Editorial: Use of small peptides in the treatment of inflammatory diseases
Bibliographic record
Abstract
Editorial on the Research Topic Use of small peptides in the treatment of inflammatory diseasesInflammation is essential in the resolution of infection or tissue damage.However, excessive inflammation, which sometimes lasts for years, leads to numerous inflammatory disorders such as cardiovascular, neurodegenerative, or gastrointestinal diseases, cancer or diabetes mellitus, or even infections.Small peptides are promising therapeutic tools as they have been shown to present higher specificity (especially peptides with allosteric properties) and binding affinity than small molecules, and reduced immunogenicity and toxicity than biologics.Moreover, some of them have a dual-anti-inflammatory and antimicrobial-activity.The source of therapeutic peptides is almost inexhaustible as they can have natural or designed origins.Interestingly, peptidic compounds entering clinical trials are more susceptible to approval.In the last 20 years, novel designs, delivery strategies, and improvements in peptide production and modification have led to a total of 33 approved peptide drugs, and more than 170 peptides are in clinical trials (Wang et al., 2022).In their comprehensive review focused on the state of clinical trials on noncancer dermatological biologics in China, Zhu et al. show that the number of dermatological biologic trials in China surged between 2016 and 2020, primarily driven by psoriasis trials.To control undesirable inflammation with reduced side effects, it is desirable to target a particular signaling pathway, cell type, tissue, or organ, without increasing susceptibility to infections or diseases secondary to the treatment.Additional to those considerations, the development of promising immunomodulatory peptide candidates needs to contemplate when and how they will best contribute to inflammation resolution.As a first step to comply with those requirements, it is necessary to elucidate the origin and potential functions of the diverse immune cells present in a tissue at a given time.Especially, macrophages' multiple functions in wounds or infections, such as the induction and resolution of inflammation, the removal of apoptotic cells, cell
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.015 | 0.016 |
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".