Theory and Applications of NMR-Based Metabolomics in Human Disease Diagnosis
Bibliographic record
Abstract
Metabolomics is a dynamic and emerging research field, joining proteomics, transcriptomics and genomics in affording a comprehensive understanding of biological systems and how these systems are affected by environmental stimuli and/or genetic modification. Metabolomics is particularly helpful for identifying biomarkers of disease processes such as the effects of a high fat diet on cardiovascular disease, providing insight into the interaction between genes and diet. Nuclear Magnetic Resonance (NMR) and Mass spectrometry (MS) are the most common analytical tools in metabolomics research. The high reproducibility of NMR-based techniques makes it superior to other analytical techniques especially in terms of searching for new and novel biomarkers in human diseases. Recently, NMR-based metabolomics approaches have been proposed as a promising and powerful technique for diagnosis of several human diseases. They have been used to investigate a wide range of diseases, through the examination of different kinds of human samples, including urine, blood plasma/serum, blister fluid, saliva, as well as intact tissue biopsies and tissue extracts. However, several factors can influence the metabolic balance within the human body and therefore in samples drawn from the body, including gender, age, fasting, diet, emotional stress, drug administration, physical activity and life style, thus complicating the use of NMR-based metabolomics approaches in diagnosing specific human disease. This chapter highlights the potential applications of NMR-based metabolomics approaches as a promising technique for diagnosis of human diseases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".