Dehydration decreases saliva antimicrobial proteins important for mucosal immunity
Bibliographic record
Abstract
The aim of the study was to investigate the effect of exercise-induced dehydration and subsequent overnight fluid restriction on saliva antimicrobial proteins important for host defence (secretory IgA (SIgA), α-amylase, and lysozyme). On two randomized occasions, 13 participants exercised in the heat, either without fluid intake to evoke progressive body mass losses (BML) of 1%, 2%, and 3% with subsequent overnight fluid restriction until 0800 h in the following morning (DEH) or with fluids to offset losses (CON). Participants in the DEH trial rehydrated from 0800 h until 1100 h on day 2. BML, plasma osmolality (Posm), and urine specific gravity (USG) were assessed as hydration indices. Unstimulated saliva samples were assessed for flow rate (SFR), SIgA, α-amylase, and lysozyme concentrations. Posm and USG increased during dehydration and remained elevated after overnight fluid restriction (BML = 3.5% ± 0.3%, Posm = 297 ± 6 mosmol·kg⁻¹, and USG = 1.026 ± 0.002; P < 0.001). Dehydration decreased SFR (67% at 3% BML, 70% at 0800 h; P < 0.01) and increased SIgA concentration, with no effect on SIgA secretion rate. SFR and SIgA responses remained unchanged in the CON trial. Dehydration did not affect α-amylase or lysozyme concentration but decreased secretion rates of α-amylase (44% at 3% BML, 78% at 0800 h; P < 0.01) and lysozyme (46% at 3% BML, 61% at 0800 h; P < 0.01), which were lower than in CON at these time points (P < 0.05). Rehydration returned all saliva variables to baseline. In conclusion, modest dehydration (~3% BML) decreased SFR, α-amylase, and lysozyme secretion rates. Whether the observed magnitude of decrease in saliva AMPs during dehydration compromises host defence remains to be shown.
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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 | 0.000 | 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) | 0.001 | 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".