RESUMO
BACKGROUND: Recently, research data are increasingly shared through social media and other digital platforms. Traditionally, the influence of a scientific article has been assessed by the publishing journal's impact factor (IF) and its citation count. The Altmetric scoring system, a new bibliometric that integrates research "mentions" over digital media platforms, has emerged as a metric of online research distribution. The aim of this study was to explore the relationship of the Altmetric Score with IF and citation number within the pathology literature. METHODS: Citation count and Altmetric scores were obtained from the top 10 most-cited articles from the 15 pathology journals with the highest IF for 2013 and 2016. These variables were analyzed and correlated with each other, as well as the age of the publishing journal's Twitter account. RESULTS: Three hundred articles were examined from the two cohorts. The total citation count of the articles decreased from 21,043 (2013) to 14,679 (2016), while the total Altmetric score increased from 830 (2013) to 4066 (2016). In 2013, Altmetric score weakly correlated with citation number (r = 0.284, P < 0.001) but not with journal IF (r = 0.024, P = 0.771). In 2016, there was strong correlation between citation count and Altmetric Score (r = 0.714, P < 0.0001) but not the IF (r = 0.0442, P = 0.591). Twitter was the single most important contributor to the Altmetric score; however, the age of the Twitter account was not associated with citation number nor Altmetric score. CONCLUSIONS: In the pathology literature studied, the Altmetric score correlates with article citation count, suggesting that the Altmetric score and conventional bibliometrics can be treated as complementary metrics. Given the trend towards increasing use of social media, additional investigation is warranted to evaluate the evolving role of social media metrics to assess the dissemination and impact of scientific findings in the field of pathology.
RESUMO
Chemical identification often relies on matching measured chemical properties and/or spectral "fingerprints" of unknowns against their precompiled libraries. Chromatography, absorption spectroscopy, and mass spectrometry are all among analytical approaches that provide chemical measurement databases amenable to library searching. Occasionally, using conventional single-library or single-domain searches can lead to misidentification of unknowns. To improve chemical identification, we present a tandem gas chromatography/vacuum ultraviolet-mass spectrometry (GC/VUV-MS) chemical identification approach that utilizes databases from GC, VUV spectroscopy, and mass spectrometry analyses for a "multidomain" library search. Using standard chemical mixtures as well as aroma compounds as test cases, we demonstrate that multidatabase library searches utilizing GC, VUV, and MS data results in fully correct identification of chemical mixtures examined here that could only be identified with a 69.2% or an 88.5% success rate with MS or VUV library searches alone, respectively. Additionally, we introduce a library- and data domain-independent metric for evaluating the confidence of library search results. Using multidomain library searches improves both the chemical assignment accuracy and confidence.
RESUMO
For wide class characterizations of volatile organic compounds (VOCs), conventional gas chromatography mass spectrometry (GC-MS)-based techniques are utilized. These GC-MS-based chemical identification approaches typically rely on library searches against ion fragmentation patterns of known compounds. Although MS library searches can often provide correct chemical identities, erroneous chemical assignments of structurally similar unknown compounds are also possible. Other detection systems, such as absorption spectrometers, have been used for VOC analysis and can provide complementary absorption data. Here, we demonstrate the analytical advantages of coupling vacuum ultraviolet (VUV) absorption spectroscopy and MS in tandem for the improved characterization of structurally similar VOCs. We also discuss technical considerations and limitations of coupling a VUV spectrometer to a quadrupole mass spectrometer. Moreover, we show that combining the isomer selectivity of VUV spectroscopy, as a nondestructive analyte detection approach, with the mass selectivity of MS in a VUV-MS detection system improves characterization of GC-eluting compounds. Utilizing GC/VUV-MS data, we demonstrate that orthogonal VUV and MS library searches improve identification of VOCs present in complex mixtures such as a mixed standard sample, a commercial perfume product, and an essential oil sample.
