Bibliographic record
Abstract
The activation of T cells is a tightly regulated process that has evolved to maximize protective immune responses to pathogens while minimizing damage to self-tissues. A delicate balance of cell-intrinsic, costimulatory, and transcriptional pathways as well as micro-environmental cues such as local cytokines controls the magnitude and nature of T-cell responses in vivo. The discovery of functional small noncoding RNAs called micro-RNAs (miRNAs) has introduced new mechanisms that contribute to the regulation of protein translation and cellular responses to stimuli. miRNAs are short (approximately 22 bp) RNA species, which bind to mRNAs and suppress translation. Due to their short length and imperfect base pairing requirements, each miRNA has the potential to regulate various pathways through the translational inhibition of multiple mRNAs. The human and mouse genomes each encode hundreds of miRNAs, and studying the function of miRNAs has led to the realization that they play important roles in diverse biological processes from development and cancer to immunity. This review focuses on the function of mir-155 in T cells and the impact of this miRNA on autoimmunity, tumor immunity, and pathogen-induced immunity.
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.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".