Engineering DNA Nanostructures to Manipulate Immune Receptor Signaling and Immune Cell Fates
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Immune cells sense, communicate, and logically integrate a multitude of environmental signals to make important cell-fate decisions and fulfill their effector functions. These processes are initiated and regulated by a diverse array of immune receptors and via their dynamic spatiotemporal organization upon ligand binding. Given the widespread relevance of the immune system to health and disease, there have been significant efforts toward understanding the biophysical principles governing immune receptor signaling and activation, as well as the development of biomaterials which exploit these principles for therapeutic immune engineering. Here, how advances in the field of DNA nanotechnology constitute a growing toolbox for further pursuit of these endeavors is discussed. Key cellular players involved in the induction of immunity against pathogens or diseased cells are first summarized. How the ability to design DNA nanostructures with custom shapes, dynamics, and with site-specific incorporation of diverse guests can be leveraged to manipulate the signaling pathways that regulate these processes is then presented. It is followed by highlighting emerging applications of DNA nanotechnology at the crossroads of immune engineering, such as in vitro reconstitution platforms, vaccines, and adjuvant delivery systems. Finally, outstanding questions that remain for further advancing immune-modulatory DNA nanodevices are outlined.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it