Proteomic discovery in sickle cell disease: Elevated neurogranin levels in children with sickle cell disease
Bibliographic record
Abstract
PURPOSE: Sickle cell disease (SCD) is an inherited hemoglobinopathy that causes stroke and silent cerebral infarct (SCI). Our aim was to identify markers of brain injury in SCD. EXPERIMENTAL DESIGN: Plasma proteomes were analyzed using a sequential separation approach of hemoglobin (Hb) and top abundant plasma protein depletion, followed by reverse phase separation of intact proteins, trypsin digestion, and tandem mass spectrometry. We compared plasma proteomes of children with SCD with and without SCI in the Silent Cerebral Infarct Multi-Center Clinical Trial (SIT Trial) to age-matched, healthy non-SCD controls. RESULTS: From the SCD group, 1172 proteins were identified. Twenty-five percent (289/1172) were solely in the SCI group. Twenty-five proteins with enriched expression in the human brain were identified in the SCD group. Neurogranin (NRGN) was the most abundant brain-enriched protein in plasma of children with SCD. Using a NRGN sandwich immunoassay and SIT Trial samples, median NRGN levels were higher at study entry in children with SCD (0.28 ng/mL, N = 100) compared to control participants (0.12 ng/mL, N = 25, p < 0.0004). CONCLUSIONS AND CLINICAL RELEVANCE: NRGN levels are elevated in children with SCD. NRGN and other brain-enriched plasma proteins identified in plasma of children with SCD may provide biochemical evidence of neurological injury.
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 | 0.001 | 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) | 0.000 | 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".