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Ethylene glycol, a widely used chemical, has a large global capacity exceeding 40 million tons per year. Nevertheless, its production is heavily reliant on fossil fuels, resulting in substantial CO 2 emissions. Herein, we report an approach for electrochemically producing ethylene glycol from biomass glycerol. This process involves glycerol electrooxidation to glycolaldehyde at anode, which is subsequently electro-reduced to ethylene glycol at cathode. While the anode reaction has been reported, the cathode reaction remains a challenge. An electrodeposited electrode with metallic Cu catalyst enables us to achieve glycolaldehyde-to-ethylene glycol conversion with an exceptional faradaic efficiency of about 80%. Experimental and theoretical studies reveal that metallic Cu catalyst facilitates the C=O activation, promoting glycolaldehyde hydrogenation into ethylene glycol. We further assemble a zero-gap electrolyzer and demonstrate ethylene glycol electrosynthesis from glycerol to give a decent production rate of 1.32 mmol cm –2  h –1 under a 3.48 V cell voltage. The carbon intensity assessment based on a valid assumption reveals that our strategy may reduce CO 2 emissions by over 80 million tons annually compared to conventional fossil fuel routes. Ethylene glycol, widely utilized with over 40 million tons produced annually, is typically made with high CO 2 emissions. Here, the authors report an electrochemical method to produce ethylene glycol from biomass glycerol, offering a more sustainable, low-emission alternative.

Acute hypercapnic respiratory failure (AHRF) is a major cause of mortality in intensive care units (ICUs). Early identification of high-risk patients with poor prognosis is crucial, as timely and effective interventions can significantly improve survival rates. However, effective models for predicting short-term mortality in AHRF patients remain limited. This study extracted clinical data from 4,302 AHRF patients in the Medical Information Mart for Intensive Care IV (MIMIC-IV) database (version 3.1), with 28-day all-cause mortality as the primary outcome. Univariate and multivariate Cox regression analyses, along with the Boruta algorithm and least absolute shrinkage and selection operator (LASSO) regression, consistently revealed a significant association between the Charlson Comorbidity Index (CCI) and 28-day mortality. Even after propensity score matching (PSM) was applied to balance the baseline characteristics between the high and low CCI groups, the difference in 28-day mortality remained statistically significant. Restricted cubic spline (RCS) analysis demonstrated an U-shaped relationship between CCI and the 28-day survival probability, indicating that higher CCI values were associated with an increased risk of adverse outcomes. Compared to the low-CCI group, patients in the high-CCI group exhibited a significantly elevated risk of 28-day mortality ( P  = 0.004). Subgroup analyses further suggested that the predictive value of CCI was more pronounced among patients with chronic pulmonary disease and those with a partial pressure of arterial oxygen/fraction of inspired oxygen (PaO₂/FiO₂) ratio between 100 and 149, compared to patients with a ratio > 150 or < 100. Feature selection using the Boruta algorithm identified CCI as a key variable with a high Z-score. Among the developed machine learning models, the Light Gradient Boosting Machine (LightGBM) algorithm achieved the best overall performance (internal validation results accuracy = 0.7793 [0.7516, 0.8070], area under the curve [AUC] = 0.8158 [0.7595, 0.8721]; external validation results on the eICU Collaborative Research Database (eICU-CRD): accuracy = 0.8003 [0.7860–0.8147], AUC = 0.7629 [0.7213–0.8045]), underscoring its robustness in predicting 28-day mortality in AHRF patients. Moreover, the Acute Physiology Score III (APSIII) also demonstrated potential predictive value for adverse clinical outcomes in this population.

Acute type A aortic dissection (ATAAD) is a serious cardiovascular emergency with high risk and mortality after surgery. Recent studies have shown that serum glucose-potassium ratio (GPR) is associated with the prognosis of cerebrovascular diseases. The purpose of this study was to investigate the relationship between GPR and in-hospital mortality in patients with ATAAD. From June 2019 to August 2021, we retrospectively analyzed the clinical data of 272 patients who underwent ATAAD surgery. According to the median value of GPR (1.74), the patients were divided into two groups. Univariate and multivariate logistic regression analysis were used to determine the risk factors of in-hospital mortality after ATAAD. In-hospital death was significantly more common in the high GPR group (> 1.74) (24.4% vs 13.9%; P  = 0.027). The incidence of renal dysfunction in the low GPR group was significantly higher than that in the high GPR group (26.3% vs 14.8%: P  = 0.019). After controlling for potential confounding variables and adjusting for multivariate logistic regression analysis, the results showed a high GPR (> 1.74) (AOR 4.70, 95% confidence interval (CI) 2.13–10.40; P  =  < 0.001), lactic acid (AOR 1.14, 95% CI 1.03–1.26; P  = 0.009), smokers (AOR 2.45, 95% CI 1.18–15.07; P  = 0.039), mechanical ventilation (AOR 9.47, 95% CI 4.00–22.38; P  =  < 0.001) was independent risk factor for in-hospital mortality in ATAAD patients, albumin (AOR 0.90, 95% CI 0.83–0.98; P  = 0.014) was a protective factor for in-hospital prognosis. High GPR is a good predictor of in-hospital mortality after ATAAD surgery.

Overproduction of reactive oxygen species (ROS), elevated synovial inflammation, synovial hyperplasia and fibrosis are the main characteristic of microenvironment in rheumatoid arthritis (RA). Macrophages and fibroblast-like synoviocytes (FLSs) play crucial roles in the progression of RA. Hence, synergistic combination of ROS scavenging, macrophage polarization from pro-inflammatory M1 phenotype towards M2 anti-inflammatory phenotype, and restoring homeostasis of FLSs will provide a promising therapeutic strategy for RA. In this study, we successfully synthesized metformin-derived carbon dots (MCDs), and investigated the antirheumatic effect in vivo and in vitro. Designed MCDs could target inflamed cells and accumulate at the inflammatory joints of collagen-induced arthritis (CIA) rats. In vivo therapeutic investigation suggested that MCDs reduced synovial inflammation and hyperplasia, ultimately prevented cartilage destruction, bone erosion, and synovial fibrosis in CIA rats. In addition, MCDs eliminated the cellular ROS in M1 phenotype macrophages in RA microenvironment through the enzyme-like catalytic activity as well as inhibiting NOD-like receptor family, pyrin domain containing 3 (NLRP3) inflammasome signaling pathway, effectively polarizing them into the M2 phenotype to realize the anti-inflammatory effect. Furthermore, MCDs could inhibit the proliferation, migration, and fibrosis of inflamed FLSs. Mechanistically, MCDs restored the homeostasis of FLSs while reducing the level of synovial inflammation by blocking IL-6/gp130 signaling pathway. Combined with preferable biocompatibility, MCDs offer a prospective treatment approach for RA.

Objectives In recent years, most studies on symptom characteristics in patients undergoing cardiac surgery have focused on the preoperative and postoperative phases. Relatively little knowledge is available related to the post-discharge phase. In this context, this paper aimed to analyze the symptoms and needs of patients with acute type A aortic dissection (AAAD) during the post-discharge phase. Methods We recruited and studied patients who underwent acute type A aortic dissection surgery at Fujian Heart Medical Center from June 2022 to August 2023. At 3 months following the surgery, these subjects were investigated using the general information questionnaire and relevant symptom assessment scales, including the Mini-Mental State Examination Scale (MMSE), Athens Insomnia Scale (AIS), Hospital Anxiety and Depression Scale (HADS), and Fatigue Severity Scale (FSS). Meanwhile, grip strength and average step per day were measured for the exercise endurance assessment. A latent class analysis (LCA) based on the symptoms was performed, and differences in demographic and disease characteristics among different subgroups of patients were identified and compared using multivariate logistic regression. Results A total of 228 patients were enrolled and categorized into three latent classes: fatigue–sleep disturbance (44.3%), anxiety–locomotion decline (16.9%), and high symptom groups (38.8%). Results showed that patients with cardiopulmonary bypass time > 200 min, higher BMI, or decreased grip strength were more likely to be classified as the high symptom group and those were unemployment status have a higher possibility of being defined as the anxiety–locomotion decline group. Conclusions The symptom characteristics in patients with AAAD during the postoperative rehabilitation phrase exhibit heterogeneity. It is suggested that Clinical healthcare personnel improve the identification of symptoms in high-risk patients, particularly patients cardiopulmonary bypass time > 200 min, overweight or obese, unemployed status or decreased grip strength, relevant nursing interventions should be carried out to prevent the occurrence of surgical stress and complications in patients with AAAD early to improve the quality of life of patients.

Pancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy characterized by significant heterogeneity. We conducted a multi-omics integrated clustering analysis to categorize PDAC molecular subtypes. Multi-omics data from The Cancer Genome Atlas-Pancreatic Adenocarcinoma (TCGA-PAAD) were integrated using ten clustering algorithms. Comparisons across PDAC subtypes were performed regarding prognosis, gene mutations, pathways, tumor microenvironment (TME), and chemotherapy sensitivity. A prognostic model was constructed utilizing Cox and Lasso regression based on subtype-related genes. Samples from the TCGA-PAAD cohort were classified into two subtypes. The CS1 subtype was identified as a high-risk, immunosilent subtype, while the CS2 subtype was characterized as a low-risk, immunoactive subtype. Compared to CS2 subtype, CS1 subtype exhibited shorter survival, higher frequency of genetic mutations, more aggressive tumor-promoting nature, lower TME immune score, and increased sensitivity to chemotherapy. The prognostic model related to PDAC subtypes displayed robust predictive efficiency; IL20RB gene emerged having superior predictive capability. We successfully identified two distinct PDAC subtypes. The developed prognostic model exhibited strong predictive efficacy; and the upregulation of IL20RB was identified as a promising therapeutic target for PDAC.

Quantum emitter-based schemes for the generation of photonic graph states offer a promising, resource-efficient methodology for realizing distributed quantum computation and communication protocols on near-term hardware. We present a heralded scheme for making photonic graph states that is compatible with the typically poor photon collection from state-of-the-art coherent quantum emitters. We demonstrate that the construction time for large graph states can be polynomial in the photon collection efficiency, as compared to the exponential scaling of current emitter-based schemes, which assume deterministic photon collection. The additional overhead here consists of an extra spin qubit plus one additional spin-spin entangling gate per photon added to the graph. While the proposed scheme requires both non-demolition measurement and efficient storage of photons in order to generate graph states for arbitrary applications, we show that many useful tasks, including measurement-based quantum computation, can be implemented without these requirements. As a use case of our scheme, we construct a protocol for secure two-party computation that can be implemented efficiently on current hardware. Estimates of the fidelity to produce graph states used in the computation are given assuming current and near-term fidelities for highly coherent quantum emitters.

This paper investigates the constrained nonlinear thrust allocation problem for the dynamic positioning system of vessels. Considering the wear, energy consumption, and allocation error of thrusters, a constrained nonlinear mathematical optimization model of thrust allocation is established based on the “Hai Yang Shi You 201”. Based on the dung beetle optimizer (DBO) algorithm, a hybrid Osprey adaptive t-distribution DBO (HOATDBO) algorithm is presented to achieve the thrust allocation. The HOATDBO algorithm introduces the global exploration strategy of the Osprey algorithm, with the addition of the initialization of the good point set and adaptive t-distribution perturbations. The proposed HOATDBO algorithm has perfect global and local optimization capabilities, which can quickly and reliably obtain the optimal thrust solution, improve the thrust allocation accuracy of vessels, and reduce energy consumption. Finally, the simulation and comparison results are presented to verify the superiority of the proposed HOATDBO algorithm.

Background The clinical treatment of patients suspected of pulmonary infections often rely on empirical antibiotics. However, preliminary diagnoses were based on clinical manifestations and conventional microbiological tests, which could later be proved wrong. In this case, we presented a patient whose initial diagnosis was lung abscess, but antibiotic treatments had no effect, and metagenomic Next-Generation Sequencing (mNGS) indicated presence of neoplasm. Case presentation A 62-year-old female was diagnosed with lung abscess at three different health facilities. However, mNGS of bronchoalveolar lavage fluid did not support pulmonary infections. Rather, the copy number variation analysis using host DNA sequences suggested neoplasm. Using H&E staining and immunohistochemistry of lung biopsy, the patient was eventually diagnosed with lung squamous cell carcinoma. Conclusions mNGS not only detects pathogens and helps diagnose infectious diseases, but also has potential in detecting neoplasm via host chromosomal copy number analysis. This might be beneficial for febrile patients with unknown or complex etiology, especially when infectious diseases were initially suspected but empirical antibiotic regimen failed.

Electrochemical conversion of CO2 to hydrocarbons is a promising approach to carbon neutrality and energy storage. The formation of reaction intermediates involves crucial steps of proton transfer, making it essential to understand the role of protons in the electrochemical process to control the product selectivity and elucidate the underlying catalytic reaction mechanism of the CO2 electrochemical reduction (CO2RR). In this work, we proposed a strategy to regulate product selectivities by tuning local proton transport rates through a surface resin layer over cuprous oxides. We systematically studied the influence of proton transfer rates on product selectivities by regulating the polymerization degree of resorcinol-formaldehyde resin (RF). The production of C2 compounds and CH4 could be switched through an RF coating with the maximum CH4 Faradaic efficiency of 51% achieved at current densities close to the amperage level. Both experimental and theoretical calculation results suggest that the resin layer can subtly alter proton transfer rates during the electrochemical process, thereby influencing the hydrogen coverage on catalytic sites and ultimately guiding the overall electrochemical performance toward product selectivity.