Plasma Level of Endothelin-1 and Vascular endothelial growth factor in Vascular Congnitive Impairment and dementia
Bibliographic record
Abstract
Objective To investigate the role of endothelin-1(ET-1)and vascular endothelial growth factor(VEGF)in the pathogenesis of Vascular Congnitive Impairment(VCI)and vascular dementia(VaD).Methods Follow-uped patients who were after Cerebral infarction 3 months,patients were divided into No cognitive Impairment(N-VCI)group﹑cognitive impairment,no dementia(CIND)group and Vascular dementia(VaD)group by Montreal Cognitive Assessment Clinical Dementia Rota scales.Radio-immunity was used to detect the concentration of ET-1 and Enzyme-linked immunosorbent assay(ELISA)was used to detect the concentration of VEGF in the plasma of the patients,then,analysed the correlation On plasma ET-1 and VEGF.Results(1)The plasma of ET-1 in VaD group were significantly higher than that of CIND group and N-VCI group,CIND group higher than that of N-VCI group(P0.01),but VEGF in VaD group were lower than that of CIND group and N-VCI group,CIND group lower than that of N-VCI group,there were significantly Statistical differences(P0.01).(2)The plasma of ET-1 showed negative correlation with VEGF(r=-0.808,P0.01).(3)The plasma of ET-1 in VaD group showed negative correlation with MoCA scales(r=-0.719,P0.01),but The plasma of VEGF in VaD was signifycantly positively correlated with MoCA scales(r=0.670,P=0.01).Conclusions High ET-1 and low VEGF take part in the process of VCI and VaD,The plasma of ET-1 showed a positive correlation with VaD condition,and VEGF showed negative correlation with VaD condition,monitoring of these indicators can be used to judge the severity of VaD.
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 | 0.000 | 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".