COPD in never smokers: results from the population-based burden of obstructive lung disease study.

To access publisher full text version of this article. Please click on the hyperlink in Additional Links field.BACKGROUND: Never smokers comprise a substantial proportion of patients with COPD. Their characteristics and possible risk factors in this population are not yet well defined. METHODS: We analyzed data from 14 countries that participated in the international, population-based Burden of Obstructive Lung Disease (BOLD) study. Participants were aged ≥ 40 years and completed postbronchodilator spirometry testing plus questionnaires about respiratory symptoms, health status, and exposure to COPD risk factors. A diagnosis of COPD was based on the postbronchodilator FEV₁/FVC ratio, according to current GOLD (Global Initiative for Obstructive Lung Disease) guidelines. In addition to this, the lower limit of normal (LLN) was evaluated as an alternative threshold for the FEV₁/FVC ratio. RESULTS: Among 4,291 never smokers, 6.6% met criteria for mild (GOLD stage I) COPD, and 5.6% met criteria for moderate to very severe (GOLD stage II+) COPD. Although never smokers were less likely to have COPD and had less severe COPD than ever smokers, never smokers nonetheless comprised 23.3% (240/1,031) of those classified with GOLD stage II+ COPD. This proportion was similar, 20.5% (171/832), even when the LLN was used as a threshold for the FEV₁/FVC ratio. Predictors of COPD in never smokers include age, education, occupational exposure, childhood respiratory diseases, and BMI alterations. CONCLUSION: This multicenter international study confirms previous evidence that never smokers comprise a substantial proportion of individuals with COPD. Our data suggest that, in addition to increased age, a prior diagnosis of asthma and, among women, lower education levels are associated with an increased risk for COPD among never smokers.ALTANA Aventis AstraZeneca Boehringer-Ingleheim Chiesi GlaxoSmithKline Merck Novartis Pfizer Inc Schering-Plough Sunovion Pharmaceuticals Inc University of Kentucky Schering Plough Sepracor AstraZeneca, Spai

Dynamics of Multiple Trafficking Behaviors of Individual Synaptic Vesicles Revealed by Quantum-Dot Based Presynaptic Probe

Although quantum dots (QDs) have provided invaluable information regarding the diffusive behaviors of postsynaptic receptors, their application in presynaptic terminals has been rather limited. In addition, the diffraction-limited nature of the presynaptic bouton has hampered detailed analyses of the behaviors of synaptic vesicles (SVs) at synapses. Here, we created a quantum-dot based presynaptic probe and characterized the dynamic behaviors of individual SVs. As previously reported, the SVs exhibited multiple exchanges between neighboring boutons. Actin disruption induced a dramatic decrease in the diffusive behaviors of SVs at synapses while microtubule disruption only reduced extrasynaptic mobility. Glycine-induced synaptic potentiation produced significant increases in synaptic and inter-boutonal trafficking of SVs, which were NMDA receptor- and actin-dependent while NMDA-induced synaptic depression decreased the mobility of the SVs at synapses. Together, our results show that sPH-AP-QD revealed previously unobserved trafficking properties of SVs around synapses, and the dynamic modulation of SV mobility could regulate presynaptic efficacy during synaptic activity

Contextualizing legal norms: a multi-dimensional view of the 2014 legal capital reform in China

This paper intends to shed light on the contentious theme of the reception of legal transplantation in the host environment, by examining the 2014 legislative reform of legal capital in China, which at least on paper imitates the enabling settings of US Revised Model Business Corporation Act (RMBCA). The paper looks at the interconnections between national-specific contextual elements, the resultant complexities, and the spillover effects of transplanted configurations in the unique Chinese socio-cultural setting, implicating the discrepancy between the ‘law in practice’ and the borrowed words ‘on the books’, and suggesting the importance of gaining a holistic understanding of ‘law’ involving the legal traditions in both the donor country and the recipient nation

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe