GP.2 Changes in Leptin, CCL16 and sTNF-RII as a distinctive plasma immune profile in patients with fast progressing ALS
Bibliographic record
Abstract
Background: Amyotrophic lateral sclerosis (ALS) is highly heterogeneous with survival rate ranging from months to decades. Approximately 10-20% of patients develop a rapidly progressive disease and may die within the first year. Therefore, there is an increasing need for an early detection of unique molecular signatures associated with more aggressive forms of disease as it may help identify therapeutic targets. Methods: To identify a unique molecular signature in fast progressing patients, we recruited 45 sporadic ALS (sALS) patients and 35 age-matched healthy controls and measured 62 immune markers in plasma using cytokines array. Results: We found that leptin was significantly downregulated in plasma of sALS patients and more importantly in fast progressing disease. Immune markers CCL16 and sTNF-RII were significantly increased in rapidly progressing disease. We also found that leptin was significantly downregulated in plasma of SOD1G93A mice across disease stage. This was caused by an increased in levels of phospho-AMPK in mice adipocytes and in adipocytes exposed to fast sALS patients’ plasma. Conclusions: We propose that the combination of decreased plasma leptin levels and up-regulation in CCL16/sTNF-RII may be used as a prognostic biomarker to identify fast progressing ALS patients. This unique immune/metabolic profile may cause dysfunction in metabolic homeostasis.
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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".