A facile strategy is developed to create a MIL-88A/Ni(OH)2 heterostructure, where the interfacial charge transfer significantly boosted the OER performance.
Background: Purulent meningitis (PM) is an important cause of mortality and morbidity in the newborn population throughout the world. The subtle of specific clinical signs and low success rates of lumbar puncture make diagnosis of PM more difficult in preterm than in older children. The objective of this study was to establish a predict model for preterm PM in hopes of helping clinicians develop new diagnostic and treatment strategies.Methods: Premature infants who were admitted to The First Affiliated Hospital of Zhengzhou University from September 2017 to March 2020 were enrolled in this study. All the patients underwent lumbar puncture. We collected data encompassing maternal diseases and neonatal clinical features. Cerebrospinal fluid (CSF) culture is the gold standard for diagnosing meningitis. The PM was diagnosed according to the diagnostic criteria. All statistical analyses were performed using R 3.63 (https://www.r-project.org/). Logistic regression and least absolute shrinkage and selection operator (LASSO) regression analyses were used to establish a risk prediction model of PM. The Brier score, calibration slope, and concordance (C)-index were used to verify the accuracy of prediction model.Results: A total of 168 preterm infants were enrolled in this study, 80 boys and 88 girls, the gestational age (GA) was 26. 43-36.86 weeks (32.45±2.79 weeks), the birth weight (BW) was 700-3,400 g (1,814.05±568.84 g).There were 77 preterm infants with PM while 91 without. We identified seven variables as independent risk factors for PM in preterm infants by LASSO analysis [the optimal λ was 0.080960, and log(λ) = −2.5138],including procalcitonin (PCT) on the 1st day after birth, prenatal glucocorticoid use, albumin, the 1-minute Apgar score, the use of non-invasive biphasic positive airway pressure, hemoglobin, and sex. These were used to construct a risk prediction nomogram and verified its accuracy. The Brier score was 0.17, the calibration slope was 0.966, and the concordance index was 0.82018.Conclusions: Our prediction model could predict the risk of PM in preterm infants. Using this prediction model, it may be able to provide reference to determine whether lumbar puncture is performed and whether antibiotics are applied as soon as possible.
Developing high-performance catalysts for oxygen evolution reaction (OER) is critical for the widespread applications of clean and sustainable energy through electrochemical devices such as zinc-air batteries and (photo) electrochemical water splitting. Constructing heterostructure and oxygen vacancies have demonstrated great promises to boost the OER performance. Herein, we report a facile strategy to fabricate hetero-structured NiFe 2 O 4 /Ni 3 S 4 nanorods, where NiFe 2 O 4 can be derived from Fe-based metalorganic frameworks (MOFs). The NiFe 2 O 4 /Ni 3 S 4 catalyst exhibited excellent OER performance, evidenced by an overpotential value of 357 mV at the current density of 20 mA cm À 2 , and a small Tafel slope of 87.46 mV dec À 1 in 1 M KOH, superior to the benchmark IrO 2 catalyst. Moreover, NiFe 2 O 4 /Ni 3 S 4 outperformed with regard to long-term durability for OER than IrO 2. Such outstanding OER performance is mainly accounted by the interface between NiFe 2 O 4 and Ni 3 S 4 , and the presence of rich oxygen vacancies. When employed as air-cathode in zinc-air batteries, the NiFe 2 O 4 / Ni 3 S 4 decorated battery had a high round-trip efficiency of 62.1% at 10 h, and possessed long-term stability of > 50 h. This study may pave the way for fabricating non-noblemetal-based cost-effective, efficient and durable electrocatalysts for OER, zinc-air batteries, and beyond.
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder caused by both environmental and genetic factors. However, its etiology and pathogenesis remain unclear. The purpose of this study was to establish an immune-related diagnostic model for ASD using bioinformatics methods and to identify ASD biomarkers. Two ASD datasets, GSE18123 and GSE29691, were integrated into the gene expression Database to eliminate batch effects. 41 differentially expressed genes were identified by microarray data linear model (limma package). Based on the results of the immune infiltration analysis, we speculated that neutrophils, B cells naive, CD8+ T cells, and Tregs are potential core immune cells in ASD and participate in the occurrence of ASD. Finally, the differential genes and immune infiltration in ASD and non-ASD patients were compared, and the most relevant genes were selected to construct the first immune correlation prediction model of ASD. After the calculation, the model exhibited better accuracy. The calculations show that the model has good accuracy.
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