Inflammatory biomarkers and lipid metabolism parameters in women with mild cognitive impairment and dementia
Bibliographic record
Abstract
The detection of specific markers of dementia and mild cognitive decline (MCI) could be the key to disease prevention and forehanded treatment. Female gender is one of the major risk factor for dementia. The aim of our study was to compare serum concentration of some factors related to lipid metabolism and the immune system in patients with MCI and dementia. The study was performed on women >65 years old: controls (n = 75), diagnosed with dementia (n = 73) and MCI (n = 142). Patients were evaluated using Mini-Mental State Examination, Clock Drawing Test and Montreal Cognitive Assessment scales in the period 2020–2021. The level of Apo A1 and HDL was significantly decreased in patients with dementia; the level of Apo A1 was also decreased in MCI. EGF, eotaxin-1, GRO-α, and IP-10 were elevated in patients with dementia compared to the controls. IL-8, MIP-1β, sCD40L, and TNF-α levels were decreased in MCI patients and increased in patients with dementia compared to the control. Serum VEGF levels were decreased in MCI and dementia patients in comparison with the control. We hypothesize that no single marker can indicate a neurodegenerative process. Future research should focus on identifying markers to determine possible diagnostic combinations that can reliably predict neurodegeneration.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".