Bibliographic record
Abstract
Nosocomial infections are associated with increased morbidity and mortality in intensive care units (ICUs) and remain an important target for intervention. Several studies have explored bovine lactoferrin as a potential tool for preventing infection and modulating the immune response. One clinical trial, the PREVAIL study, investigated the efficacy of lactoferrin in preventing nosocomial infections in critically ill, mechanically ventilated patients. A subset of patients in this study had blood samples collected at up to 6 different time points during their stay in the ICU, which were used to extract RNA for gene expression profiling. The resulting gene expression data were analyzed to determine the impact of lactoferrin on gene expression. Differential expression analysis was performed using both a single-gene approach and weighted gene co-expression network analysis (WGCNA). A single-gene analysis revealed that there was an increased number of differentially expressed genes in the lactoferrin group at all time points, including genes associated with the inflammatory response. Results from WGCNA revealed that groups of genes associated with innate immunity and defence response to virus were significantly differentially expressed over time in the lactoferrin group. These results contribute to our understanding of critical illness at the molecular level and provide evidence that lactoferrin has a biological effect.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".