Correlation of surface-enhanced Raman spectroscopic fingerprints of kidney transplant recipient urine with kidney function parameters
Bibliographic record
Abstract
Abstract Routine monitoring of kidney transplant function is required for the standard care in post-transplantation management, including frequent measurements of serum creatinine with or without kidney biopsy. However, the invasiveness of these methods with potential for clinically significant complications makes them less than ideal. The objective of this study was to develop a non-invasive tool to monitor the kidney transplant function by using Surface-Enhanced Raman Spectroscopy (SERS). Urine and blood samples were collected from kidney transplant recipients after surgery. Silver nanoparticle-based SERS spectra of the urine were measured and evaluated using partial least squires (PLS) analysis. The SERS spectra were compared with conventional chemical markers of kidney transplant function to assess its predictive ability. A total of 110 kidney transplant recipients were included in this study. PLS results showed significant correlation with urine protein (R2 = 0.4660, p < 0.01), creatinine (R2 = 0.8106, p < 0.01), and urea (R2 = 0.7808, p < 0.01). Furthermore, the prediction of the blood markers of kidney transplant function using the urine SERS spectra was indicated by R2 = 0.7628 (p < 0.01) for serum creatinine and R2 = 0.6539 (p < 0.01) for blood urea nitrogen. This preliminary study suggested that the urine SERS spectral analysis could be used as a convenient method for rapid assessment of kidney transplant function.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".