Synchrotron Radiation X-ray Fluorescence Elemental Mapping in Healthy versus Malignant Prostate Tissues Provides New Insights into the Glucose-Stimulated Zinc Trafficking in the Prostate As Discovered by MRI
Bibliographic record
Abstract
Prostatic zinc content is a known biomarker for discriminating normal healthy tissue from benign prostatic hyperplasia (BPH) and prostate cancer (PCa). Given that zinc content is not readily measured without a tissue biopsy, we have been exploring noninvasive imaging methods to detect these diagnostic differences using a zinc-responsive MRI contrast agent. During imaging studies in mice, we observed that a bolus of glucose stimulates secretion of zinc from the prostate of fasted mice. This discovery allowed the use of a Gd-based zinc sensor to detect differential zinc secretion in regions of healthy versus malignant prostate tissue in a transgenic adenocarcinoma mouse model of PCa. Here, we used a zinc-responsive MRI agent to detect zinc release across the prostate during development of malignancy and confirm the loss of total tissue zinc by synchrotron radiation X-ray fluorescence (μSR-XRF). Quantitative μSR-XRF results show that the lateral lobe of the mouse prostate uniquely accumulates high concentrations of zinc, 1.06 ± 0.08 mM, and that the known loss of zinc content in the prostate is only observed in the lateral lobe during development of PCa. Additionally, we confirm that lesions identified by a loss of zinc secretion indeed represent malignant neoplasia and that the relative zinc concentration in the lesion is reduced to 0.370 ± 0.001 mM. The μSR-XRF data also provided insights into the mechanism of zinc secretion by showing that glucose promotes movement of zinc pools (∼1 mM) from the glandular lumen of the lateral lobe of the mouse prostate into the stromal/smooth muscle surrounding the glands. Co-localization of zinc and gadolinium in the stromal/smooth muscle areas as detected by μSR-XRF confirm that glucose initiates secretion of zinc from intracellular compartments into the extracellular spaces of the gland where it binds to the Gd-based agent and albumin promoting MR image enhancement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".