Unraveling the unknown areas of the human metabolome: the role of infrared ion spectroscopy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The identification of molecular biomarkers is critical for diagnosing and treating patients and for establishing a fundamental understanding of the pathophysiology and underlying biochemistry of inborn errors of metabolism. Currently, liquid chromatography/high-resolution mass spectrometry and nuclear magnetic resonance spectroscopy are the principle methods used for biomarker research and for structural elucidation of small molecules in patient body fluids. While both are powerful techniques, several limitations exist that often make the identification of unknown compounds challenging. Here, we describe how infrared ion spectroscopy has the potential to be a valuable orthogonal technique that provides highly-specific molecular structure information while maintaining ultra-high sensitivity. Here, we characterize and distinguish two well-known biomarkers of inborn errors of metabolism, glutaric acid for glutaric aciduria and ethylmalonic acid for short-chain acyl-CoA dehydrogenase deficiency, using infrared ion spectroscopy. In contrast to tandem mass spectra, in which ion fragments can hardly be predicted, we show that the prediction of an IR spectrum allows reference-free identification in the case that standard compounds are either commercially or synthetically unavailable. Finally, we illustrate how functional group information can be obtained from an IR spectrum for an unknown and how this is valuable information to, for example, narrow down a list of candidate structures resulting from a database query. Early diagnosis in inborn errors of metabolism is crucial for enabling treatment and depends on the identification of biomarkers specific for the disorder. Infrared ion spectroscopy has the potential to play a pivotal role in the identification of challenging biomarkers.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it