The fungal gut microbiota in pediatric-onset multiple sclerosis
Bibliographic record
Abstract
Evidence suggests that the gut microbiome may play a role in multiple sclerosis (MS). However, the majority of the studies have focused on gut bacterial communities; none have examined the fungal microbiota (mycobiota) in persons with pediatric-onset multiple sclerosis (POMS). We examined the gut mycobiota in persons with and without POMS through a cross-sectional examination of the gut mycobiota from 46 participants’ stool samples (three groups: 18 POMS, 13 acquired monophasic demyelinating syndromes [monoADS], and 15 unaffected controls). Using metataxonomic sequencing of the fungal internal transcribed spacer region 2, the fungal profiles were compared between participants using visualizations, statistical tests, and predictive analysis. While the mycobiome α- (Shannon and inverse Simpson indices) and β-diversity differed across the three groups [analysis of variance (ANOVA), p < 0.05], further post-hoc analysis of the β-diversity identified a difference between monoADS vs. POMS participants [p = 0.005 (adjusted)]. At the genus level of taxonomy, 7 out of 10 of the majority of abundant genera were similar among all three groups, with Saccharomyces spp. and Candida spp. being in the highest abundance. The Agaricus genus was especially high in POMS participants, dominated primarily due to the species Agaricus bisporus (widely consumed as white button mushrooms). The commonality of high abundance fungi found in our cohort suggests a possible connection to diet. Predictive modeling of differential abundance associated with Candida albicans, Cyberlindera jadinii, and Fusarium poae revealed that these fungi were strongly associated with the POMS participants. Our study provides novel insight into the fungal gut mycobiota in POMS. While findings indicate that the gut mycobiome of participants with POMS may largely comprise fungi considered transient from the diet, the differential predictive analysis suggested rare or under-detected fungal markers being of potential importance, warranting consideration in future mycobiome-MS-related studies.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".