In one paragraphArticle in Analytical chemistry, 2026. 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 itWhat 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 registryThe 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 literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
5 · Who and what moneyAuthors and funding
12 authors.
Richard GrohsLeibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Research Alliance INFECTIONS , Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745Jena, Germany.ORCID 0009-0004-5109-5481 Franziska HornungInstitute of Medical Microbiology, Jena University Hospital, Am Klinikum 1, 07747Jena, Germany.
Marie-Luise EnghardtLeibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Research Alliance INFECTIONS , Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745Jena, Germany.
Anja SilgeLeibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Research Alliance INFECTIONS , Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745Jena, Germany.ORCID 0000-0002-6262-6020 Uwe GlaserLeibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Research Alliance INFECTIONS , Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745Jena, Germany.
Uwe HübnerLeibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Research Alliance INFECTIONS , Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745Jena, Germany.
Stefanie Deinhardt-EmmerInstitute of Medical Microbiology, Jena University Hospital, Am Klinikum 1, 07747Jena, Germany.
Oleg RyabchykovLeibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Research Alliance INFECTIONS , Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745Jena, Germany.ORCID 0000-0002-4655-8080 Karina WeberLeibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Research Alliance INFECTIONS , Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745Jena, Germany.ORCID 0000-0003-4907-8645 Thomas BocklitzLeibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Research Alliance INFECTIONS , Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745Jena, Germany.ORCID 0000-0003-2778-6624 Bettina LöfflerInstitute of Medical Microbiology, Jena University Hospital, Am Klinikum 1, 07747Jena, Germany.
Jürgen PoppLeibniz Institute of Photonic Technology, Member of Leibniz Health Technologies, Member of the Leibniz Research Alliance INFECTIONS , Member of the Leibniz Centre for Photonics in Infection Research (LPI), Albert-Einstein-Straße 9, 07745Jena, Germany.ORCID 0000-0003-4257-593X Funding
Bundesministerium für Forschung und Technologie 13GW0432EBundesministerium für Forschung und Technologie 13GW0459DBundesministerium für Forschung und Technologie FKZ 13N15466Bundesministerium für Forschung und Technologie FKZ 13N15704Bundesministerium für Forschung und Technologie FKZ 13N15708Deutsche Forschungsgemeinschaft EXC 2051 390713860Freistaat Thüringen 2020FGI0016
6 · The paper itselfAbstract
Bloodstream infections are associated with considerable morbidity and mortality, necessitating timely and accurate antimicrobial susceptibility testing (AST) to guide appropriate therapy. Current diagnostic methods primarily rely on culture-based AST, which is time-consuming, or on genotypic approaches that lack phenotypic relevance. We present the RamanBioAssay (RBA) platform, a novel diagnostic tool integrating dielectrophoretic on-chip bacterial enrichment with label-free Raman spectroscopy, to enable rapid phenotypic AST and simultaneous bacterial identification (ID). The RBA platform delivers AST results within 3.5 h from a positive blood culture, substantially reducing the diagnostic turnaround time compared to standard culture-based techniques. The RBA platform demonstrated high concordance with conventional AST using quality control strains: 94.4% and 100% for E. coli treated with ciprofloxacin and S. aureus treated with oxacillin in medium controls, 91.7% and 97.2% in artificial blood cultures, respectively. For proof-of-concept evaluation, six patient blood cultures were analyzed, yielding concordance rates of 91.7% and 83.3% for E. coli treated with ciprofloxacin and S. aureus treated with oxacillin, respectively (1/6 samples showed a S/R mismatch, 1/6 samples was nonconclusive). The mean diagnostic turnaround time for clinical samples was 3 h and 6 min (±24 min). Additionally, the family level classification of E. coli and S. aureus, shown exemplary in medium controls, was achieved with 97.2% accuracy. These findings highlight the RBA platform as a promising tool for rapid phenotypic AST combined with bacterial ID, providing comprehensive and clinically actionable results significantly faster than conventional methods.
Indexed as
Anti-Bacterial AgentsBlood CultureEscherichia coliLab-On-A-Chip DevicesSpectrum Analysis, RamanStaphylococcus aureusCiprofloxacinHumansMicrobial Sensitivity TestsOxacillinTime FactorsAnti-Bacterial AgentsCiprofloxacinOxacillin
Identifiers
PMID42330052
PMCPMC13449216
What OpenQuestion holds
Textmetadata
LicenceCC BY
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