Human rhinovirus recognition in non-immune cells is mediated by<i>Toll</i>-like receptors and MDA-5, which trigger a synergetic pro-inflammatory immune response
Bibliographic record
Abstract
The early detection of invading viruses by the host depends on their identification by pathogen sensors. These include Toll-like receptors (TLRs) as well as cytoplasmic RNA helicases such as retinoic acid inducible protein I (RIG-I) and melanoma differentiation associated gene 5 (MDA-5). These pathogen sensors recognize specific molecular patterns found in viruses and trigger inflammatory and antiviral responses that result in the eradication of invading pathogens. In this study we investigated the specific recognition of Human rhinovirus 6 (HRV6) the common cold pathogen by the innate immune response in lung epithelial cells. Our experiments established that in the first stages on infection the TLRs play a crucial role in HRV recognition and that different constituents of HRV6 are recognized by different TLRs, while upon viral replication and generation of dsRNA the type I IFN inflammatory response is mediated by MDA-5. The HRV6 capsid is recognized via TLR2, whereas upon HRV6 ssRNA internalization the virus genome is recognized by TLR7 and TLR8. Upon generation of dsRNA the type I IFN response is mediated by MDA-5. The combined recognition by different TLRs and MDA5 and their upregulation concurs with the huge inflammatory response seen in the common cold caused by human rhinoviruses.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".