Suppression of high-mobility group box 1 ameliorates xerostomia in a Sjögren syndrome-triggered mouse model
Bibliographic record
Abstract
Xerostomia is a self-conscious symptom. High-mobility group box 1 (HMGB1) promotes pro-inflammatory effects in many diseases. This study aimed to clarify the role of HMGB1 in Sjögren syndrome (SS)-triggered xerostomia. Nonobese diabetic (NOD)/Ltj mice were used to establish an SS-triggered xerostomia model. The results showed that saliva production was decreased and anti-Sjögren syndrome B (anti-SSB) level was increased in SS. PCR, Western blot, and immunohistochemistry experiments indicated that the HMGB1 and aquaporin 5 (AQP5) levels were enhanced and diminished in SS compared with those in the control, respectively. While the mice were treated with anti-HMGB1, xerostomia was reversed due to the elevated saliva production and reduced anti-SSB level. In addition, it was found that the inhibition of HMGB1 restrained the toll-like receptor 4 (TLR4)/nuclear factor kappa B (NF-κB) axis activation. The TLR4 and p-IκB levels were alleviated, while the IκBα and NF-κB p65 levels were augmented. The NF-κB p65 binding activity was attenuated via the electrophoretic mobility shift assay (EMSA) after anti-HMGB1 treatment. Moreover, the repression of HMGB1 facilitated the expression of AQP5. These findings demonstrate that suppression of HMGB1 ameliorates SS-triggered xerostomia via suppressing the HMGB1/TLR4/NF-κB signaling pathway and upregulating AQP5 expression.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".