Interrogating Cellular Communication in Cancer with Genetically Encoded Imaging Reporters
Bibliographic record
Abstract
Cells continuously communicate changes in their microenvironment, both locally and globally, with other cells in the organism. Integration of information arising from signaling networks impart continuous, time-dependent changes of cell function and phenotype. Use of genetically encoded reporters enable researchers to noninvasively monitor time-dependent changes in intercellular and intracellular signaling, which can be interrogated by macroscopic and microscopic optical imaging, nuclear medicine imaging, MRI, and even photoacoustic imaging techniques. Reporters enable noninvasive monitoring of changes in cell-to-cell proximity, transcription, translation, protein folding, protein association, protein degradation, drug action, and second messengers in real time. Because of their positive impact on preclinical research, attempts to improve the sensitivity and specificity of these reporters, and to develop new types and classes of reporters, remain an active area of investigation. A few reporters have migrated to proof-of-principle clinical demonstrations, and recent advances in genome editing technologies may enable the use of reporters in the context of genome-wide analysis and the imaging of complex genomic regulation in vivo that cannot be readily investigated through standard methodologies. The combination of genetically encoded imaging reporters with continuous improvements in other molecular biology techniques may enhance and expedite target discovery and drug development for cancer interventions and treatment. © RSNA, 2020.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".