Evidence map›Paper›PMID 38746179›Full record

ArticlebioRxiv : the preprint server for biology2024

Investigating the translational value of Periprosthetic Joint Infection (PJI) models to determine the risk and severity of Staphylococcal biofilms.

Amita Sekar, Yingfang Fan, Peyton Tierney, Madeline McCanne, Parker Jones, Fawaz Malick, Devika Kannambadi, Keith K Wannomae, Nicoletta Inverardi, Orhun Muratoglu and 1 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Amita SekarHarris Orthopaedics laboratory, Massachusetts General Hospital, Boston, USA.ORCID 0000-0002-2057-1332
Yingfang FanHarris Orthopaedics laboratory, Massachusetts General Hospital, Boston, USA.
Peyton TierneyHarris Orthopaedics laboratory, Massachusetts General Hospital, Boston, USA.
Madeline McCanneHarris Orthopaedics laboratory, Massachusetts General Hospital, Boston, USA.
Parker JonesHarris Orthopaedics laboratory, Massachusetts General Hospital, Boston, USA.
Fawaz MalickHarris Orthopaedics laboratory, Massachusetts General Hospital, Boston, USA.
Devika KannambadiHarris Orthopaedics laboratory, Massachusetts General Hospital, Boston, USA.
Keith K WannomaeHarris Orthopaedics laboratory, Massachusetts General Hospital, Boston, USA.
Nicoletta InverardiHarris Orthopaedics laboratory, Massachusetts General Hospital, Boston, USA.
Orhun MuratogluHarris Orthopaedics laboratory, Massachusetts General Hospital, Boston, USA.
Ebru OralHarris Orthopaedics laboratory, Massachusetts General Hospital, Boston, USA.

Funding

Drug-eluting joint implants with synergistic antimicrobial release and risk stratified models of preclinical efficacy testingR01AR077023 · NIAMS · MASSACHUSETTS GENERAL HOSPITAL · PI ORAL, EBRU · 2020 to 2024
$1.8M
NIAMS NIH HHS R01 AR077023
6 · The paper itself

Abstract

With the advent of antibiotic-eluting polymeric materials for targeting recalcitrant infections, using preclinical models to study biofilm is crucial for improving the treatment efficacy in periprosthetic joint infections. The stratification of risk and severity of infections is needed to develop an effective clinical dosing framework with better outcomes. Here, using in-vivo and in-vitro implant-associated infection models, we demonstrate that methicillin-sensitive and resistant

Indexed as

antibiotic resistancedrug delivery devicesImplant infectionsin vivo modelsStaphylococcus aureus

Identifiers

PMID38746179
PMCPMC11092509

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.