Cerebrolysin modulates pronerve growth factor/nerve growth factor ratio and ameliorates the cholinergic deficit in a transgenic model of Alzheimer's disease
Bibliographic record
Abstract
Alzheimer's disease (AD) is characterized by degeneration of neocortex, limbic system, and basal forebrain, accompanied by accumulation of amyloid-β and tangle formation. Cerebrolysin (CBL), a peptide mixture with neurotrophic-like effects, is reported to improve cognition and activities of daily living in patients with AD. Likewise, CBL reduces synaptic and behavioral deficits in transgenic (tg) mice overexpressing the human amyloid precursor protein (hAPP). The neuroprotective effects of CBL may involve multiple mechanisms, including signaling regulation, control of APP metabolism, and expression of neurotrophic factors. We investigate the effects of CBL in the hAPP tg model of AD on levels of neurotrophic factors, including pro-nerve growth factor (NGF), NGF, brain-derived neurotrophic factor (BDNF), neurotropin (NT)-3, NT4, and ciliary neurotrophic factor (CNTF). Immunoblot analysis demonstrated that levels of pro-NGF were increased in saline-treated hAPP tg mice. In contrast, CBL-treated hAPP tg mice showed levels of pro-NGF comparable to control and increased levels of mature NGF. Consistently with these results, immunohistochemical analysis demonstrated increased NGF immunoreactivity in the hippocampus of CBL-treated hAPP tg mice. Protein levels of other neurotrophic factors, including BDNF, NT3, NT4, and CNTF, were unchanged. mRNA levels of NGF and other neurotrophins were also unchanged. Analysis of neurotrophin receptors showed preservation of the levels of TrKA and p75(NTR) immunoreactivity per cell in the nucleus basalis. Cholinergic cells in the nucleus basalis were reduced in the saline-treated hAPP tg mice, and treatment with CBL reduced these cholinergic deficits. These results suggest that the neurotrophic effects of CBL might involve modulation of the pro-NGF/NGF balance and a concomitant protection of cholinergic neurons.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.001 | 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".