MétaCan
Menu
← Back to cohort
Record W4411287882 · doi:10.1101/2025.06.09.658763

Thermally controlled state switches for macrophage immunotherapy

2025· preprint· en· W4411287882 on OpenAlexfundno aff
Ann Liu, Mohamad H. Abedi, Cameron A. B. Smith, Di Wu, Mikhail G. Shapiro

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2025
Typepreprint
Languageen
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCalifornia Institute of TechnologySontag FoundationBeckman Institute, California Institute of TechnologyNational Institutes of HealthNational Science Foundation
KeywordsImmunotherapyImmune systemCancer immunotherapyIn vivoCytokineCell biologyMacrophageBiologyCancer researchImmunologyIn vitroGenetics

Abstract

fetched live from OpenAlex

ABSTRACT Advances in cellular immunotherapy promise new treatments for conditions such as cancer, autoimmune disease, and heart disease. While engineered cells have the ability to recognize clinically relevant signals, traffic to disease sites and interface with the host immune system, their activity must be tightly controlled to minimize undesirable effects in healthy tissues. One approach to obtaining specificity is to activate the cells spatially using externally applied energy, such as ultrasound-delivered heating. To facilitate such control, we designed and characterized a genetic circuit that enables stable transcriptional activation of macrophages after a brief thermal stimulus, resulting in the expression of reporters or secretion of the cytokine IL-12. We demonstrate that in vivo activation of a mouse macrophage cell line containing this bioswitch results in spatially localized gene expression for at least 14 days after ultrasound heating. This thermal bioswitch provides a precise control element for cell-therapeutic agents.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.019
GPT teacher head0.277
Teacher spread0.259 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicCAR-T cell therapy research→French-language works237,207→