Raman Spectroscopy of Blood and Blood Components
Bibliographic record
Abstract
Blood is a bodily fluid that is vital for a number of life functions in animals. To a first approximation, blood is a mildly alkaline aqueous fluid (plasma) in which a large number of free-floating red cells (erythrocytes), white cells (leucocytes), and platelets are suspended. The primary function of blood is to transport oxygen from the lungs to all the cells of the body and move carbon dioxide in the return direction after it is produced by the cells' metabolism. Blood also carries nutrients to the cells and brings waste products to the liver and kidneys. Measured levels of oxygen, nutrients, waste, and electrolytes in blood are often used for clinical assessment of human health. Raman spectroscopy is a non-destructive analytical technique that uses the inelastic scattering of light to provide information on chemical composition, and hence has a potential role in this clinical assessment process. Raman spectroscopic probing of blood components and of whole blood has been on-going for more than four decades and has proven useful in applications ranging from the understanding of hemoglobin oxygenation, to the discrimination of cancerous cells from healthy lymphocytes, and the forensic investigation of crime scenes. In this paper, we review the literature in the field, collate the published Raman spectroscopy studies of erythrocytes, leucocytes, platelets, plasma, and whole blood, and attempt to draw general conclusions on the state of the field.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".