Longitudinal effects of oral administration of antimicrobial drugs on fecal microbiota of horses
Bibliographic record
Abstract
BACKGROUND: Antimicrobial drug-associated diarrhea (AAD) is the most common adverse effect in horses receiving antimicrobials. Little information on how oral administration of antimicrobials alters intestinal microbiota in horses is available. OBJECTIVE: Investigate changes of the fecal microbiota in response to oral administration of antimicrobials. ANIMALS: Twenty healthy horses. METHODS: Prospective, longitudinal study. Horses were randomly assigned to 4 groups comprising 4 horses each: group 1 (metronidazole); group 2 (erythromycin); group 3 (doxycycline); group 4 (sulfadiazine/trimethoprim, SMZ-TMP); and group 5 (control). Antimicrobials were administered for 5 days. Fecal samples were obtained before (day 0) and at 1, 2, 3, 4, 5, 6, and 30 days of the study period. Fecal microbiota was characterized by high throughput sequencing of the V4 region of the 16S rRNA. RESULTS: Horses remained healthy throughout the study. Richness and diversity in doxycycline, erythromycin, and metronidazole, but not SMZ-TMP groups, was significantly lower (P < .05) at multiple time points after administration of antimicrobials compared with samples from day 0. Main changes in the microbiota were observed during the time of antimicrobial administration (day 2-5; weighted and unweighted UniFrac PERMANOVA P < .05). Administration of erythromycin, doxycycline and, to a lesser extent, metronidazole produced a pronounced alteration in the microbiota compared with day 0 samples by decreasing the abundance of Treponema, Fibrobacter, and Lachnospiraceae and increasing Fusobacterium and Escherichia-Shigella. CONCLUSIONS AND CLINICAL IMPORTANCE: Oral administration of antimicrobials alters the intestinal microbiota of healthy horses resembling horses with dysbiosis, potentially resulting in intestinal inflammation and predisposition to diarrhea.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".