The Combination of Human Urinary Kallidinogenase and Mild Hypothermia Protects Adult Rats Against Hypoxic-Ischemic Encephalopathy-Induced Injury by Promoting Angiogenesis and Regeneration
Bibliographic record
Abstract
Objectives Human Urinary Kallidinogenase (HUK) is a tissue kallikrein that plays neuroprotective roles in ischemic conditions via different mechanisms. Mild hypothermia (MH) is another robust neuroprotectant that reduces mortality but does not profoundly ameliorate the neurological outcome in hypoxic-ischemic encephalopathy (HIE) patients. However, whether the combination of HUK and MH can be used as a promising neuroprotective treatment in HIE is unknown. Methods One-hundred-forty-four adult Wistar rats were randomly divided into 5 groups: Sham, HIE, HUK, MH and a combination of HUK and MH treatment. The HIE rat model was established by right carotid dissection followed by hypoxia aspiration. The survival curve was created within 7 days, and the neurological severity scores (NSS) were assessed at days 0, 1, 3, and 7. Nissl staining, TUNEL, immunofluorescent staining and western blotting were used to evaluate neuronal survival, apoptosis and necrosis, tight-junction proteins Claudin-1 and ZO-1, vascular endothelial growth factor (VEGF), doublecortex (DCX), bradykinin receptor B1, B2 and Ki67 staining. Results: The combined treatment rescued all HIE rats from death and had a best survival curve compared to HIE. The Combination also reduced the NSS scores after HIE at days 7, better than HUK or MH alone. The combination of HUK and MH reserved more cells in Nissl staining and inhibited neuronal apoptosis and necrosis as well as significantly attenuated HIE-induced decreases in claudin-1, ZO-1, cyclin D1, and BDKRB1/B2 receptors in comparison to HUK or MH treatment alone. Moreover, the combined treatment increased the expression of VEGF and DXC as well as the number of Ki67-labeled cells. Conclusions This study demonstrates that both HUK and MH are neuroprotective after HIE insult; however, the combined therapy with HUK and MH enhanced the efficiency and efficacy of either therapy alone in the treatment of HIE, at least partially by promoting angiogenesis and regeneration and rescuing tight-junction loss. The combination of HUK and MH seems to be a feasible and promising clinical strategy to alleviate cerebral injury following HIE insult.
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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) | 0.001 | 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 | 0.001 |
| 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".