A systematic review of peptide nucleic acids (PNAs) with antibacterial activities: Efficacy, potential and challenges
Bibliographic record
Abstract
Peptide nucleic acids (PNAs) are synthetic molecules that are like DNA/RNA, but with different building blocks. PNAs target and bind to mRNAs and disrupt the function of a targeted gene, hence they have been studied as potential antibacterials. The aim of this systematic review was to provide an in-depth analysis of the current status of PNAs as antibacterial agents, define the characteristics of the effective PNA constructs, and address the gap in advancing PNAs to become clinically competent agents. Following the PRISMA model, four electronic databases were searched: Web of Science, PubMed, SciFinder and Scopus. A total of 627 articles published between 1994 and 2023 were found. After screening and a rigorous selection process using explicit inclusion and exclusion criteria, 65 scientific articles were selected, containing 656 minimum inhibitory concentration (MIC) data. The antibacterial activity of PNAs was assessed against 20 bacterial species. The most studied Gram-negative and Gram-positive bacteria were Escherichia coli (n=266) and Staphylococcus aureus (n=53), respectively. In addition, the effect of PNA design, including construct length, binding location, and carrier agents, on antibacterial activity was shown. Finally, antibacterial test models to assess the inhibitory effects of PNAs were examined, emphasising gaps and prospects. This systematic review provides a comprehensive assessment of the potential of PNAs as antibacterial agents and offers valuable insights for researchers and clinicians seeking novel therapeutic strategies in the context of increasing rates of antibiotic-resistant bacteria.
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 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) | 0.002 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".