Increase in repulsive guidance molecule-a (RGMa) in lacunar and cortical stroke patients is related to the severity of the insult
Bibliographic record
Abstract
Repulsive guidance molecule-a (RGMa) inhibits angiogenesis and increases inflammation. Animal models of cerebral ischemia have shown that an increased expression of RGMa leads to larger infarction and its inhibition attenuates effects of ischemia. We report on the relationship of RGMa to stroke types and severity. This is a prospective study in patients admitted to the stroke service in Qatar. We collected the clinical determinants, including NIHSS at admission, imaging and outcome at discharge and 90-days. RGMa levels were determined by measuring mRNA levels extracted from peripheral blood mononuclear cells (PBMCs) within 24 h of onset and at 5 days. There were 90 patients (lacunar: 64, cortical: 26) and 35 age-matched controls. RGMa mRNA levels were significantly higher in the stroke patients: day 1: 1.007 ± 0.13 versus 2.152 ± 0.19 [p < 0.001] and day-5: 3.939 ± 0.36 [p < 0.0001]) and significantly higher in patients with severe stroke (NIHSS ≥ 8) compared to milder symptoms (NIHSS < 8) at day 1 (NIHSS ≥ 8: 2.563 ± 0.36; NIHSS < 8: 1.947 ± 0.2) and day 5 (NIHSS ≥ 8: 5.25 ± 0.62; NIHSS < 8: 3.259 ± 0.419). Cortical stroke patients had marginally higher RGMa mRNA levels compared to lacunar stroke at day 1 (cortical stroke: 2.621 ± 0.46 vs lacunar stroke: 1.961 ± 0.19) and day 5 (cortical stroke: 4.295 ± 0.76 vs lacunar stroke: 3.774 ± 0.39). In conclusion, there is an increase in the level of RGMa mRNA in patients with acute stroke and seen in patients with lacunar and cortical stroke. The increase in RGMa mRNA levels is related to the severity of the stroke and increases over the initial 5 days. Further studies are required to determine the effects of the increase in RGMa on stroke recovery.
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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 | 0.001 | 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".