Evidence map›Paper›PMID 37815435›Full record

ArticleBiology open2023

Fully automated point spread function analysis using PyCalibrate.

Jeremy Metz, Michele Gintoli, Alexander David Corbett

Abstract read
In one paragraph

Article in Biology open, 2023. 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
–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

3 citing papers in PubMed.

  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

3 authors.

Jeremy MetzJeremy Metz, Järfälla, 17739, Sweden.
Michele GintoliFaculty of Science and Technology, University of Twente, 7500 AE Enschede, The Netherlands.ORCID 0000-0002-5254-9447
Alexander David CorbettDepartment of Physics and Astronomy, Stocker Road, Exeter, EX4 4QL, UK.ORCID 0000-0003-1645-5475

Funding

Engineering and Physical Sciences Research Council EP/R021252/1Royal Society SIF\R2\202044University of Exeter
6 · The paper itself

Abstract

Reproducibility is severely limited if instrument performance is assumed rather than measured. Within optical microscopy, instrument performance is typically measured using sub-resolution fluorescent beads. However, the process is performed infrequently as it is requires time and suitably trained staff to acquire and then process the bead images. Analysis software still requires the manual entry of imaging parameters. Human error from repeatedly typing these parameters can significantly affect the outcome of the analysis, rendering the results less reproducible. To avoid this issue, PyCalibrate has been developed to fully automate the analysis of bead images. PyCalibrate can be accessed either by executing the Python code locally or via a user-friendly web portal to further improve accessibility when moving between locations and machines. PyCalibrate interfaces with the BioFormats library to make it compatible with a wide range of proprietary image formats. In this study, PyCalibrate analysis performance is directly compared with alternative free-access analysis software PSFj, MetroloJ QC and DayBook 3 and is demonstrated to have equivalent performance but without the need for user supervision.

Indexed as

MicroscopySoftwareFluorescent DyesHumansReproducibility of ResultsFluorescent DyesAutomationFluorescence microscopyImage analysisMicroscope calibration

Identifiers

PMID37815435
PMCPMC10651089

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.