Laminar Fluid Diffusion Interface Preconditioning of Serum and Urine for Reagent-Free Infrared Clinical Analysis and Diagnostics
Bibliographic record
Abstract
A number of reagent-free infrared spectroscopic diagnostic and analytical methods have been established previously making use of dry biofluid films. For example, this approach has successfully measured high concentration analytes of serum and urine. However, a number of low concentration diagnostically relevant analytes presently elude detection by infrared spectroscopy. This is due in part to their relatively low concentration and in part to spectral interference by other strongly absorbing constituents. The applicability of the technique would be broadened substantially if it were possible to concentrate and separate lower concentration analytes, e. g., serum creatinine and urine proteins, from the obscuring presence of relatively high concentration compounds. One possible means to achieve this is through microfluidic sample preconditioning based on laminar fluid diffusion interfaces. The objective of this study was therefore to qualitatively assess the performance of this technology in preferentially separating certain serum and urine analytes of clinical interest that presently lie just below the threshold of detection by infrared spectroscopy. Observations from simulated and genuine urine and serum samples strongly suggest that this process should improve existing accuracy and extend the range of detectable analytes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".