Reticular network as the lymph nodes railroad system: T cells migration modelling by the free energy minimization technique
Bibliographic record
Abstract
Abstract One of the most important structural and functional elements of lymph nodes (LNs) is the fibroblasts reticular network (RN). Placed in vivo in the LN space, lymphocytes can move directionally, in fact, just along the RN, which acts as a central immune highway. However, despite the multiple experimental studies, mechanisms regulating the lymphocytes motion are not fully understood. In this paper, we propose a modelling study of the basic mechanisms of the lymphocyte migration along the reticulum linear part at the subcellular level. Model simulations were performed in order to test several possibilities of the stochastic T cells motion along the RN driven by chemotaxis. The main goal of the work is to answer the question, what mechanisms are required to provide persistent and non-detached T cells gliding along whole length of the fibronectin fiber, maintaining the T cell integrity, using free energy minimization technique – Cellular Potts Modeling. As a result, a wide range of possible hypotheses and various CPM Hamiltonians were tested. The spatial chemokine gradient is not a universal solution to the problem. The linear chemokine gradient (haptotaxis) of the concentration distributed along the fiber does not solve the problem. Additionally, the production of chemokines by FRC fibers and their diffusion from the fiber into the lymph are not sufficient for a satisfactory solution as well. According to the proposed model, biologically relevant description of immune cells gliding along the RN can be achieved via a combination of haptotaxis and a spatially distributed gradient without a component normal to the fiber. The spatially distributed chemokine gradient becomes a successful solution in combination with the active type of cell motion and fibronectin fibers defined as spatial corridors, which in fact is in line with various experimental evidence.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".