Mass spectrometric based approaches in urine metabolomics and biomarker discovery
Bibliographic record
Abstract
Urine metabolomics has recently emerged as a prominent field for the discovery of non-invasive biomarkers that can detect subtle metabolic discrepancies in response to a specific disease or therapeutic intervention. Urine, compared to other biofluids, is characterized by its ease of collection, richness in metabolites and its ability to reflect imbalances of all biochemical pathways within the body. Following urine collection for metabolomic analysis, samples must be immediately frozen to quench any biogenic and/or non-biogenic chemical reactions. According to the aim of the experiment; sample preparation can vary from simple procedures such as filtration to more specific extraction protocols such as liquid-liquid extraction. Due to the lack of comprehensive studies on urine metabolome stability, higher storage temperatures (i.e. 4°C) and repetitive freeze-thaw cycles should be avoided. To date, among all analytical techniques, mass spectrometry (MS) provides the best sensitivity, selectivity and identification capabilities to analyze the majority of the metabolite composition in the urine. Combined with the qualitative and quantitative capabilities of MS, and due to the continuous improvements in its related technologies (i.e. ultra high-performance liquid chromatography [UPLC] and hydrophilic interaction liquid chromatography [HILIC]), liquid chromatography (LC)-MS is unequivocally the most utilized and the most informative analytical tool employed in urine metabolomics. Furthermore, differential isotope tagging techniques has provided a solution to ion suppression from urine matrix thus allowing for quantitative analysis. In addition to LC-MS, other MS-based technologies have been utilized in urine metabolomics. These include direct injection (infusion)-MS, capillary electrophoresis-MS and gas chromatography-MS. In this article, the current progresses of different MS-based techniques in exploring the urine metabolome as well as the recent findings in providing potentially diagnostic urinary biomarkers are discussed. © 2015 Wiley Periodicals, Inc. Mass Spec Rev 36:115-134, 2017.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".