Evidence map›Paper›PMID 41996406›Full record

ArticlePloS one2026

Non-lethal imaging and modeling approaches for estimating dry mass in aquatic larvae.

Daniela Granados Frias, Najva Akbari, Lauren A O'Connell, Bryan H Juarez

Abstract read
In one paragraph

Article in PloS one, 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

4 authors.

Daniela Granados FriasDepartment of Biology, Stanford University, Stanford, California, United States of America.
Najva AkbariDepartment of Biology, Stanford University, Stanford, California, United States of America.
Lauren A O'ConnellDepartment of Biology, Stanford University, Stanford, California, United States of America.ORCID https://orcid.org/0000-0002-2706-4077
Bryan H JuarezDepartment of Biology, Stanford University, Stanford, California, United States of America.ORCID https://orcid.org/0000-0002-5474-596X

Funding

Dietary tuning of infant social communicationDP2HD102042 · NICHD · STANFORD UNIVERSITY · PI O'CONNELL, LAUREN A · 2019 to 2021
$2.5M
Dopamine regulation of infant perceptual motor development and communicationR01HD110514 · NICHD · STANFORD UNIVERSITY · PI Lauren A O'Connell · 2023 to 2026
$2.3M
Multiphon imaging for understanding social brain function in tadpolesR34NS132841 · NINDS · STANFORD UNIVERSITY · PI JI, NA, O'CONNELL, LAUREN A · 2023 to 2023
$637k
NICHD NIH HHS DP2 HD102042NICHD NIH HHS R01 HD110514NINDS NIH HHS R34 NS132841
6 · The paper itself

Abstract

Body mass is crucial for scaling and comparing physiological rates. Specifically, dry body mass is important in determining an organism's metabolic rate since it excludes metabolically inactive water weight. While obtaining repeated measurements of body mass throughout an individual's lifetime is trivial, we can obtain only a single estimate of dry body mass since classical methods require end-point euthanasia. Here, we present imaging and modeling techniques for estimating individual dry body mass in African clawed frog (Xenopus laevis) tadpoles, which allow repeated sampling of the same individuals. We applied allometric principles and tested whether external anatomy would yield reliable estimates of dry body mass. Specifically, we describe a procedure to embed tadpoles in agarose media for obtaining morphological data in 3-D and then we evaluate dry mass predictions among nine cross-validated maximum likelihood and machine learning models. The best performing and most flexible model was an allometric model that used estimates of body volume to predict dry body mass. However, other models based on wet body mass or fewer input variables may also be logistically tractable. This research develops a foundation for continued research on the biological importance of dry body mass, particularly in the context of development and physiological ecology.

Indexed as

Body WeightLarvaModels, BiologicalXenopus laevisAnimalsImaging, Three-DimensionalMachine Learning

Identifiers

PMID41996406
PMCPMC13089712

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.