GPER-induced signaling is essential for the survival of breast cancer stem cells.

G protein-coupled estrogen receptor-1 (GPER), a member of the G protein-coupled receptor (GPCR) superfamily, mediates estrogen-induced proliferation of normal and malignant breast epithelial cells. However, its role in breast cancer stem cells (BCSCs) remains unclear. Here we showed greater expression of GPER in BCSCs than non-BCSCs of three patient-derived xenografts of ER- /PR+ breast cancers. GPER silencing reduced stemness features of BCSCs as reflected by reduced mammosphere forming capacity in vitro, and tumor growth in vivo with decreased BCSC populations. Comparative phosphoproteomics revealed greater GPER-mediated PKA/BAD signaling in BCSCs. Activation of GPER by its ligands, including tamoxifen (TMX), induced phosphorylation of PKA and BAD-Ser118 to sustain BCSC characteristics. Transfection with a dominant-negative mutant BAD (Ser118Ala) led to reduced cell survival. Taken together, GPER and its downstream signaling play a key role in maintaining the stemness of BCSCs, suggesting that GPER is a potential therapeutic target for eradicating BCSCs

Impact of the COVID-19 pandemic on use of anti-dementia medications in 34 European and North American countries

INTRODUCTION: The impact of the COVID-19 pandemic on the global use of anti-dementia medication is unknown. We aimed to determine the changes of anti-dementia medication use in Europe (EU) and North America (NA) during the pandemic. METHODS: This is a cross-sectional study using sales data of anti-dementia medications in 2019 and 2020 from 34 EU and NA countries. The monthly uses of anti-dementia medications from January through June in 2020 were compared to the corresponding months in 2019 for each country. RESULTS: In the pre-pandemic period of January to March 2020, 70 out of 102 (3 months x 34 countries) measurements (68.6%) of monthly sales volume showed an increase. In contrast, 76.5% and 85.3% countries showed reduced sales in April and May 2020, respectively. DISCUSSION: These findings indicate changes in use of anti-dementia medications during the pandemic. The delivery of pharmaceutical care for dementia patients may be heavily disrupted in certain countries

Single-walled carbon nanotubes as excitonic optical wires

Although metallic nanostructures are useful for nanoscale optics, all of their key optical properties are determined by their geometry. This makes it difficult to adjust these properties independently, and can restrict applications. Here we use the absolute intensity of Rayleigh scattering to show that single-walled carbon nanotubes can form ideal optical wires. The spatial distribution of the radiation scattered by the nanotubes is determined by their shape, but the intensity and spectrum of the scattered radiation are determined by exciton dynamics, quantum-dot-like optical resonances and other intrinsic properties. Moreover, the nanotubes display a uniform peak optical conductivity of ~8 e^2/h, which we derive using an exciton model, suggesting universal behaviour similar to that observed in nanotube conductance. We further demonstrate a radiative coupling between two distant nanotubes, with potential applications in metamaterials and optical antennas

On-Chip Rayleigh Imaging and Spectroscopy of Carbon Nanotubes

We report a novel on-chip Rayleigh imaging technique using wide-field laser illumination to measure optical scattering from individual single-walled carbon nanotubes (SWNTs) on a solid substrate with high spatial and spectral resolution. This method in conjunction with calibrated AFM measurements accurately measures the resonance energies and diameters for a large number of SWNTs in parallel. We apply this technique for fast mapping of key SWNT parameters, including the electronic-types and chiral indices for individual SWNTs, position and frequency of chirality-changing events, and intertube interactions in both bundled and distant SWNTs

Derivation of an electronic frailty index for predicting short-term mortality in heart failure: a machine learning approach

Aims Frailty may be found in heart failure patients especially in the elderly and is associated with a poor prognosis. However, assessment of frailty status is time-consuming, and the electronic frailty indices developed using health records have served as useful surrogates. We hypothesized that an electronic frailty index developed using machine learning can improve short-term mortality prediction in patients with heart failure. Methods and results This was a retrospective observational study that included patients admitted to nine public hospitals for heart failure from Hong Kong between 2013 and 2017. Age, sex, variables in the modified frailty index, Deyo's Charlson co-morbidity index (≥2), neutrophil-to-lymphocyte ratio (NLR), and prognostic nutritional index at baseline were analysed. Gradient boosting, which is a supervised sequential ensemble learning algorithm with weak prediction submodels (typically decision trees), was applied to predict mortality. Variables were ranked in the order of importance with a total score of 100 and used to build the frailty models. Comparisons were made with decision tree and multivariable logistic regression. A total of 8893 patients (median: age 81, Q1–Q3: 71–87 years old) were included, in whom 9% had 30 day mortality and 17% had 90 day mortality. Prognostic nutritional index, age, and NLR were the most important variables predicting 30 day mortality (importance score: 37.4, 32.1, and 20.5, respectively) and 90 day mortality (importance score: 35.3, 36.3, and 14.6, respectively). Gradient boosting significantly outperformed decision tree and multivariable logistic regression. The area under the curve from a five-fold cross validation was 0.90 for gradient boosting and 0.87 and 0.86 for decision tree and logistic regression in predicting 30 day mortality. For the prediction of 90 day mortality, the area under the curve was 0.92, 0.89, and 0.86 for gradient boosting, decision tree, and logistic regression, respectively. Conclusions The electronic frailty index based on co-morbidities, inflammation, and nutrition information can readily predict mortality outcomes. Their predictive performances were significantly improved by gradient boosting techniques

Global trends in symptomatic medication use against dementia in 66 countries/regions from 2008 to 2018

Background and purpose: The aim was to determine trends and patterns of symptomatic medication used against dementia in 66 countries and regions. Methods: This was a cross-sectional study that used the wholesale data from the IQVIA Multinational Integrated Data Analysis System database. Sale data for symptomatic medication against dementia from 66 countries and regions from 2008 to 2018 were analysed and stratified by income level (low/middle-income countries [LMICs], n = 27; high-income countries [HICs], n = 37; regions, n = 2). The medication use volume was estimated by defined daily dose (DDD) per 1000 inhabitants per day (World Health Organization DDD harmonized the size, strength and form of each pack and reflects average dosing). Changes in medication use over time were quantified as percentage changes in compound annual growth rates (CAGRs). Results: Total symptomatic medication against dementia sales increased from 0.85 to 1.33 DDD per 1000 inhabitants per day between 2008 and 2018 (LMICs 0.094–0.396; HICs 3.88–5.04), which is an increase of CAGR of 4.53% per year. The increase was mainly driven by the LMICs (CAGR = 15.42%) in comparison to the HICs (CAGR = 2.65%). The overall medication use from 2008 to 2018 increased for all four agents: memantine (CAGR = 8.51%), rivastigmine (CAGR = 6.91%), donepezil (CAGR = 2.72%) and galantamine (CAGR = 0.695%). In 2018, the most commonly used medication globally was donepezil, contributing to 49.8% of total use volume, followed by memantine (32.7%), rivastigmine (11.24%) and galantamine (6.36%). Conclusion: There was an increasing trend in the use of symptomatic medications against dementia globally, but the use remained low in LMICs. Interventions may be needed to support the medication use in some countries

A biochemical network can control formation of a synthetic material by sensing numerous specific stimuli

Developing bio-compatible smart materials that assemble in response to environmental cues requires strategies that can discriminate multiple specific stimuli in a complex milieu. Synthetic materials have yet to achieve this level of sensitivity, which would emulate the highly evolved and tailored reaction networks of complex biological systems. Here we show that the output of a naturally occurring network can be replaced with a synthetic material. Exploiting the blood coagulation system as an exquisite biological sensor, the fibrin clot end-product was replaced with a synthetic material under the biological control of a precisely regulated cross-linking enzyme. The functions of the coagulation network remained intact when the material was incorporated. Clot-like polymerization was induced in indirect response to distinct small molecules, phospholipids, enzymes, cells, viruses, an inorganic solid, a polyphenol, a polysaccharide, and a membrane protein. This strategy demonstrates for the first time that an existing stimulus-responsive biological network can be used to control the formation of a synthetic material by diverse classes of physiological triggers

Global, regional, and national trends in opioid analgesic consumption from 2015 to 2019: a longitudinal study.

BACKGROUND: Previous studies have reported an extremely unbalanced global access to opioid analgesics. We aimed to determine contemporary trends and patterns of opioid analgesic consumption at the global, regional, and national levels. METHODS: We analysed the global pharmaceutical sales data of 66 countries or regions from the IQVIA-Multinational Integrated Data Analysis System database on opioid analgesics between 2015 and 2019. Opioid analgesic consumption was measured in milligram morphine equivalent per 1000 inhabitants per day (MME per 1000/day). The global, regional, and national trend changes were estimated using linear regressions. Factors associated with consumption patterns and trend changes were explored in multivariable linear regression analyses. FINDINGS: Overall opioid analgesic sales in the 66 countries or regions increased from 27·52 MME per 1000/day (16·63-45·54) in 2015 to 29·51 MME per 1000/day (17·85-48·79) in 2019 (difference per year 3·96%, 95% CI 0·26 to 7·80). Sales reduced yearly in North America (-12·84%; 95% CI -15·34 to -10·27) and Oceania (-2·96%; -4·20 to -1·70); increased in South America (28·69%; 7·18 to 54·53), eastern Europe (7·68%; 3·99 to 11·49), Asia (5·74%; 0·61 to 11·14), and western and central Europe (1·64%; 0·52 to 2·78); and did not differ in Africa or central America and the Caribbean. The global opioid consumption patterns were associated with country-level Human Development Index (p=0·040), cancer death rate excluding leukaemia (p=0·0072), and geographical location (p<0·0001). In 2019, opioid analgesic consumption ranged from 0·01 MME per 1000/day to 5·40 MME per 1000/day in the 17 countries and regions in the lowest consumption quartile, despite high income levels and cancer death rates in some of them. INTERPRETATION: Global opioid analgesic consumption increased from 2015 to 2019. The trend changes were distinctive across regions, which could reflect the different actions in response to known issues of opioid use and misuse. Disparities in opioid analgesic consumption remained, indicating potential inadequate access to essential pain relief in countries with low consumption. FUNDING: None