Neuronal apoptosis inhibitory protein is implicated in amyotrophic lateral sclerosis symptoms
Bibliographic record
Abstract
Abstract The delineation of the molecular pathology underlying amyotrophic lateral sclerosis (ALS) is being hampered by the lack of suitable biomarkers. We have previously reported that bromocriptine upregulates the endogenous antioxidative factor, neuronal apoptosis inhibitory protein (NAIP), sustains motor function and slows disease progression in ALS patients, implying the NAIP’s implication in ALS. Here, we aimed to verify a correlation of NAIP level with disease progression in ALS patients. The amount of NAIP in mononuclear cells (MNC) from peripheral blood from ALS patients (n = 18) and the age matched healthy controls (n = 12) was validated by NAIP-Dot blotting. Notably, the MNC-NAIP level in ALS patients (0.62 ± 0.29 ng) was nearly half of that in the healthy controls (1.34 ± 0.61 ng, P = 0.0019). Furthermore, the MNC-NAIP level in ALS patients and their ALS Functional Rating Scale-Revised (ALSFRS-R) score were evaluated through 1 year. Regression analysis of the MNC-NAIP vs ALSFRS-R indicated that a higher amount of MNC-NAIP was associated with a smaller change in ALSFRS-R at 12 months (R2 = 0.799; P = 0.016), suggesting that a progressive increment of the MNC-NAIP led to slower ALS progression. Our present report implies that NAIP will have broad implications for ALS symptoms as a risk factor and a promising prognostic biomarker.
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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) | 0.001 | 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".