Developing Insoluble Polyoxometalate Clusters to Bridge Homogeneous and Heterogeneous Water Oxidation Photocatalysis.

Cluster catalysts are attractive for their atomically precise structures, defined compositions, tunable coordination environments, uniform active sites, and their ability to transfer multiple electrons, but they suffer from poor stability and recyclability. Here, we report a general approach to the direct insolubilization of a water soluble polyoxometalate (POM) [{(B-α-PW9 O34 )Co3 (OH)(H2 O)2 (O3 PC(O)-(C3 H6 NH3 )PO3 )}2 Co]14- (Co7 ) and formation of a series of POM-based solid catalysts with the counter-cations Ag+ , Cs+ , Sr2+ , Ba2+ , Pb2+ , Y3+ , and Ce3+ . They exhibit improved catalytic activities for visible-light-driven water oxidation following the trend CsCo7 >SrCo7 >AgCo7 >CeIII Co7 >BaCo7 >YCo7 >PbCo7 . While CsCo7 exhibits mainly homogeneous catalysis, the others are predominantly heterogeneous catalysts. An optimal oxygen yield of 41.3 % and a high apparent quantum yield (AQY) of 30.6 % for SrCo7 is obtained, which is comparable to that of the parent homogeneous POM. Band gap structures, UV/Vis spectra, and real-time laser flash photolysis experiments collectively suggest that easier electron transfer from the solid POM catalyst to the photosensitizer promotes photocatalytic water oxidation performance. These solid POM catalysts exhibit good stability, which is directly confirmed by a combination of Fourier-transform infrared spectroscopy, electron microscopy, X-ray diffraction patterns, Raman spectroscopy, X-ray photoelectron spectroscopy, five cycles of tests, and poisoning experiments.

Polyoxometalates tailoring of frustrated Lewis pairs on Ce-doped Bi2O3 for boosting photocatalytic CO2 reduction.

Constructing frustrated Lewis pairs (FLPs) on catalysts will provide catalytic sites to activate CO2 and boost photocatalytic CO2 reduction. Herein, a Ce-doped bismuth oxide (CeBiOX) with FLPs was designed by loading [(α-SbW9O33)2Cu3(H2O)3]12- (Cu3) via strong electrostatic interactions to create oxygen vacancies (OVs). Detailed experiments and measurements showed that Cu3 could regulate the FLPs and optimize the band structure of CeBiOX to boost photocatalytic CO2 reduction. In particular, the Cu3/CeBiOX composite exhibited the highest yields of CO (42.85 µmoL g-1) and CH4 (13.23 µmoL g-1), being 6.6 and 3.3 times, and 4.9 and 6.3 times higher than those of pristine Bi2O3 and CeBiOX, respectively. This work provides a significant and mild approach to obtaining advanced catalysts with tuneable FLPs for more fields.

Machine learning for the prediction of all-cause mortality in patients with sepsis-associated acute kidney injury during hospitalization.

Background: Sepsis-associated acute kidney injury (S-AKI) is considered to be associated with high morbidity and mortality, a commonly accepted model to predict mortality is urged consequently. This study used a machine learning model to identify vital variables associated with mortality in S-AKI patients in the hospital and predict the risk of death in the hospital. We hope that this model can help identify high-risk patients early and reasonably allocate medical resources in the intensive care unit (ICU). Methods: A total of 16,154 S-AKI patients from the Medical Information Mart for Intensive Care IV database were examined as the training set (80%) and the validation set (20%). Variables (129 in total) were collected, including basic patient information, diagnosis, clinical data, and medication records. We developed and validated machine learning models using 11 different algorithms and selected the one that performed the best. Afterward, recursive feature elimination was used to select key variables. Different indicators were used to compare the prediction performance of each model. The SHapley Additive exPlanations package was applied to interpret the best machine learning model in a web tool for clinicians to use. Finally, we collected clinical data of S-AKI patients from two hospitals for external validation. Results: In this study, 15 critical variables were finally selected, namely, urine output, maximum blood urea nitrogen, rate of injection of norepinephrine, maximum anion gap, maximum creatinine, maximum red blood cell volume distribution width, minimum international normalized ratio, maximum heart rate, maximum temperature, maximum respiratory rate, minimum fraction of inspired O2, minimum creatinine, minimum Glasgow Coma Scale, and diagnosis of diabetes and stroke. The categorical boosting algorithm model presented significantly better predictive performance [receiver operating characteristic (ROC): 0.83] than other models [accuracy (ACC): 75%, Youden index: 50%, sensitivity: 75%, specificity: 75%, F1 score: 0.56, positive predictive value (PPV): 44%, and negative predictive value (NPV): 92%]. External validation data from two hospitals in China were also well validated (ROC: 0.75). Conclusions: After selecting 15 crucial variables, a machine learning-based model for predicting the mortality of S-AKI patients was successfully established and the CatBoost model demonstrated best predictive performance.

Identification of common molecular signatures of SARS-CoV-2 infection and its influence on acute kidney injury and chronic kidney disease.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the main cause of COVID-19, causing hundreds of millions of confirmed cases and more than 18.2 million deaths worldwide. Acute kidney injury (AKI) is a common complication of COVID-19 that leads to an increase in mortality, especially in intensive care unit (ICU) settings, and chronic kidney disease (CKD) is a high risk factor for COVID-19 and its related mortality. However, the underlying molecular mechanisms among AKI, CKD, and COVID-19 are unclear. Therefore, transcriptome analysis was performed to examine common pathways and molecular biomarkers for AKI, CKD, and COVID-19 in an attempt to understand the association of SARS-CoV-2 infection with AKI and CKD. Three RNA-seq datasets (GSE147507, GSE1563, and GSE66494) from the GEO database were used to detect differentially expressed genes (DEGs) for COVID-19 with AKI and CKD to search for shared pathways and candidate targets. A total of 17 common DEGs were confirmed, and their biological functions and signaling pathways were characterized by enrichment analysis. MAPK signaling, the structural pathway of interleukin 1 (IL-1), and the Toll-like receptor pathway appear to be involved in the occurrence of these diseases. Hub genes identified from the protein-protein interaction (PPI) network, including DUSP6, BHLHE40, RASGRP1, and TAB2, are potential therapeutic targets in COVID-19 with AKI and CKD. Common genes and pathways may play pathogenic roles in these three diseases mainly through the activation of immune inflammation. Networks of transcription factor (TF)-gene, miRNA-gene, and gene-disease interactions from the datasets were also constructed, and key gene regulators influencing the progression of these three diseases were further identified among the DEGs. Moreover, new drug targets were predicted based on these common DEGs, and molecular docking and molecular dynamics (MD) simulations were performed. Finally, a diagnostic model of COVID-19 was established based on these common DEGs. Taken together, the molecular and signaling pathways identified in this study may be related to the mechanisms by which SARS-CoV-2 infection affects renal function. These findings are significant for the effective treatment of COVID-19 in patients with kidney diseases.

Rothia nasimurium as a Cause of Disease: First Isolation from Farmed Geese.

Rothia nasimurium was known previously as an opportunistic pathogen of animals. However, there are few reports regarding the pathogenicity of Rothia nasimurium. In September 2020, geese contracted a disease of unknown cause which brought economic losses to a farm in Jiangsu Province, China, prompting a series of investigations. The bacterium was isolated, cultured, and purified, and then identified using Gram staining, biochemical tests, matrix-assisted laser desorption/ionization time of flight mass spectrometry, and 16S rRNA sequence analysis. After determining the obtained bacteria species, antibiotic susceptibility tests and animal regression experiments were carried out. A strain of bacterium was successfully isolated from the livers of the diseased geese, which was identified as a strain of the Gram-positive bacterium Rothia nasimurium according to the 16S rRNA sequencing results. By indexing references, no goose was reported to have been infected with Rothia nasimurium. The antibiotic susceptibility testing showed that only four antibiotics (amikacin, cefazolin, fosfomycin, and ampicillin/sulbactam) could effectively inhibit the growth of the Rothia nasimurium strain. The animal regression experiments showed that the novel isolated strain could infect goslings, and it also causes serious depilation of goslings. The results of the manuscript expanded the range of pathogenic microorganisms in geese, which is helpful to develop methods for avian endemic control.

Direct infusion electrospray ionization-ion mobility-mass spectrometry for rapid metabolite marker discovery of medicinal Phellodendron Bark.

Ion-mobility mass spectrometry (IM-MS) currently serves as a powerful tool for the structural identification of numerous biological compounds and small molecules. In this work, rapid metabolomic analysis of closely-related herbal medicines by direct injection ion mobility-quadrupole time-of-flight mass spectrometry (DI-IM-QTOF MS) was established. Phellodendron chinense Bark (PC) and Phellodendron amurense Bark (PA) were studied as a case. Thirty-three batches of PC and twenty-two batches of PA have been directly injected in electrospray ionization-IM-QTOF MS in positive mode. Without chromatographic separation, each run was completed within 3 min. After data alignment and statistical analysis, a total of seven chemical markers were found (p-value < 0.05, VIP > 1.00). Among them, the ion m/z 342.17 and m/z 356.18 present a single peak in the drift spectrum, respectively, but their drift time has a certain deviation compared with the pure substance of known compounds. In addition, the MS/MS spectra also confirmed that the single peak includes two chemical isomers. To investigate the composition ratio of individual isomers, the calibration curves of relative drift time (rDT) based on the standard superposition method were established, which were found to fit the least square regression. The ion [M]+m/z 342.17 was recognized consisting of magnoflorine (MAG) and phellodendrine (PHE), and their composition ratio in PA and PC samples was calculated. The results were compared with those obtained by the HPLC quantitative method, which produced equivalent quantification results. Our DI-IM-QTOF MS methodology provides an additional methodology for the relative quantification of unresolved isomers in drift tube IM-MS and offers DI-IM-QTOF MS based metabolomics.