The many roles of myeloperoxidase: From inflammation and immunity to biomarkers, drug metabolism and drug discovery
Bibliographic record
Abstract
This review provides a practical guide to myeloperoxidase (MPO) and presents to the reader the diversity of its presence in biology. The review provides a historical background, from peroxidase activity to the discovery of MPO, to its role in disease and drug development. MPO is discussed in terms of its necessity, as specific individuals lack MPO expression. An underlying theme presented throughout brings up the question of the benefit and burden of MPO activity. Enzyme structure is discussed, including accurate masses and glycosylation sites. The catalytic cycle of MPO and its corresponding pathways are presented, with a discussion of the importance of the redox couples of the different states of MPO. Cell lines expressing MPO are discussed and practically summarized for the reader, and locations of MPO (primary and secondary) are provided. Useful methods of MPO detection are discussed, and how these can be used for studying disease processes are implied through the presentation of MPO as a biomarker. The presence of MPO in neutrophil extracellular traps is presented, and the activators of the former are provided. Lastly, the transition from drug metabolism to a target for drug development is where the review concludes.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".