The authors would like to thank the Deanship of Scientific Research for its fund to support the video production for this study. The authors would also like to thank Dr. Aseel Zabin, Department of English Language and Literature, The University of Jordan for English language review of this study.

NOTE: The following are search methods and an example search for each method is provided. Note that the part related specifically to bibliometric analysis is also supplied.
1. PubMed
<ol>
	<li>Choose Advanced Search from PubMed homepage (www.ncbi.nlm.nih.gov/pubmed).</li>
	<li>Enter the desired search term in the search field. Choose the search terms from the medical subject heading (MeSH) database. The following example details how to assess research in &#34;cancer&#34;.
	<ol>
		<li>Open the MeSH database:&#160;https://www.ncbi.nlm.nih.gov/mesh.</li>
		<li>Enter the word &#34;cancer&#34; in the search field.</li>
		<li>Ensure that the results show the word &#34;neoplasms&#34; at the top of the search, where it notes that using the word &#34;neoplasms&#34; is more suitable, as &#34;neoplasms&#34; is the word used to index relevant topics.</li>
		<li>Further check other terms listed under the term &#34;neoplasms&#34; by clicking on it. The results will show other terms used to describe similar topics (e.g., tumor), and will also list other subcategories (subheadings) under the term neoplasm.</li>
		<li>Use the drop-down lists in the Builder section to specify the field of the article that PubMed will search for the term in. Note that the following search fields are available: all fields, title, abstract, authors, affiliation, conflict of interest, language, journal, publisher, publication type, grant number, ISBN, and MeSH terms.</li>
		<li>Add as many fields as needed and choose the relation between these fields (AND, OR, or NOT). See Table 1 for further details.</li>
	</ol>
	</li>
	<li>Click Search.</li>
	<li>Refine the search results further in the results using different filters available as detailed in Table 2.&#160;Note that from now on, the final search done will be saved in the history of the Advanced Search, which was accessed in the first step. This means that the search can be paused at this step and resumed later.</li>
	<li>Ensure that each new search is saved in the Advanced Search history screen where it will be assigned a number (e.g., #2). Use this number in the search field above to subtract searched queries (e.g., &#34;#1 NOT #2&#34; to subtract results in search #2 from results of search #1).</li>
	<li>Export the results to further analyze them.
	<ol>
		<li>Use the FLink tool (https://www.ncbi.nlm.nih.gov/Structure/flink/flink.cgi) to export the results in a comma separated values (CSV) format, and choose PubMed from the Please choose a database to start drop-down list.</li>
		<li>Choose Input From Entrez History from the input screen, and an advanced PubMed search history will appear at the drop-down list.</li>
		<li>Choose the search performed in the previous steps at the PubMed advanced search and click Submit.</li>
		<li>View the resulting report since it provides the option of exporting the search results in a CSV format file.</li>
	</ol>
	</li>
	<li>Perform the following steps to analyze Jordanian research output during a 5-year period between 1/1/2013 and 31/12/2017 using PubMed.
	<ol>
		<li>Open the Advanced search form in the document search form at the PubMed website (www.ncbi.nlm.nih.gov/pubmed).&#160;</li>
		<li>Use Jordan as the search term and specify Affiliation as the search field. Note that PubMed interprets affiliation as all information related to the author (i.e., author&#39;s name, address, affiliation), so exclude any document authored by an author named &#34;Jordan&#34; in which the affiliation country is not Jordan. Follow the steps below to avoid including such irrelevant results.</li>
		<li>Type Jordan in another field and choose the field type Author.</li>
		<li>Choose the operator NOT as the relation between the two fields and click on Search.</li>
		<li>Specify Publication Dates from 1/1/2013 to 31/12/2017 in the results window, and choose Journal Article and Review from Article Types.</li>
		<li>Open FLink (https://www.ncbi.nlm.nih.gov/Structure/flink/flink.cgi), and choose PubMed as the database.</li>
		<li>Choose Input From Entrez History from the input screen, and choose the search from the drop-down list.</li>
		<li>Click Download CSV. 
		NOTE: Figure 1 details the PubMed search report with annotation for each section of the report.</li>
	</ol>
	</li>
</ol>
2. Scopus
<ol>
	<li>Register in order to access the full search capabilities of the Scopus database. Check if the local institution is already registered and has access to the database since academic institutions are usually registered in Scopus.</li>
	<li>Go to the website (www.scopus.com) and by default, Scopus opens the document search form screen.</li>
	<li>Enter the search term desired in the search field available.</li>
	<li>Specify the fields in the article to be searched for. Note that the following search fields are available: all fields, title, abstract, keywords, authors, affiliations, funding information, language, references, conference, ISSN, CODEN, DOI, ORCID, and CAS number.</li>
	<li>Add other fields to search for and indicate the relation between the newly added field and the other field already entered (AND, OR, or AND NOT). See ( Table 1) for further details.</li>
	<li>Use the Limit option to limit the search based on the options provided by Scopus, as detailed in Table 2.After executing the search, save the search and continue later if needed.
	<ol>
		<li>In this case, set an alert using the option Set alert, where an email will be sent when an article satisfying the searching criteria is added.</li>
	</ol>
	</li>
	<li>Refine the search results further from the results directly by choosing from the options provided by Scopus ( Table 2), where Scopus shows the number of documents included for each option.</li>
	<li>Choose to either analyze the results directly on the Scopus website (click Analyze Search Results), or to export the results in either zip or CSV formats after completing the search criteria.</li>
	<li>Perform the following steps to analyze Jordanian research output during a 5-year period between 1/1/2013 and 31/12/2017 using Scopus.
	<ol>
		<li>Go to the website (www.scopus.com) and by default, Scopus opens the document search form screen.</li>
		<li>Type Jordan as the searching term in the document search form.</li>
		<li>Specify Affiliation Country as the search field.</li>
		<li>Limit the search duration from 2013 to 2017. Note that from 2013 means from 1/1/2013, and to 2017 means to 31/12/2017.</li>
		<li>Limit the document type to Article or Review, and then click Search. 
		NOTE: Figure 2 details the Scopus search report with annotation for each section of the report.</li>
	</ol>
	</li>
</ol>
3. Web of Science
<ol>
	<li>Register in order to access the full search capabilities of the Web of Science database. Check if the local institution is already registered and has access to the database as academic institutions are usually registered in Web of Science.</li>
	<li>Go to the Web of Science home page&#160;(www.webofknowledge.com). The website opens the basic search and includes the Web of Science Core Collection as the selected database for search.</li>
	<li>Search for the fields as detailed in Table 2.</li>
	<li>Add another field (if needed) to connect both fields by either AND, OR, or NOT. See Table 1.</li>
	<li>Define the duration that is searched down to 1945. After completing the search, the results are saved in the history and can be returned to at any time. If needed, set an alert if any new document is added to the search report.</li>
	<li>Sort the results according to either data, times cited, usage counts, or to other categories from the drop-down list provided.</li>
	<li>Refine the search results further from the results directly by choosing from the options provided by Web of Science ( Table 2), where Web of Science shows the number of documents included for each option.</li>
	<li>View the results and analyze them via a tree map or bar graph. Note that there is a table showing the count in each category.</li>
	<li>Download the results. Note that unlike Scopus and PubMed, Web of Science only allows the downloading of 5,000 records at a time (e.g., a 10,000 result search is downloaded in two batches, the first batch for the first 5,000 records, and a second batch for the next 5,000 records).</li>
	<li>Perform the following steps to analyze Jordanian research output during a 5-year period between 1/1/2013 and 31/12/2017 using Web of Science.
	<ol>
		<li>Type Jordan in the search field and specify Address as the search field.</li>
		<li>Identify the duration of search between 2013 and 2017 and click Search.</li>
		<li>Restrict the search using Article and Review filters.</li>
		<li>Choose to analyze the results that are saved in Search History now or later.</li>
		<li>Choose to analyze the results in the form of tables or visual tree map and bars. 
		NOTE: Figure 3 details the Web of Science search report with annotation for each section of the report.</li>
	</ol>
	</li>
</ol>

<ol>
	<li>McGowan, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=PRESS+peer+review+of+electronic+search+strategies:+2015+guideline+statement.">PRESS peer review of electronic search strategies: 2015 guideline statement.</a> Journal of Clinical Epidemiology. 75, 40-46 (2016).</li><li>Hood, W., Wilson, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+literature+of+bibliometrics,+scientometrics,+and+informetrics.">The literature of bibliometrics, scientometrics, and informetrics.</a> Scientometrics. 52 (2), 291-314 (2001).</li><li>Bar-Ilan, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Citations+to+the+Introduction+to+infometrics+indexed+by+WOS,+Scopus+and+Google+Scholar.">Citations to the Introduction to infometrics indexed by WOS, Scopus and Google Scholar.</a> Scientometrics. 82 (3), 495-506 (2010).</li><li>Boudry, C., Baudouin, C., Mouriaux, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=International+publication+trends+in+dry+eye+disease+research:+A+bibliometric+analysis.">International publication trends in dry eye disease research: A bibliometric analysis.</a> The Ocular Surface. 16 (1), 173-179 (2018).</li><li>Kulkarni, A. V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparisons+of+citations+in+Web+of+Science,+Scopus,+and+Google+Scholar+for+articles+published+in+general+medical+journals.">Comparisons of citations in Web of Science, Scopus, and Google Scholar for articles published in general medical journals.</a> Journal of the American Medical Association. 302 (10), 1092-1096 (2009).</li><li>AlRyalat, S. A., Malkawi, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=International+Collaboration+and+Openness+in+Jordanian+Research+Output:+A+10-year+Publications+Feedback.">International Collaboration and Openness in Jordanian Research Output: A 10-year Publications Feedback.</a> Publishing Research Quarterly. 34 (2), 265-274 (2018).</li><li>Falagas, M. E., Karavasiou, A. I., Bliziotis, I. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Estimates+of+global+research+productivity+in+virology.">Estimates of global research productivity in virology.</a> Journal of Medical Virology. 76 (2), 223-229 (2005).</li><li>Young, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> The ALA glossary of library and information science. , (1983).</li><li>Broadus, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Toward+a+definition+of+bibliometrics.">Toward a definition of bibliometrics.</a> Scientometrics. 12 (5-6), 373-379 (1987).</li><li>Falagas, M. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+PubMed,+Scopus,+web+of+science,+and+Google+scholar:+strengths+and+weaknesses.">Comparison of PubMed, Scopus, web of science, and Google scholar: strengths and weaknesses.</a> The FASEB Journal. 22 (2), 338-342 (2008).</li><li>Guz, A. N., Rushchitsky, J. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Scopus:+A+system+for+the+evaluation+of+scientific+journals.">Scopus: A system for the evaluation of scientific journals.</a> International Applied Mechanics. 45 (4), 351 (2009).</li><li>Jacso, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=As+we+may+search-comparison+of+major+features+of+the+Web+of+Science,+Scopus,+and+Google+Scholar+citation-based+and+citation-enhanced+databases.">As we may search-comparison of major features of the Web of Science, Scopus, and Google Scholar citation-based and citation-enhanced databases.</a> CurrentScience. 89 (9), 1537-1547 (2005).</li><li>Li, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Citation+analysis:+Comparison+of+Web+of+Science,+Scopus,+Scifinder,+And+Google+Scholar.">Citation analysis: Comparison of Web of Science, Scopus, Scifinder, And Google Scholar.</a> Journal of Electronic Resources in Medical Libraries. 7 (3), 196-217 (2010).</li><li>Levine-Clark, M., Kraus, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Finding+chemistry+information+using+Google+Scholar:+a+comparison+with+Chemical+Abstracts+Service.">Finding chemistry information using Google Scholar: a comparison with Chemical Abstracts Service.</a> Science &amp; Technology Libraries. 27 (4), 3-17 (2007).</li><li>Gavel, Y., Iselid, L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Web+of+Science+and+Scopus:+a+journal+title+overlap+study.">Web of Science and Scopus: a journal title overlap study.</a> Online Information Review. 32 (1), 8-21 (2008).</li><li>Harzing, A. W., Alakangas, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Google+Scholar,+Scopus+and+the+Web+of+Science:+a+longitudinal+and+cross-disciplinary+comparison.">Google Scholar, Scopus and the Web of Science: a longitudinal and cross-disciplinary comparison.</a> Scientometrics. 106 (2), 787-804 (2016).</li><li>Aghaei Chadegani, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> A comparison between two main academic literature collections: Web of Science and Scopus databases. , (2013).</li><li>Testa, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Thomson+Reuters+journal+selection+process.">The Thomson Reuters journal selection process.</a> Transnational Corporations Review. 1 (4), 59-66 (2009).</li><li>Burnham, J. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Scopus database: a review. 3 (1), 1 (2006).</li><li>Small, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Visualizing+science+by+citation+mapping.">Visualizing science by citation mapping.</a> Journal of the American society for Information Science and Technology. 50 (9), 799-813 (1999).</li><li>Cobo, M. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Science+mapping+software+tools:+Review,+analysis,+and+cooperative+study+among+tools.">Science mapping software tools: Review, analysis, and cooperative study among tools.</a> Journal of the American Society for Information Science and Technology. 62 (7), 1382-1402 (2011).</li><li>. Statistical Yearbook – 60th issue Available from: <a target="_blank" href="https://unstats.un.org/unsd/publications/statistical-yearbook/">https://unstats.un.org/unsd/publications/statistical-yearbook/</a> (2018)</li><li>Mongeon, P., Paul-Hus, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+journal+coverage+of+Web+of+Science+and+Scopus:+a+comparative+analysis.">The journal coverage of Web of Science and Scopus: a comparative analysis.</a> Scientometrics. 106 (1), 213-228 (2016).</li></ol>

All authors disclose no potential conflicts of interest.

In this study, the steps through which PubMed, Scopus, and Web of Science databases are used to perform a bibliometric analysis were provided. It was indicated that the friendliest and the easiest tool to use for bibliometric analysis services is Web of Science; however, its drawback is that its services are not available for free. PubMed is devoted for biomedical sciences and is affiliated with several other National Library of Medicine (NLM) tools that can help to optimize analysis of biomedical subjects. Medical Subje...

Classically, researchers have used literature databases to perform literature review for their studies1. Another use of these literature databases arose at the end of the 19th century, where researchers analyzed the body of literature, a use that has slowly grown since2. In the last few decades, digitizing literature and the formation of online literature databases provided an opportunity to researchers to analyze the body of literature and research performance easily and efficiently. An example would be analyzing the research performance for a document3, a subject4, a discipline5, a country6, or even a region in the world7. This type of analysis is known as bibliometric analysis. Heartsill Young defined bibliometric analysis as the use of statistical methods to analyze a body of literature to reveal historical development8. In other words, bibliometrics is the quantitative study of published units on the basis of citation and text analysis9.
Different databases are used to do bibliometric analysis and each database has different characteristics and can provide different services10. Currently, the most commonly used literature databases are the Web of Science and Scopus for almost all disciplines, both only available on a subscription basis11, and PubMed for biomedical and life sciences, a freely available database10. There is also Google Scholar, which might be an easy tool to handle, but it should not be used as a bibliometric analysis tool currently due to some deficiencies such as its unclear scope and coverage, its lack of citation analysis tools, and its inclusion of non-peer reviewed non-scientific contents12,13. Moreover, Google Scholar lacks the tools for performing advanced search and keyword optimization14.
Several previous studies have compared the features of the previously mentioned literature databases for literature review purposes3,5,10,12,13,15,16,17. However, in this study, the means by which PubMed, Scopus, and Web of Science databases are used to perform a bibliometric analysis will be provided, and the pros and cons for each of them will be compared. Bibliometric analysis can be used to analyze the research output in almost any discipline, so the target audience would be any researcher who intends to analyze publication trends. An example of analyzing a publication trend in Jordan as a country will be presented using each database. Jordan was chosen because doing a bibliometric analysis for a country (in contrast to a subject) is not very straightforward. In addition, Jordan, specifically, is poorly studied in a bibliometric way as it can be both an author name and a country name. We explain how to overcome such a challenge in the search.

<table border="0" cellpadding="0" cellspacing="0" style="width:1267px;" width="1266">
	<tbody>
		<tr height="40">
			<td height="40" style="height:40px;width:216px;"></td>
			<td style="width:123px;">clarivate</td>
			<td style="width:344px;">N/A</td>
			<td style="width:584px;">Web of Science provider, where the access was provided by the subscription made by the University of Jordan.</td>
		</tr>
		<tr height="40">
			<td height="40" style="height:40px;"></td>
			<td>Elsevier</td>
			<td>N/A</td>
			<td>Scopus provider, where the access was provided by the subscription made by the University of Jordan.</td>
		</tr>
	</tbody>
</table>

comparing bibliometric analysis using pubmed, scopus, and web of science databases

Literature databases (i.e., PubMed, Scopus, and Web of Science) differ in terms of their coverage, focus, and the tool they provide. PubMed focuses mainly on life sciences and biomedical disciplines, whereas Scopus and Web of Science are multidisciplinary. The protocol described in the current study was used to search for publications from Jordanian authors in the years 2013-2017. In this protocol, how to use each database to conduct this type of search is explained in detail. A Scopus search resulted in the highest number of documents (11,444 documents), followed by a Web of Science search (10,943 documents). PubMed resulted in a smaller number of documents due to its narrower scope and coverage (4,363 documents). The results also show a yearly trend in: (1) the number of publications, (2) the disciplines that have the most publications, (3) the countries of collaboration, and (4) the number of open access publications. In contrast, PubMed has a sophisticated keyword optimization service (i.e., Medical Subject Heading, or MeSH), while both Scopus and Web of Science provide search analysis tools that can produce representative figures. Finally, the features of each database are explained in detail and several indices that can be extracted using the search results are provided. This study provides a base for using literature databases for bibliometric analysis.

Results from PubMed search
A total of 4,363 documents were retrieved based on the search conducted in this study. Free full text was available for 1,767 documents (40.5%). In 2013, a total of 532 documents were published, 663 documents in 2014, 811 documents in 2015, 952 documents in 2016, and 1,405 documents in 2017.
The results reveal that 1,008 (23....

Watch this Scientific Journal Video about Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases at JoVE.com

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Literature databases are commonly used to assess publications in a certain subject, discipline, country, or region of the world, a practice known as bibliometric analysis. The current protocol details how to use PubMed, Scopus, and Web of Science databases to do bibliometric analysis.

Literature databases (i.e., PubMed, Scopus, and Web of Science) differ in terms of their coverage, focus, and the tool they provide. PubMed focuses mainly ...

comparing-bibliometric-analysis-using-pubmed-scopus-web-science

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30.8K Views.  University of Jordan. Literature databases are commonly used to assess publications in a certain subject, discipline, country, or region of the world, a practice known as bibliometric analysis. The current protocol details how to use PubMed, Scopus, and Web of Science databases to do bibliometric analysis.

Video: Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

Contrast Enhanced Vessel Imaging using MicroCT

Microscopic computed tomography (microCT) offers high-resolution volumetric imaging of the anatomy of living small animals. However, the contrast between different soft tissues and body fluids is inherently poor in micro-CT images 1. Under these circumstances, visualization of blood vessels becomes a nearly impossible task. To overcome this and to improve the visualization of blood vessels exogenous contrast agents can be used. Herein, we present a methodology for visualizing the vascular network in a rodent model. By using a long-acting aqueous colloidal polydisperse iodinated blood-pool contrast agent, eXIA 160XL, we optimized image acquisition parameters and volume-rendering techniques for finding blood vessels in live animals. Our findings suggest that, to achieve a superior contrast between bone and soft tissue from vessel, multiple-frames (at least 5-8/ frames per view), and 360-720 views (for a full 360&deg; rotation) acquisitions were mandatory. We have also demonstrated the use of a two-dimensional transfer function (where voxel color and opacity was assigned in proportion to CT value and gradient magnitude), in visualizing the anatomy and highlighting the structure of interest, the blood vessel network. This promising work lays a foundation for the qualitative and quantitative assessment of anti-angiogenesis preclinical studies using transgenic or xenograft tumor-bearing mice.

Contrast enhanced small animal vessel imaging by microCT is a rapid, cost-effective and high-throughput technique for serial in situ examination for tumor development, for analyzing the network of blood vessels that nourish them, and for following the response of tumors to preclinical therapeutic intervention(s).

Microscopic computed tomography (microCT) offers high-resolution volumetric imaging of the anatomy of living small animals. However, the contrast between ...

Quantitative Analysis of Cancer Metastasis using an Avian Embryo Model

During metastasis cancer cells disseminate from the primary tumor, invade into surrounding tissues, and spread to distant organs. Metastasis is a complex process that can involve many tissue types, span variable time periods, and often occur deep within organs, making it difficult to investigate and quantify. In addition, the efficacy of the metastatic process is influenced by multiple steps in the metastatic cascade making it difficult to evaluate the contribution of a single aspect of tumor cell behavior. As a consequence, metastasis assays are frequently performed in experimental animals to provide a necessarily realistic context in which to study metastasis. Unfortunately, these models are further complicated by their complex physiology. The chick embryo is a unique in vivo model that overcomes many limitations to studying metastasis, due to the accessibility of the chorioallantoic membrane (CAM), a well-vascularized extra-embryonic tissue located underneath the eggshell that is receptive to the xenografting of tumor cells (figure 1). Moreover, since the chick embryo is naturally immunodeficient, the CAM readily supports the engraftment of both normal and tumor tissues. Most importantly, the avian CAM successfully supports most cancer cell characteristics including growth, invasion, angiogenesis, and remodeling of the microenvironment. This makes the model exceptionally useful for the investigation of the pathways that lead to cancer metastasis and to predict the response of metastatic cancer to new potential therapeutics. The detection of disseminated cells by species-specific Alu PCR makes it possible to quantitatively assess metastasis in organs that are colonized by as few as 25 cells. Using the Human Epidermoid Carcinoma cell line (HEp3) we use this model to analyze spontaneous metastasis of cancer cells to distant organs, including the chick liver and lung. Furthermore, using the Alu-PCR protocol we demonstrate the sensitivity and reproducibility of the assay as a tool to analyze and quantitate intravasation, arrest, extravasation, and colonization as individual elements of metastasis.

Using quantitative PCR, we demonstrate how the well-established chick CAM model can be used to quantitatively analyze the metastasis of human tumor cells to distant organs.

During metastasis cancer cells disseminate from the primary tumor, invade into surrounding tissues, and spread to distant organs. Metastasis is a complex ...

Visualization and Analysis of Blood Flow and Oxygen Consumption in Hepatic Microcirculation: Application to an Acute Hepatitis Model

There is a considerable discrepancy between oxygen supply and demand in the liver because hepatic oxygen consumption is relatively high but about 70% of the hepatic blood supply is poorly oxygenated portal vein blood derived from the gastrointestinal tract and spleen. Oxygen is delivered to hepatocytes by blood flowing from a terminal branch of the portal vein to a central venule via sinusoids, and this makes an oxygen gradient in hepatic lobules. The oxygen gradient is an important physical parameter that involves the expression of enzymes upstream and downstream in hepatic microcirculation, but the lack of techniques for measuring oxygen consumption in the hepatic microcirculation has delayed the elucidation of mechanisms relating to oxygen metabolism in liver. We therefore used FITC-labeled erythrocytes to visualize the hepatic microcirculation and used laser-assisted phosphorimetry to measure the partial pressure of oxygen in the microvessels there. Noncontact and continuous optical measurement can quantify blood flow velocities, vessel diameters, and oxygen gradients related to oxygen consumption in the liver. In an acute hepatitis model we made by administering acetaminophen to mice we observed increased oxygen pressure in both portal and central venules but a decreased oxygen gradient in the sinusoids, indicating that hepatocyte necrosis in the pericentral zone could shift the oxygen pressure up and affect enzyme expression in the periportal zone. In conclusion, our optical methods for measuring hepatic hemodynamics and oxygen consumption can reveal mechanisms related to hepatic disease.

An optical system was developed to visualize hepatic microcirculation with FITC-labeled erythrocytes and to measure the partial pressure of oxygen in the microvessels with laser-assisted phosphorimetry. This method can be used to investigate physiological and pathological mechanisms by analyzing microvascular structure, diameter, blood flow velocity, and oxygen tension.

There is a considerable discrepancy between oxygen supply and demand in the liver because hepatic oxygen consumption is relatively high but about 70% of ...

Transcriptomic Analysis of Human Retinal Surgical Specimens Using jouRNAl

Retinal detachment (RD) describes a separation of the neurosensory retina from the retinal pigmented epithelium (RPE). The RPE is essential for normal function of the light sensitive neurons, the photoreceptors. Detachment of the retina from the RPE creates a physical gap that is filled with extracellular fluid. RD initiates cellular and molecular adverse events that affect both the neurosensory retina and the RPE since the physiological exchange of ions and metabolites is severely perturbed. The consequence for vision is related to the duration of the detachment since a rapid reapposition of the two tissues results in the restoration of vision 1. The treatment of RD is exclusively surgical. Removal of vitreous gel (vitrectomy) is followed by the removal non essential part of the retina around the detached area to favor retinal detachment. The removed retinal specimens are res nullius (nothing) and consequently normally discarded. To recover RNA from these surgical specimens, we developed the procedure jouRNAl that allows RNA conservation during the transfer from the surgical block to the laboratory. We also standardized a protocol to purify RNA by cesium chloride ultracentrifugation to assure that the purified RNAs are suitable for global gene expression analysis. The quality of the RNA was validated both by RT-PCR and microarray analysis. Analysis of the data shows a simultaneous involvement of inflammation and photoreceptor degeneration during RD.

We used retinal samples from retinectomy for a transcriptomic analysis of retinal detachment. We developed a procedure that allows RNA conservation between the surgical blocks and the laboratory. We standardized a protocol to purify RNA by cesium chloride ultracentrifugation to assure that the purified RNAs are suitable for microarray analysis.

Retinal detachment (RD) describes a separation of the neurosensory retina  from the retinal pigmented epithelium (RPE). The RPE is essential for normal  ...

Clinicopathological Analysis of miRNA Expression in Breast Cancer Tissues by Using miRNA In Situ Hybridization

In this article, we describe a detailed protocol for miRNA detection in breast cancer tissue using in situ hybridization with a digoxigenin-labelled LNA (Locked Nucleic Acid) probe. The probe was recognized by anti-DIG alkaline phosphatase antibodies and later developed using alkaline phosphatase substrate producing fluorescence signals. Here we utilized miRNA in situ hybridization (MISH) technique to analyze expression of miR-489 in tissues from breast cancer patients. This technique can detect the localization of miRNA of interest in individual tissue samples. This technique can be used to compare the expression of desired miRNA in tumor tissue with that in adjacent normal tissue and to identify the specific structures responsible for expressing this miRNA. This technique can be very useful in answering certain clinical questions, such as role of specific miRNA in the development of cancer. Our results indicate that mammary epithelial cells express significantly higher levels of miR-489 than adjacent tumor cells.

Clinicopathological Analysis of miRNA Expression in Breast Cancer Tissues by Using miRNA In Situ Hybridization

Here we present a protocol to detect miRNA expression in breast cancer patient samples using miRNA in situ hybridization.

In this article, we describe a detailed protocol for miRNA detection in breast cancer tissue using in situ hybridization with a digoxigenin-labelled LNA ...

Strategic Endothelial Cell Tube Formation Assay: Comparing Extracellular Matrix and Growth Factor Reduced Extracellular Matrix

Malignant tumors require a blood supply in order to survive and spread. These tumors obtain their needed blood from the patient's blood stream by hijacking the process of angiogenesis, in which new blood vessels are formed from existing blood vessels. The CXCR2 (chemokine (C-X-C motif) receptor 2) receptor is a transmembrane G-protein-linked molecule found in many cells that is closely associated with angiogenesis1. Specific blockade of the CXCR2 receptor inhibits angiogenesis, as measured by several assays such as the endothelial tube formation assay. The tube formation assay is useful for studying angiogenesis because it is an excellent method of studying the effects that any given compound or environmental condition may have on angiogenesis. It is a simple and quick in vitro assay that generates quantifiable data and requires relatively few components. Unlike in vivo assays, it does not require animals and can be carried out in less than two days. This protocol describes a variation of the extracellular matrix supporting endothelial tube formation assay, which tests the CXCR2 receptor.

This manuscript describes a tube formation assay to quantify the effects of a given compound or condition on angiogenesis by using endothelial cell tube formation in a controlled environment.

Malignant tumors require a blood supply in order to survive and spread. These tumors obtain their needed blood from the patient's blood stream by ...

Video Movement Analysis Using Smartphones (ViMAS): A Pilot Study

The use of smartphones in clinical practice is steadily increasing with the availability of low cost/freely available &#34;apps&#34; that could be used to assess human gait. The primary aim of this manuscript is to test the concurrent validity of kinematic measures recorded by a smartphone application in comparison to a 3D motion capture system in the sagittal plane. The secondary aim was to develop a protocol for clinicians on the set up of the smartphone camera for video movement analysis.
The sagittal plane knee angle was measured during heel strike and toe off events using the smart phone app and a 3D motion-capture system in 32 healthy subjects. Three trials were performed at near (2-m) and far (4-m) smartphone camera distances. The order of the distances was randomized. Regression analysis was performed to estimate the height of the camera based on either the subject&#39;s height or leg length.
Absolute measurement errors were least during toe off (3.12 &#177; 5.44 degrees) compared to heel strike (5.81 &#177; 5.26 degrees). There were significant (p &#60; 0.05) but moderate agreements between the application and 3D motion capture measures of knee angles. There were also no significant (p &#62; 0.05) differences between the absolute measurement errors between the two camera positions. The measurement errors averaged between 3 - 5 degrees during toe off and heel strike events of the gait cycle.
The use of smartphone apps can be a useful tool in the clinic for performing gait or human movement analysis. Further studies are needed to establish the accuracy in measuring movements of the upper extremity and trunk.

Video Movement Analysis Using Smartphones ViMAS: A Pilot Study

This manuscript describes the method to test the concurrent validity of kinematic measures recorded by the smartphone application in comparison to a 3D motion capture system in the sagittal plane. This protocol will enable clinicians to set up smartphones for video capture of human movement.

The use of smartphones in clinical practice is steadily increasing with the availability of low cost/freely available &#34;apps&#34; that could be used to ...

Comparing the Effects of Electronic Cigarette Vapor and Cigarette Smoke in a Novel In Vivo Exposure System

Electronic cigarettes (E-cigarettes) are being widely used, and growing in popularity. It is estimated that more than 9 million adults use them regularly. The potential adverse health effects of electronic cigarette vapor (E-vapor) exposure are poorly defined. While several animal models of E-vapor exposure have been developed, few models expose rodents to clinically relevant quantities of nicotine and make direct comparisons to cigarette smoke within the same exposure system. Here, we present a method for constructing and operating an E-vapor chamber and cigarette smoke chamber. The chambers are constructed by outfitting anesthesia chambers with a computer controlled pumping system that delivers consistent amounts of E-vapor or cigarette smoke to rodents. Nicotine exposure is measured indirectly by quantifying pre and post-exposure serum cotinine levels. This exposure system can be modified to accommodate various types of E-cigarettes&#160;and tobacco cigarettes, and can be used to compare the effects of E-vapor&#160;and cigarette smoke in vivo.

Comparing the Effects of Electronic Cigarette Vapor and Cigarette Smoke in a Novel In Vivo Exposure System

This protocol describes a method for exposing rodents to electronic cigarette vapor (E-vapor) and cigarette smoke. Exposure chambers are constructed by modifying anesthesia chambers with an automated pumping system that delivers E-vapor or cigarette smoke to rodents. This system can easily be modified to accommodate many experimental endpoints.

Electronic cigarettes (E-cigarettes) are being widely used, and growing in popularity. It is estimated that more than 9 million adults use them regularly. ...

Executing Complexity-Increasing Queries in Relational (MySQL) and NoSQL (MongoDB and EXist) Size-Growing ISO/EN 13606 Standardized EHR Databases

This research shows a protocol to assess the computational complexity of querying relational and non-relational (NoSQL (not only Structured Query Language)) standardized electronic health record (EHR) medical information database systems (DBMS). It uses a set of three doubling-sized databases, i.e. databases storing 5000, 10,000 and 20,000 realistic standardized EHR extracts, in three different database management systems (DBMS): relational MySQL object-relational mapping&#160;(ORM), document-based NoSQL MongoDB, and native extensible markup language (XML) NoSQL eXist.
The average response times to six complexity-increasing queries were computed, and the results showed a linear behavior in the NoSQL cases. In the NoSQL field, MongoDB presents a much flatter linear slope than eXist.
NoSQL systems may also be more appropriate to maintain standardized medical information systems due to the special nature of the updating policies of medical information, which should not affect the consistency and efficiency of the data stored in NoSQL databases.
One limitation of this protocol is the lack of direct results of improved relational systems such as archetype relational mapping (ARM) with the same data. However, the interpolation of doubling-size database results to those presented in the literature and other published results suggests that NoSQL systems might be more appropriate in many specific scenarios and problems to be solved. For example, NoSQL may be appropriate for document-based tasks such as EHR extracts used in clinical practice, or edition and visualization, or situations where the aim is not only to query medical information, but also to restore the EHR in exactly its original form.

Executing Complexity-Increasing Queries in Relational MySQL and NoSQL MongoDB and EXist Size-Growing ISO/EN 13606 Standardized EHR Databases

This study compares relational and non-relational (NoSQL) standardized medical information systems. The computational complexity of the response times of querying such database management systems (DBMS) is computed using doubling-sized databases. These results help the discussion of the appropriateness of each database approach to different scenarios and problems.

This research shows a protocol to assess the computational complexity of querying relational and non-relational (NoSQL (not only Structured Query ...

'Boden Food Plate': Novel Interactive Web-based Method for the Assessment of Dietary Intake

Different methods can be used in research to assess dietary intake, many of which are still paper-based. Written estimated food diaries are often utilized in clinical trials, despite being a burden for both study participants and researchers. This method requires participant literacy, it is time consuming, labor intensive, and can easily lead to under-reporting. With advancements in technology, there is a growing interest in electronic diaries that automate the dietary assessment process. These are focused on improving accuracy, reducing both time and cost and providing users with a visual and more enjoyable experience. The methodology presented here aimed to validate the &#39;Boden Food Plate&#39;, a novel web-based platform for self-recording of food and drink items, compared to a traditional estimated food diary. The application was also rated on a satisfaction scale by study participants using a paper-based questionnaire. Sixty-seven participants completed the dietary measures on both the three-day electronic and paper food diaries. For the analysis, only dietary data completed at both study time points (baseline and week six) was utilized. Despite small mean differences between dietary data collection methods, Bland Altman analysis showed fairly wide 95% limits of agreement between the electronic platform and the written estimated food diary and there were few cases which did not fall within the 95% confidence intervals. Overall, participants found the electronic food diary to be more fun than the paper method and as easy to use as hard copy diaries. The new platform has potential as a self-recording tool for the collection of dietary data, particularly when utilized in clinical trial settings. However, further validation studies are needed to improve the validity of this novel electronic dietary data collection tool.

The &#39;Boden Food Plate&#39; is an electronic food diary designed to be an interactive and fun method to collect dietary intake using visual depictions. The purpose of this study was to validate the web-based application against a traditional three-day estimated food diary method.

Different methods can be used in research to assess dietary intake, many of which are still paper-based. Written estimated food diaries are often utilized ...

Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases

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Quantitative Analysis of Cancer Metastasis using an Avian Embryo Model

Visualization and Analysis of Blood Flow and Oxygen Consumption in Hepatic Microcirculation: Application to an Acute Hepatitis Model

Transcriptomic Analysis of Human Retinal Surgical Specimens Using jouRNAl

Clinicopathological Analysis of miRNA Expression in Breast Cancer Tissues by Using miRNA In Situ Hybridization

Strategic Endothelial Cell Tube Formation Assay: Comparing Extracellular Matrix and Growth Factor Reduced Extracellular Matrix

Video Movement Analysis Using Smartphones (ViMAS): A Pilot Study

Comparing the Effects of Electronic Cigarette Vapor and Cigarette Smoke in a Novel In Vivo Exposure System

Executing Complexity-Increasing Queries in Relational (MySQL) and NoSQL (MongoDB and EXist) Size-Growing ISO/EN 13606 Standardized EHR Databases

'Boden Food Plate': Novel Interactive Web-based Method for the Assessment of Dietary Intake