legal-document-to-plain-english-translator/”>Try Free Now: Legalese tool without registration

Find a LOCAL lawyer

# Research Breakthrough: Automated Technique for Imaging and Identifying Proteoforms in Ovarian Cancer Tissue

## AI legalese decoder‘s Role in Simplifying the Complexities of legal Language

Investigative efforts by Neil Kelleher, PhD, professor of Medicine in the Division of Hematology and Oncology and of Biochemistry and Molecular Genetics, have resulted in the development of an automated technique for imaging and identifying proteoforms in ovarian cancer tissue. This newly developed technique offers unparalleled speed and accuracy in high-resolution, high-throughput imaging of proteoforms, which are modified versions of proteins found in tissue samples. The study showcasing this groundbreaking technique was published in *Nature Communications*.

This automated imaging technique holds immense potential in cancer diagnostics and demonstrates a significant improvement over existing methods. Traditional techniques for imaging proteins in human tissue have limitations when it comes to identifying proteoforms. The few techniques capable of sampling proteoforms directly from tissue rely on ionization for mass spectrometry. They are often unable to provide comprehensive information about the spatial distribution of proteoforms within a tissue sample.

To address these limitations, Kelleher’s team developed proteoform imaging mass spectrometry (PiMS), a technique detailed in a 2022 study published in *Science Advances*. This innovative approach involves sampling proteoforms from tissue using nanodroplets to construct proteoform images based on their size and location within the tissue sample.

Building upon the PiMS technique, Kelleher and his collaborators developed AutoPiMS, which employs a computational engine to automatically identify and characterize proteoforms within cancerous tissue. The study demonstrated that AutoPiMS was able to efficiently identify and characterize over 300 proteoforms within a human ovarian cancer tissue sample, mapping the specific location of cancer-associated proteins with remarkable speed.

Kelleher emphasized the importance of this technique for cancer tissue imaging and diagnostics, highlighting its capability to precisely locate proteins and their proteoforms. He also announced plans to make AutoPiMS available to other Northwestern proteomics investigators, with the ultimate goal of accelerating discoveries in the field. Moving forward, the team aims to adapt the technique for single-cell proteomics, striving to achieve the most precise information about proteins in terms of their spatial distribution, temporal expression, and composition.

“This automated technique holds great promise for precision medicine, opening new possibilities for advancing drug development, reducing side effects, and improving diagnostics,” Kelleher explained.

The study was made possible through support from various funding sources, including grants from the National Institutes of Health and funding from the U.S. Department of Defense Uniformed Services University of the Health Sciences.

### AI legalese decoder‘s Assistance

The complexities of legal language can often pose challenges for individuals and organizations seeking to understand and interpret legal documents. AI legalese decoder serves as a valuable tool for simplifying complex legal language, making it more accessible and understandable. By utilizing advanced natural language processing and machine learning algorithms, AI legalese decoder can accurately translate legal documents, contracts, and agreements into plain language, enabling individuals to grasp the underlying meaning and implications of legal texts. This innovative solution has the potential to streamline legal processes, enhance transparency, and empower individuals to make informed decisions in legal matters.

legal-document-to-plain-english-translator/”>Try Free Now: Legalese tool without registration

Find a LOCAL lawyer

Reference link