New insights into the systemic effects of oral lactoferrin: transcriptome profiling
Bibliographic record
Abstract
The immunomodulatory nature of lactoferrin (LF) derives from its ability to bridge innate and adaptive immunity in obtaining physiological equilibrium. LF is an attractive molecule for treatment of diseases that compromise immune homeostasis. Oral delivery is a preferable method for LF administration; however, its bioavailability is affected by protein degradation and absorption. The aim of this study was to evaluate the systemic effects of orally and intravenously (IV) administered recombinant human LF (rhLF) on blood cell transcriptome profiling. Rats were administered a single dose of rhLF by gavage or IV. The transcriptome profiles from the control and the rhLF-treated rats after 3, 6, and 24 h were analyzed using a Clariom D microarray. The results showed differentially expressed genes in response to IV as well as oral administered rhLF including coding and noncoding RNAs. Moreover, a comparison of the differentially expressed genes between oral and IV administration of LF, after 6 h, revealed that the majority (72.8%) of the genes altered in response to oral administration of rhLF were the same as for the IV treatment. The pathway profiles showed similarities in up-regulation of specific genes involved in oxidative stress and inflammatory responses for both routes of treatments. These findings provide evidence of the systemic signal transduction effects of orally administered rhLF.
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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) | 0.001 | 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".