High-resolution single-photon emission computed tomography and X-ray computed tomography imaging of Tc-99m–labeled anti-DR5 antibody in breast tumor xenografts
Bibliographic record
Abstract
A murine, apoptosis-inducing monoclonal antibody (mTRA-8) targeting human DR5 was radiolabeled with Tc-99m. The binding affinity (K(d)) and the number of DR5 receptors were measured in MD MBA-231-derived 2LMP cell lines that were "sensitive" or "resistant" to mTRA-8 killing. Single-photon emission computed tomography and X-ray computed tomography (SPECT/CT) evaluated the Tc-99m-mTRA-8 retention and distribution within xenograft tumors; biodistribution analyses confirmed the levels. Scatchard assays showed specific and high binding affinity of Tc-99m-mTRA-8 to DR5; the killing efficacy of mTRA-8 was unchanged by Tc-99m labeling. There was no significant difference between sensitive and resistant 2LMP cells for K(d) values (1.5 +/- 0.3 nmol/L = acid labile), or DR5 receptors (mean/cell = 11,000). SPECT/CT imaging analyses at 6 h after injection of Tc-99m-mTRA-8 revealed the second 1.5 mm shell from the surface of the mammary fat pad tumors (n = 5; 5,627 mm(3)) retained 12.7 +/- 1.4%ID/g, higher than the other shells, with no difference between the sensitive and resistant 2LMP tumors. Binding of Tc-99m-labeled mTRA-8 in tumor was specific; excess unlabeled mTRA-8 blocked Tc-99m-mTRA-8 retention in tumor by 45%. Retention of Tc-99m-labeled isotype antibody in tumor was consistent with the blocking study, and 30% lower. These studies show that SPECT/CT imaging provided detailed distribution information of Tc-99m-labeled mTRA-8 within breast tumor xenografts. Imaging could provide a mechanism to assess DR5 modulation when DR5 therapy is combined with chemotherapy and radiation, and thereby aid in optimizing the dosing schedule.
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 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 | 0.001 |
| 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".