Cutaneous Bacteria in the Gut Microbiome as Biomarkers of Systemic Malodor and People Are Allergic to Me (PATM) Conditions: Insights From a Virtually Conducted Clinical Trial
Bibliographic record
Abstract
Abstract Summary The proposed study will identify microbial communities associated with flare ups and remissions of MEBO (systemic malodor of metabolic origin) or PATM (“People allergic to me”) conditions. Background Human odor-prints, mostly owing to the microbiome, have proven their value as biomarkers of health and environmental exposures. In recent years, microbial networks responsible for localized malodors (e.g., halitosis [1,2], groin area, foot and axillary odor [3,4]) have been mapped by using next generation sequencing approaches. Intestinal microbes responsible for psychologically debilitating systemic malodor (whole-body and extraoral halitosis), however, remain to be identified. Even a relatively straightforward disorder of choline metabolism trimethylaminuria (TMAU) is thought to exhibit complex host-gene microbiome interactions [5] and has not been sufficiently studied. Mapping gut microbiome is needed to understand human metabolic disfunction, make proper dietary recommendations and develop targeted treatments such as microbial therapies [6–8]. Our preliminary analysis of culture-, PCR- and 16S-RNA-based data found several Operational Taxonomic Units (OTUs) potentially linked to systemic malodor. Proposed controlled pilot study will provide a more comprehensive evaluation and, combined with our prior data [9–12], will help to develop new therapies and treatments.
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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 | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".