Differential effects of prolonged post-fixation on immunohistochemical and histochemical staining for postmortem human brains
Bibliographic record
Abstract
Abstract Immunohistochemical (IHC) and histochemical (HC) staining is widely used for human brains post-fixed in formalin and stored in brain banks worldwide for months, years and decades. Understanding the effect of prolonged post-fixation, postmortem interval (PMI) and age on these staining procedures is important for interpreting their outcomes, thus improving diagnosis and research of brain disorders afflicting millions of world populations. In this study, we performed both IHC and HC staining for prefrontal cortex (PFC) of postmortem human brains post-fixed for 1, 5, 10, 15 and 20 years. A negative correlation was detected between the intensity of neuron marker neuron nuclear specific marker (NeuN), microglia marker ionized calcium-binding adaptor molecule 1 (Iba1), cresyl violet (CV) and Luxol fast blue (LFB) staining versus post-fixation durations. By contrast, a positive correlation was seen between the intensity of astrocyte marker glial fibrillary acidic protein (GFAP) and hemaetoxylin and eosin Y (H&E) staining versus post-fixation durations. No correlation was found between the staining intensity of NeuN, GFAP, Iba1, H&E, CV and LFB versus PMI. Moreover, no correlation was seen between NeuN, Iba1, H&E, CV and LFB, except GFAP, versus age. These data suggest that prolonged post-fixation exerts both positive and negative effects, but age and PMI have limited effects, on these IHC and HC parameters. Hence these differential changes need to be considered in interpretation of the results when using tissues with prolonged post-fixation. Furthermore, if feasible, it is recommended to perform IHC and HC staining for human brains with the same post-fixation time windows and to use the most optimal antibodies to offset its impact on downstream analyses.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".