Discovery and Identification of Arsenolipids Using a Precursor-Finder Strategy and Data-Independent Mass Spectrometry
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Arsenolipids are a class of lipid-soluble arsenic species. They are present in seafoods and show high potentials of cytotoxicity and neurotoxicity. Hindered by traditional low-throughput analytical techniques, the characterization of arsenolipids is far from complete. Here, we report on a sensitive and high-throughput screening method for arsenolipids in krill oil, tuna fillets, hairtail heads, and kelp. We demonstrate the detection and identification of 23 arsenolipids, including novel arsenic-containing fatty acids (AsFAs), hydroxylated AsFAs, arsenic-containing hydrocarbons (AsHCs), hydroxylated AsHCs, thiolated trimethylarsinic acids, and arsenic-containing lysophosphatidylcholines not previously reported. The new method incorporated precursor ion scan (PIS) into data-independent acquisition. High-performance liquid chromatography (HPLC) electrospray ionization quadrupole time-of-flight mass spectrometry (ESI–qToF–MS) was used to perform the sequential window acquisition of all theoretical spectra (SWATH). Comprehensive HPLC–MS and MS/MS data were further processed using a fragment-guided chromatographic computational program Precursorfinder developed here. Precursorfinder achieved efficient peak-picking, retention time comparison, hierarchical clustering, and wavelet coherence calculations to assemble fragment features with their target precursors. The identification of arsenolipids was supported by coeluting the HPLC–MS peaks detected with the characteristic fragments of arsenolipids. Method validation using available arsenic standards and the successful identification of previously unknown arsenolipids in seafood samples demonstrated the applicability of the method for environmental research.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".