Evidence map›Paper›PMID 28945305›Full record

ArticleJournal of biophotonics2018

In silico optimization of radioluminescence microscopy.

Qian Wang, Debanti Sengupta, Tae Jin Kim, Guillem Pratx

Open access · greenAbstract read
In one paragraph

Article in Journal of biophotonics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.6field-weighted citation impact, top 33% of its field
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

3 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Article
  3. 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

4 authors at 1 institution in 1 country.

Qian WangDepartment of Radiation Oncology, Stanford University, California.
Debanti SenguptaDepartment of Radiation Oncology, Stanford University, California.
Tae Jin KimDepartment of Radiation Oncology, Stanford University, California.
Guillem PratxDepartment of Radiation Oncology, Stanford University, California.
Stanford University · US

Funding

Stanford Molecular Imaging Scholars (SMIS) ProgramT32CA118681 · NCI · STANFORD UNIVERSITY · PI CRAIG S LEVIN · 2016 to 2026
$4.3M
Quantitative Imaging of Cancer Drug Resistance via Radioluminescence MicroarraysR01CA186275 · NCI · STANFORD UNIVERSITY · PI PRATX, GUILLEM · 2014 to 2018
$1.7M
Bright and Fast Sensors for Radioluminescence Microscopy of Single Living CellsR44GM110888 · NIGMS · RADIATION MONITORING DEVICES, INC. · PI MILLER, STUART R · 2016 to 2017
$1.5M
NCI NIH HHS R01 CA186275NCI NIH HHS T32 CA118681NIGMS NIH HHS R44 GM110888
6 · The paper itself

Abstract

Radioluminescence microscopy (RLM) is a high-resolution method for imaging radionuclide uptake in live cells within a fluorescence microscopy environment. Although RLM currently provides sufficient spatial resolution and sensitivity for cell imaging, it has not been systematically optimized. This study seeks to optimize the parameters of the system by computational simulation using a combination of numerical models for the system's various components: Monte-Carlo simulation for radiation transport, 3D optical point-spread function for the microscope, and stochastic photosensor model for the electron multiplying charge coupled device (EMCCD) camera. The relationship between key parameters and performance metrics relevant to image quality is examined. Results show that Lu

Indexed as

Computer SimulationBiological TransportCell SurvivalMicroscopy, FluorescenceRadioisotopesSingle-Cell AnalysisRadioisotopescomputational simulationimaging system optimizationLu2O3 thin film scintillatorradioluminescence microscopy

Identifiers

PMID28945305
PMCPMC5839938
OpenAlexW2757920410

What OpenQuestion holds

Textmetadata
LicenceTDM
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.