No abstract

No abstract

The influence of metals in biology has become more and more apparent within the past century. Metal ions perform essential roles as critical scaffolds for structure and as catalysts in reactions. Speciation is a key concept that assists researchers in investigating processes that involve metal ions. However, translation of the essential area across scientific fields has been plagued by language discrepancies. To rectify this, the IUPAC Commission provided a framework in which speciation is defined as the distribution of species. Despite these attempts, contributions from inorganic chemists to the area of speciation have not fully materialized in part because the past decade's contributions focused on technological advances, which are not yet to the stage of measuring speciation distribution in biological solutions. In the following, we describe how speciation influences the area of metals in medicine and how speciation distribution has been characterized so far. We provide two case studies as an illustration, namely, vanadium and iron. Vanadium both has therapeutic importance and is known as a cofactor for metalloenzymes. In addition to being a cation, vanadium(V) has analogy with phosphorus and as such is a potent inhibitor for phosphorylases. Because speciation can change the metal's existence in cationic or anionic forms, speciation has profound effects on biological systems. We also highlight how speciation impacts iron metabolism, focusing on the rather low abundance of biologically relevant iron cation that actually exists in biological fluids. fluids. Furthermore, we point to recent investigations into the mechanism of Fenton chemistry, and that the emerging results show pH dependence. The studies suggest formation of Fe(IV)-intermediates and that the generally accepted mechanism may only apply at low pH. With broader recognition toward biological speciation, we are confident that future investigations on metal-based systems will progress faster and with significant results. Studying metal complexes to explore the properties of a potential "active species" and further uncovering the details associated with their specific composition and geometry are likely to be important to the action.

Simple procedures and characterization of a series of well-defined precursors are described for preparation of a unique microenvironment in nanoreactors, reverse micelles. The Na(+), K(+), Rb(+), Cs(+), and Mg(2+) surfactants were prepared using liquid-liquid ion exchange using chloride and nitrate salts. The surfactants were characterized using (1)H NMR spectroscopy and a variety of other techniques. (1)H NMR spectroscopy was found to be a sensitive probe for characterization of the size of the nanoreactor as well as its water content. (1)H NMR spectra can be used for detailed characterization of reactions in confined environments when counterion effects are likely to be important. (1)H NMR spectroscopy revealed two separate peaks corresponding to water in Mg(AOT)2 samples; one peak arises from water coordinated to the Mg(2+) ion while the other peak arises from bulk water. The two water signals arise directly from the slow exchange of the water coordinated to Mg(2+) in these microemulsions with water in the water pool, and provide an opportunity to study hydration of Mg(2+). This work thus extends the potential use of MAOT microemulsions for applications such as in green chemistry.

Background As a major chronic disease, asthma causes many emergency department (ED) visits and hospitalizations each year. Predictive modeling is a key technology to prospectively identify high-risk asthmatic patients and enroll them in care management for preventive care to reduce future hospital encounters, including inpatient stays and ED visits. However, existing models for predicting hospital encounters in asthmatic patients are inaccurate. Usually, they miss over half of the patients who will incur future hospital encounters and incorrectly classify many others who will not. This makes it difficult to match the limited resources of care management to the patients who will incur future hospital encounters, increasing health care costs and degrading patient outcomes. Objective The goal of this study was to develop a more accurate model for predicting hospital encounters in asthmatic patients. Methods Secondary analysis of 334,564 data instances from Intermountain Healthcare from 2005 to 2018 was conducted to build a machine learning classification model to predict the hospital encounters for asthma in the following year in asthmatic patients. The patient cohort included all asthmatic patients who resided in Utah or Idaho and visited Intermountain Healthcare facilities during 2005 to 2018. A total of 235 candidate features were considered for model building. Results The model achieved an area under the receiver operating characteristic curve of 0.859 (95% CI 0.846-0.871). When the cutoff threshold for conducting binary classification was set at the top 10.00% (1926/19,256) of asthmatic patients with the highest predicted risk, the model reached an accuracy of 90.31% (17,391/19,256; 95% CI 89.86-90.70), a sensitivity of 53.7% (436/812; 95% CI 50.12-57.18), and a specificity of 91.93% (16,955/18,444; 95% CI 91.54-92.31). To steer future research on this topic, we pinpointed several potential improvements to our model. Conclusions Our model improves the state of the art for predicting hospital encounters for asthma in asthmatic patients. After further refinement, the model could be integrated into a decision support tool to guide asthma care management allocation. International Registered Report Identifier (IRRID) RR2-10.2196/resprot.5039

Sumwzary A o-bonded cobalt allene has been prepared by the reaction of the bis (dimethylg1yoximato)pyridinecobalt (I) anion with prop-2-ynyl bromide. Although another o-bonded allenyl-metal compound had been prepared previously, it is incorrectly described as a uprop-2-ynyl compound.

reported complexes containing the related 0=Tc-OR2+ core.12'13

No abstract