Cerebrospinal Fluid in a Small Cohort of Patients with Multiple Sclerosis Was Generally Free of Microbial DNA
Bibliographic record
Abstract
Multiple sclerosis (MS) is a common cause of non-traumatic neurologic disability with high incidence in many developed countries. Although the etiology of the disease remains elusive, it is thought to entail genetic and environmental causes, and microbial pathogens have also been envisioned as contributors to the phenotype. We conducted a metagenomic survey in cerebrospinal fluid (CSF) from 28 MS patients and 15 patients suffering other type of neurological conditions. We detected bacterial reads in eight out of the 15 non-MS patients and in a single MS patient, at an abundance > 1% of total classified reads. Two patients were of special interest: one non-MS patient harbored ~ 73% bacterial reads, while an MS patient had ~ 83% bacterial reads. In the former case, Veillonella parvula, a bacterium occasionally found associated with meningitis was the predominant species, whilst Kocuria flava, apparently an environmental bacterium, predominated in the latter case. Thirty-four out of 43 samples contained less than 1% bacterial reads, which we regard as cross- or environmental contamination. A few viral reads corresponding to Epstein-Barr virus, cytomegalovirus and parvovirus were also identified. Our results suggest that CSF of MS patients is often (but not always) free of microbial DNA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".