Transfection and Transcription of Genes in Developing Thymocytes
Bibliographic record
Abstract
Thymocyte development is characterized by the stage-specific expression of CD4 and CD8 surface molecules ( 1 ). The earliest thymic immigrants, arriving from the fetal liver or bone marrow, lack CD4 and CD8 expression (CD4 − CD8 − , double negative (DN) ( 2 ). This population can be further subdivided into four discrete subsets defined by the differential expression of CD117 (stem cell factor, SCF, receptor; c-kit) and CD25 (IL-2Rα) ( 3 ). The earliest population is identified as CD117 + CD25 − , and contains precursors for T, B, and natural killer (NK) lymphocyte lineages. Induction of CD25 expression on progenitor CD117 + thymocytes characterizes commitment to the T cell lineage ( 4 ). The CD117 + CD25 + stage is also accompanied by an increased rate of cellular proliferation ( 5 ). Loss of CD117 expression correlates with the initiation of TCR-β gene rearrangement ( 6 ). Only thymocytes that successfully rearrange their TCR-β locus expand and differentiate to the next stage; this important developmental checkpoint is known as β-selection ( 7 , 8 ). Expression of CD25 ends with the generation of a functionally rearranged TCR-β chain, which together with the pre-TCR-α (pre-Tβ) chain forms the pre-TCR complex ( 9 ). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".