Evidence map›Paper›PMID 38787397›Full record

ArticleEuropean journal of nuclear medicine and molecular imaging2024

PET imaging of Aspergillus infection using Zirconium-89 labeled anti-β-glucan antibody fragments.

Jianhao Lai, Swati Shah, Neysha Martinez-Orengo, Rekeya Knight, Eyob Alemu, Mitchell L Turner, Benjamin Wang, Anna Lyndaker, Jianfeng Shi, Falguni Basuli and 1 more

Abstract read
In one paragraph

Article in European journal of nuclear medicine and molecular imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Review
  3. Review
  4. Nature communications · 2025
    Observational
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

11 authors.

Jianhao Lai *Center for Infectious Disease Imaging (CIDI), Radiology and Imaging Sciences, Clinical Center (CC), National Institutes of Health (NIH), 10 Center Drive, Room 1C368, Bethesda, MD, 20892, USA.
Swati Shah *Center for Infectious Disease Imaging (CIDI), Radiology and Imaging Sciences, Clinical Center (CC), National Institutes of Health (NIH), 10 Center Drive, Room 1C368, Bethesda, MD, 20892, USA.
Neysha Martinez-OrengoCenter for Infectious Disease Imaging (CIDI), Radiology and Imaging Sciences, Clinical Center (CC), National Institutes of Health (NIH), 10 Center Drive, Room 1C368, Bethesda, MD, 20892, USA.
Rekeya KnightCenter for Infectious Disease Imaging (CIDI), Radiology and Imaging Sciences, Clinical Center (CC), National Institutes of Health (NIH), 10 Center Drive, Room 1C368, Bethesda, MD, 20892, USA.
Eyob AlemuCenter for Infectious Disease Imaging (CIDI), Radiology and Imaging Sciences, Clinical Center (CC), National Institutes of Health (NIH), 10 Center Drive, Room 1C368, Bethesda, MD, 20892, USA.
Mitchell L TurnerCenter for Infectious Disease Imaging (CIDI), Radiology and Imaging Sciences, Clinical Center (CC), National Institutes of Health (NIH), 10 Center Drive, Room 1C368, Bethesda, MD, 20892, USA.
Benjamin WangCenter for Infectious Disease Imaging (CIDI), Radiology and Imaging Sciences, Clinical Center (CC), National Institutes of Health (NIH), 10 Center Drive, Room 1C368, Bethesda, MD, 20892, USA.
Anna LyndakerCenter for Infectious Disease Imaging (CIDI), Radiology and Imaging Sciences, Clinical Center (CC), National Institutes of Health (NIH), 10 Center Drive, Room 1C368, Bethesda, MD, 20892, USA.
Jianfeng ShiChemistry and Synthesis Center, National Heart, Lung, and Blood Institute (NHLBI), NIH, Rockville, MD, USA.
Falguni BasuliChemistry and Synthesis Center, National Heart, Lung, and Blood Institute (NHLBI), NIH, Rockville, MD, USA.
Dima A HammoudCenter for Infectious Disease Imaging (CIDI), Radiology and Imaging Sciences, Clinical Center (CC), National Institutes of Health (NIH), 10 Center Drive, Room 1C368, Bethesda, MD, 20892, USA. hammoudd@cc.nih.gov.ORCID 0000-0001-7406-7871

Funding

Imaging of fungal infectionsZIACL090055 · CLC · CLINICAL CENTER · PI HAMMOUD, DIMA A · 2018 to 2025
$0k
Artificial Intelligence with Chest Imaging in COVID-19 and Isolation and Ventilator Devices for COVID-19ZIACL090072 · CLC · CLINICAL CENTER · PI WOOD, BRADFORD · 2020 to 2025
$0k
NIH Clinical Center Z99 CL090055
6 · The paper itself

Abstract

purposeInvasive fungal diseases, such as pulmonary aspergillosis, are common life-threatening infections in immunocompromised patients and effective treatment is often hampered by delays in timely and specific diagnosis. Fungal-specific molecular imaging ligands can provide non-invasive readouts of deep-seated fungal pathologies. In this study, the utility of antibodies and antibody fragments (Fab) targeting β-glucans in the fungal cell wall to detect Aspergillus infections was evaluated both in vitro and in preclinical mouse models.

methodsThe binding characteristics of two commercially available β-glucan antibody clones and their respective antigen-binding Fabs were tested using biolayer interferometry (BLI) assays and immunofluorescence staining. In vivo binding of the Zirconium-89 labeled antibodies/Fabs to fungal pathogens was then evaluated using PET/CT imaging in mouse models of fungal infection, bacterial infection and sterile inflammation.

resultsOne of the evaluated antibodies (HA-βG-Ab) and its Fab (HA-βG-Fab) bound to β-glucans with high affinity (K

conclusions[

Indexed as

Aspergillosisbeta-GlucansRadioisotopesZirconiumAnimalsAspergillusImmunoglobulin Fab FragmentsImmunoglobulin FragmentsMicePositron Emission Tomography Computed Tomographybeta-GlucansImmunoglobulin Fab FragmentsImmunoglobulin FragmentsRadioisotopesZirconiumZirconium-89Antibody and fragmentAspergillus infectionFungal β-glucanPET imaging

Identifiers

PMID38787397
PMCPMC11368974

What OpenQuestion holds

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Registered trials

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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.