SERS Characterization of Filtrate Portions of Typhoid Blood Serum Samples Using 30 kDa Filtration Devices
Bibliographic record
Abstract
ABSTRACT Typhoid fever remains a significant global public health concern and continues to pose serious diagnostic challenges, particularly in the differentiation of different stages of infection. In this study, surface‐enhanced Raman spectroscopy (SERS) combined with ultracentrifugation was explored to design a reliable method for characterization and identification of typhoid serum filtrate. During the analysis of serum samples by SERS, the presence of high molecular weight fractions (HMWF) occupying greater surface area masks the presence of low molecular weight fractions (LMWF). Therefore, HMWF was removed from the healthy and typhoid serum samples, and SERS was employed for the biomolecular analysis and differentiation of filtrate portions of serum containing LMWF less than 30 kDa. Silver nanoparticles, as substrates, were used that enhanced Raman signals of the biomolecules in the filtrate samples. The results show notable differences in the spectra of two stages of typhoid and healthy samples (control group) at 394, 648, 742, 771, 930, 1012, 1218, 1424, and 1538 cm −1 . A chemometric tool, principal component analysis (PCA), was used to differentiate early‐ and late‐stage typhoid from each other and control group. PCA highlighted the spectral differences between healthy and diseased samples and classified them separately that proves the diagnostic ability of SERS from LMWF of serum samples. SERS has characterized and differentiated effectively early‐ and late‐stage typhoid from each other as well as from healthy individuals by using LMWF of the blood serum samples. The results proved the diagnostic ability of SERS for typhoid fever and offered a noninvasive, rapid, and cost‐effective method for disease detection and progression study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".