Complement-Associated Deposits in the Human Retina
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to investigate complement activation and associated inflammatory mechanisms in normal, aged human retina. METHODS: Evidence of complement activation and associated mechanisms was assessed in normal human retina (n = 52) using a panel of antibodies directed against membrane attack complex (C5b-9), microglia (CD11b), amyloid precursor protein (APP), scavenger receptor (CD36), and a phytolectin (RCA-I). Fifty-two eyes, categorized into two age groups, were used. Nineteen "younger" eyes (<56 years) and 33 "older" eyes (>69 years) with no history of ocular disease were processed between 4 and 22 hours, with a median delay of 14 hours postmortem. RESULTS: Age-dependent expression was evident in C5b-9, APP, CD11b, and RCA-I, but not CD36, immunoreactivity. Immunoreactivity for C5b-9 was robust in Bruch membrane (BM) and the intercapillary pillars of Bruch. Immunoreactivity for APP was robust in the basal cytoplasm of the retinal pigment epithelium. Immunoreactivity for CD11b was robust on the surface of the retinal pigment epithelial cell, in the choriocapillaris, and in BM. Lectin binding of RCA-I was strong throughout the neuroretina. CONCLUSIONS: Robust immunostaining for APP in older donor eyes suggested that amyloid beta peptides may be one of the triggers of complement activation during the normal aging process. Microglial markers CD11b and RCA-I also increase with age, suggesting a concomitant inflammatory response to C5b-9 deposits in the retinal pigment epithelium, BM, and CC. Immunoreactivity for CD36 was strong in both age groups; the lack of age dependence in this candidate receptor for amyloid beta suggested that complement activation may arise from interactions of amyloid beta with other candidate receptors in normal human retina.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".