An international assessment of surgeon practices in abdominal wound closure and surgical site infection prevention by the European Society for Coloproctology

Aim: The burden of abdominal wound failure can be profound. Recent clinical guidelines have highlighted the heterogeneity of laparotomy closure techniques. The aim of this study was to investigate current midline closure techniques and practices for prevention of surgical site infection (SSI).Method: An online survey was distributed in 2021 among the membership of the European Society of Coloproctology and its partner societies. Surgeons were asked to provide information on how they would close the abdominal wall in three specific clinical scenarios and on SSI prevention practices.Results: A total of 561 consultants and trainee surgeons participated in the survey, mainly from Europe (n = 375, 66.8%). Of these, 60.6% identified themselves as colorectal surgeons and 39.4% as general surgeons. The majority used polydioxanone for fascial closure, with small bite techniques predominating in clean-contaminated cases (74.5%, n = 418). No significant differences were found between consultants and trainee surgeons. For SSI prevention, more surgeons preferred the use of mechanical bowel preparation (MBP) alone over MBP and oral antibiotics combined. Most surgeons preferred 2% alcoholic chlorhexidine (68.4%) or aqueous povidone-iodine (61.1%) for skin preparation. The majority did not use triclosan-coated sutures (73.3%) or preoperative warming of the wound site (78.5%), irrespective of level of training or European/non-European practice.Conclusion: Abdominal wound closure technique and SSI prevention strategies vary widely between surgeons. There is little evidence of a risk-stratified approach to wound closure materials or techniques, with most surgeons using the same strategy for all patient scenarios. Harmonization of practice and the limitation of outlying techniques might result in better outcomes for patients and provide a stable platform for the introduction and evaluation of further potential improvements

SDSS-III: Massive Spectroscopic Surveys of the Distant Universe, the Milky Way Galaxy, and Extra-Solar Planetary Systems

Building on the legacy of the Sloan Digital Sky Survey (SDSS-I and II), SDSS-III is a program of four spectroscopic surveys on three scientific themes: dark energy and cosmological parameters, the history and structure of the Milky Way, and the population of giant planets around other stars. In keeping with SDSS tradition, SDSS-III will provide regular public releases of all its data, beginning with SDSS DR8 (which occurred in Jan 2011). This paper presents an overview of the four SDSS-III surveys. BOSS will measure redshifts of 1.5 million massive galaxies and Lya forest spectra of 150,000 quasars, using the BAO feature of large scale structure to obtain percent-level determinations of the distance scale and Hubble expansion rate at z<0.7 and at z~2.5. SEGUE-2, which is now completed, measured medium-resolution (R=1800) optical spectra of 118,000 stars in a variety of target categories, probing chemical evolution, stellar kinematics and substructure, and the mass profile of the dark matter halo from the solar neighborhood to distances of 100 kpc. APOGEE will obtain high-resolution (R~30,000), high signal-to-noise (S/N>100 per resolution element), H-band (1.51-1.70 micron) spectra of 10^5 evolved, late-type stars, measuring separate abundances for ~15 elements per star and creating the first high-precision spectroscopic survey of all Galactic stellar populations (bulge, bar, disks, halo) with a uniform set of stellar tracers and spectral diagnostics. MARVELS will monitor radial velocities of more than 8000 FGK stars with the sensitivity and cadence (10-40 m/s, ~24 visits per star) needed to detect giant planets with periods up to two years, providing an unprecedented data set for understanding the formation and dynamical evolution of giant planet systems. (Abridged)Comment: Revised to version published in The Astronomical Journa

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

A Two-Stage Meta-Analysis Identifies Several New Loci for Parkinson's Disease

A previous genome-wide association (GWA) meta-analysis of 12,386 PD cases and 21,026 controls conducted by the International Parkinson's Disease Genomics Consortium (IPDGC) discovered or confirmed 11 Parkinson's disease (PD) loci. This first analysis of the two-stage IPDGC study focused on the set of loci that passed genome-wide significance in the first stage GWA scan. However, the second stage genotyping array, the ImmunoChip, included a larger set of 1,920 SNPs selected on the basis of the GWA analysis. Here, we analyzed this set of 1,920 SNPs, and we identified five additional PD risk loci (combined p <5x10(-10), PARK16/1q32, STX1B/16p11, FGF20/8p22, STBD1/4q21, and GPNMB/7p15). Two of these five loci have been suggested by previous association studies (PARK16/1q32, FGF20/8p22), and this study provides further support for these findings. Using a dataset of post-mortem brain samples assayed for gene expression (n = 399) and methylation (n = 292), we identified methylation and expression changes associated with PD risk variants in PARK16/1q32, GPNMB/7p15, and STX1B/16p11 loci, hence suggesting potential molecular mechanisms and candidate genes at these risk loc

The Radial Velocity Experiment (RAVE):Second data release

We present the second data release of the Radial Velocity Experiment (RAVE), an ambitious spectroscopic survey to measure radial velocities (RVs) and stellar atmosphere parameters of up to one million stars using the 6dF multi-object spectrograph on the 1.2-m UK Schmidt Telescope of the Anglo-Australian Observatory (AAO). It is obtaining medium resolution spectra (median R=7,500) in the Ca-triplet region (8,410--8,795 \AA) for southern hemisphere stars in the magnitude range 9<I<12. Following the first data release (Steinmetz et al. 2006) the current release doubles the sample of published RVs, now containing 51,829 RVs for 49,327 individual stars observed on 141 nights between April 11 2003 and March 31 2005. Comparison with external data sets shows that the new data collected since April 3 2004 show a standard deviation of 1.3 km/s, about twice better than for the first data release. For the first time this data release contains values of stellar parameters from 22,407 spectra of 21,121 individual stars. They were derived by a penalized \chi^2 method using an extensive grid of synthetic spectra calculated from the latest version of Kurucz models. From comparison with external data sets, our conservative estimates of errors of the stellar parameters (for a spectrum with S/N=40) are 400 K in temperature, 0.5 dex in gravity, and 0.2 dex in metallicity. We note however that the internal errors estimated from repeat RAVE observations of 822 stars are at least a factor 2 smaller. We demonstrate that the results show no systematic offsets if compared to values derived from photometry or complementary spectroscopic analyses. The data release includes proper motion and photometric measurements. It can be accessed via the RAVE webpage: http://www.rave-survey.org and through CDS.

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 science. © The Author(s) 2019. Published by Oxford University Press