Dataset related to article: "Calpain-1 and calpain-2 promote breast cancer metastasis"
Bibliographic record
Abstract
This dataset accompanies the manuscript “Calpain-1 and calpain-2 promote breast cancer metastasis” and contains raw image data underlying the in vivo tumor biology and metastasis figures (Fig. 3 and Fig. 5) and the related supplementary analyses. Using MDA-MB-231 triple-negative breast cancer cells, we generated CRISPR/Cas9 knockouts of CAPN1, CAPN2, and CAPNS1 (the two catalytic and the common regulatory subunit required for calpain-1/2 heterodimers). The data contain images of lungs and of microscopic randomly selected regions of interest thereof harvested from the calpain-deficient tumor bearing Rag2-/-IL2Rgc-/- mice, fluorescently imaged in a GFP channel (Ex/Em: 488/510nm); each hyperintense nodule is a GFP-positive metastatic lesion. Abbreviations: CAPN1, C1 - calpain-1 catalytic subunit. CAPN2, C2 - calpain-2 catalytic subunit. CAPNS1, S1 - calpain small regulatory subunit 1. KO - CRISPR/Cas9 gene knockout. R - gene rescue, addback.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.126 | 0.066 |
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".