Endogenous Biotin in Rat Brain: Implications for False-Positive Results With Avidin-Biotin and Streptavidin-Biotin Techniques
Bibliographic record
Abstract
The interaction between avidin and biotin or streptavidin and biotin forms the basis of several widely used immunohistochemical techniques. An assumption inherent to these techniques is that endogenous biotin is not present in the tissue in detectable quantities, as neither avidin nor streptavidin can discriminate between endogenous biotin and biotinylated antibodies. However, biotin is a known cofactor for numerous carboxylases required in oxidative metabolism, raising the possibility for potential false-positive results in many tissues. This issue has been appreciated in liver and kidney tissue, but has received very little attention in nervous tissue. To address this concern, we examined the distribution of biotin throughout the rat central nervous system using avidin- and streptavidin-based detection systems, as well as a monoclonal antibody raised against biotin. Significant levels of endogenous biotin were identified within specific neuronal types, particularly in neurons associated with the cerebellar motor system and the brainstem auditory system. Non-specific (non-biotin) interactions of avidin and streptavidin conjugates with rat brain tissue were further identified and were most pronounced in the lower brainstem. The binding of avidin- and streptavidin-conjugated markers to endogenous biotin and other non-specific interactions with neural tissue were overcome by several methods including the use of blocking kits, prolonged post-fixation of the tissue in paraformaldehyde, or omission of Triton X-100 from the working solution. Without these measures, a reliable estimate of immunolabel may only be achieved in certain brain regions with markers conjugated directly to secondary antibodies.
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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 | 0.014 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".