Reagent-free, Simultaneous Determination of Serum Cholesterol in HDL and LDL by Infrared Spectroscopy
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to assess the feasibility of infrared (IR) spectroscopy for the simultaneous quantification of serum LDL-cholesterol (LDL-C) and HDL-cholesterol (HDL-C) concentrations. METHODS: Serum samples (n = 90) were obtained. Duplicate aliquots (5 microL) of the serum specimens were dried onto IR-transparent barium fluoride substrates, and transmission IR spectra were measured for the dry films. In parallel, the HDL-C and LDL-C concentrations were determined separately for each specimen by standard methods (the Friedewald formula for LDL-C and an automated homogeneous HDL-C assay). The proposed IR method was then developed with a partial least-squares (PLS) regression analysis to quantitatively correlate IR spectral features with the clinical analytical results for 60 randomly chosen specimens. The resulting quantification methods were then validated with the remaining 30 specimens. The PLS model for LDL-C used two spectral ranges (1700-1800 and 2800-3000 cm(-1)) and eight PLS factors, whereas the PLS model for HDL-C used three spectral ranges (800-1500, 1700-1800, and 2800-3500 cm(-1)) with six factors. RESULTS: For the 60 specimens used to train the IR-based method, the SE between IR-predicted values and the clinical laboratory assays was 0.22 mmol/L for LDL-C and 0.15 mmol/L for HDL-C (r = 0.98 for LDL-C; r = 0.91 for HDL-C). The corresponding SEs for the test spectra were 0.34 mmol/L (r = 0.96) and 0.26 mmol/L (r = 0.82) for LDL-C and HDL-C, respectively. The precision for the IR-based assays was estimated by the SD of duplicate measurements to be 0.11 mmol/L (LDL-C) and 0.09 mmol/L (HDL-C). CONCLUSIONS: IR spectroscopy has the potential to become the clinical method of choice for quick and simultaneous determinations of LDL-C and HDL-C.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".