Intestinal dysbiosis enhances gut microflora‐induced neutrophil extracellular traps (LB517)
Bibliographic record
Abstract
Antibiotic (Abx) therapy alters gut microbial composition and is associated with an increased risk of pathogen infection. However, little is known about the ability of Abx to foster the growth of pathobionts ‐ commensal microbes that, as a result of select environmental stresses, become pathogenic. Here, we measured the ability of Abx‐disturbed gut microflora to induce neutrophil extracellular traps (NET), protease‐rich ‘footprints’ left by neutrophils to capture and kill microbes. C57BL/6 mice were administered vancomycin (40 mg/kg/d) and gentamicin (3 mg/kg/d) once daily for 3d. Fecal pellets were collected prior to Abx and for 16d thereafter, and incubated in either Tryptic Soy (TS), deMan‐Rogosa‐Sharpe (MRS) or Luria‐Bertani (LB) broth (37°C, 16 h) to enrich either all microbes, Lactobacilli , or Escherichia coli , respectively. The capacity to induce NET from bone marrow‐derived neutrophils (BMDN) was quantified by measurement of extracellular DNA and visualized by fluorescence microscopy. Microbial composition was monitored by qPCR and DGGE. Baseline NET‐activating capacity was present in TS and LB cultures, and was inhibited following Abx. At 10d post‐Abx, LB cultures induced significantly more NET release, indicating overgrowth of immuno‐stimulatory microbes. In combination with compromised epithelial function, the growth of NET‐activating microbes may exacerbate intestinal disease. Grant Funding Source : This work is supported by CIHR (operating grants MOP‐89894, IOP‐92890)
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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 | 0.001 |
| 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".