In Vivo Molecular Imaging of Somatostatin Receptors in Pancreatic Islet Cells and Neuroendocrine Tumors by Miniaturized Confocal Laser-Scanning Fluorescence Microscopy
Bibliographic record
Abstract
The aim of the study was to evaluate real time in vivo molecular imaging of somatostatin receptors (sstrs) using a handheld miniaturized confocal laser scan microscope (CLM) in conjunction with fluorescein-labeled octreotate (OcF) in healthy mice and murine models of neuroendocrine tumors. For CLM a small rigid probe (diameter 7 mm) with an integrated single line laser (488 nm) was used (optical slice thickness 7 mum; lateral resolution 0.7 mum). OcF was synthesized via Fmoc solid-phase peptide synthesis and purified by HPLC showing high-affinity binding to the sstr2 (IC(50) 6.2 nmol). For in vitro evaluation, rat and human pancreatic cancer cells were used and characterized with respect to its sstr subtype expression and functional properties. For in vivo confocal imaging, healthy mouse pancreatic islet and renal tubular cells as well as immunoincompetent nude mice harboring sstr-expressing tumors were evaluated. Incubation of sstr-positive cells with OcF showed a specific time- and dose-dependent staining of sstr-positive cells. CLM showed rapid internalization and homogenous cytoplasmatic distribution. After systemic application to mice (n = 8), specific time-dependent internalization and cytoplasmatic distribution into pancreatic islet cells and tubular cells of the renal cortex was recorded. After injection in tumor-harboring nude mice (n = 8), sstr-positive cells selectively displayed a cell surface and cytoplasmatic staining. CLM-targeted biopsies detected sstr-positive tumor cells with a sensitivity of 87.5% and a specificity of 100% as correlated with ex vivo immunohistochemistry. CLM with OcF permits real-time molecular, functional, and morphological imaging of sstr-expressing cell structures, allowing the specific visualization of pancreatic islet cells and neuroendocrine tumors in vivo.
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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 | 0.000 | 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".