Semiquantitative proteomic analysis of human hippocampal tissues from Alzheimer’s disease and age-matched control brains
Bibliographic record
Abstract
BACKGROUND: Alzheimer's disease (AD) is the most common type of dementia affecting people over 65 years of age. The hallmarks of AD are the extracellular deposits known as amyloid β plaques and the intracellular neurofibrillary tangles, both of which are the principal players involved in synaptic loss and neuronal cell death. Tau protein and Aβ fragment 1-42 have been investigated so far in cerebrospinal fluid as a potential AD biomarkers. However, an urgent need to identify novel biomarkers which will capture disease in the early stages and with better specificity remains. High-throughput proteomic and pathway analysis of hippocampal tissue provides a valuable source of disease-related proteins and biomarker candidates, since it represents one of the earliest affected brain regions in AD. RESULTS: In this study 2954 proteins were identified (with at least 2 peptides for 1203 proteins) from both control and AD brain tissues. Overall, 204 proteins were exclusively detected in AD and 600 proteins in control samples. Comparing AD and control exclusive proteins with cerebrospinal fluid (CSF) literature-based proteome, 40 out of 204 AD related proteins and 106 out of 600 control related proteins were also present in CSF. As most of these proteins were extracellular/secretory origin, we consider them as a potential source of candidate biomarkers that need to be further studied and verified in CSF samples. CONCLUSIONS: Our semiquantitative proteomic analysis provides one of the largest human hippocampal proteome databases. The lists of AD and control related proteins represent a panel of proteins potentially involved in AD pathogenesis and could also serve as prospective AD diagnostic biomarkers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| 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".