Mn-Doped Highly Dispersed RuO₂ Catalyst with Abundant Oxygen Vacancies for Efficient Decarboxylation of l‑Lysine to Cadaverine

Chemical decarboxylation of l-lysine is a promising route for producing cadaverine, which is the key monomer of new polyamide, polyurethane, and nylon materials. Currently, the wide application of Ru-based catalysts is restricted by its low efficiency which was mainly caused by the severe agglomeration of ruthenium nanoparticle. In this study, manganese (Mn) doped ruthenium oxide catalyst was synthesized through the wetness impregnation method with Beta zeolite as the candidate support for efficient decarboxylation of l-lysine to cadaverine. Structure characterization showed that RuO2 was the main phase of ruthenium oxide nanoparticles. The prepared Ru–Mn/Beta catalysts exhibited a high dispersion of ruthenium oxide nanaoparticle on Beta, which maximized the utilization of active sites. Meanwhile, abundant oxygen vacancies were generated after Mn doping to balance the charge of the disturbed long-term periodic structure in the RuO2 crystalline, which greatly facilitated the adsorption and activation of l-lysine by the capture of carboxylic groups. A full conversion was obtained with Ru–Mn/Beta, and a selectivity of cadaverine up to 54% was reached in a short time of 1.5 h. The cadaverine production rate in Ru–Mn/Beta was 60.8 mg/L/min, which was almost triple that in Ru/Beta (17.7 mg/L/min). The synergetic catalysis of metal active sites and oxygen vacancies provides a new opportunity to design efficient catalyst of decarboxylation of amino acids

Table1_Evaluation and clinical implications of interactions between compound Danshen dropping pill and warfarin associated with the epoxide hydrolase gene.DOCX

Introduction: In clinical practice, warfarin is often combined with Compound Danshen dripping pill (CDDP) for the treatment of cardiovascular diseases. However, warfarin has a narrow therapeutic index, wide interindividual variability (genetic and non-genetic factors), and is susceptible to drug-drug interactions. Our previous study indicated that CDDP might interact with warfarin in individuals with the epoxide hydrolase gene (EPHX1; single-nucleotide polymorphism: rs2292566) A/A subtype. We sought to clarify the interaction between CDDP and warfarin associated with EPHX1 in a comprehensive and accurate manner.Methods: Here, EPHX1 A and EPHX1 G cell lines were established. Expression of microsomal epoxide hydrolase (mEH), vitamin K epoxide reductase (VKOR), and vitamin K-dependent clotting factors (FII, FVII, FIX, FX) was measured by western blotting upon incubation with CDDP and warfarin. mEH activity was evaluated by measuring the transformation of epoxyeicosatrienoic acids into dihydroxyeicosatrienoic acids. Then, healthy volunteers (HVs) with the EPHX1 A/A genotype were recruited and administered warfarin and CDDP to investigate the pharmacokinetics and pharmacodynamics of warfarin.Results: CDDP combined with warfarin could decrease expression of mEH and VKOR, and increase protein expression of FII, FVII, FIX, and FX, in EPHX1 A cells. CDDP could slightly influence the pharmacokinetics/pharmacodynamics of warfarin in HVs with the EPHX1 A/A genotype.Discussion: Rational combination of CDDP and warfarin was safe with no risk of bleeding, but the therapeutic management is also needed. The clinical study is posted in the China Clinical Trial Registry (ChiCTR190002434).</p

Interfacial Defect Engineering on Electronic States of Two-Dimensional AlN/MoS₂ Heterostructure

The effects of vacancies and doping defects on the electronic and magnetic states of two–dimensional (2D) AlN/MoS₂ heterostructure are investigated by first–principle calculation. Because of charge transfer from the AlN layer to the interstitial region between layers, the energy band structure of the AlN/MoS₂ heterostructure is not a simple superposition of those of AlN and MoS₂. The energy band alignment can be further tuned by introducing vacancies and doping at the interface. When the AlN layer is decorated by N vacancies or n–type doped, noticeable charge transfer to the conduction band of the MoS₂ layer is observed and the band alignment maintains type–I. However, in the case of p–type doping, for instance, C substituting for N (C_N) in the AlN sublayer, the band alignment changes to type–II. Moreover, Al vacancies and Be_Al/C_N doping produce asymmetrical spin–up and spin–down states, which leads to magnetization of the AlN/MoS₂ heterostructure. The results demonstrate the significant effects of interfacial defects on the physical properties of 2D heterostructures

Ionozymes for Efficient Synthesis of Cadaverine: Offering a Sustainable Way for Bio-nylon 5X Production

Bio-based cadaverine, a crucial monomer for the production of bio-nylon 5X, can be synthesized during the bioconversion of l-lysine HCl relying on lysine decarboxylases. The rationally designed lysine decarboxylase ΔLdcEt3 has exhibited outstanding alkaline stability in pH 8.0 (half-life: 362 h); however, its catalytic activity still needs to be improved to meet the requirements of industrial cadaverine production. A novel ionozyme strategy can create a preferable reaction microenvironment, affect the intermediate formation, and/or improve the enzymatic stability and activity. Therefore, ionozymes ΔLdcEt3-[Emim]Cl and ΔLdcEt3-[Ch][Ser] have been successfully developed for efficient cadaverine production in this study. The results showed that the catalytic activities of ΔLdcEt3-[Emim]Cl and ΔLdcEt3-[Ch][Ser] improved 124.2 and 116.2%, respectively. Meanwhile, the catalytic efficiencies (kcat/Km) of ΔLdcEt3-[Emim]Cl and ΔLdcEt3-[Ch][Ser] were also increased by 1.5- and 1.2-fold, respectively. Particle size, circular dichroism, and Raman spectrum analyses showed that [Emim]Cl and [Ch][Ser] could affect short-range attractions related to the aggregation state and change the secondary structure. Protein surface analysis demonstrated that the addition of ionic liquids changed the hydrophobicity of ΔLdcEt3. In addition, isothermal titration calorimetry and molecular docking revealed that [Emim]Cl and [Ch][Ser] could promote the protein–ligand complexation during the enthalpy-driven l-lysine and ΔLdcEt3 binding process, which was confirmed by molecular dynamics. Therefore, ionozymes ΔLdcEt3-[Emim]Cl/[Ch][Ser] provide a novel possibility for high-level cadaverine production

Oxygen Vacancy Enhanced Gas-Sensing Performance of CeO₂/Graphene Heterostructure at Room Temperature

Oxygen vacancies (O_v) as the active sites have significant influences on the gas sensing performance of metal oxides, and self-doping of Ce³⁺ in CeO₂ might promote the formation of oxygen vacancies. In this work, hydrothermal process is adopted to fabricate the composites of graphene and CeO₂ nanoparticles, and the influences of oxygen vacancies as well as Ce³⁺ ions on the sensing response to NO₂ are studied. It is found that the sensitivity of the composites to NO₂ increases gradually, as the proportion of Ce³⁺ relative to all of the cerium ions is increased from 14.6% to 50.7% but decreases after that value. First-principles calculations illustrate that CeO₂ becomes metallic at the Ce³⁺ proportion of <50.7%, the chemical potential of electrons on surface decreases, and the Fermi level shifts upward due to the existence of low-electronegativity Ce³⁺ ions, resulting in reduced Schottky barrier height (SBH) at the CeO₂/graphene interface, enhanced interfacial charge transfer, and high gas sensing performance. However, deep energy level will be induced at the Ce³⁺ proportion of >50.7%, and the Fermi level is pinned at the interface. As a result, the density of free electrons is reduced, leading to increased SBH and poor gas sensing response. It demonstrates that an appropriate concentration of oxygen vacancies in CeO₂ is needed to enhance the gas sensing performance to NO₂

Polar Cubic CeO₂ Nanoparticles on Graphene for Enhanced Room-Temperature NO₂ Sensing Performance

Composites of graphene and CeO2 nanoparticles with high-energy {100} facets exposed are prepared by the hydrothermal method. The percentage of high-energy {100} polar plane increases with elevating temperature, but the proportion of Ce3+ and oxygen vacancy (Ov) decreases. As a result, the sensitivity of CeO2{100}/graphene composites toward NO2 at room temperature is enhanced. First-principles calculations are done to uncover the mechanism. The adsorption energy of NO2 on the six-coordinated Ce of the CeO2{100} polar plane is the most negative, indicating the most preferred sites for the adsorption of NO2. However, if more Ov are generated, the proportion of six-coordinated Ce atoms will be reduced, and the interaction with NO2 will be reduced because of charge recombination. Consequently, the sensing performances toward NO2 are deteriorated. The results provide new information pertaining to the design and fabrication of room-temperature sensing materials for NO2

Stability and Sensing Enhancement by Nanocubic CeO₂ with {100} Polar Facets on Graphene for NO₂ at Room Temperature

Metal oxides with a polar surface interact strongly with polar NO2 molecules, thus facilitating sensitive detection of NO2. In this work, the composites comprising graphene and cubic CeO2 nanoparticles with the {100} polar surface are prepared by a hydrothermal technique, and they exhibit fast response, excellent selectivity, stable recovery, and sensitive detection with a low detection limitation of 1 ppm for NO2 at room temperature. According to the first-principle calculations, the adsorption energy of NO2 on the CeO2{100} polar surface is the most negative corresponding to the strongest interactions between them. The formation energy of oxygen vacancies (Ov) on the {100} polar plane is also negative, and the abundant Ov facilitates the adsorption of NO2. The internal electric field near the polar surface promotes the charge separation and accelerates the charge exchange between NO2 and the composites. In addition, graphene promotes electron transfer at the interface and improves the stability of the CeO2{100} polar surface. The composites of graphene and metal oxides with a polar surface are excellent for NO2 detection, and the discovery reveals a new sensing strategy

DataSheet_1_Multi-Parametric MRI-Based Radiomics Models for Predicting Molecular Subtype and Androgen Receptor Expression in Breast Cancer.docx

ObjectiveTo investigate whether radiomics features extracted from multi-parametric MRI combining machine learning approach can predict molecular subtype and androgen receptor (AR) expression of breast cancer in a non-invasive way.Materials and MethodsPatients diagnosed with clinical T2–4 stage breast cancer from March 2016 to July 2020 were retrospectively enrolled. The molecular subtypes and AR expression in pre-treatment biopsy specimens were assessed. A total of 4,198 radiomics features were extracted from the pre-biopsy multi-parametric MRI (including dynamic contrast-enhancement T1-weighted images, fat-suppressed T2-weighted images, and apparent diffusion coefficient map) of each patient. We applied several feature selection strategies including the least absolute shrinkage and selection operator (LASSO), and recursive feature elimination (RFE), the maximum relevance minimum redundancy (mRMR), Boruta and Pearson correlation analysis, to select the most optimal features. We then built 120 diagnostic models using distinct classification algorithms and feature sets divided by MRI sequences and selection strategies to predict molecular subtype and AR expression of breast cancer in the testing dataset of leave-one-out cross-validation (LOOCV). The performances of binary classification models were assessed via the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). And the performances of multiclass classification models were assessed via AUC, overall accuracy, precision, recall rate, and F1-score.ResultsA total of 162 patients (mean age, 46.91 ± 10.08 years) were enrolled in this study; 30 were low-AR expression and 132 were high-AR expression. HR+/HER2− cancers were diagnosed in 56 cases (34.6%), HER2+ cancers in 81 cases (50.0%), and TNBC in 25 patients (15.4%). There was no significant difference in clinicopathologic characteristics between low-AR and high-AR groups (P > 0.05), except the menopausal status, ER, PR, HER2, and Ki-67 index (P = 0.043, ConclusionsMulti-parametric MRI-based radiomics combining with machine learning approaches provide a promising method to predict the molecular subtype and AR expression of breast cancer non-invasively.</p