Evidence map›Paper›PMID 42339368›Full record

ArticleProceedings of SPIE--the International Society for Optical Engineering2026

Deep learning‑based cell type prediction in lung tissue from brightfield histology using CODEX-derived labels.

Sumanth Devarasetty, Nicholas Lucarelli, Sayat Mimar, Sanjay Jain, Tarek M El-Achkar, Jeffrey Purkerson, Gloria Pryhuber, Pinaki Sarder

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings of SPIE--the International Society for Optical Engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Sumanth DevarasettyDivision of Nephrology, Hypertension, and Renal Transplantation, Department of Medicine, University of Florida, Gainesville, FL.
Nicholas LucarelliDivision of Nephrology, Hypertension, and Renal Transplantation, Department of Medicine, University of Florida, Gainesville, FL.
Sayat MimarDivision of Nephrology, Hypertension, and Renal Transplantation, Department of Medicine, University of Florida, Gainesville, FL.
Sanjay JainDepartment of Medicine, Division of Nephrology, Washington University School of Medicine, St. Louis, MO.
Tarek M El-AchkarDepartment of Medicine, Indiana University School of Medicine, Indianapolis, IN.
Jeffrey PurkersonDivision of Neonatology, Department of Pediatrics, University of Rochester Medical Center, Rochester, NY.
Gloria PryhuberDivision of Neonatology, Department of Pediatrics, University of Rochester Medical Center, Rochester, NY.
Pinaki SarderDivision of Nephrology, Hypertension, and Renal Transplantation, Department of Medicine, University of Florida, Gainesville, FL.

Funding

A Computational IMage Analysis Platform (CIMAP) for HuBMAPOT2OD033753 · OD · UNIVERSITY OF FLORIDA · PI JAIN, SANJAY, SARDER, PINAKI · 2022 to 2025
$4.9M
A Cloud Based Distributed Tool for Computational Renal PathologyR21DK128668 · NIDDK · UNIVERSITY OF FLORIDA · PI SARDER, PINAKI · 2022 to 2023
$444k
NIDDK NIH HHS R21 DK128668NIH HHS OT2 OD033753
6 · The paper itself

Abstract

Identifying cell types present in lung biopsies can provide critical information on pathological processes, tissue organization, and organ health, and are valuable in both clinical and research settings. Knowledge of cell types, their distributions, and spatial relationships can assist pathologists and researchers in diagnosis, prognosis, and mechanistic investigations. Multiplex imaging technologies such as Phenocycler

Indexed as

cell type predictionComputational pathologycomputer visionhistologyPhenocyclerproteomicsspatial omics

Identifiers

PMID42339368
PMCPMC13286254

What OpenQuestion holds

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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.