ArticleJournal of the Royal Society, Interface2023
Sequential antibiotic therapy in the laboratory and in the patient.
Article in Journal of the Royal Society, Interface, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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.
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.
Who cites it
15 citing papers in PubMed, 28 citations in OpenAlex.
- Evolutionary drivers of divergent collateral sensitivity responses during antibiotic therapy.Nature ecology & evolution · 2026Review
- Computational framework for streamlining the success of sequential antibiotic therapy.npj antimicrobials and resistance · 2025Article
- Tripartite Loops Reverse Antibiotic Resistance.Molecular biology and evolution · 2025Article
- SAAP-148 and halicin exhibit synergistic antimicrobial activity against antimicrobial-resistant bacteria in skin but not airway epithelial culture models.JAC-antimicrobial resistance · 2025Article
- Phage-phage competition and biofilms affect interactions between two virulent bacteriophages and Pseudomonas aeruginosa.The ISME journal · 2025Article
- Environmental Antimicrobial Resistance: Implications for Food Safety and Public Health.Antibiotics (Basel, Switzerland) · 2024Review
- Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs.eLife · 2024Article
- The many dimensions of combination therapy: How to combine antibiotics to limit resistance evolution.Evolutionary applications · 2024Article
- Distinguishing mutants that resist drugs via different mechanisms by examining fitness tradeoffs.bioRxiv : the preprint server for biology · 2024Article
- Navigating collateral sensitivity: insights into the mechanisms and applications of antibiotic resistance trade-offs.Frontiers in microbiology · 2024Review
- A Pharmacodynamic Study of Aminoglycosides against PathogenicPharmaceuticals (Basel, Switzerland) · 2023Article
- A stochastic analysis of the interplay between antibiotic dose, mode of action, and bacterial competition in the evolution of antibiotic resistance.PLoS computational biology · 2023Article
- The pharmacokinetic-pharmacodynamic modelling framework as a tool to predict drug resistance evolution.Microbiology (Reading, England) · 2023Article
- The evolution of antibiotic resistance is associated with collateral drug phenotypes in Mycobacterium tuberculosis.Nature communications · 2023Article
- Sequential antibiotic therapy in the laboratory and in the patient.Journal of the Royal Society, Interface · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Laboratory experiments suggest that rapid cycling of antibiotics during the course of treatment could successfully counter resistance evolution. Drugs involving collateral sensitivity could be particularly suitable for such therapies. However, the environmental conditions
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.