Direct and Indirect Antimicrobial Activities of Neuropeptides and their Therapeutic Potential
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
As global resistance to conventional antibiotics rises we need to develop new strategies to develop future novel therapeutics. In our quest to design novel anti-infectives and antimicrobials it is of interest to investigate host-pathogen interactions and learn from the complexity of host defense strategies that have evolved over millennia. A myriad of host defense molecules are now known to play a role in protection against human infection. However, the interaction between host and pathogen is recognized to be a multifaceted one, involving countless host proteins, including several families of peptides. The regulation of infection and inflammation by multiple peptide families may represent an evolutionary failsafe in terms of functional degeneracy and emphasizes the significance of host defense in survival. One such family is the neuropeptides (NPs), which are conventionally defined as peptide neurotransmitters but have recently been shown to be pleiotropic molecules that are integral components of the nervous and immune systems. In this review we address the antimicrobial and anti-infective effects of NPs both in vitro and in vivo and discuss their potential therapeutic usefulness in overcoming infectious diseases. With improved understanding of the efficacy of NPs, these molecules could become an important part of our arsenal of weapons in the treatment of infection and inflammation. It is envisaged that targeted therapy approaches that selectively exploit the anti-infective, antimicrobial and immunomodulatory properties of NPs could become useful adjuncts to our current therapeutic modalities.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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