Bibliographic record
Abstract
Apical periodontitis is an inflammatory reaction of the periradicular tissues as a consequence of multispecies microbial communities organized as biofilms within the root canal system. Periradicular tissue changes at the molecular level initiate and orchestrate the inflammatory process and precede the presentation of clinical symptoms. Inflammatory mediators have been studied at either the proteomic, metabolomic, or transcriptomic levels. Analysis at the protein level is the most common approach used to identify and quantify analytes from diseased periradicular tissues during root canal treatment, since it is more representative of definitive and active periradicular inflammatory mediator than its transcript expression level. In disease, proteins expressed in an altered manner could be utilized as biomarkers. Biomarker proteins in periradicular tissues have been qualitatively and quantitatively assessed using antibodies (immunoassays and immunostaining) or mass spectrometry-based approaches. Herein, we aim to provide a comprehensive understanding of biomarker proteins identified in clinical studies investigating periradicular lesions and pulp tissue associated with apical periodontitis using proteomics. The high throughput mass spectrometry-based proteomics has the potential to improve the current methods of monitoring inflammation while distinguishing between progressive, stable, and healing lesions for the identification of new diagnostic and therapeutic targets. This method would provide more objective tools to (a) discover biomarkers related to biological processes for better clinical case selection, and (b) determine tissue response to novel therapeutic interventions for more predictable outcomes in endodontic treatment.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".