Expression of matrix metalloproteinase in patients with Alzheimer and mechanism of huangqi granule (astragalus saponins) intervention
Bibliographic record
Abstract
This study investigated expression of matrix metalloproteinases in senile dementia patients and mechanism of Huangqi granule intervention. 78 cases of senile dementia patients were selected as observation group from June 2017 to June 2019, while 62 cases of healthy people were enrolled as control group. Blood samples were collected after admission and levels of matrix metalloproteinase 3,9,13 (MMP-3,9,13) were determined. The observation group was randomly and equally assigned into donepezil hydrochloride group and combined drug group. Mental state examination (MMSE) and Boston diagnostic aphasia test (BDAE) were used to compare the two groups. Montreal Cognitive Assessment (MoCA), Dementia Scale (HDS) scores, biochemical index levels and drug safety were also used. MMP-2, MMP-9 and urinary plasminogen activator levels in observation group were higher and ZO-1 was lower than control group along with higher MMP-3,9,13 mRNA levels (p < 0.05) which were reduced after 3 months of treatment. MMSE scale, BDAE, MoCA, and HDS scores in the combined drug group were higher after 3 months of treatment (p < 0.05). The level of NSE (neuron-specific enolase) was higher and SOD level was lower than in the donepezil hydrochloride group (p < 0.05); nausea and vomiting, muscle spasms, insomnia bradycardia and gastrointestinal bleeding incidence in two groups showed no differences (p > 0.05). The intervention of Huangqi granules can improve cognitive function of patients, inhibit matrix metalloproteinase, thereby improving the level of biochemical indicators without increasing the incidence of complications.
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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 | 0.000 |
| 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".