Evidence map›Paper›PMID 39440326›Full record

ArticleiRadiology2024

Imaging of Pain using Positron Emission Tomography.

Yu-Peng Zhou, Lauren L Zhang, Yang Sun, Pedro Brugarolas

Abstract read
In one paragraph

Article in iRadiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Review
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

4 authors.

Yu-Peng ZhouDepartment of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.
Lauren L ZhangDepartment of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.
Yang SunDepartment of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.
Pedro BrugarolasDepartment of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts.

Funding

Development of a PET radioligand for alpha2delta-1 receptors to visualize and quantify neuropathic pain mechanisms neuropathic pain mechanismsR21NS120139 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI BRUGAROLAS, PEDRO · 2020 to 2020
$456k
Development of next generation of α2δ-1 PET tracers for neuroimagingK99EB033407 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI ZHOU, YU-PENG · 2022 to 2023
$182k
NIBIB NIH HHS K99 EB033407NINDS NIH HHS R21 NS120139
6 · The paper itself

Abstract

Positron emission tomography (PET) is a noninvasive molecular imaging technique that utilizes biologically active radiolabeled compounds to image biochemical processes. As such, PET can provide important pathophysiological information associated with pain of different etiologies. As such, the information obtained using PET often combined with MRI or CT can provide useful information for diagnosing and monitoring changes associated with pain. This review covers the most important PET tracers that have been used to image pain including tracers for fundamental biological processes such as glucose metabolism and cerebral blood flow to receptor-specific tracers such as ion channels and neurotransmitters. For tracer type, we describe the structure and radiochemical synthesis of the tracer followed by a brief summary of the available preclinical and clinical studies. By providing a summary of the PET tracers that have been employed for PET imaging of pain, this review aims to serve as a reference for preclinical, translational and clinical investigators interested in molecular imaging of pain. Finally, the review ends with an outlook of the needs and opportunities in this area.

Indexed as

molecular imagingPainPET tracerPositron emission tomography

Identifiers

PMID39440326
PMCPMC11493400

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.