<i>Acinetobacter baumannii</i> : much more than a human pathogen
Bibliographic record
Abstract
ABSTRACT Acinetobacter baumannii is a major human nosocomial pathogen. Due to this, a significant amount of knowledge has been gained about human clinical isolates over a substantial period of time. More recently, studies have begun to pay attention to non-human isolates of A. baumannii . In reviewing these studies, we highlight some major trends. First, A. baumannii has been found in a variety of sources/hosts: from diverse types of animals, to food products, to plants and even aquatic environments. Second, considering the molecular epidemiology of A. baumannii , two scenarios are possible. One implies transmission between human and non-human populations, and this has been described in several international clones (ICs): IC1, IC2, IC5, IC7, and IC8. In the other scenario, human populations are well differentiated from non-human populations, and there is no exchange between them. Third, in terms of antibiotic resistance in the non-human populations, these populations tend to have fewer antibiotic resistance genes, mostly intrinsic in nature. However, when non-clinical bacterial populations come into closer contact with humans, the antibiotic resistance profiles of the non-human bacterial population become more similar to those of clinical populations. Also, there are some instances of non-human isolates showing extensive drug resistance phenotypes. By far, the least studied aspect is the virulence potential of A. baumannii from non-human sources. A small number of studies suggest that some non-human isolates can be as virulent as the human isolates. Finally, we discuss gaps in knowledge and future research avenues when considering non-human populations of A. baumannii and their relationship with human populations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".