Significance of S100β, Endothelin-1 and Vascular Endothelial Growth Factor in the Elderly Cognitive Dysfunction
Bibliographic record
Abstract
Objective: There are still some difficulties in the diagnosis of the elderly cognitive dysfunction,especially the mild cognitive dysfunction early,the significance of S100β,endothelin-1(ET-1) and vascular endothelial growth factor(VEGF) in the elderly cognitive dysfunction were observed in this study.Methods: 117 patients with elderly cognitive dysfunction from October 2008 to April 2012,were divided into AD group(55 cases with Alzheimer's disease) and MCI group(62 cases with mild cognitive impairment).20 healthy elderly volunteers(control group) were selected at the same period.The levels of S100β,ET-1,VEGF and Montreal cognitive score(MoCA) were detected.Results: The serum levels of S100β and ET-1,from AD group and MIC group to control group in turns,reduced significantly.Meanwhile,the levels of VEGF and MoCA score increased,the difference was statistically significant(P 0.01).The levels of S100β and ET-1 in AD group were negatively correlated to the MoCA score(r =-0.387,r =-0.408,P 0.05),and the levels of VEGF was positively correlated to the MoCA score(r = 0.363,P 0.05);Meanwhile,the levels of S100β,ET-1,VEGF in MIC group was no significant correlation to the MoCA score(P 0.05).Conclusions: The S100β,ET-1 and VEGF participate in the formation process of the elderly cognitive dysfunction.The plasma of S100β and ET-1 showed a positive correlation with AD condition,and VEGF showed negative correlation with AD condition,monitoring of these indicators can be used to judge the severity of AD,it is of important significance to the early detection of MCI.
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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) | 0.001 | 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".