Evidence map›Paper›PMID 40515693›Full record

ArticleJournal of the American Chemical Society2025

Deep Learning for Fluorescence Lifetime Predictions Enables High-Throughput In Vivo Imaging.

Sofia Kapsiani, Nino F Läubli, Edward N Ward, Ana Fernandez-Villegas, Bismoy Mazumder, Clemens F Kaminski, Gabriele S Kaminski Schierle

Abstract read
In one paragraph

Article in Journal of the American Chemical Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Discovery of TDP-43 aggregation inhibitorsbioRxiv : the preprint server for biology · 2026
    Article
  5. Review
  6. Review
  7. Article
  8. 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

7 authors.

Sofia KapsianiDepartment of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, U.K.ORCID 0000-0001-6234-8246
Nino F LäubliDepartment of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, U.K.ORCID 0000-0003-2894-2385
Edward N WardDepartment of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, U.K.
Ana Fernandez-VillegasDepartment of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, U.K.
Bismoy MazumderDepartment of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, U.K.
Clemens F KaminskiDepartment of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, U.K.ORCID 0000-0002-5194-0962
Gabriele S Kaminski SchierleDepartment of Chemical Engineering and Biotechnology, University of Cambridge, Cambridge CB3 0AS, U.K.ORCID 0000-0002-1843-2202

Funding

Wellcome Trust
6 · The paper itself

Abstract

Fluorescence lifetime imaging microscopy (FLIM) is a powerful optical tool widely used in biomedical research to study changes in a sample's microenvironment. However, data collection and interpretation are often challenging, and traditional methods such as exponential fitting and phasor plot analysis require a high number of photons per pixel for reliably measuring the fluorescence lifetime of a fluorophore. To satisfy this requirement, prolonged data acquisition times are needed, which makes FLIM a low-throughput technique with limited capability for

Indexed as

Deep LearningFluorescent DyesOptical ImagingAnimalsCaenorhabditis elegansFluorescenceHigh-Throughput Screening AssaysMicroscopy, FluorescenceFluorescent Dyes

Identifiers

PMID40515693
PMCPMC12232305

What Socratic 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 Socratic graph.