A review of analytical strategies for the detection of ‘endogenous’ steroid abuse in food production
Bibliographic record
Abstract
Detection of the abuse of synthetic steroids in food production is nowadays relatively straightforward using modern techniques such as gas or liquid chromatography coupled to mass spectrometry (GC-MS/MS or LC-MS/MS, respectively). However, proving the abuse of 'endogenous' (or naturally occurring) steroids is more difficult. Despite these difficulties, significant progress in this area has recently been made and a number of methods are now available. The aim of the current review was to systematically review the available analytical approaches, which include threshold concentrations, qualitative 'marker' metabolites, intact steroid esters, gas chromatography-combustion-isotope ratio mass spectrometry (GC-C-IRMS), longitudinal testing and 'omics' biomarker profiling. The advantages/disadvantages of these methods are considered in detail, but the choice of which to adopt is dictated by a number of practical, political, and economic factors, which vary in different parts of the world. These include the steroid/species combination requiring analysis, the matrix tested, whether samples are collected from live or slaughtered animals, available analytical instrumentation, sample throughput/cost, and the relevant legal/regulatory frameworks. Furthermore, these approaches could be combined in a range of different parallel and/or sequential screening/confirmatory testing streams, with the final choice being determined by the aforementioned considerations. Despite these advances, more work is required to refine the different techniques and to respond to the ever increasing list of compounds classified as 'endogenous'. At this advanced stage, however, it is now more important than ever for scientists and regulators from across the world to communicate and collaborate in order to harmonize and streamline research efforts.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".