Current overview on viroid–host interactions
Bibliographic record
Abstract
Viroids are one of the most enigmatic highly structured, circular, single-stranded RNA phytopathogens. Although they are not known to code for any peptide, viroids induce visible symptoms in susceptible host plants that resemble those associated with many plant viruses. It is known that viroids induce disease symptoms by direct interaction with host factors; however, the precise mechanism by which this occurs remains poorly understood. Studies on the host's responses to viroid infection, host susceptibility and nonhost resistance have been underway for several years, but much remains to be done in order to fully understand the complex nature of viroid-host interactions. Recent progress using molecular biology techniques combined with computational algorithms, in particular evidence of the role of viroid-derived small RNAs in the RNA silencing pathways of a disease network, has widened the knowledge of viroid pathogenicity. The complexity of viroid-host interactions has been revealed in the past decades to include, but not be limited to, the involvement of host factors, viroid structural complexity, and viroid-induced ribosomal stress, which is further boosted by the discovery of long noncoding RNAs (lncRNAs). In this review, the current understanding of the viroid-host interaction has been summarized with the goal of simplifying the complexity of viroid biology for future research. This article is categorized under: RNA in Disease and Development > RNA in Disease.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".