The volatile metabolome and microbiome in pulmonary and gastro-intestinal disease
Bibliographic record
Abstract
Omics-technologies allow detailed characterization of biochemical molecular families enabling data-driven and hypothesis-generating research. In this thesis we explore potential merits and pitfalls of such an approach by studying the volatile metabolome and microbiome in disease diagnosis, phenotyping and prognosis. Diagnosis - The volatile metabolome constitutes of Volatile Organic Compounds (VOCs) reflecting metabolic activity. We describe that analysis of exhaled and fecal VOCs by eNose can discriminate controls from patients with cancer (lung and colorectal), chronic inflammatory conditions (asthma, Cystic Fibrosis, Primary Cilliary Dyskinesia, Crohn’s disease, Ulcerative Colitis) and help to identify patients suffering from fungal (invasive Aspergillosis), bacterial (exacerbation in CF and PCD) and viral (Rhinovirus) infections. These VOCs likely originate from the primary disease process, the systemic response and pathogens directly. Phenotyping - VOCs can differentiate between clinically similar diseases such as CF-PCD and Crohn-Ulcerative Colitis. Furthermore, VOCs can predict whether patients with asthma respond to steroid treatment. These VOCs associate with disease activity, potentially allowing monitoring. Prognosis - We showed VOCs differentiate between children with and without an increased risk to develop asthma, irrespective of symptoms. We made similar observation by analysis of the microbiome (the agglomerate of bacterial species) in the nasopharynx of these children. Omics technologies have a broad potential in medicine allowing diagnosis and characterization of disease processes, potentially prior to clinical onset. Several hurdles however need to be overcome in validation, data-analysis, data-integration, technological development and experiment uniformity. If adequately addressed and integrated into systems medicine, the potential healthcare impact of omics-technologies is profound.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".