Effects of different sources of lactoferrin on cytokine response to SARS-COV-2, respiratory syncytial virus, and rotavirus infection in vitro
Bibliographic record
Abstract
Lactoferrin (Lf) is a multifunctional iron-binding glycoprotein, involved in a wide range of bioactivities, including immunomodulatory and antiviral activities. Lf in human milk and bovine Lf added to infant formula may provide some protection against viral infections. However, functions of Lfs from different sources may differ due to varying manufacturing processes and posttranslational modifications. Here, effects of Lfs (11 commercial bovine milk Lfs, 2 recombinant Lfs, and native human/bovine milk Lf) on cytokine responses to virus infection were examined by infecting human intestinal epithelial cells (Caco-2 cells) with rotavirus (naked) or normal human bronchial epithelial cells (BEAS-2B cells) with respiratory syncytial virus (RSV, enveloped) or severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein 1. Effects of Lf on viral infection were evaluated by quantitative real-time polymerase chain reaction analysis of transcripts of cytokines/chemokines (TNF-α, IL-1β, IL-6, IL-8, IL-10, IFN-β, and CXCL10). Our results show that viral infection changes transcription of these cytokines and that Lfs significantly and variously influence immune responses to rotavirus, RSV, and SARS-CoV-2 in vitro. Thus, Lf may provide protection against virus infection by down-regulating pro-inflammatory cytokine/chemokine responses. Recombinant bovine and human Lf show similar effects as bovine milk Lfs suggesting that different posttranslational modifications do not affect the antiviral activity on cytokine response.
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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) | 0.000 | 0.000 |
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".