Diet-Induced Obesity Does Not Alter Tigecycline Treatment Efficacy in Murine Lyme Disease
Bibliographic record
Abstract
Obese individuals more frequently suffer from infections, as a result of increased susceptibility to a number of bacterial pathogens. Furthermore, obesity can alter antibiotic treatment efficacy due to changes in drug pharmacokinetics which can result in under-dosing. However, studies on the treatment of bacterial infections in the context of obesity are scarce. To address this research gap, we assessed efficacy of antibiotic treatment in diet-induced obese mice infected with the Lyme disease pathogen, Borrelia burgdorferi. Diet-induced obese C3H/HeN mice and normal-weight controls were infected with B. burgdorferi, and treated during the acute phase of infection with two doses of tigecycline, adjusted to the weights of diet-induced obese and normal-weight mice. Antibiotic treatment efficacy was assessed one month after the treatment by cultivating bacteria from tissues, measuring severity of Lyme carditis, and quantifying bacterial DNA clearance in ten tissues. In addition, B. burgdorferi-specific IgG production was monitored throughout the experiment. Tigecycline treatment was ineffective in reducing B. burgdorferi DNA copies in brain. However, diet-induced obesity did not affect antibiotic-dependent bacterial DNA clearance in any tissues, regardless of the tigecycline dose used for treatment. Production of B. burgdorferi-specific IgGs was delayed and attenuated in mock-treated diet-induced obese mice compared to mock-treated normal-weight animals, but did not differ among experimental groups following antibiotic treatment. No carditis or cultivatable B. burgdorferi were detected in any antibiotic-treated group. In conclusion, obesity was associated with attenuated and delayed humoral immune responses to B. burgdorferi, but did not affect efficacy of antibiotic treatment.
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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".