Toward point-of-care diagnostic metabolic fingerprinting: quantification of plasma creatinine by infrared spectroscopy of microfluidic-preprocessed samples
Bibliographic record
Abstract
Infrared (IR) spectroscopy has previously been established as a means to accurately quantify several serum and urine metabolites, based upon spectroscopy of dry films. The same technique has also provided the basis to develop certain diagnostic tests, developed in the 'metabolomics' spirit. Here, we report on the further development of an integrated microfluidic-IR technology and technique, customized with the aim of dramatically extending the capabilities of IR spectroscopy in both analytical and diagnostic (metabolomic) applications. By exploiting the laminar fluid diffusion interface (LFDI), serum specimens are processed to yield product streams that are better suited for metabolic fingerprinting; metabolites are captured within the aqueous product stream, while proteins (which otherwise dominate the spectra of films dried from serum) are present in much reduced concentration. Spectroscopy of films dried from the aqueous stream then provides enhanced diagnostic and analytical sensitivity. The manuscript introduces an LFDI card design that is customized for integration with IR spectroscopy, and details the development of a quantitative assay for serum creatinine--based upon LFDI-processed serum samples--that is substantially more accurate (standard error of calibration, SEC = 43 micromol/L) than the corresponding assay based upon unprocessed serum specimens (SEC = 138 micromol/L). Preliminary results of diffusion modeling are reported, and the prospects for further optimization of the technique, guided by accurate modeling, are discussed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".