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The process of jatropha oil transesterification was studied for biodiesel production. Rhizomucor miehei lipase (RML) immobilized on 3-aminopropyltriethoxysilane functionalized magnetic Fe3O4 was employed as a biocatalyst. The immobilized lipase was confirmed by scanning electron microcopy (SEM), Fourier transform infrared spectra (FT-IR), thermogravimetric analysis (TGA), vibrating sample magnetometer (VSM), and confocal laser scanning microscopy (CLSM) techniques. The efficient biodiesel synthesis in ionic liquids using immobilized RML was demonstrated. Fifteen kinds of ionic liquids based on different alkyl chain lengths of the methyl imidazolium cation ([C2MIM], [C4MIM], [C8MIM], [C12MIM], and [C16MIM]) combined with [NTf2], [N(CN)2], [PF6] or [BF4] anions were assayed as reaction media for RML producing biodiesel. The highest synthetic activity of immobilized RML appeared in [BMIM][PF6], which is more than five times higher than the free enzyme. The enzyme catalytic activity remained 60% even after the magnetic nano-biocatalyst and ionic liquid had been reused 5 times in a 48-h reaction cycle. In addition, the immobilized enzyme exhibited excellent storage stability with almost no decrease in catalytic activity after storing at − 20 °C for 98 days. [ABSTRACT FROM AUTHOR]

The common method for the determination of Au in geological samples is by Atomic Absorption Spectrometry (AAS) and Inductively Coupled Plasma-Optical Emission Spectrometry (ICP-OES) after enrichment by polyurethane foam (PUF). Compared with activated carbon, PUF has a good selectivity but has a low absorption capacity. PUF loaded by different extraction agents or modified with different functional groups can improve the absorption capacity. PUF was treated with hydrochloric acid to prepare PUF-NH2. Infrared Spectrum and Scanned Electron Microscope results show the peak of PUF-NH2 shows obvious red shift, and the amount of amino group increases obviously. Macromolecule of PUF-NH2 shows obvious fracture. The amino group after hydrolysis is reduced, which is easy to achieve in an oxide-reduction reaction with Au ions during the absorption of Au to form Au nanoparticles. Modified PUF-NH2 achieved absorption capacity of 96 mg/g, which is an 8-fold improvement compared to PUF. Using PUF-NH2 to enrich Au in geological samples, the samples were first ashed and then digested by 50% aqua regia. Gold was determined by ICP-OES. The recovery by standard-addition ranged from 95.0% to 105.0% and the detection limit was 0.15 μg/g. The experimental results show that the enrichment degree and analytical sensitivity are improved by using PUF-NH2, which is suitable for the determination Au in low grade ores.

Peroxidase (POD)-like nanozymes have been found to act as nanoreactors for the generation of reactive oxygen species (ROS) to resolve drug resistance in the tumor microenvironment (TME). Amplifying cellular oxidative stress is considered to be a drug-free strategy to efficiently induce apoptosis in tumor cells. However, the limited content of intracellular hydrogen peroxide (H2O2) extremely restricts the performance of POD-like nanozymes to amplify cellular oxidative stress. Moreover, additional operational processes combined with exogenous reagents to achieve oxidative stress lead to a dilemma of extra cytotoxicity. Here, an integrated iron-porphyrin-MOF-based nanozyme composite named HA@GOx@PCN-224(Fe) (HGPF) was precisely designed and constructed. Generally, the POD-like nanozyme PCN-224(Fe) was used as a platform to immobilize glucose oxidase (GOx), and further embedded with hyaluronic acid (HA) to enable the targeting ability of tumor cells. When endocytosed by tumor cells, intracellular glucose was oxidized to H2O2 and gluconic acid catalyzed by immobilized GOx of HGPF. Afterwards, inspired by heme analogs, H2O2 was catalyzed by iron-porphyrin active sites of the HGPF nanozyme to generate hydroxyl radicals (˙OH). Under light irradiation, the iron-porphyrin of HGPF acted as a photosensitizer to facilely produce singlet oxygen (1O2). Such a synergistic generation of ROS strikingly amplified oxidative stress and induced severe apoptosis in tumor cells. HGPF was expected to integrate intracellular oxygen sources and overcome the dilemma of limited intracellular H2O2 content. Consequently, HGPF was constructed as an integrated nanoreactor to simultaneously achieve light-enhanced catalytic oxidation cascades, providing a promising strategy for a synergistic amplification of cellular oxidative stress. [ABSTRACT FROM AUTHOR]

Aims: To compare the performance of logistic regression and machine learning methods in predicting postoperative delirium (POD) in elderly patients. Method: This was a retrospective study of perioperative medical data from patients undergoing non‐cardiac and non‐neurology surgery over 65 years old from January 2014 to August 2019. Forty‐six perioperative variables were used to predict POD. A traditional logistic regression and five machine learning models (Random Forest, GBM, AdaBoost, XGBoost, and a stacking ensemble model) were compared by the area under the receiver operating characteristic curve (AUC‐ROC), sensitivity, specificity, and precision. Results: In total, 29,756 patients were enrolled, and the incidence of POD was 3.22% after variable screening. AUCs were 0.783 (0.765–0.8) for the logistic regression method, 0.78 for random forest, 0.76 for GBM, 0.74 for AdaBoost, 0.73 for XGBoost, and 0.77 for the stacking ensemble model. The respective sensitivities for the 6 aforementioned models were 74.2%, 72.2%, 76.8%, 63.6%, 71.6%, and 67.4%. The respective specificities for the 6 aforementioned models were 70.7%, 99.8%, 96.5%, 98.8%, 96.5%, and 96.1%. The respective precision values for the 6 aforementioned models were 7.8%, 52.3%, 55.6%, 57%, 54.5%, and 56.4%. Conclusions: The optimal application of the logistic regression model could provide quick and convenient POD risk identification to help improve the perioperative management of surgical patients because of its better sensitivity, fewer variables, and easier interpretability than the machine learning model. [ABSTRACT FROM AUTHOR]