Metagenomic datasets of cerebrospinal fluid from a small cohort of MS/non-MS patients do not show DNA from the fungal genus Trichosporon
Bibliographic record
Abstract
In [2] Alonso et al used nested PCR assays together with next-generation sequencing to find Trichosporon species in the nervous tissue of 10 patients with MS. We deemed it possible that the fungus would be present in cerebrospinal fluid (CSF) samples. Whole metagenomic shotgun (WMGS) sequencing allows detection of any organism in a sample. With Trichosporon any detection would be a true positive because these fungi are not known to be on the skin, or as typical lab contamination. We screened public WMGS datasets of CSF from a cohort of 43 Canadian patients (28 MS, 13 non-MS)[1], using Kraken2[7], the ultrafast kmer-based classifier, using a fungal database augmented with all cleaned available Trichosporon genome assemblies from the NCBI. Blasting the marked reads against an equally augmented blastn database revealed no alignments with an evalue <= 1e-50. In general Kraken2 marked not more than 5 consecutive kmers in any read, which is a clear negative.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".