A synthetic BBB-permeable tripeptide GCF confers neuroprotection by increasing glycine in the ischemic brain
Bibliographic record
Abstract
Background: We and others have previously demonstrated that glycine is neuroprotective in cerebral ischemia-reperfusion injury. But glycine has low permeability to the blood–brain barrier (BBB). To deliver glycine into the ischemic brain to confer neuroprotection, we designed a novel glycine-containing and BBB-permeable tripeptide, the H-glycine-cysteine-phenylalanine-OH (GCF). Methods: For the synthesis of GCF, phenylalanine was included to increase the BBB permeability of the tripeptide. Cysteine was conjugated with glycine to enable the release of glycine from GCF. With the use of immunofluorescence labeling and HPLC assays, we measured the distribution and level of GCF. We used TTC labeling, LDH release, and MTT assays to evaluate the neuroprotective effect of GCF. Results: Following intravenous injection in a rat model of cerebral ischemia-reperfusion injury, GCF was intensively distributed in the ischemic neurons. Intravenous injection of GCF, but not the non-cleavable acetyl-GCF, resulted in the elevation of glycine in the ischemic brain. GCF but not acetyl-GC conferred neuroprotection in ischemic stroke animals. Conclusion: GCF protects against cerebral ischemia-reperfusion injury in the rat. In contrast to peptide drugs that exert therapeutic effect by interfering with signaling interaction, GCF acts as a BBB shuttle and prodrug to deliver glycine to confer neuroprotection, representing a novel therapeutic strategy for acute ischemic stroke.
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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".