Serum amyloid P ameliorates radiation-induced oral mucositis and fibrosis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: To evaluate the effect of the anti-fibrotic protein serum amyloid P (SAP) on radiation-induced oral mucositis (OM) and fibrosis in a hamster cheek-pouch model. EXPERIMENTAL DESIGN: Hamsters received a single dose of radiation (40 Gy) to the left everted cheek pouch to induce significant OM. The protective therapeutic potential of SAP was evaluated using varying dosing regimens. The extent of OM was measured using a validated six-point scoring scheme ranging from 0 (normal tissue, no mucositis) to 5 (complete ulceration). Fibrotic remodeling was also visualized histologically and quantified at later time points using collagen gene expression. RESULTS: SAP treatment attenuated the profile of radiation-induced oral mucositis by delaying the time of onset, reducing the peak value, and enhancing the resolution of injury. The peak mucositis score was reduced by approximately 0.5 grade in SAP-treated animals. The number of animal days with a score of >/= 3 was reduced by 48% in the SAP-treated group, compared with the saline control group (P < 0.01). SAP also inhibited the extent of tissue remodeling and decreased radiation-induced increases in myofibroblast number. Attenuated collagen deposition and gene expression was also observed in the cheek pouches of hamsters treated with SAP at both 16 and 28 days post-radiation. CONCLUSIONS: SAP treatment significantly attenuated radiation-induced injury. In particular, SAP attenuated the severity of OM and inhibited pathogenic remodeling. This suggests that SAP may be a useful therapy for the palliation of side effects observed during treatment for head and neck cancer.
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.
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) | 0.001 | 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 it