Zeolitic imidazolate framework derived Fe catalyst electrocatalytic-driven atomic hydrogen for efficient reduction of nitrate to N2.

Excessive discharge of nitrogen-containing chemical products into the natural water environment leads to the serious environmental problem of nitrate-nitrogen pollution, threatening the ecological balance and human health. In this study, we propose an efficient denitrification electrochemical method utilizing iron-doped zeolite imidazolium framework derived defective nitrogen-doped carbon (d-FeNC) catalysts. The d-FeNC catalyst exhibited 97 % nitrate removal efficiency and 94 % total nitrogen (TN) removal, and the reaction rate constant was increased from 0.73 h-1 of the Fe-undoped electrocatalyst (d-NC) to 1.11 h-1. The successful synthesis of d-FeNC with carbon defect sites and encapsulated Fe was confirmed by in-depth characterization. In situ electron paramagnetic resonance (EPR) analysis in conjunction with cyclic voltammetry (CV) tests confirmed the carbon substrates with defect enhanced the trapping of atomic hydrogen (H*) on the catalyst surface. Density functional theory (DFT) calculations clarified the doping of Fe facilitated the adsorption of nitrate, resulting in contact of H* with nitrate on the catalyst surface. In the synergy of the defective state organic framework and metal Fe, H* and nitrate realized a collision process. The electrochemical denitrification system achieved an excellent nitrate removal capacity of 7587 mgN·g-1cat in high-concentration nitrate solution and showed excellent stability under various conditions. Overall, this study underscores the potential of defective iron-doped carbon catalysts for efficient electrocatalytic denitrification, providing a promising approach for sustainable wastewater treatment.

Breviscapine attenuates lead­induced myocardial injury by activating the Nrf2 signaling pathway.

The present study investigated the therapeutic potential of breviscapine (Bre) in mitigating lead (Pb)-induced myocardial injury through activation of the nuclear factor erythroid-2 related factor 2 (Nrf2) pathway. Rat cardiomyocytes (H9C2 cells) were exposed to Pb to model Pb poisoning, and various parameters, including cell viability, apoptosis and reactive oxygen species (ROS) production, were assessed using Cell Counting Kit-8, flow cytometry and 2',7'-dichlorfluoresceindiacetate assays, respectively. Additionally, a rat model of Pb poisoning was established in which blood Pb levels were measured using a graphite furnace atomic absorption spectrophotometer, and alterations in myocardial tissue, oxidative stress markers, inflammatory indicators, protein expression related to apoptosis and the Nrf2 pathway were evaluated via histopathology, ELISA and western blotting. The results showed that Bre treatment enhanced cell viability, decreased apoptosis, and reduced ROS production in Pb-exposed H9C2 cells. Moreover, Bre modulated oxidative stress markers and inflammatory factors while enhancing the expression of proteins in the Nrf2 pathway. In a rat model, Bre mitigated the lead-induced increase in blood Pb levels and myocardial injury biomarkers, and reversed the downregulation of Nrf2 pathway proteins. In conclusion, the current findings suggested that Bre mitigates Pb-induced myocardial injury by activating the Nrf2 signaling pathway, highlighting its potential as a therapeutic agent for protecting the heart from the harmful effects of Pb exposure. Further research is required to elucidate the exact mechanisms and explore the clinical applicability of Bre in mitigating Pb-induced myocardial damage.

Phosphate Adsorption on Cerium/Terephthalic Acid Metal-Organic Frameworks (Ce-MOF) Driven by Effective Electrostatic Attraction and Ligand Exchange in a Wide pH Range.

Eutrophication has posed a threat to aquatic ecosystems, so it's urgent to remove excessive phosphate from water. In this study, we developed an adsorbent material, cerium/terephthalic-acid metal-organic-frameworks (Ce-MOF), to remove phosphate from different water systems. The optimal Ce-MOF presented a maximum phosphate adsorption capacity of 377.2 mg/g, approximately 3.7 times higher than that of the commercial phosphate adsorbent (Phoslock: 101.6 mg/g). Experimental and computational analysis suggested that pH dominated the adsorption process. The main forces driving the adsorption process changed from the synergistic effect of electrostatic attraction and ligand exchange at lower pH to only ligand exchange at the increased pH values. Hence, the Ce-MOF is applicable for phosphate adsorption in a wide pH range. Impressively, the adsorbent remained an excellent phosphate adsorption performance in the real water containing various interfering ions and organic matters, indicating the potential of Ce-MOF for the practical use to solve the water eutrophication issue.

Integrating anodic sulfate activation with cathodic H2O2 production/activation to generate the sulfate and hydroxyl radicals for the degradation of emerging organic contaminants.

Conventional electrocatalytic degradation of pollutants involves either cathodic reduction or anodic oxidation process, which caused the low energy utilization efficiency. In this study, we successfully couple the anodic activation of sulfates with the cathodic H2O2 production/activation to boost the generation of sulfate radical (SO4·-) and hydroxyl radical (·OH) for the efficient degradation of emerging contaminants. The electrocatalysis reactor is composed of a modified-graphite-felt (GF) cathode, in-situ prepared by the carbonization of polyaniline (PANI) electrodeposited on a GF substrate, and a boron-doped diamond (BDD) anode. In the presence of sulfates, the electrocatalysis system shows superior activities towards the degradation of pharmaceutical and personal care products (PPCPs), with the optimal performance of completely degrading the representative pollutant carbamazepine (CBZ, 0.2 mg L-1) within 150 s. Radicals quenching experiments indicated that ·OH and SO4·- act as the main reactive oxygen species for CBZ decomposition. Results from the electron paramagnetic resonance (EPR) and chronoamperometry studies verified that the sulfate ions were oxidized to SO4·-radicals at the anode, while the dissolve oxygen molecules were reduced to H2O2 molecules which were further activated to produce ·OH radicals at the cathode. It was also found that during the catalytic reactions SO4·-radicals could spontaneously convert into peroxydisulfate (PDS) which were subsequently reduced back to SO4·-at the cathodes. The quasi-steady-state concentrations of ·OH and SO4·-were estimated to be 0.51×10-12 M and 0.56×10-12 M, respectively. This study provides insight into the synergistic generation of ·OH/SO4·- from the integrated electrochemical anode oxidation of sulfate and cathode reduction of dissolved oxygen, which indicates a potential practical approach to efficiently degrade the emerging organic water contaminants.

Mapping developmental paths of monkey primordial germ-like cells differentiation from pluripotent stem cells by single cell ribonucleic acid sequencing analysis†.

The induction of primordial germ-like cells (PGCLCs) from pluripotent stem cells (PSCs) provides a powerful system to study the cellular and molecular mechanisms underlying germline specification, which are difficult to study in vivo. The studies reveal the existence of a species-specific mechanism underlying PGCLCs between humans and mice, highlighting the necessity to study regulatory networks in more species, especially in primates. Harnessing the power of single-cell RNA sequencing (scRNA-seq) analysis, the detailed trajectory of human PGCLCs specification in vitro has been achieved. However, the study of nonhuman primates is still needed. Here, we applied an embryoid body (EB) differentiation system to induce PGCLCs specification from cynomolgus monkey male and female PSCs, and then performed high throughput scRNA-seq analysis of approximately 40 000 PSCs and cells within EBs. We found that EBs provided a niche for PGCLCs differentiation by secreting growth factors critical for PGCLC specification, such as bone morphogenetic protein 2 (BMP2), BMP4, and Wnt Family Member 3. Moreover, the developmental trajectory of PGCLCs was reconstituted, and gene expression dynamics were revealed. Our study outlines the roadmap of PGCLC specification from PSCs and provides insights that will improve the differentiation efficiency of PGCLCs from PSCs.

Prediction of microvascular invasion in hepatocellular carcinoma with expert-inspiration and skeleton sharing deep learning.

BACKGROUND AND AIMS: Radiological prediction of microvascular invasion (MVI) of hepatocellular carcinoma (HCC) is essential but few models were clinically implemented because of limited interpretability and generalizability. METHODS: Based on 2096 patients in three independent HCC cohorts, we established and validated an MVI predicting model. First, we used data from the primary cohort to train a 3D-ResNet network for MVI prediction and then optimised the model with "expert-inspired training" for model construction. Second, we implemented the model to the other two cohorts using three implementation strategies, the original model implementation, data sharing model implementation and skeleton sharing model implementation, the latter two of which used part of the cohorts' data for fine-tuning. The areas under the receiver operating characteristic curve (AUCs) were calculated to compare the performances of different models. RESULTS: For the MVI predicting model, the AUC of the expert-inspired model was 0.83 (95% CI: 0.77-0.88) compared to 0.54 (95% CI: 0.46-0.62) of model before expert-inspiring. Taking this model as an original model, AUC on the second cohort was 0.76 (95% CI: 0.67-0.84). The AUC was improved to 0.83 (95% CI: 0.77-0.90) with the data-sharing model, and further improved to 0.85 (95% CI: 0.79-0.92) with the skeleton sharing model. The trend that the skeleton sharing model had an advantage in performance was similar in the third cohort. CONCLUSIONS: We established an expert-inspired model with better predictive performance and interpretability than the traditional constructed model. Skeleton sharing process is superior to data sharing and direct model implementation in model implementation.

Decoding the molecular subtypes of breast cancer seen on multimodal ultrasound images using an assembled convolutional neural network model: A prospective and multicentre study.

BACKGROUND: Preoperative determination of breast cancer molecular subtypes facilitates individualized treatment plan-making and improves patient prognosis. We aimed to develop an assembled convolutional neural network (ACNN) model for the preoperative prediction of molecular subtypes using multimodal ultrasound (US) images. METHODS: This multicentre study prospectively evaluated a dataset of greyscale US, colour Doppler flow imaging (CDFI), and shear-wave elastography (SWE) images in 807 patients with 818 breast cancers from November 2016 to February 2021. The St. Gallen molecular subtypes of breast cancer were confirmed by postoperative immunohistochemical examination. The monomodal ACNN model based on greyscale US images, the dual-modal ACNN model based on greyscale US and CDFI images, and the multimodal ACNN model based on greyscale US and CDFI as well as SWE images were constructed in the training cohort. The performances of three ACNN models in predicting four- and five-classification molecular subtypes and identifying triple negative from non-triple negative subtypes were assessed and compared. The performance of the multimodal ACNN was also compared with preoperative core needle biopsy (CNB). FINDING: The performance of the multimodal ACNN model (macroaverage area under the curve [AUC]: 0.89-0.96) was superior to that of the dual-modal ACNN model (macroaverage AUC: 0.81-0.84) and the monomodal ACNN model (macroaverage AUC: 0.73-0.75) in predicting four-classification breast cancer molecular subtypes, which was also better than that of preoperative CNB (AUC: 0.89-0.99 vs. 0.67-0.82, p < 0.05). In addition, the multimodal ACNN model outperformed the other two ACNN models in predicting five-classification molecular subtypes (AUC: 0.87-0.94 vs. 0.78-0.81 vs. 0.71-0.78) and identifying triple negative from non-triple negative breast cancers (AUC: 0.934-0.970 vs. 0.688-0.830 vs. 0.536-0.650, p < 0.05). Moreover, the multimodal ACNN model obtained satisfactory prediction performance for both T1 and non-T1 lesions (AUC: 0.957-0.958 and 0.932-0.985). INTERPRETATION: The multimodal US-based ACNN model is a potential noninvasive decision-making method for the management of patients with breast cancer in clinical practice. FUNDING: This work was supported in part by the National Natural Science Foundation of China (Grants 81725008 and 81927801), Shanghai Municipal Health Commission (Grants 2019LJ21 and SHSLCZDZK03502), and the Science and Technology Commission of Shanghai Municipality (Grants 19441903200, 19DZ2251100, and 21Y11910800).

Assessing MicroRNA-375 Levels in Type 2 Diabetes Mellitus (T2DM) Patients and Their First-Degree Relatives with T2DM.

PURPOSE: The pancreatic islet specific microRNA-375 (miR-375) is overexpressed in type 2 diabetes mellitus (T2DM) patients suppressing the glucose-induced insulin secretion. Thus, miR-375 may serve as a biomarker for the early prediction of T2DM among high-risk individuals. We conducted this clinical study to assess the significance of miR-375 among type 2 diabetes mellitus (T2DM) patients and their first-degree relatives. PATIENTS AND METHODS: We included 56 Han Chinese individuals (N: NGT = 21, T2DM = 10, FD-NGT =13 and FD-T2DM = 12) who received medical health check-ups from January 2018 to September 2018 at The Third Hospital of Yunnan Province, China. They were categorized as normal glucose tolerance (NGT), T2DM, first-degree relatives with normal glucose tolerance (FD-NGT) and first-degree relatives with T2DM (FD-T2DM). OGTT, C-peptide and Insulin tests were performed to confirm the diagnosis. The miR-375 levels were determined by Quantitative real-time RT-PCR (qRT-PCR). RESULTS: The OGTT test showed a significant difference in T2DM and FD-T2DM groups compared with NGT and FD-NGT (p< 0.05). Similar results were observed during C-peptide and insulin tests. Interestingly, the 2-hour insulin test showed FD-NGT group having a significantly higher mean ± standard error of (64.240 ± 12.775) compared to NGT (28.836 ± 10.875). Assessment of miR-375 expression levels in 4 groups showed a significant up-regulation in T2DM and FD-T2DM compared with NGT and FD-NGT groups. A slight increase in miRNA expression was observed in FD-NGT compared with NGT group but was not statistically significant. CONCLUSION: The OGTT, C-peptide and insulin tests revealed a statistically significant difference in T2DM and FD-T2DM compared with NGT and FD-NGT groups. A significantly higher miR-375 expression was also observed in T2DM and FD-T2DM groups compared with NGT and FD-NGT and thus, miR-375 may serve as a stable biomarker for the early prediction of T2DM among high-risk individuals.

COVID-19's Impact on China's Strategic Emerging Industries: An Observation of Policy Difficulties.

The study investigates the influence of the COVID-19 on the rate of R&D investment and foreign exchange development of China's most important emerging industry firms. From 2010 to 2020, data were collected from 26 locations across China, focusing on seven different types of critical creating companies. To analyze the data, we have applied Fourier Increased Unit Root Test, Granger causality assessments test, Pattern Assessment test, Poisson pseudo most excellent probability (PPML) approach, Wald test, and Regression analysis test. The results of the tests reveal a clear underlying association among COVID-19 relates Chinese exports and imports. COVID-19's instant effects on imports and exports lack working capital have been calculated, but the short-term, medium-to-long-term products are composite and unidentified. The article result main results are following: (i) The COVID-19 impacts the R&D investment is main industries like as high-end equipment industry, new materials industry, and new-era data innovation. (ii) The COVID-19 highly affects the imports and exports development network of Chinese strategic emerging industries which emphasizes cross-industry grouping features. The study provides the guidance to the future researchers to focus on COVID-19 affects on the strategic emerging industries of developed and underdeveloped countries to determine of foreign direct investment inflow and unemployment growth rates. JEL: G20, O10, O40.

Function of T regulatory type 1 cells is down-regulated and is associated with the clinical presentation of coronary artery disease.

T regulatory type 1 (Tr1) cells can promote tolerance and suppress inflammation. Atherosclerosis may be induced by the proinflammatory activation of cells in the vasculature and the immune system. Hence, we wondered whether defects in Tr1 function were a contributing factor to coronary artery disease (CAD). Data showed that the frequency of IL-10+ Tr1 cells was significantly lower in CAD patients than in controls. Compared to healthy controls, Tr1 cells from CAD patients presented lower CTLA-4 but higher PD-1 expression, in addition to lower IL-10 secretion. When co-incubated with Tconv cells, the CD4+CD49b+LAG-3+CD45RO+ Tr1 cells presented IL-10-dependent inhibitory effects, and those from CAD patients presented significantly lower suppression capacity than those from healthy controls. Interestingly, the characteristics of Tr1 cells were associated with clinical features of CAD patients. The frequency of Tr1 cells and the IL-10 and LAG-3 expression by Tr1 cells were negatively correlated with the BMI of the CAD patients. In addition, the Tr1 frequency and the LAG-3 and CTLA-4 expression on Tr1 cells were lower in CAD patients with higher numbers of narrowed vessels. Together, these results suggest that in CAD, Tr1 cells present multiple defects, which are associated with the clinical presentation of the disease.