Additional file 1 of Radioimmunotherapy of PANC-1 human pancreatic cancer xenografts in NOD/SCID or NRG mice with Panitumumab labeled with Auger electron emitting, 111In or β-particle emitting, 177Lu
Bibliographic record
Abstract
Additional file 1: Fig. S1. (a) Percent cell bound radioactivity at selected times after incubation of 2 × 105 PANC-1 cells with 1.2 MBq (2.5 nmoles/L) of panitumumab-DOTA-[177Lu]Lu, panitumumab-DOTA-[177Lu]Lu combined with an excess of unlabeled panitumumab, or non-specific hIgG-DOTA-[177Lu]Lu. (b) Percent of cell bound radioactivity at selected times on the cell membrane, internalized into the cytoplasm or transported to the nucleus in PANC-1 cells incubated with panitumumab-DOTA-177Lu. The time-integrated radioactivity (Bq × sec) in each subcellular compartment (Ãs) was calculated and used to estimate the absorbed doses in the nucleus as described in the Methods of the main article and shown in the Results (Table 1). Fig. S2. Radioactivity vs. time in the tumor and normal organs in NOD/SCID mice with s.c. PANC-1 xenografts injected i.v. (tail vein) with (a) panitumumab-DOTA-[111In]In or (b) panitumumab-MCP-[111In]In, or (c) in NRG mice with s.c. PANC-1 xenografts injected with panitumumab-DOTA-[177Lu]Lu. The time-integrated radioactivity (Bq × sec) in the tumor and source organs (Ãs) was obtained by integration and used to estimate the absorbed doses in the tumor and normal organs as described in the Methods of the main article and shown in the Results (Table 2).
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.874 | 0.153 |
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".