PSAPP mice exhibit regionally selective reductions in gliosis and plaque deposition in response to S100B ablation
Bibliographic record
Abstract
BACKGROUND: Numerous studies have reported that increased expression of S100B, an intracellular Ca2+ receptor protein and secreted neuropeptide, exacerbates Alzheimer's disease (AD) pathology. However, the ability of S100B inhibitors to prevent/reverse AD histopathology remains controversial. This study examines the effect of S100B ablation on in vivo plaque load, gliosis and dystrophic neurons. METHODS: Because S100B-specific inhibitors are not available, genetic ablation was used to inhibit S100B function in the PSAPP AD mouse model. The PSAPP/S100B-/- line was generated by crossing PSAPP double transgenic males with S100B-/- females and maintained as PSAPP/S100B+/- crosses. Congo red staining was used to quantify plaque load, plaque number and plaque size in 6 month old PSAPP and PSAPP/S100B-/- littermates. The microglial marker Iba1 and astrocytic marker glial fibrillary acidic protein (GFAP) were used to quantify gliosis. Dystrophic neurons were detected with the phospho-tau antibody AT8. S100B immunohistochemistry was used to assess the spatial distribution of S100B in the PSAPP line. RESULTS: PSAPP/S100B-/- mice exhibited a regionally selective decrease in cortical but not hippocampal plaque load when compared to PSAPP littermates. This regionally selective reduction in plaque load was accompanied by decreases in plaque number, GFAP-positive astrocytes, Iba1-positive microglia and phospho-tau positive dystrophic neurons. These effects were not attributable to regional variability in the distribution of S100B. Hippocampal and cortical S100B immunoreactivity in PSAPP mice was associated with plaques and co-localized with astrocytes and microglia. CONCLUSIONS: Collectively, these data support S100B inhibition as a novel strategy for reducing cortical plaque load, gliosis and neuronal dysfunction in AD and suggest that both extracellular as well as intracellular S100B contribute to AD histopathology.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".