Nuclear Factor of Activated T Cells Signalling and Viral Pathogens: A Dynamic Cross‐Talk
Bibliographic record
Abstract
The signalling pathway of the nuclear factor of activated T cells (NFAT) plays a crucial role in regulating various cellular processes such as cardiac hypertrophy, adipose differentiation, chondrocyte development, angiogenesis, inflammation, immune system activation, organogenesis, cancer cell migration, differentiation and survival. In addition, the NFAT signalling pathway acts as a key regulator of viral infections. Accordingly, it is plausible to assume that viruses have developed different mechanisms to manipulate this pathway to promote their pathogenicity. Viral pathogens can either inhibit or upregulate NFAT signalling through various mechanisms, including modulation of calcineurin activity, calcineurin/NFAT interaction, NFAT stability and translocation, NFAT-DNA-binding activity and NFAT-transcription partner interaction. Therefore, the NFAT signalling pathway can be regarded as a promising target to control viral infections. This review discusses the dynamic interactions between the NFAT signalling pathway and viral pathogens. It also addresses several drugs and agents that can target the NFAT signalling pathway at different levels to control viral infections.
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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".