Mass spectrometry‐based extracellular vesicle micromolecule detection in cancer biomarker discovery: An overview of metabolomics and lipidomics is a research paper published in VIEW (2023). On theSindex it has a DataRank of 1.6. It has been cited 53 times, with 52 citing works in its 1-hop citation network. Its calibrated FAIR score is 55/100.
AbstractExtracellular vesicles (EVs) become one of the most important sources of cancer biomarkers during the past decade due to their wide distribution in body fluids and physiological stability. Previous studies mainly focused on nucleic acids and proteins, while lipids and metabolites were largely neglected. Noticeably, many of those micromolecules exhibited a high abundance in EVs. Revealing the metabolomics and lipidomics of EVs would provide more comprehensive information for biomarker discovery. With the rapid development of mass spectrometry (MS) facilities, MS‐based micromolecule detection has become an emerging technique for EVs studies. Increasing evidence demonstrated the presence of EV‐associated metabolites and lipids in different types of samples (e.g., cell, urine, serum, stool), which exhibited promising performance in cancer diagnosis, prognosis, and prediction of treatment responses. This review aims to summarize advances in micromolecule profiling of EVs for cancer biomarker discovery, with an emphasis on MS‐based metabolomic and lipidomic analytical techniques. Challenges in this field, including the minimum sample quantity, normalization methods, and compound identifications, are also discussed along with possible solutions.
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Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →
Base Score Contribution
0.598
From this paper's citation signal
Citation Network Contribution
0.952
From 44 citing papers with measurable signal
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 39% comes from its base citations and 61% from the citation network (44 citing papers contributed measurable signal).
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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