Bibliographic record
Abstract
In the field of immunology, to understand the progression of an immune response against a vaccine, an infection or a tumour, the response is often followed over time. Similarly, the study of lymphocyte homeostasis requires time course experiments. Performing these studies within the same mouse is ideal to reduce the experimental variability as well as the number of mice used. Blood withdrawal allows performance of time course experiments, but it only gives information about circulating lymphocytes and provides a limited number of cells. Since lymphocytes circulating through the body and residing in the lymph nodes have different properties, it is important to examine both locations. The sequential removal of lymph nodes by surgery provides a unique opportunity to follow an immune response or immune cell expansion in the same mouse over time. Furthermore, this technique yields between 1-2x10(6) cells per lymph node which is sufficient to perform phenotypic characterization and/or functional assays. Sequential lymph node surgery or lymphadenectomy has been successfully used by us and others. Here, we describe how the brachial and inguinal lymph nodes can be removed by making a small incision in the skin of an anesthetised mouse. Since the surgery is superficial and done rapidly, the mouse recovers very quickly, heals well and does not experience excessive pain. Every second day, it is possible to harvest one or two lymph nodes allowing for time course experiments. This technique is thus suitable to study the characteristics of lymph node-residing lymphocytes over time. This approach is suitable to various experimental designs and we believe that many laboratories would benefit from performing sequential lymph node surgeries.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".