Evidence map›Paper›PMID 36671268›Full record

ReviewAntibiotics (Basel, Switzerland)2022

Current Promising Strategies against Antibiotic-Resistant Bacterial Infections.

Jinzhou Ye, Xinhai Chen

Abstract readReview
In one paragraph

Review in Antibiotics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.

0numbers the graph read from it
0cells of the map it votes in
32citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

32 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Review
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Novel Antibacterial Approaches and Therapeutic Strategies.Antibiotics (Basel, Switzerland) · 2025
    Review
  11. Photodynamic Inactivation ofBiomacromolecules · 2025
    Article
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Review
  18. Exploring Biosurfactants as Antimicrobial Approaches.Pharmaceuticals (Basel, Switzerland) · 2024
    Review
  19. SynthesizedNanomaterials (Basel, Switzerland) · 2024
    Article
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Jinzhou YeInstitute of Infectious Diseases, Shenzhen Bay Laboratory, Shenzhen 518132, China.ORCID 0000-0003-0193-9581
Xinhai ChenInstitute of Infectious Diseases, Shenzhen Bay Laboratory, Shenzhen 518132, China.ORCID 0000-0002-6771-6316

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Infections caused by antibiotic-resistant bacteria (ARB) are one of the major global health challenges of our time. In addition to developing new antibiotics to combat ARB, sensitizing ARB, or pursuing alternatives to existing antibiotics are promising options to counter antibiotic resistance. This review compiles the most promising anti-ARB strategies currently under development. These strategies include the following: (i) discovery of novel antibiotics by modification of existing antibiotics, screening of small-molecule libraries, or exploration of peculiar places; (ii) improvement in the efficacy of existing antibiotics through metabolic stimulation or by loading a novel, more efficient delivery systems; (iii) development of alternatives to conventional antibiotics such as bacteriophages and their encoded endolysins, anti-biofilm drugs, probiotics, nanomaterials, vaccines, and antibody therapies. Clinical or preclinical studies show that these treatments possess great potential against ARB. Some anti-ARB products are expected to become commercially available in the near future.

Indexed as

antibiotic efficacyantibiotic resistanceantibodybacteriophagesnew antibioticsvaccine

Identifiers

PMID36671268
PMCPMC9854991

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.