Therapeutic monitoring and prediction of the efficacy of neurotrophic treatment in patients with amnestic type of mild cognitive impairment
Bibliographic record
Abstract
AIM: To perform therapeutic monitoring and prediction of the neurotrophic therapy efficacy in patients with amnestic type of mild cognitive impairment (aMCI) in a model of course cerebrolysin therapy. MATERIAL AND METHODS: The study involved a group of 19 elderly patients who met the diagnostic criteria of aMCI. All patients received a course of neurotrophic therapy consisting of 20 intravenous infusions of cerebrolysin (30 ml of cerebrolysin in 100 ml of isotonic sodium chloride solution). To assess the therapy efficacy, psychometric scales (CGI, MMSE, MoCA-test, МDRS, FAB, Clock Drawing Test, BNT, Word Recall test, delayed reproduction of 10 words, naming digits in a direct and reverse order) were used at 0, 4, 10 and 26 weeks of the study. Antibodies to p75 neurotrophin receptor (NTR) were measured by ELISA in blood serum of 19 patients before cerebrolysin therapy and after 10 and 26 weeks of treatment. RESULTS AND CONCLUSION: The study showed that аMCI patients had an increased level of antibodies against P75NTR that was significantly decreased after 5.5 month of cerebrolysin treatment. Therefore, it can be a potential biomarker of long-term therapeutic effect of cerebrolysin treatment in aMCI patients. The modified fragment 155-164 of P75 NTR determined in the serum of patients can be an effective indicator for monitoring and predicting the efficacy of long-term neurotrophic therapy.
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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 | 0.001 |
| 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) | 0.000 | 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".