Evidence map›Paper›PMID 39269158›Full record

ArticleMicrobiology spectrum2024

Protein and transcriptional biomarker profiling may inform treatment strategies in lower respiratory tract infections by indicating bacterial-viral differentiation.

Dhanasekaran Sivakumaran, Synne Jenum, Dagfinn Lunde Markussen, Sondre Serigstad, Aashish Srivastava, Christina Skår Saghaug, Elling Ulvestad, Siri Tandberg Knoop, Harleen M S Grewal

Abstract read
In one paragraph

Article in Microbiology spectrum, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

9 authors.

Dhanasekaran SivakumaranDepartment of Clinical Science, Bergen Integrated Diagnostic Stewardship Cluster, University of Bergen, Bergen, Norway.ORCID 0000-0003-1688-8182
Synne JenumDepartment of Clinical Science, Bergen Integrated Diagnostic Stewardship Cluster, University of Bergen, Bergen, Norway.
Dagfinn Lunde MarkussenDepartment of Clinical Science, Bergen Integrated Diagnostic Stewardship Cluster, University of Bergen, Bergen, Norway.
Sondre SerigstadEmergency Care Clinic, Haukeland University Hospital, Bergen, Norway.ORCID 0000-0002-2582-8328
Aashish SrivastavaGenome Core-Facility, Clinical Laboratory (K2), Haukeland University Hospital, University of Bergen, Bergen, Norway.
Christina Skår SaghaugDepartment of Clinical Science, Bergen Integrated Diagnostic Stewardship Cluster, University of Bergen, Bergen, Norway.ORCID 0000-0003-0836-7889
Elling UlvestadDepartment of Clinical Science, Bergen Integrated Diagnostic Stewardship Cluster, University of Bergen, Bergen, Norway.
Siri Tandberg KnoopDepartment of Clinical Science, Bergen Integrated Diagnostic Stewardship Cluster, University of Bergen, Bergen, Norway.ORCID 0000-0002-9382-8186
Harleen M S GrewalDepartment of Clinical Science, Bergen Integrated Diagnostic Stewardship Cluster, University of Bergen, Bergen, Norway.

Funding

Norges Forskningsråd (Forskningsrådet) 288718Trond Mohn stiftelse (Trond Mohn Foundation) TMS2020TMT07
6 · The paper itself

Abstract

Lower respiratory tract infections (LRTIs) remain a significant global cause of infectious disease-related mortality. Accurate discrimination between acute bacterial and viral LRTIs is crucial for optimal patient care, prevention of unnecessary antibiotic prescriptions, and resource allocation. Plasma samples from LRTI patients with bacterial ( IMPORTANCE: Accurate differentiation between bacterial and viral lower respiratory tract infections (LRTIs) is vital for effective patient care and resource allocation. This study investigated specific protein signatures and gene expression patterns in plasma and blood samples from LRTI patients that distinguished bacterial and viral infections. The identified signatures can inform the design of point-of-care tests that can aid healthcare providers in making informed decisions about antibiotic prescriptions in order to reduce unnecessary use, thereby contributing to reduced side effects and antibiotic resistance. Furthermore, the potential for faster and more accurate diagnoses for improved patient management in acute LRTIs is compelling.

Indexed as

Bacterial InfectionsBiomarkersCOVID-19Respiratory Tract InfectionsSARS-CoV-2AdultAgedAnti-Bacterial AgentsFemaleGene Expression ProfilingHumansMaleMiddle AgedVirus DiseasesAnti-Bacterial AgentsBiomarkersacute respiratory infectionsantibiotic treatmentbacterial vs viral diagnosisbiosignatureshost-based biomarkers

Identifiers

PMID39269158
PMCPMC11448388

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.