Distinctive function of Tetraspanins: Implication in viral infections
Bibliographic record
Abstract
Harboring four transmembrane domains in their structural hallmark, Tetraspanins (Tspans) are a family of glycoproteins with pivotal functions in a variety of biological and cellular processes. Through interacting laterally with each other or specific membrane proteins, Tspans organize tetraspanin-enriched microdomains (TEMs), modulating cellular signaling, adhesion, fusion, and proliferation. An abundance of evidence has identified the multiple functions in the progression of cancer as well as the underlying molecular mechanisms. Recently, plenty of studies have focused on the utilities of Tspans by pathogens for infection, especially the infection of viruses. The expression of Tspans correlates with the phase of viral infection, the type of virus, and targeted therapies. In particular, perturbations of Tspans in host cells can affect viral attachment, intracellular trafficking, translation, virus assembly, and release. In this review, we summarize and provide a historical overview of the discovery and characterization of various kinds of virus infection and highlight their diversity and complexity, along with the virus life cycle. Furthermore, we examined the current understanding of how various Tspans are involved in the regulatory mechanisms underlying viral infection. This review aims to offer a comprehensive understanding of the targeting of Tspans for therapeutic intervention in infections caused by diverse pathogens.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".