This work was supported in part by National Heart, Lung, and Blood Institute: R35 HL135772 (to P. Ping); National Institute of General Medical Sciences: U54 GM114833 (to P. Ping, K. Watson, and W. Wang); U54 GM114838 (to J. Han); a gift from the Hellen &#38; Larry Hoag Foundation and Dr. S. Setty; and the T.C. Laubisch endowment at UCLA (to P. Ping).

NOTE: We have developed this protocol based on the Python programming language. To run this program, have Anaconda Python and Git pre-installed on the device. The commands provided in this protocol are based on Unix environment. This protocol provides the detail of downloading data from PubMed (MEDLINE) database, parsing the data, and setting up a cloud computing platform for the phrase mining and quantification of user-defined entity-category association.
1.Getting code and python environment setup
<ol>
	<li>Download or clone the code repository from Github (<a href="https://github.com/CaseOLAP/caseolap.git">https://github.com/CaseOLAP/caseolap</a>) or by typing &#8216;git clone <a href="https://github.com/CaseOLAP/caseolap.git">https://github.com/CaseOLAP/caseolap.git</a>&#8217; in the terminal window.</li>
	<li>Navigate to the &#8216;caseolap&#8217; directory. This is the root directory of the project. Within this directory, the &#8216;data&#8217; directory will be populated with multiple data sets as you progress through these steps in the protocol. The &#8216;input&#8217; directory is for user-provided data. The &#8216;log&#8217; directory has log files for troubleshooting purposes. The &#8216;result&#8217; directory is where the final results will be stored.</li>
	<li>Using the terminal window, go to the directory where you cloned our GitHub repository. Create the CaseOLAP environment using the &#8216;environment.yml&#8217; file by typing &#8216;conda env create -f environment.yaml&#8217; in the terminal. Then activate the environment by typing &#8216;source activate caseolap&#8217; in the terminal.</li>
</ol>
2. Downloading documents
<ol>
	<li>Make sure that the FTP address in &#8216;ftp_configuration.json&#8217; in the config directory is the same as the Annual Baseline or Daily Update Files link address, found in the link (<a href="https://www.nlm.nih.gov/databases/download/pubmed_medline.html">https://www.nlm.nih.gov/databases/download/pubmed_medline.html</a>).</li>
	<li>To download baseline only or update files only, set &#8216;true&#8217; in the &#8216;download_config.json&#8217; file in the &#8216;config&#8217; directory. By default, it downloads and extracts both baseline and update files. A sample of extracted XML data can be viewed at (<a href="https://github.com/CaseOLAP/caseolap-pipelines/blob/master/data/extracted-data-sample.xml">https://github.com/CaseOLAP/caseolap-pipelines/blob/master/data/extracted-data-sample.xml</a>)</li>
	<li>Type &#8216;python run_download.py&#8217; in the terminal window to download abstracts from the Pubmed database. This will create a directory called &#8216;ftp.ncbi.nlm.nih.gov&#8217; in the current directory. This process checks the integrity of the downloaded data and extracts it to the target directory.</li>
	<li>Go to the &#8216;log&#8217; directory to read the log messages in &#8216;download_log.txt&#8217; in case the download process fails. If the process is completed successfully, the debugging messages of the download process will be printed out in this log file.</li>
	<li>When the download is complete, navigate through &#8216;ftp.ncbi.nlm.nih.gov&#8217; to make sure that there is &#8216;updatefiles&#8217; or &#8216;basefiles&#8217; or both directories based on download configuration in &#8216;download_config.json&#8217;. The file statistics become available at &#8216;filestat.txt&#8217; in the &#8216;data&#8217; directory.</li>
</ol>
3. Parsing documents
<ol>
	<li>Make sure that downloaded and extracted data is available at &#8216;ftp.ncbi.nlm.nih.gov&#8217; directory from step 2. This directory is the input data directory in this step.</li>
	<li>To modify the data-parsing schema, select parameters in &#8216;parsing_config.json&#8217; file in the &#8216;config&#8217; directory by setting their value to &#8216;true&#8217;. By default, it parses the PMID, authors, abstract, MeSH, location, journal, publication date.</li>
	<li>Type &#8216;python run_parsing.py&#8217; in the terminal to parse the documents from downloaded (or extracted) files. This step parses all downloaded XML files and creates a python dictionary for each document with keys (e.g., PMID, authors, abstract, MeSH of the file based on parsing schema setup at step 3.2).</li>
	<li>Once data parsing is completed, make sure that parsed data is saved in the file called &#8216;pubmed.json&#8217; in the data directory. A sample of parsed data is available at Figure 3.</li>
	<li>Go to the &#8216;log&#8217; directory to read the log messages in &#8216;parsing_log.txt&#8217; in case the parsing process fails. If the process is completed successfully, the debugging messages will be printed out in the log file.</li>
</ol>
4. Mesh to PMID mapping
<ol>
	<li>Make sure that parsed data (&#8216;pubmed.json&#8217;) is available at the &#8216;data&#8217; directory.</li>
	<li>Type &#8216;python run_mesh2pmid.py&#8217; in the terminal to perform MeSH to PMID mapping. This creates a mapping table where each of the MeSH collects associated PMIDs. A Single PMID may fall under the multiple MeSH terms.</li>
	<li>Once the mapping is completed, make sure that there is &#8216;mesh2pmid.json&#8217; in the data directory. A sample of the top 20 mapping statistics is available in Table-2, Figures 4 and 5.</li>
	<li>Go to the &#8216;log&#8217; directory to read the log messages in &#8216;mesh2pmid_mapping_log.txt&#8217; in case this process fails. If the process is completed successfully, the debugging messages of the mapping will be printed out in this log file.</li>
</ol>
5. Document indexing
<ol>
	<li>Download the Elasticsearch application from <a href="https://www.elastic.co/downloads">https://www.elastic.co</a>. Currently, the download is available at (<a href="https://www.elastic.co/downloads/elasticsearch">https://www.elastic.co/downloads/elasticsearch</a>). To download the software in the remote cloud, type &#8216;wget <a href="https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-x.x.x.tar.gz">https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-x.x.x.tar.gz</a>&#8217; in the terminal. Make sure that &#8216;x.x.x&#8217; in the above command is replaced by proper version number.</li>
	<li>Make sure that downloaded &#8216;elasticsearch-x.x.x.tar.gz&#8217; file appears in the root directory then extract the files by typing &#8216;tar xvzf elasticsearch-x.x.x.tar.gz&#8217; in the terminal window.</li>
	<li>Open a new terminal and go to the ElasticSearch bin directory by typing &#8216;cd Elasticsearch/bin&#8217; in the terminal from the root directory.</li>
	<li>Start the Elasticsearch server by typing &#8216;./Elasticsearch&#8217; in the terminal window. Make sure that the server is started without error messages. In case of error on starting Elasticsearch server, follow the instructions at (<a href="https://www.elastic.co/guide/en/elasticsearch/reference/current/index.html">https://www.elastic.co/guide/en/elasticsearch/reference/current/index.html</a>).</li>
	<li>Modify the contents in the &#8216;index_init_config.json&#8217; in &#8216;config&#8217; directory to set index initiation. By default, it will select all items present.</li>
	<li>Type &#8216;python run_index_init.py&#8217; in the terminal to initiate an index-database in the Elasticsearch server. This initializes the index with a set of criteria known as index information (e.g., index name, type name, number of shards, number of replicas). You will see the message mentioning index is successfully created.</li>
	<li>Select the items in the &#8216;index_populate_config.json&#8217; in the &#8216;config&#8217; directory by setting their value to &#8216;true&#8217;. By default, it will select all items present.</li>
	<li>Make sure that parsed data (&#8216;pubmed.json&#8217;) is present in the &#8216;data&#8217; directory.</li>
	<li>Type &#8216;python run_index_populate.py&#8217; in the terminal to populate the index by creating bulk data with two components.A first component is a dictionary with metadata information on the index name, type name, and bulk id (e.g., &#8216;PMID&#8217;). A second component is a data dictionary containing all the information on the tags (e.g., &#8216;title&#8217;,&#8217;abstract&#8217;,&#8217;MeSH&#8217;). </li>
	<li>Go to the &#8216;log&#8217; directory to read the log messages in &#8216;indexing_log.txt&#8217; in case this process fails. If the process is completed successfully, the debugging messages of the indexing will be printed out in the log file.</li>
</ol>
6. Text-cube creation
<ol>
	<li>Download the latest MeSH Tree available at (<a href="https://www.nlm.nih.gov/mesh/filelist.html">https://www.nlm.nih.gov/mesh/filelist.html</a>). The current version of the code is using MeSH Tree 2018 as &#8216;meshtree2018.bin&#8217; in the input directory.</li>
	<li>Define the categories of interest (e.g., Disease names, Age groups, Gender). A category may include one or more MeSH descriptors (<a href="https://meshb-prev.nlm.nih.gov/treeView">https://meshb-prev.nlm.nih.gov/treeView</a>). Collect MeSH IDs for a category. Save the names of the categories in the file &#8216;textcube_config.json&#8217; in the config directory (see a sample of the category in &#8216;Age Group&#8217; in the downloaded version of &#8216;textcube_config.json&#8217; file).</li>
	<li>Put the collected categories of MeSH IDs in a line separated by a space. Save the category file as &#8216;categories.txt&#8217; in the &#8216;input&#8217; directory (see a sample of &#8216;Age Group&#8217; MeSH IDs in the downloaded version of &#8216;categories.txt&#8217; file). This algorithm automatically selects all descendent MeSH descriptors. An example of root nodes and descendants are presented in Figure 4.</li>
	<li>Make sure that &#8216;mesh2pmid.json&#8217; is in the &#8216;data&#8217; directory. If the MeSH Tree has been updated with a different name (e.g., &#8216;meashtree2019.bin&#8217;) in &#8216;input&#8217; directory, make sure that this is properly represented in the input data path in the &#8216;run_textube.py&#8217; file.</li>
	<li>Type &#8216;python run_textcube.py&#8217; in the terminal to create a document data structure called Text-Cube. This creates a collection of documents (PMIDs) for each category. A single document (PMID) may fall under multiple categories, (see Table 3A, Table 3B, Figure 6A and Figure 7A).</li>
	<li>Once Text-Cube creation step is completed, make sure that following data files are saved in the &#8216;data&#8217; directory: (1) a cell to PMID table as &#8220;textcube_cell2pmid.json&#8221;, (2) a PMID to cell mapping table as &#8220;textcube_pmid2cell.json&#8221;, (3) a collection of all descendant MeSH terms for a cell as &#8220;meshterms_per_cat.json&#8221; (4) Text-Cube data statistics as &#8220;textcube_stat.txt&#8221;.</li>
	<li>Go to the &#8216;log&#8217; directory to read the log messages in &#8216;textcube_log.txt&#8217; in case this process fails. If the process is completed successfully, the debugging messages of the Text-Cube creation will be printed out in the log file.</li>
</ol>
7. Entity count
<ol>
	<li>Create user-defined entities (e.g., protein names, genes, chemicals). Put one entity and its abbreviations in a single line separated by &#8220;|&#8221;. Save the entity file as &#8216;entities.txt&#8217; in the &#8216;input&#8217; directory. A sample of entities can be found in Table 4.</li>
	<li>Make sure that Elasticsearch server is running. Otherwise, go to step 5.2 and 5.3 to restart the Elasticsearch server. It is expected to have an indexed database called &#8216;pubmed&#8217; in your Elasticsearch server which was established in step 5.</li>
	<li>Make sure that &#8216;textcube_pmid2cell.json&#8217; is in the &#8216;data&#8217; directory.</li>
	<li>Type &#8216;python run_entitycount.py&#8217; in the terminal to perform Entity Count operation. This searches the documents from the indexed database and counts the entity in each document as well as collects the PMIDs in which entities were found.</li>
	<li>Once the Entity Count is completed, make sure that the final results are saved as &#8216;entitycount.txt&#8217; and &#8216;entityfound_pmid2cell.json&#8217; in the &#8216;data&#8217; directory.</li>
	<li>Go to the &#8216;log&#8217; directory to read the log messages in &#8216;entitycount_log.txt&#8217; in case this process fails. If the process is completed successfully, the debugging messages of the Entity Count will be printed out in the log file.</li>
</ol>
8. Metadata update
<ol>
	<li>Make sure that all input data (&#8216;entitycount.txt&#8217;, &#8216;textcube_pmid2cell.json&#8217;, &#8216;entityfound_pmid2cell.txt&#8217;) are in the &#8216;data&#8217; directory. These are the input data for Metadata Update.</li>
	<li>Type &#8216;python run_metadata_update.py&#8217; in the terminal to update the metadata. This prepares a collection of metadata (e.g., cell name, associated MeSH, PMIDs) representing each text document in the cell. A sample of Text-Cube metadata is presented in Table 3A and Table 3B.</li>
	<li>Once the Metadata Update is completed, make sure that &#8216;metadata_pmid2pcount.json&#8217; and &#8216;metadata_cell2pmid.json&#8217; files are saved in &#8216;data&#8217; directory.</li>
	<li>Go to the &#8216;log&#8217; directory to read the log messages in &#8216;metadata_update_log.txt&#8217; in case this process fails. If the process is completed successfully, the debugging messages of the metadata update will be printed out in the log file.</li>
</ol>
9. CaseOLAP score calculation
<ol>
	<li>Make sure that &#8216;metadata_pmid2pcount.json&#8217; and &#8216;metadata_cell2pmid.json&#8217; files are present in the &#8216;data&#8217; directory. These are the input data for score calculation.</li>
	<li>Type &#8216;python run_caseolap_score.py&#8217; in the terminal to perform CaseOLAP score calculation. This calculates the CaseOLAP score of the entities based on user-defined categories. The CaseOLAP score is the product of Integrity, Popularity, and Distinctiveness.</li>
	<li>Once the score computation is completed, make sure that this saves the results in multiple files (e.g., popularity as &#8216;pop.csv&#8217;, distinctiveness as &#8216;dist.csv&#8217;, CaseOLAP score as &#8217;caseolap.csv&#8217;), in the &#8216;result&#8217; directory. The summary of the CaseOLAP score calculation is also presented in Table 5.</li>
	<li>Go to the &#8216;log&#8217; directory to read the log messages in &#8216;caseolap_score_log.txt&#8217; in case this process fails. If the process is completed successfully, the debugging messages of the CaseOLAP score calculation will be printed out in the log file.</li>
</ol>

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The authors have nothing to disclose.

We have demonstrated that the CaseOLAP algorithm can create a phrase based quantitative association to a knowledge-based category over large volumes of textual data for extraction of meaningful insights. Following our protocol, one can build the CaseOLAP framework to create a desired Text-Cube and quantify entity-category associations through CaseOLAP score calculation. The obtained raw CaseOLAP scores can be taken to integrative analyses including dimensionality reduction, clustering, temporal and geographical analysis, as well as the creation of a graphical database which enables semantic mapping of the documents.
Applicability of the algorithm. Examples of user-defined entities, other than proteins, could be a list of gene names, drugs, specific signs and symptoms including their abbreviations and synonyms. Furthermore, there are many choices for category selection to facilitate specific user-defined biomedical analyses (e.g., Anatomy [A], Discipline and Occupation [H], Phenomena and Processes [G]). In our two use cases, all scientific publications and their textual data are retrieved from the MEDLINE database using PubMed as the search engine, both managed by the National Library of Medicine. However, the CaseOLAP platform may be applied to other databases of interest containing biomedical documents with textual data such as the FDA Adverse Event Reporting System (FAERS). This is an open database containing information on medical adverse events and medication error reports submitted to FDA. In contrast to MEDLINE and FAERS, databases in hospitals containing electronic health records from patients are not open to the public and are restricted by the Health Insurance Portability and Accountability Act known as HIPAA.
CaseOLAP algorithm has been successfully applied to the different types of data (e.g., news articles)1. The implementation of this algorithm in biomedical documents has been made in 20185. The requirements for applicability of CaseOLAP algorithm is that each of the documents should be assigned with keywords associated with the concepts (e.g., MeSH descriptors in biomedical publications, keywords in news articles). If keywords are not found, one can apply Autophrase6,7&#160;to collect top representative phrases and build the entity list before implementing our protocol. Our protocol does not provide the step to perform Autophrase.
Comparison with other algorithms. The concept of using a Data-Cube8,9,10 and a Text-Cube2,3,4 has been evolving since 2005 with new advancements to make data mining more applicable. The concept of Online Analytical Processing (OLAP)11,12,13,14,15 in data mining and business intelligence goes back to 1993. OLAP, in general, aggregates the information from multiple systems, and stores it in a multi-dimensional format. There are different types of OLAP systems implemented in data mining. For example (1) Hybrid Transaction/Analytical Processing (HTAP)16,17, (2) Multidimensional OLAP (MOLAP)18,19-Cube based, and (3) Relational OLAP (ROLAP)20.
Specifically, the CaseOLAP algorithm has been compared with numerous existing algorithms, specifically, with their phrase segmentation enhancements, including TF-IDF+Seg, MCX+Seg, MCX, and SegPhrase. Moreover, RepPhrase (RP, also known as SegPhrase+) has been compared with its own ablation variations, including (1) RP without the Integrity measure incorporated (RP No INT), (2) RP without the Popularity measure incorporated (RP No POP), and (3) RP without the Distinctiveness measure incorporated (RP No DIS). The benchmark results are shown in the study by Fangbo Tao et al.1.
There are still challenges on data mining which can add additional functionality over saving and retrieving the data from the database. Context-aware semantic Analytical Processing (CaseOLAP) systematically implements the Elasticsearch to build an indexing database of millions of documents (Protocol 5). The Text-Cube is a document structure built over the indexed data with user-provided categories (Protocol 6). This enhances the functionality to the documents within and across the cell of the Text-Cube and allow us to calculate term frequency of the entities over a document and document frequency over a specific cell (Protocol 8). The final CaseOLAP score utilizes these frequency calculations to output a final score (Protocol 9). In 2018, we implemented this algorithm to study ECM proteins and six heart diseases to analyze protein-disease associations. The details of this study can be found in the study by Liem, D.A. et al.5. indicating that CaseOLAP could be widely used in the biomedical community exploring a variety of diseases and mechanisms.
Limitations of the algorithm. Phrase mining itself is a technique to manage and retrieve important concepts from textual data. While discovering entity-category association as a mathematical quantity (vector), this technique is unable to figure out the polarity (e.g., positive or negative inclination) of the association. One can build the quantitative summarization of the data utilizing the Text-Cude document structure with assigned entities and categories, but a qualitative concept with microscopic granularities cannot be reached. Some concepts are continuously evolving from past till now. The summarization presented for a specific entity-category association includes all incidences throughout the literature. This may lack the temporal propagation of the innovation. In the future, we plan to address these limitations.
Future Applications. About 90% of the accumulated data in the world is in the unstructured text data. Finding a representative phrase and relation to the entities embedded in the text is a very important task for the implementation of new technologies (e.g., Machine Learning, Information Extraction, Artificial Intelligence). To make the text-data machine readable, data need to be organized in the database over which the next layer of tools could be implemented. In the future, this algorithm can be a crucial step in making data mining more functional for the retrieval of information and the quantification of the entity-category associations.

Manual evaluation of millions of text files for the study of phrase-category association (e.g., Age group to protein association) is incomparable with the efficiency provided by an automated computational method. We want to introduce the cloud-based Context-aware Semantic Online Analytical Processing (CaseOLAP) platform as a phrase-mining method for automated computation of phrase-category association in the biomedical context.
The CaseOLAP platform, which was first defined in 20161, is very efficient compared to the traditional methods of data management and computation because of its functional document management called Text-Cube2,3,4,&#160;which distributes the documents while maintaining underlying hierarchy and neighbourhoods. It has been applied in biomedical research5&#160;to study entity-category association. The CaseOLAP platform consists of six major steps including download and extraction of data, parsing, indexing, Text-Cube creation, entity count, and CaseOLAP score calculation; which is the main focus of the protocol&#160;(Figure 1, Figure 2, Table 1).
To implement the CaseOLAP algorithm, the user sets up categories of interest (e.g., disease, signs and symptoms, age groups, diagnosis) and entities of interest (e.g., proteins, drugs). One example of a category included in this article is the &#39;Age Groups&#39;, which has &#39;infant&#39;, &#39;child&#39;, &#39;adolescent&#39;, and &#39;adult&#39; subcategories as cells of the Text-Cube and protein names (synonyms) and abbreviations as entities. Medical Subject Headings (MeSH) are implemented to retrieve publications corresponding to the defined categories (Table 2). MeSH descriptors are organized in a hierarchical tree structure to permit search for publications at varying levels of specificity&#160;(a sample shown in Figure 3). The CaseOLAP platform utilizes the data indexing and search functionality for curation of the documents associated with an entity which further facilitate document to entity count mapping and CaseOLAP score calculation.
The details of the CaseOLAP score calculation is available in previous publications1,5. This score is computed using specific ranking criteria based on underlying Text-Cube document structure. The final score is the product of Integrity, Popularity, and Distinctiveness. Integrity describes whether a representative entity is an integral semantic unit that collectively refers to a meaningful concept. The integrity of the user-defined phrase is taken to be 1.0 because it stands as a standard phrase in the literature. Distinctiveness represents the relative relevance of a phrase in one subset of documents compared to the rest of the other cells. It first calculates the relevance of an entity to a specific cell by comparing the occurrence of the protein name in the target data set and provides a normalized Distinctiveness score. Popularity represents the fact that phrase with a higher popularity score appears more frequently in one subset of documents. Rare protein names in a cell are ranked low, while an increase in their frequency of mention has a diminishing return due to the implementation of the logarithmic function of frequency. Quantitatively measuring these three concepts depends on the (1) term frequency of the entity over a cell and across the cells and (2) number of documents having that entity (document frequency) within the cell and across the cells.
We have studied two representative scenarios using a PubMed dataset and our algorithm. We are interested in how mitochondrial proteins are associated with two unique categories of MeSH descriptors; &#34;Age Groups&#34; and &#34;Nutritional and Metabolic Diseases&#34;. Specifically, we retrieved 15,728,250 publications from 20 years publications collected by PubMed (1998 to 2018), among them, 8,123,458 unique abstracts have had full MeSH descriptors. Accordingly, 1,842 human mitochondrial protein names (including abbreviations and synonyms), acquired from UniProt (uniprot.org) as well as from MitoCarta2.0 (http://mitominer.mrc-mbu.cam.ac.uk/release-4.0/begin.do&#62;), are systematically examined. Their associations with these 8,899,019 publications and entities were studied using our protocol; we constructed a Text-Cube and calculated the respective CaseOLAP scores.

cloud-based phrase mining and analysis of user-defined phrase-category association in biomedical publications

The rapid accumulation of biomedical textual data has far exceeded the human capacity of manual curation and analysis, necessitating novel text-mining tools to extract biological insights from large volumes of scientific reports. The Context-aware Semantic Online Analytical Processing (CaseOLAP) pipeline, developed in 2016, successfully quantifies user-defined phrase-category relationships through the analysis of textual data. CaseOLAP has many biomedical applications.
We have developed a protocol for a cloud-based environment supporting the end-to-end phrase-mining and analyses platform. Our protocol includes data preprocessing (e.g., downloading, extraction, and parsing text documents), indexing and searching with Elasticsearch, creating a functional document structure called Text-Cube, and quantifying phrase-category relationships using the core CaseOLAP algorithm.
Our data preprocessing generates key-value mappings for all documents involved. The preprocessed data is indexed to carry out a search of documents including entities, which further facilitates the Text-Cube creation and CaseOLAP score calculation. The obtained raw CaseOLAP scores are interpreted using a series of integrative analyses, including dimensionality reduction, clustering, temporal, and geographical analyses. Additionally, the CaseOLAP scores are used to create a graphical database, which enables semantic mapping of the documents.
CaseOLAP defines phrase-category relationships in an accurate (identifies relationships), consistent (highly reproducible), and efficient manner (processes 100,000 words/sec). Following this protocol, users can access a cloud-computing environment to support their own configurations and applications of CaseOLAP. This platform offers enhanced accessibility and empowers the biomedical community with phrase-mining tools for widespread biomedical research applications.

Watch this Scientific Journal Video about Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications at JoVE.com

Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

We present a protocol and associated programming code as well as metadata samples to support a cloud-based automated identification of phrases-category association representing unique concepts in user selected knowledge domain in biomedical literature. The phrase-category association quantified by this protocol can facilitate in depth analysis in the selected knowledge domain.

The rapid accumulation of biomedical textual data has far exceeded the human capacity of manual curation and analysis, necessitating novel text-mining ...

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8.6K Views.  University of California, Los Angeles. We present a protocol and associated programming code as well as metadata samples to support a cloud-based automated identification of phrases-category association representing unique concepts in user selected knowledge domain in biomedical literature. The phrase-category association quantified by this protocol can facilitate in depth analysis in the selected knowledge domain.

Video: Cloud-Based Phrase Mining and Analysis of User-Defined Phrase-Category Association in Biomedical Publications

Generation of Alginate Microspheres for Biomedical Applications

Alginate-based materials have received considerable attention for biomedical applications because of their hydrophilic nature, biocompatibility, and physical architecture. Applications include cell encapsulation, drug delivery, stem cell culture, and tissue engineering scaffolds. In fact, clinical trials are currently being performed in which islets are encapsulated in PLO coated alginate microbeads as a treatment of type I diabetes. However, large numbers of islets are required for efficacy due to poor survival following transplantation. The ability to locally stimulate microvascular network formation around the encapsulated cells may increase their viability through improved transport of oxygen, glucose and other vital nutrients. Fibroblast growth factor-1 (FGF-1) is a naturally occurring growth factor that is able to stimulate blood vessel formation and improve oxygen levels in ischemic tissues. The efficacy of FGF-1 is enhanced when it is delivered in a sustained fashion rather than a single large-bolus administration. The local long-term release of growth factors from islet encapsulation systems could stimulate the growth of blood vessels directly towards the transplanted cells, potentially improving functional graft outcomes. In this article, we outline procedures for the preparation of alginate microspheres for use in biomedical applications. In addition, we describe a method we developed for generating multilayered alginate microbeads. Cells can be encapsulated in the inner alginate core, and angiogenic proteins in the outer alginate layer. The release of proteins from this outer layer would stimulate the formation of local microvascular networks directly towards the transplanted islets.

In the following sections, we outline procedures for the preparation of alginate microspheres for use in biomedical applications. We specifically illustrate a technique for creating multilayered alginate microspheres for the dual purpose of cell and protein encapsulation as a potential treatment for type 1 diabetes.

Alginate-based materials have received considerable attention for biomedical applications because of their hydrophilic nature, biocompatibility, and ...

Quantitative Analysis and Characterization of Atherosclerotic Lesions in the Murine Aortic Sinus

Atherosclerosis is a disease of the large arteries and a major underlying cause of myocardial infarction and stroke. Several different mouse models have been developed to facilitate the study of the molecular and cellular pathophysiology of this disease. In this manuscript we describe specific techniques for the quantification and characterization of atherosclerotic lesions in the murine aortic sinus and ascending aorta. The advantage of this procedure is that it provides an accurate measurement of the cross-sectional area and total volume of the lesion, which can be used to compare atherosclerotic progression across different treatment groups. This is possible through the use of the valve leaflets as an anatomical landmark, together with careful adjustment of the sectioning angle. We also describe basic staining methods that can be used to begin to characterize atherosclerotic progression. These can be further modified to investigate antigens of specific interest to the researcher. The described techniques are generally applicable to a wide variety of existing and newly created dietary and genetically-induced models of atherogenesis.

We describe procedures to quantify and characterize atherosclerotic lesions in mouse models using precision sectioning of the aortic sinus and ascending aorta combined with histochemical and immunohistochemical analysis.

Atherosclerosis is a disease of the large arteries and a major underlying cause of myocardial infarction and stroke. Several different mouse models have ...

The Bovine Lung in Biomedical Research: Visually Guided Bronchoscopy, Intrabronchial Inoculation and In Vivo Sampling Techniques

There is an ongoing search for alternative animal models in research of respiratory medicine. Depending on the goal of the research, large animals as models of pulmonary disease often resemble the situation of the human lung much better than mice do. Working with large animals also offers the opportunity to sample the same animal repeatedly over a certain course of time, which allows long-term studies without sacrificing the animals.
The aim was to establish in vivo sampling methods for the use in a bovine model of a respiratory Chlamydia psittaci infection. Sampling should be performed at various time points in each animal during the study, and the samples should be suitable to study the host response, as well as the pathogen under experimental conditions.
Bronchoscopy is a valuable diagnostic tool in human and veterinary medicine. It is a safe and minimally invasive procedure. This article describes the intrabronchial inoculation of calves as well as sampling methods for the lower respiratory tract. Videoendoscopic, intrabronchial inoculation leads to very consistent clinical and pathological findings in all inoculated animals and is, therefore, well-suited for use in models of infectious lung disease. The sampling methods described are bronchoalveolar lavage, bronchial brushing and transbronchial lung biopsy. All of these are valuable diagnostic tools in human medicine and could be adapted for experimental purposes to calves aged 6-8 weeks. The samples obtained were suitable for both pathogen detection and characterization of the severity of lung inflammation in the host.

The Bovine Lung in Biomedical Research: Visually Guided Bronchoscopy, Intrabronchial Inoculation and In Vivo Sampling Techniques

This article describes bronchoscopic techniques in the bovine lung under experimental conditions, i.e. bronchoscopically guided inoculation, bronchoalveolar lavage, bronchial brushing, and transbronchial lung biopsy.

There is an ongoing search for alternative animal models in research of respiratory medicine. Depending on the goal of the research, large animals as ...

Carotid Artery Infusions for Pharmacokinetic and Pharmacodynamic Analysis of Taxanes in Mice

When proposing the use of a drug, drug combination, or drug delivery into a novel system, one must assess the pharmacokinetics of the drug in the study model. As the use of mouse models are often a vital step in preclinical drug discovery and drug development1-8, it is necessary to design a system to introduce drugs into mice in a uniform, reproducible manner. Ideally, the system should permit the collection of blood samples at regular intervals over a set time course. The ability to measure drug concentrations by mass-spectrometry, has allowed investigators to follow the changes in plasma drug levels over time in individual mice1, 9, 10. In this study, paclitaxel was introduced into transgenic mice as a continuous arterial infusion over three hours, while blood samples were simultaneously taken by retro-orbital bleeds at set time points. Carotid artery infusions are a potential alternative to jugular vein infusions, when factors such as mammary tumors or other obstructions make jugular infusions impractical. Using this technique, paclitaxel concentrations in plasma and tissue achieved similar levels as compared to jugular infusion. In this tutorial, we will demonstrate how to successfully catheterize the carotid artery by preparing an optimized catheter for the individual mouse model, then show how to insert and secure the catheter into the mouse carotid artery, thread the end of the catheter out through the back of the mouse&#8217;s neck, and hook the mouse to a pump to deliver a controlled rate of drug influx. Multiple low volume retro-orbital bleeds allow for analysis of plasma drug concentrations over time.

This method was developed with the goal of delivering a steady drug solution via the carotid artery, to assess the pharmacokinetics of novel drugs in mouse models.

When proposing the use of a drug, drug combination, or drug delivery into a novel system, one must assess the pharmacokinetics of the drug in the study ...

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI&#8212;Application in Premanifest Huntington's Disease

Recent advances in MRI offer a variety of useful markers to identify neurodegenerative diseases. In Huntington&#39;s disease (HD), regional brain atrophy begins many years prior to the motor onset (during the &#34;premanifest&#34; period), but the spatiotemporal pattern of regional atrophy across the brain has not been fully characterized. Here we demonstrate an online cloud-computing platform, &#34;MRICloud&#34;, which provides atlas-based whole-brain segmentation of T1-weighted images at multiple granularity levels, and thereby, enables us to access the regional features of brain anatomy. We then describe a regression model that detects statistically significant inflection points, at which regional brain atrophy starts to be noticeable, i.e. the &#34;change-point&#34;, with respect to a disease progression index. We used the CAG-age product (CAP) score to index the disease progression in HD patients. Change-point analysis of the volumetric measurements from the segmentation pipeline, therefore, provides important information of the order and pattern of structural atrophy across the brain. The paper illustrates the use of these techniques on T1-weighted MRI data of premanifest HD subjects from a large multicenter PREDICT-HD study. This design potentially has wide applications in a range of neurodegenerative diseases to investigate the dynamic changes of brain anatomy.

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI&amp;#8212;Application in Premanifest Huntington&#039;s Disease

This paper describes a statistical model for volumetric MRI data analysis, which identifies the &#34;change-point&#34; when brain atrophy begins in premanifest Huntington&#39;s disease. Whole-brain mapping of the change-points is achieved based on brain volumes obtained using an atlas-based segmentation pipeline of T1-weighted images.

Recent advances in MRI offer a variety of useful markers to identify neurodegenerative diseases. In Huntington&#39;s disease (HD), regional brain atrophy ...

Sampling Strategies and Processing of Biobank Tissue Samples from Porcine Biomedical Models

In translational medical research, porcine models have steadily become more popular. Considering the high value of individual animals, particularly of genetically modified pig models, and the often-limited number of available animals of these models, establishment of (biobank) collections of adequately processed tissue samples suited for a broad spectrum of subsequent analyses methods, including analyses not specified at the time point of sampling, represent meaningful approaches to take full advantage of the translational value of the model. With respect to the peculiarities of porcine anatomy, comprehensive guidelines have recently been established for standardized generation of representative, high-quality samples from different porcine organs and tissues. These guidelines are essential prerequisites for the reproducibility of results and their comparability between different studies and investigators. The recording of basic data, such as organ weights and volumes, the determination of the sampling locations and of the numbers of tissue samples to be generated, as well as their orientation, size, processing and trimming directions, are relevant factors determining the generalizability and usability of the specimen for molecular, qualitative, and quantitative morphological analyses. Here, an illustrative, practical, step-by-step demonstration of the most important techniques for generation of representative, multi-purpose biobank specimen from porcine tissues is presented. The methods described here include determination of organ/tissue volumes and densities, the application of a volume-weighted systematic random sampling procedure for parenchymal organs by point-counting, determination of the extent of tissue shrinkage related to histological embedding of samples, and generation of randomly oriented samples for quantitative stereological analyses, such as isotropic uniform random (IUR) sections generated by the &#34;Orientator&#34; and &#34;Isector&#34; methods, and vertical uniform random (VUR) sections.

The practical application and performance of methods for generation of representative tissue samples of porcine animal models for a broad spectrum of downstream analyses in biobank projects are demonstrated, including volumetry, systematic random sampling, and differential processing of tissue samples for qualitative and quantitative morphologic and molecular analyses types.

In translational medical research, porcine models have steadily become more popular. Considering the high value of individual animals, particularly of ...

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Clinical case reports (CCRs) are a valuable means of sharing observations and insights in medicine. The form of these documents varies, and their content includes descriptions of numerous, novel disease presentations and treatments. Thus far, the text data within CCRs is largely unstructured, requiring significant human and computational effort to render these data useful for in-depth analysis. In this protocol, we describe methods for identifying metadata corresponding to specific biomedical concepts frequently observed within CCRs. We provide a metadata template as a guide for document annotation, recognizing that imposing structure on CCRs may be pursued by combinations of manual and automated effort. The approach presented here is appropriate for organization of concept-related text from a large literature corpus (e.g., thousands of CCRs) but may be easily adapted to facilitate more focused tasks or small sets of reports. The resulting structured text data includes sufficient semantic context to support a variety of subsequent text analysis workflows: meta-analyses to determine how to maximize CCR detail, epidemiological studies of rare diseases, and the development of models of medical language may all be made more realizable and manageable through the use of structured text data.

We present a protocol and associated metadata template for the extraction of text describing biomedical concepts in clinical case reports. The structured text values produced through this protocol can support deep analysis of thousands of clinical narratives.

Clinical case reports (CCRs) are a valuable means of sharing observations and insights in medicine. The form of these documents varies, and their content ...

Quantitative [18F]-Naf-PET-MRI Analysis for the Evaluation of Dynamic Bone Turnover in a Patient with Facetogenic Low Back Pain

Imaging techniques that reflect dynamic bone turnover may aid in characterizing a wide range of bone pathologies. Bone is a dynamic tissue undergoing continuous remodeling with the competing activity of osteoblasts, which produce the new bone matrix, and osteoclasts, whose function is to eliminate mineralized bone. [18F]-NaF is a positron emission tomography (PET) radiotracer that enables visualization of bone metabolism. [18F]-NaF is chemically absorbed into hydroxyapatite in the bone matrix by osteoblasts and can thus noninvasively detect osteoblastic activity, which is occult to conventional imaging techniques. Kinetic modeling of dynamic [18F]-NaF-PET data provides detailed quantitative measures of bone metabolism. Conventional semi-quantitative PET data, which utilizes standardized uptake values (SUVs) as a measure of radiotracer activity, is referred to as a static technique due to its snapshot of tracer uptake in time.&#160; Kinetic modeling, however, utilizes dynamic image data where tracer levels are continuously acquired providing tracer uptake temporal resolution. From the kinetic modeling of dynamic data, quantitative values like blood flow and metabolic rate (i.e., potentially informative metrics of tracer dynamics) can be extracted, all with respect to the measured activity in the image data. When combined with dual modality PET-MRI, region-specific kinetic data can be correlated with anatomically registered high-resolution structural and pathologic information afforded by MRI. The goal of this methodological manuscript is to outline detailed techniques for performing and analyzing dynamic [18F]-NaF-PET-MRI data. The lumbar facet joint is a common site of degenerative arthritis disease and a common cause for axial low back pain.&#160; Recent studies suggest [18F]-NaF-PET may serve as a useful biomarker of painful facetogenic disease.&#160; The human lumbar facet joint will, therefore, be used as a prototypical region of interest for dynamic [18F]-NaF-PET-MRI analysis in this manuscript.

Quantitative [18F]-Naf-PET-MRI Analysis for the Evaluation of Dynamic Bone Turnover in a Patient with Facetogenic Low Back Pain

Imaging techniques that reflect dynamic bone turnover may aid in characterizing a wide range of bone pathologies. We present detailed methodologies for performing and analyzing dynamic [18F]-NaF-PET-MRI data in a patient with facetogenic low back pain using the lumbar facet joints as a prototypical region of interest.

Imaging techniques that reflect dynamic bone turnover may aid in characterizing a wide range of bone pathologies. Bone is a dynamic tissue undergoing ...

On-Site Sampling and Extraction of Brain Tumors for Metabolomics and Lipidomics Analysis

Despite the variety of tools available for cancer diagnosis and classification, methods that enable fast and simple characterization of tumors are still in need. In recent years, mass spectrometry has become a method of choice for untargeted profiling of discriminatory compound as potential biomarkers of a disease. Biofluids are generally considered as preferable matrices given their accessibility and easier sample processing while direct tissue profiling provides more selective information about a given cancer. Preparation of tissues for the analysis via traditional methods is much more complex and time-consuming, and, therefore, not suitable for fast on-site analysis. The current work presents a protocol combining sample preparation and extraction of small molecules on-site, immediately after tumor resection. The sampling device, which is of the size of an acupuncture needle, can be inserted directly into the tissue and then transported to the nearby laboratory for instrumental analysis. The results of metabolomics and lipidomics analyses demonstrate the capability of the approach for the establishment of phenotypes of tumors related to the histological origin of the tumor, malignancy, and genetic mutations, as well as for the selection of discriminating compounds or potential biomarkers. The non-destructive nature of the technique permits subsequent performance of routinely used tests e.g., histological tests, on the same samples used for SPME analysis, thus enabling attainment of more comprehensive information to support personalized diagnostics.

The manuscript presents on-site sampling of human brain tumors with solid phase microextraction followed by their biochemical profiling towards the discovery of biomarkers.

Despite the variety of tools available for cancer diagnosis and classification, methods that enable fast and simple characterization of tumors are still ...

Standardization and Maintenance of 3D Canine Hepatic and Intestinal Organoid Cultures for Use in Biomedical Research

Dogs develop complex multifactorial diseases analogous to humans, including inflammatory diseases, metabolic diseases, and cancer. Therefore, they represent relevant large animal models with the translational potential to human medicine. Organoids are 3-dimensional (3D), self-assembled structures derived from stem cells that mimic the microanatomy and physiology of their organ of origin. These translational in vitro models can be used for drug permeability and discovery applications, toxicology assessment, and to provide a mechanistic understanding of the pathophysiology of multifactorial chronic diseases. Furthermore, canine organoids can enhance the lives of companion dogs, providing input in various areas of veterinary research and facilitating personalized treatment applications in veterinary medicine. A small group of donors can create a biobank of organoid samples, reducing the need for continuous tissue harvesting, as organoid cell lines can be sub-cultured indefinitely. Herein, three protocols that focus on the culture of intestinal and hepatic canine organoids derived from adult stem cells are presented. The Canine Organoid Isolation Protocol outlines methods to process tissue and embedding of the cell isolate in a supportive matrix (solubilized extracellular membrane matrix). The Canine Organoid Maintenance Protocol describes organoid growth and maintenance, including cleaning and passaging along with appropriate timing for expansion. The Organoid Harvesting and Biobanking Protocol describes ways to extract, freeze, and preserve organoids for further analysis.

Experimental methods to harvest adult stem cells from canine intestinal and hepatic tissues to establish 3D organoid cultures are described. Furthermore, the laboratory techniques to ensure consistent growth and provide standard operating procedures to harvest, biobank, and revive canine intestinal and hepatic organoid cultures are discussed.