Single Input Multi Output Model of Molecular Communication via Diffusion With Spheroidal Receivers
Bibliographic record
Abstract
Spheroids are aggregates of cells that can mimic the cellular organization often found in tissues. Spheroids can be created from various cell types, including cancer cells, stem cells, and primary cells, and they serve as valuable tools in biological research. Although there are initial results on how a molecular signal can propagate between a pair of spheroids, practical experiments typically use clusters of spheroids and there isn’t a good understanding of how neighboring spheroids impact the spatiotemporal dynamics of local molecule propagation. This paper simulates a series of scenarios to gain intuition about propagation in such multi-spheroid systems for applications such as transport and drug delivery. The spheroids are modeled as porous media with a corresponding effective diffusion coefficient. System variations are considered with a higher spheroid porosity (i.e., with a higher effective diffusion coefficient) and molecule uptake by the spheroid cells (approximated as a first-order degradation reaction while molecules diffuse within the spheroid). Results show that a local crowd of spheroids will eventually slow overall propagation, such that molecules stay in the vicinity of the transmitter for longer. The results demonstrate that a single-spheroid receiver model is insufficient to accurately model propagation under these conditions.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".