Diabetic Ketoacidosis Alters Plasma Levels of Matrix Metalloproteinases and PMN‐Specific Elastase in Children
Bibliographic record
Abstract
Introduction Diabetic ketoacidosis (DKA) is associated with cerebrovascular complications in children such as vasogenic edema. Circulating matrix metalloproteinases (MMPs), their endogenous inhibitors (TIMPs), and other proteases may contribute to loss of cerebrovascular endothelial cell junctional integrity. Our aim was to determine the effects of acute DKA on systemic MMP/TIMP levels, as well as polymorphonuclear neutrophil(PMN)‐specific elastase. Methods Plasma was obtained from children ages 4‐17 years old, with type‐1 diabetes, either in an acute DKA state or insulin‐controlled (CON). DKA and CON samples were age‐ and sex‐matched for all experiments. Gelatin zymography was used to assess gelatinolytic activity. ELISA‐based methods were used to assess plasma concentration of proteins. Results DKA plasma showed significantly increased MMP‐9 activity(P<0.01) and decreased MMP‐2 activity(P<0.001), compared to CON plasma. MMP‐9 activity in DKA plasma was inversely correlated with pH(P<0.05). DKA plasma had increased circulating levels of MMP‐3(P<0.05), MMP‐8(P<0.001) and TIMP‐4(P<0.01). In addition, a significant increase in circulating levels of PMN elastase(P<0.001) was detected in DKA plasma. Summary DKA is associated with dynamic changes in plasma levels of MMP‐2, MMP‐3, MMP‐8, MMP‐9 and TIMP‐4, as well as PMN elastase, which may contribute to loss of blood‐brain barrier integrity.
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) | 0.002 | 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".