the risk of SIC each day after the diagnosis of sepsis using 15 predictive models. The best model was selected based on its accuracy and area under the receiver operating characteristic curve (AUC), followed by fine-grained hyperparameter adjustment using the Bayesian Optimization Algorithm. A compact model was developed, based on 15 features selected according to their importance and clinical availability. These two models were compared with Logistic Regression and SIC scores in terms of SIC prediction.
Of 11,362 patients in MIMIC-IV included in the final cohort, a total of 6,744 (59%) patients developed SIC during sepsis. The model named Categorical Boosting (CatBoost) had the greatest AUC in our study (0.869; 95% CI: 0.850-0.886). Coagulation profile and renal function indicators were the most important features for predicting SIC. A compact model was developed with an AUC of 0.854 (95% CI: 0.832-0.872), while the AUCs of Logistic Regression and SIC scores were 0.746 (95% CI: 0.735-0.755) and 0.709 (95% CI: 0.687-0.733), respectively. A cohort of 35,252 septic patients in eICU-CRD was analyzed. The AUCs of the full and the compact models in the external validation were 0.842 (95% CI: 0.837-0.846) and 0.803 (95% CI: 0.798-0.809), respectively, which were still larger than those of Logistic Regression (0.660; 95% CI: 0.653-0.667) and SIC scores (0.752; 95% CI: 0.747-0.757). Prediction results were illustrated by SHapley Additive exPlanations (SHAP) values, which made our models clinically interpretable.
We developed two models which were able to dynamically predict the risk of SIC in septic patients better than conventional Logistic Regression and SIC scores.
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