Identification of Inflammatory Mediators in Saliva Samples From Hospitalized Newborns: Potential Biomarkers?
Bibliographic record
Abstract
Saliva measurements serve as a noninvasive tool for clinically monitoring newborns (NB) and children, a vulnerable population with promising potential for both research and clinical practice. Saliva acts as a repository for various inflammatory biomarkers involved in diverse biological functions. Particularly for children, it offers numerous advantages when compared to plasma and urine sampling. Nevertheless, there is a significant knowledge gap regarding detectable levels of cytokines in the saliva of newborns and children, as well as studies aiming to assess the relationship of this content with physiological and pathological processes. OBJECTIVES: To characterize the levels of 11 inflammatory mediators (IFNg, IL1b, IL2, IL4, IL6, IL8, IL10, IL12, IL17, TNF, and VEGF) in saliva samples from NB on the first and second day of hospitalization in the Neonatal Intensive Care Unit (NICU). METHOD: Exploratory study, descriptive, nested within a primary clinical, observational, and prospective study, conducted in the NICU of a public hospital in São Paulo, Brazil. Demographic data and vital signs were recorded in the clinical records of 90 NB, and five saliva samples from 5 NB were collected between the first and second day of life (D1-D2) at approximately 8-hr intervals (8-9 am, 4-5 pm, and 11-12 pm). Saliva samples were used for the measurement of 11 cytokines (IFNg, IL1b, IL2, IL4, IL6, IL8, IL10, IL12, IL17, TNF, and VEGF). RESULTS: Five NBs participated in this exploratory study, and the vital signs showed variability from the first (D1) to the second day (D2) of hospitalization, variability similar to that of the total population of the primary study. The presence and levels of the 11 cytokines were detected in the saliva samples, as well as a statistical correlation between 10 cytokines (IFNg, IL1b, IL2, IL4, IL6, IL10, IL12, IL17, TNF, and VEGF) and vital signs. CONCLUSIONS: The novelty of measuring inflammatory mediators in saliva samples from hospitalized NBs in the NICU is highlighted, providing support and new perspectives for the development of clinical and experimental research and an opportunity for developing and implementing new salivary biomarkers in different population segments.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".