Bovine lactoferrin increases the poly(I:C)-induced antiviral response in vitro
Bibliographic record
Abstract
Bovine lactoferrin (bLF) is a naturally occurring glycoprotein with antibacterial and antiviral activities. We evaluated whether bLF can prevent viral infections in the human intestinal epithelial cell line Caco-2. To assess antiviral responses, we measured the levels of interferon (IFN) expression, IFN-stimulated gene expression, and infection with a pseudotyped virus bearing either severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein or vesicular stomatitis virus (VSV)-G protein after treatment of cells with both bLF and polyinosinic-polycytidylic acid, an analog of double-stranded RNA that mimics viral infection. Combination treatment of cells with both bLF and polyinosinic-polycytidylic acid increased mRNA and protein expression of several IFN genes ( IFNB, IFNL1, and IFNL2) and IFN-stimulated genes ( ISG15, MX1, IFITM1, and IFITM3) in Caco-2 cells. However, treatment with bLF alone did not induce an antiviral response. Furthermore, combination treatment suppressed infection of the SARS-CoV-2 pseudotyped virus more efficiently than did bLF treatment alone, even though combination treatment increased the expression of mRNA encoding ACE2. These results indicate that bLF increases the antiviral response associated with the double-stranded RNA-stimulated signaling pathway. Our results also suggest that bLF and double-stranded RNA analogs can be used to treat viral infections, including those caused by SARS-CoV-2.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".