ArticleHardwareX2025
Microscope Upcycling: Transforming legacy microscopes into automated cloud-integrated imaging systems.
Article in HardwareX, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computerized microscopes improve repeatability, throughput, antisepsis, data analysis and data sharing in the biological laboratory, but these machines are cost-prohibitive in most academic environments. This is a barrier into collecting the large and consistent datasets required for machine learning analyses of microscopy data. We demonstrate hardware modifications and software to bring the features of modern computerized microscopes to decades-old legacy laboratory inverted microscopes. We demonstrate automation of X-Y positioning, focus stacking, image acquisition and image storage.
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Registered trials
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.