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Record W4404110295 · doi:10.1038/s44303-024-00051-1

Impact of dietary zinc on stimulated zinc secretion MRI in the healthy and malignant mouse prostate

2024· article· en· W4404110295 on OpenAlexaff
Veronica Clavijo Jordan, André F. Martins, E. Han Dao, Kalotina Geraki, Sara Chirayil, Xiaodong Wen, Pooyan Khalighinejad, Daniel Parrott, Xiaojing Wang, Patricia Gonzalez Pagan, Neil M. Rofsky, Michael Farquharson, A. Dean Sherry

Bibliographic record

Venuenpj Imaging · 2024
Typearticle
Languageen
FieldNursing
TopicTrace Elements in Health
Canadian institutionsMcMaster University
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesNational Cancer InstituteNational Institutes of HealthCancer Prevention and Research Institute of Texas
KeywordsZincSecretionProstateProstate glandInternal medicineMedicineEndocrinologyCancer researchPathologyOncologyChemistryCancer

Abstract

fetched live from OpenAlex

Previous studies have shown that the zinc-responsive MRI probe, GdL1, can distinguish healthy versus malignant prostate tissues based upon differences in zinc content and secretion. In this study, mice were fed chow containing low, normal, or high zinc content for 3 weeks before imaging glucose stimulated zinc secretion (GSZS) by MRI. The distribution of zinc in prostate tissue in these three groups was imaged by synchrotron radiation X-ray fluorescence (SR-XRF). A zinc deficiency caused systemic and organ-level dysregulation, weight loss, and altered zinc bioavailability. Zinc efflux from the prostate increased in parallel to dietary zinc in healthy mice but not in TRAMP mice, consistent with a lowered capacity to store dietary zinc in malignant cells. This differential zinc efflux suggests that a dietary supplement of zinc prior to a GSZS study may enhance image contrast between healthy and malignant prostate tissue, thereby improving the accuracy of prostate cancer detection in man.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.194
Threshold uncertainty score0.475

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.355
Teacher spread0.333 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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