Joint Application of Magnetic Resonance Imaging and Biochemical Biomarkers in Diagnosis of Multiple Sclerosis
Bibliographic record
Abstract
Multiple Sclerosis (MS), an autoimmune disorder associated with spinal cord and brain, chiefly affects the white matter. Regarding the complexity as well as heterogenic etiology of this disease, the treatment of MS has been a challenging issue up to now. Researchers are working to develop new therapeutic strategies and drugs as complementary therapies. MS diagnosis significantly depends on the findings of Magnetic Resonance Imaging (MRI) examination. In this imaging technique, gadolinium is used as a contrast agent to reveal active plaques intending to destroy the bloodbrain barrier. It also detects plaques that are not correlated with the neurological symptoms. It has been attempted to determine biomarkers related to different dimensions of MS in various organizational hierarchy levels of the human anatomy (i.e., cells, proteins, RNA, and DNA). These biomarkers are appropriate diagnostic tools for MS diagnosis. In this review, we summarized the application of MRI and biochemical biomarkers to monitor MS patients. Moreover, we highlighted the joint application of MRI and biomarkers for the diagnosis of MS subjects.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".