Study of cytokine induced neuropathology by high resolution proton NMR spectroscopy of rat urine
Bibliographic record
Abstract
Multiple sclerosis is a major cause of non-traumatic neurological disability. The identification of markers that differentiate disease progression is critical to effective therapy. A combination of NMR spectroscopic metabolic profiling of urine and statistical pattern recognition was used to detect focal inflammatory central nervous system (CNS) lesions induced by microinjection of a replication-deficient recombinant adenovirus expressing TNF-alpha or IL1-beta cDNA into the brains of Wistar rats. These animals were compared with a group of naïve rats and a group of animals injected with an equivalent null adenovirus. Urine samples were collected 7 days after adenovirus injection, when the inflammatory lesion is maximally active. Principal components analysis and Partial Least Squares-Discriminate analysis of the urine (1)H NMR spectra revealed significant differences between each of the cytokine adenovirus groups and the control groups; for the TNF-alpha group the main differences lay in citrate and succinate, while for the IL-1beta group the predominant changes occurred in leucine, isoleucine, valine and myo-inositol. Thus, we can identify urinary metabolic vectors that not only separate rats with inflammatory lesions in the brain from control animals, but also distinguish between different types of CNS inflammatory lesions.
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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 | 0.000 | 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".