Human urinary kallidinogenase decreases the incidence of post-stroke cognitive impairment in acute ischemic stroke patients
Bibliographic record
Abstract
Background: Post-stroke cognitive impairment (PSCI) is a common symptom of stroke and affects the quality of life and prognosis of stroke survivors. In our study, we evaluated the efficacy of Human urinary kallidinogenase (HUK) on cognitive function in acute ischemic stroke (AIS) patients, and discussed the role of cystatin C (CysC) in improving PSCI. Methods: We enrolled a retrospective cohort with prospective follow-up. From August 2020 to May 2021, 130 patients completed the final follow-up. Among them, 61 patients received HUK combined with basic treatment, which we defined as the HUK group, and 69 patients received basic treatment, which we defined as the control group. We compared the changes of CysC, urea nitrogen and creatinine levels after one week of treatment between the two groups. Cognitive function was assessed by Montreal Cognitive Assessment (MoCA) at 3-month after AIS. Results: No significant differences in demographic data and Laboratory tests between two groups before treatment. A total of 67 patients (51.5%) were diagnosed as PSCI at 3-month follow-up, among which, 25 patients were in the HUK group and 42 patients were in the control group. Compared with the control group (60.9%), the incidence of PSCI was significantly lower in the HUK group (41.0%). In addition, the serum CysC level after a week of treatment significantly decreased from baseline in HUK group (p = 0.037), in comparison, the serum CysC level in the control group was basically unchanged (p = 0.951). There was a significant negative correlation between MoCA score and the level of CysC after treatment (p = 0.003, r = –0.373). Conclusions: HUK can reduce the risk of PSCI at 3-month in AIS patients. The decrease of serum CysC level may be one of the mechanisms by which HUK reduces the incidence of PSCI.
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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".