Quantification of human plasma metalloproteins in multiple sclerosis, ischemic stroke and healthy controls reveals an association of haptoglobin-hemoglobin complexes with age
Bibliographic record
Abstract
Advanced analytical methods play an important role in quantifying serum disease biomarkers. The problem of separating thousands of proteins can be reduced by analyzing for a 'sub-proteome', such as the 'metalloproteome', defined as all proteins that contain bound metals. We employed size exclusion chromatography (SEC) coupled to an inductively coupled plasma atomic emission spectrometer (ICP-AES) to analyze plasma from multiple sclerosis (MS) participants (n = 21), acute ischemic stroke (AIS) participants (n = 17) and healthy controls (n = 21) for Fe, Cu and Zn-metalloproteins. Using ANOVA analysis to compare the mean peak areas among the groups revealed no statistically significant differences for ceruloplasmin (p = 0.31), α2macroglobulin (p = 0.51) and transferrin (p = 0.31). However, a statistically significant difference was observed for the haptoglobin-hemoglobin (Hp-Hb) complex (p = 0.04), being driven by the difference between the control group and AIS (p = 0.012), but not with the MS group (p = 0.13), based on Dunnes test. A linear regression model for Hp-Hb complex with the groups now adjusted for age found no statistically significant differences between the groups (p = 0.95), but was suggestive for age (p = 0.057). To measure the strength of association between the Hp-Hb complex and age without possible modifications due to disease, we calculated the Spearman rank correlation in the healthy controls. The latter revealed a positive association (r = 0.39, 95% Confidence Interval = (-0.05, 0.83), which suggests that either the removal of Hp-Hb complexes from the blood circulation slows with age or that the release of Hb from red blood cells increases with age. We also observed that the Fe-peak corresponding to the Hp-Hb complex eluted ~100 s later in ~14% of all study samples, which was not correlated with age or disease diagnosis, but is consistent with the presence of the smaller Hp (1-1) isoform in 15% of the population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".