Analysis and differentiation of seminal plasma via polarized SERS spectroscopy
Bibliographic record
Abstract
Polarized surface-enhanced Raman scattering (SERS) spectroscopy was applied for obtaining biochemical information about the seminal plasma. The effect of different laser polarizations (nonpolarized, linear-polarized, right-handed circularly polarized, and left-handed circularly polarized) on seminal plasma SERS spectroscopy was explored for the first time. The diagnostic performance in differentiating abnormal seminal plasma (n = 37) from normal seminal plasma (n = 24) was evaluated. A combination of principal component analysis (PCA) and linear discriminant analysis (LDA) was employed to develop diagnostic algorithms. Classification results of different laser polarizations demonstrated different diagnostic sensitivities and specificities, among which, left-handed circularly polarized laser excitation showed the best diagnostic result (95.8% sensitivity and 64.9% specificity). Our exploratory study demonstrated that SERS spectroscopy with left-handed circularly polarized laser excitation has the potential for becoming a new diagnostic method in semen-quality assessment.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".