ArticleThe Journal of pathology2020
Ex vivo perfusion of human lymph nodes
Article in The Journal of pathology, 2020. 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
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.
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.
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Who cites it
0 citing papers in PubMed, 2 citations in OpenAlex.
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In a recent study published in The Journal of Pathology, Barrow-McGee et al described a feasibility study of a real-time ex vivo perfusion model of the axillary lymph nodes (ALNs) from breast cancer patients for immune-oncological investigations. The study showed that perfused ALNs remain viable for up to 24 h, and perfusion of therapeutic antibodies confirmed the ability to reach ALN-resident cells. This work is a highly encouraging demonstration of feasibility for further research into lymphatic system function, with applications to immune function, vaccinations, and cancer. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.
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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.