Biomolecular alterations detected in multiple sclerosis skin fibroblasts using Fourier transform infrared spectroscopy
Bibliographic record
Abstract
Multiple sclerosis (MS) is the leading cause of non-traumatic disability in young adults. New avenues are needed to help predict individuals at risk for developing MS and aid in diagnosis, prognosis, and outcome of therapeutic treatments. Previously, we showed that skin fibroblasts derived from patients with MS have altered signatures of cell stress and bioenergetics, which likely reflects changes in their protein, lipid, and biochemical profiles. Here, we used Fourier transform infrared (FTIR) spectroscopy to determine if the biochemical landscape of MS skin fibroblasts were altered when compared to age- and sex-matched controls (CTRL). More so, we sought to determine if FTIR spectroscopic signatures detected in MS skin fibroblasts are disease specific by comparing them to amyotrophic lateral sclerosis (ALS) skin fibroblasts. Spectral profiling of skin fibroblasts from MS individuals suggests significant alterations in lipid and protein organization and homeostasis, which may be affecting metabolic processes, cellular organization, and oxidation status. Sparse partial least squares-discriminant analysis of spectral profiles show that CTRL skin fibroblasts segregate well from diseased cells and that changes in MS and ALS may be unique. Differential changes in the spectral profile of CTRL, MS, and ALS cells support the development of FTIR spectroscopy to detect biomolecular modifications in patient-derived skin fibroblasts, which may eventually help establish novel peripheral biomarkers.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".