On The Crosstalk of Circadian Rhythm and Th17 cells: An Integrated Biological Regulatory Pathway
Bibliographic record
Abstract
Th 17 cells play a pivotal role in cell-mediated immunity and also have implications for autoimmune disorders. The interplay between the circadian rhythm and the immune system has driven interest in developing novel therapies. Th17 cells have a robust relationship with the circadian rhythm through clock-controlled genes such as NFIL3 (E4BP4), RORA, RORB, NR3C1,and RORC. The purpose of this study is to construct a literature-curatedupdated biological regulatory network (BRN) of the molecular regulators of circadian rhythm and CD4+ Th17 cells. The integrated BRN will provide a holistic view of the differentiation process of Th17 cells from a circadian rhythm perspective, which will enhance our understanding of the interplay between the two systems. We aim to perform formal modelling and analysis of this BRN using our previously developed approaches to gain system-wide insights into various molecular expression dynamics and identify the significance of biological clocks in immunity in the future. In addition, biological pathway databases are an integral part of omics analytical workflows, and their continuous updates with the latest knowledge are crucial for gaining biological insights from such studies. Therefore, with this additional objective, we have also uploaded this pathway to WikiPathways (Database),to facilitate its use in future studies, which can be accessed via the following URL: https://classic.wikipathways.org/index.php/Pathway:WP5130. To our knowledge, this is the first study to report a literature-curated pathway of comprehensive regulatory interactions and crosstalk between Th17 cell differentiation and circadian genes.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".