THE ROLE OF BRAIN-DERIVED NEUROTROPHIC FACTOR AND INTERLEUKIN-6 IN THE FORMATION OF COGNITIVE IMPAIRMENTS IN PATIENTS WITH MULTIPLE SCLEROSIS
Bibliographic record
Abstract
This study analysed the relationship between levels of brain-derived neurotrophic factor (BDNF) and pro-inflammatory interleukin-6 (IL-6) and cognitive impairment in patients with multiple sclerosis (MS) with different disease durations.The study included 72 patients with relapsing-remitting MS, who were divided into three groups depending on the duration of the disease: Group 1 -27 patients with a disease duration of up to 5 years, Group 2 -from 5 to 10 years (23 patients), Group 3 -over 10 years (22 patients).The neurocognitive status of patients was assessed using the MoCA, SDMT and PASAT-3 tests, and the serum levels of BDNF and IL-6 were determined by enzyme-linked immunosorbent assay.The results showed that in patients with MS, the formation of cognitive impairment (as assessed by the MoCA, SDMT, and PASAT-3 scores) was associated with a decrease in serum BDNF and an increase in IL-6 levels.In particular, a positive correlation was found between BDNF levels and cognitive test scores (MoCA r=0.61, p=0.000;SDMT r=0.668, p=0.000), while IL-6 levels were negatively correlated with cognitive scores p=0.000; p=0.000).These results confirm the importance of neurotrophic and inflammatory dysfunction in the development of cognitive impairment in patients with MS.
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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 | 0.001 | 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".