Author Archives: Marina

What are the Differences Between Google Scholar, OneSearch and Bibliographic Databases When Looking for Journal Articles?

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Bibliographic databases contain references to journal articles (as well as sometimes other formats such as book chapters, etc.) with metadata that may typically include a title, authors, publisher, publication date and place, pages, abstract, index terms, etc. Examples: PubMed, Embase, Web of Science, Scopus, and PsycINFO.

When you search bibliographic databases you are actually searching for metadata, so you can specify what “field” you want your search terms to be found in (eg. title, abstract, author, journal, etc.). Available full text is linked to references/bibliographic records but cannot be searched.

Advantages to Searching Bibliographic Databases

  • The ability to use database functionality to do the best quality search and retrieve the best results. Such functionality in biomedical databases includes advanced functionality such as subject heading (e.g. MeSH) mapping.
  • Searching only essential information (title, abstract, author supplied keywords, index terms/subject headings) when searching on a topic may be a safeguard against overwhelming a searcher with an abundance of results.

    Unlike in bibliographic databases, searching for journal articles via OneSearch (the library’s catalog found on our homepage) or Google Scholar involves searching the full text of journal articles.

    Searching OneSearch
  • OneSearch is a library catalog plus, available at many Libraries, including MSK Library, that allows finding journals and other serials, books, etc., and also databases, newspapers, dissertations, and other print and electronic media typically available in the library’s catalog.
  • It also is able to search the full-text content of e-journals and e-books (owned/subscribed to by the Library) such as journal articles and book chapters.
  • Advanced Search also allows keyword searching in the Title, Author or Subject fields alone, as well as searching in Any Field, which includes the full text of an article.
  • OneSearch relies on keywords and phrases (using quotations), but does not include any mapping or controlled vocabulary.


Searching Google Scholar

  • In Google Scholar you can find journal articles by searching within their full text.
  • Google Scholar includes multidisciplinary content (e.g. Medicine, Physics, Computer Science, Humanities)
  • The articles come from journals that Google Scholar has authority to search regardless of and well beyond any institutional subscription.
  • You have poor control of how you design and execute the search and view your search results (no proper search tools, no abundance of limits and sorting options typical to bibliographic databases).
  • As you are searching the whole “universe” of journals with Google Scholar search, you will find articles that your institution does and does not have access to. An older MSK Library blog post explains how to get access to the full text or request the full text.

Disadvantages of Searching OneSearch and Google Scholar

-The lack of advanced search functionality. While essential search functionality is available (better in OneSearch, worse in Google Scholar) it is still not as advanced as in major biomedical databases.

-Searching the full text in either OneSearch or Google Scholar may end up in the overwhelming number of search results because you are searching the full text of journal articles.

Takeaways and Recommendations

  • Using OneSearch or Google Scholar involves searching the full text of journal articles.
  • To find journal articles, use bibliographic databases as the first choice; use OneSearch and Google Scholar as complementary to using bibliographic journal literature databases.
  • Searching OneSearch and GoogleScholar are most effective when the content being searched is likely not found in the title or the abstract, and thus searching full-text is required.

Non-Smokers At Higher Risk of Resistance to Lung Cancer Treatment, Low Risk of Secondary Cancers in Patients on CAR-T Cell Therapy And More

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  • Researchers from University College London, the Francis Crick Institute, AstraZeneca, etc., identified the genetic basis of an increased risk of resistance to treatment of non-small cell lung cancer (NSCLC) in non-smokers. The study was published in Nature Communications.
  • The Institute for Bioengineering of Catalonia (IBEC) study showed that different physical properties of colorectal cancer cells had different potential for cancer metastasis. The study was published in Nature Communications.
  • Researchers from Stanford University found that, contrary to an earlier FDA warning, the risk of secondary cancers in patients on CAR-T cell therapy is low. The study was published in the New England Journal of Medicine.
  • A study found that statins (in particular, pitavastatin), a class of drugs used to lower cholesterol, may be instrumental in suppressing chronic inflammation, a finding that could help prevent inflammatory-related cancers, e.g., pancreatic cancer. The study was published in Nature Communications.
  • “Father of tamoxifen”, Pharmacologist V. Craig Jordan, a professor of Breast and Medical Oncology and Molecular and Cellular Oncology at The University of Texas MD Anderson Cancer Center who discovered selective estrogen receptor modulators and developed breakthrough breast cancer treatment died last week.
  • Note: Kendra contributed this entry. The National Institutes of Health is developing a fairly simple (in today’s terms) model to predict whether a given cancer patient will likely respond to immunotherapy. This is a far cry from a large foundational model, but there’s still value in training simpler classifiers. The current state for predicting patient responses to immunotherapy is that one or both biomarkers approved by the FDA will be measured for a particular patient to help oncologists select the drugs most likely to work. 
    The model in beta right now is described in Nature Cancer (lead author Tian-Gen Chang). The model “makes predictions based on five clinical features that are routinely collected from patients: a patient’s age, cancer type, history of systemic therapy, blood albumin level, and blood neutrophil-to-lymphocyte ratio, a marker of inflammation. The model also considers tumor mutational burden.” The model has shown decent predictive ability and is available here: https://loris.ccr.cancer.gov.

Triple Negative Breast Cancer Research Advancements, Bacteria That Promote Colorectal Cancer, and More

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  • Researchers from the National Cancer Institute (NCI) created an artificial intelligence (AI) tool “that uses data from individual cells” in tumors to predict patients’ response to a specific drug. The report was published in Nature Cancer.
  • Purdue University researchers are developing nanoparticles capable of enhancing immunotherapy effects in cancer treatment. The study was published in ACS Nano.
  • Researchers from the University of Pennsylvania gained insights into how cancer-caused liver inflammation hinders the immune system’s ability to fight cancer. The study was published in Nature Immunology.
  • The researchers from Fred Hutchinson Cancer Center found that a specific type of bacteria, Fusobacterium nucleatum, known as related to gum disease, may promote colorectal cancer. These findings pave the way for therapies targeting these bacteria in colorectal cancer patients. The findings were published in Nature Immunology.
  • Researchers from the University of Colorado Cancer Center explored the potential of a two-drug combination, doxorubicin plus bocodepsin, as a promising treatment option for triple-negative breast cancer. The preclinical study paves the way for future human clinical trials. The study was published in Breast Cancer Research.
  • A new multicenter study found that women with early-stage triple-negative breast cancer who have high levels of immune cells in the tumor may be at a lower risk of recurrence and have better survival rates. The study was published today in the Journal of American Medical Association (JAMA).