Preliminary Evidence for the Role of Fungi, Specifically <i>Chaetomium</i>, in Gulf War Illness
Bibliographic record
Abstract
INTRODUCTION: For veterans of the Persian Gulf War (1990-1991), dozens of possible causes for their illness have been proposed. We hypothesize that all may be correct. These may have weakened the immunity of the military personnel to fungal pathogens in the soil. These microbes, in turn, may have afflicted the veterans either directly by infection or indirectly by toxins. MATERIALS AND METHODS: In 1990, the military (source confidential) provided the first author with soil samples from the Persian Gulf to determine if there were biothreats present. His team found that per gram of soil, there had few bacteria but many fungi. The National Centre for Human Mycotic Diseases (Edmonton) identified some of these fungi. They sent to the first author reference cultures of 12 pathogenic fungal species isolated from Canadian patients. Supernatant antigens of these fungi were used to assess if control and Gulf War Illness (GWI) patient sera had IgG antibodies against them. RESULTS: Human sera were tested on pathogenic fungal supernatant antigens. Controls had low IgG titers against all 12 fungal sources. Gulf War Illness (GWI) patient sera had low IgG titers against 11 of the 12 fungal antigens. However, 12 of 28 GWI patient sera (43%, P ≤ .0002 compared to controls) had high IgG titers against one fungus, Chaetomium, supernatant antigen. CONCLUSIONS: We suggest that the military personnel in the Persian Gulf War (1990-1991) may have had their immunity weakened from a variety of causes. The role of pathogenic fungi and/or their supernatant antigens or toxins as a contributing factor to GWI should be further investigated.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".