Bibliographic record
Abstract
Bacteriophages (phages) are viruses that infect bacteria with species- and strain-level specificity and are the most abundant biological entities across all known ecosystems. Within bacterial communities, such as those found in the gut microbiota, phages are implicated in regulating microbiota population dynamics and driving bacterial evolution. There has been renewed interest in phage research in the last decade, in part due to the host-specific killing capabilities of lytic phages, which offer a promising tool to counter the increasing threat of antimicrobial resistant bacteria. Furthermore, recent studies demonstrating that phages adhere to intestinal mucus suggest they may have a protective role in preventing bacterial invasion into the underlying epithelium. Importantly, like bacterial microbiomes, disrupted phageomes have been associated with worsened outcomes in diseases such as inflammatory bowel disease. Previous studies have demonstrated that phages can modulate the microbiome of animals and humans through fecal filtrate transplants, benefiting the host's health. With this recent wave of research comes the necessity to establish and standardize protocols for studying phages in the context of the gut microbiome. This protocol provides a set of procedures to study isolated T4 phages and their bacterial host, Escherichia coli, in the context of the murine gastrointestinal tract. The methods described here outline how to start from a phage lysate, administer it to mice and assess effects on bacterial host and phage levels. This protocol can be modified and applied to other phage-bacterial pairs and provides a starting point for studying host-phage dynamics in vivo.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.516 | 0.350 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".