Human salivary protein extraction from RNAPro·SAL™, Pure·SAL™, and passive drooling method
Bibliographic record
Abstract
ABSTRACT Objective: The aim of the current study was to carry out a preliminary validation of devices for standardized collection of whole mouth fluid (WMF) in comparison to the passive drooling method for protein analysis in healthy subjects.Materials and Methods: A carefully designed sample collection/pretreatment protocol is crucial to the success of any saliva proteomics project. In this study, WMF was collected from healthy volunteers (n = 10, ages: 18–26 years). Individuals with any oral disease were excluded from the study group. In our study, we evaluated the following collection methods; the classical passive drooling method (unstimulated whole saliva) and standardized tools for saliva collection (Pure·SAL™, and RNAPro·SAL™) from Oasis Diagnostics® Corporation (Vancouver WA, USA). For estimation of protein levels, we used the bicinchoninic acid assay and protein assay kit (Thermo Fisher). The two-dimensional gel electrophoresis sample analysis was carried out for the estimation of proteins in one of the samples.Results: When gels were compared, the difference was seen in the resolution of spots. Protein spots were fading from high- to low-molecular weight masses. Hence, advanced devices in comparison to spitting method resulted in much clearer protein spots which in turn prove the validation of devices.Conclusions: In this study, we concluded that protein extraction could be possible by both methods such as passive drooling method and through advanced saliva collection devices (Pure·SAL™ and RNAPro·SAL™).
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 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 | 0.001 | 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".