Sulfur-Modulated Tin Sites Enable Highly Selective Electrochemical Reduction of CO2 to Formate

Electrochemical reduction of carbon dioxide (CO2RR) to formate provides an avenue to the synthesis of value-added carbon-based fuels and feedstocks powered using renewable electricity. Here, we hypothesized that the presence of sulfur atoms in the catalyst surface could promote undercoordinated sites, and thereby improve the electrochemical reduction of CO2 to formate. We explored, using density functional theory, how the incorporation of sulfur into tin may favor formate generation. We used atomic layer deposition of SnSx followed by a reduction process to synthesize sulfur-modulated tin (Sn(S)) catalysts. X-ray absorption near-edge structure (XANES) studies reveal higher oxidation states in Sn(S) compared with that of tin in Sn nanoparticles. Sn(S)/Au accelerates CO2RR at geometric current densities of 55 mA cm−2 at −0.75 V versus reversible hydrogen electrode with a Faradaic efficiency of 93%. Furthermore, Sn(S) catalysts show excellent stability without deactivation (<2% productivity change) following more than 40 hours of operation. With rapid advances in the efficient and cost-effective conversion of sunlight to electrical power, the development of storage technologies for renewable energy is even more urgent. Using renewable electricity to convert CO2 into formate simultaneously addresses the need for storage of intermittent renewable energy sources and the need to reduce greenhouse gas emissions. We report an increase of greater than 4-fold in the current density (hence the rate of reaction) in formate electrosynthesis compared with relevant controls. Our catalysts also show excellent stability without deactivation (<2% productivity change) following more than 40 hours of operation. The electrochemical reduction of carbon dioxide (CO2RR) offers a compelling route to energy storage and high-value chemical manufacture. The presence of sulfur atoms in catalyst surfaces promotes undercoordinated sites, thereby improving the electrochemical reduction of CO2 to formate. The resulting sulfur-modulated tin catalysts accelerate CO2RR at geometric current densities of 55 mA cm−2 at −0.75 V versus RHE with a Faradaic efficiency of 93%

Expression of paclitaxel-inactivating CYP3A activity in human colorectal cancer: implications for drug therapy

Cytochrome P450 3A is a drug-metabolising enzyme activity due to CYP3A4 and CYP3A5 gene products, that is involved in the inactivation of anticancer drugs. This study analyses the potential of cytochrome P450 3A enzyme in human colorectal cancer to impact anticancer therapy with drugs that are cytochrome P450 3A substrates. Enzyme activity, variability and properties, and the ability to inactivate paclitaxel (taxol) were analysed in human colorectal cancer and healthy colorectal epithelium. Cytochrome P450 3A enzyme activity is present in healthy and tumoral samples, with a nearly 10-fold interindividual variability. Nifedipine oxidation activity±s.d. for colorectal cancer microsomes was 67.8±36.6 pmol min−1 mg−1. The Km of the tumoral enzyme (42±8 μM) is similar to that in healthy colorectal epithelium (36±8 μM) and the human liver enzyme. Colorectal cancer microsomes metabolised the anticancer drug paclitaxel with a mean activity was 3.1±1.2 pmol min−1 mg−1. The main metabolic pathway is carried out by cytochrome P450 3A, and it is inhibited by the cytochrome P450 3A-specific inhibitor ketoconazole with a KI value of 31 nM. This study demonstrates the occurrence of cytochrome P450 3A-dependent metabolism in colorectal cancer tissue. The metabolic activity confers to cancer cells the ability to inactivate cytochrome P450 3A substrates and may modulate tumour sensitivity to anticancer drugs

Acute heroin intoxication in a baby chronically exposed to cocaine and heroin: a case report

<p>Abstract</p> <p>Introduction</p> <p>Acute intoxication with drugs of abuse in children is often only the tip of the iceberg, actually hiding chronic exposure. Analysis using non-conventional matrices such as hair can provide long-term information about exposure to recreational drugs.</p> <p>Case presentation</p> <p>We report the case of a one-month-old Caucasian boy admitted to our pediatric emergency unit with respiratory distress and neurological abnormalities. A routine urine test was positive for opiates, suggesting an acute opiate ingestion. No other drugs of misuse, such as cocaine, cannabis, amphetamines or derivatives, were detected in the baby's urine. Subsequently, hair samples from the baby and the parents were collected to evaluate the possibility of chronic exposure to drug misuse by segmental analysis. Opiates and cocaine metabolites were detected in hair samples from the baby boy and his parents.</p> <p>Conclusions</p> <p>In light of these and previous results, we recommend hair analysis in babies and children from risky environments to detect exposure to heroin and other drug misuse, which could provide the basis for specific social and health interventions.</p

Demonstration of the event identification capabilities of the NEXT-White detector

In experiments searching for neutrinoless double-beta decay, the possibility of identifying the two emitted electrons is a powerful tool in rejecting background events and therefore improving the overall sensitivity of the experiment. In this paper we present the first measurement of the efficiency of a cut based on the different event signatures of double and single electron tracks, using the data of the NEXT-White detector, the first detector of the NEXT experiment operating underground. Using a 228Th calibration source to produce signal-like and background-like events with energies near 1.6 MeV, a signal efficiency of 71.6 ± 1.5 stat± 0.3 sys% for a background acceptance of 20.6 ± 0.4 stat± 0.3 sys% is found, in good agreement with Monte Carlo simulations. An extrapolation to the energy region of the neutrinoless double beta decay by means of Monte Carlo simulations is also carried out, and the results obtained show an improvement in background rejection over those obtained at lower energies. [Figure not available: see fulltext.

Radiogenic backgrounds in the NEXT double beta decay experiment

Natural radioactivity represents one of the main backgrounds in the search for neutrinoless double beta decay. Within the NEXT physics program, the radioactivity- induced backgrounds are measured with the NEXT-White detector. Data from 37.9 days of low-background operations at the Laboratorio Subterráneo de Canfranc with xenon depleted in 136Xe are analyzed to derive a total background rate of (0.84±0.02) mHz above 1000 keV. The comparison of data samples with and without the use of the radon abatement system demonstrates that the contribution of airborne-Rn is negligible. A radiogenic background model is built upon the extensive radiopurity screening campaign conducted by the NEXT collaboration. A spectral fit to this model yields the specific contributions of 60Co, 40K, 214Bi and 208Tl to the total background rate, as well as their location in the detector volumes. The results are used to evaluate the impact of the radiogenic backgrounds in the double beta decay analyses, after the application of topological cuts that reduce the total rate to (0.25±0.01) mHz. Based on the best-fit background model, the NEXT-White median sensitivity to the two-neutrino double beta decay is found to be 3.5σ after 1 year of data taking. The background measurement in a Qββ±100 keV energy window validates the best-fit background model also for the neutrinoless double beta decay search with NEXT-100. Only one event is found, while the model expectation is (0.75±0.12) events. [Figure not available: see fulltext.]

Distinguishing Asthma Phenotypes Using Machine Learning Approaches.

Asthma is not a single disease, but an umbrella term for a number of distinct diseases, each of which are caused by a distinct underlying pathophysiological mechanism. These discrete disease entities are often labelled as asthma endotypes. The discovery of different asthma subtypes has moved from subjective approaches in which putative phenotypes are assigned by experts to data-driven ones which incorporate machine learning. This review focuses on the methodological developments of one such machine learning technique-latent class analysis-and how it has contributed to distinguishing asthma and wheezing subtypes in childhood. It also gives a clinical perspective, presenting the findings of studies from the past 5 years that used this approach. The identification of true asthma endotypes may be a crucial step towards understanding their distinct pathophysiological mechanisms, which could ultimately lead to more precise prevention strategies, identification of novel therapeutic targets and the development of effective personalized therapies

Energy calibration of the NEXT-White detector with 1% resolution near Q ββ of 136Xe

Excellent energy resolution is one of the primary advantages of electroluminescent high-pressure xenon TPCs. These detectors are promising tools in searching for rare physics events, such as neutrinoless double-beta decay (ββ0ν), which require precise energy measurements. Using the NEXT-White detector, developed by the NEXT (Neutrino Experiment with a Xenon TPC) collaboration, we show for the first time that an energy resolution of 1% FWHM can be achieved at 2.6 MeV, establishing the present technology as the one with the best energy resolution of all xenon detectors for ββ0ν searches. [Figure not available: see fulltext.

Investigating the Bidirectional Associations of Adiposity with Sleep Duration in Older Adults: The English Longitudinal Study of Ageing (ELSA)

Cross-sectional analyses of adiposity and sleep duration in younger adults suggest that increased adiposity is associated with shorter sleep. Prospective studies have yielded mixed findings, and the direction of this association in older adults is unclear. We examined the cross-sectional and potential bi-directional, prospective associations between adiposity and sleep duration (covariates included demographics, health behaviours, and health problems) in 5,015 respondents from the English Longitudinal Study of Ageing (ELSA), at baseline and follow-up. Following adjustment for covariates, we observed no significant cross-sectional relationship between body mass index (BMI) and sleep duration [(unstandardized) B?=??0.28?minutes, (95% Confidence Intervals (CI)?=??0.012; 0.002), p?=?0.190], or waist circumference (WC) and sleep duration [(unstandardized) B?=??0.10?minutes, (95% CI?=??0.004; 0.001), p?=?0.270]. Prospectively, both baseline BMI [B?=??0.42?minutes, (95% CI?=??0.013; ?0.002), p?=?0.013] and WC [B?=??0.18?minutes, (95% CI?=??0.005; ?0.000), p?=?0.016] were associated with decreased sleep duration at follow-up, independently of covariates. There was, however, no association between baseline sleep duration and change in BMI or WC (p?>?0.05). In older adults, our findings suggested that greater adiposity is associated with decreases in sleep duration over time; however the effect was very small