Anabolic Steroids in Fattening Food-Producing Animals—A Review
Bibliographic record
Abstract
Anabolic steroids are chemically synthetic derivatives of the male sex hormone testosterone. They are used in medicine for their ability to support muscle growth and healing and by athletes for esthetic purposes and to increase sports performance, but another major use is in fattening animals to increase meat production. The more people there are on Earth, the greater the need for meat production and anabolic steroids accelerate the growth of animals and, most importantly, increase the amount of muscle mass. Anabolic steroids also have proven side effects that affect all organs and tissues, such as liver and kidney parenchymal damage, heart muscle degeneration, organ growth, coagulation disorders, and increased risk of muscle and tendon rupture. Anabolic steroids also have a number of harmful effects on the developing brain, such as brain atrophy and changes in gene expression with consequent changes in the neural circuits involved in cognitive functions. Behavioral changes such as aggression, irritability, anxiety and depression are related to changes in the brain. In terms of long-term toxicity, the greatest impact is on the reproductive system, i.e., testicular shrinkage and infertility. Therefore, their abuse can be considered a public health problem. In many countries around the world, such as the United States, Canada, China, Argentina, Australia, and other large meat producers, the use of steroids is permitted but in all countries of the European Union there is a strict ban on the use of anabolic steroids in fattening animals. Meat from a lot of countries must be carefully inspected and monitored for steroids before export to Europe. Gas or liquid chromatography methods in combination with mass spectrometry detectors and immunochemical methods are most often used for the analysis of these substances. These methods have been considered the most modern for decades, but can be completely ineffective if they face new synthetic steroid derivatives and want to meet meat safety requirements. The problem of last years is the application of "cocktails" of anabolic substances with very low concentrations, which are difficult to detect and are difficult to quantify using conventional detection methods. This is the reason why scientists are trying to find new methods of detection, mainly based on changes in the structure of tissues and cells and their metabolism. This review gathered this knowledge into a coherent form and its findings could help in finding such a combination of changes in tissues that would form a typical picture for evidence of anabolic misuse.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".