Clinical implications of glycoproteomics for<i>Acinetobacter baumannii</i>
Bibliographic record
Abstract
The opportunistic human pathogen Acinetobacter baumannii persists in the healthcare setting because of its ability to survive exposure to various antimicrobial and sterilization agents. A. baumannii's ability to cause multiple infection types complicates diagnosis and treatment. Rapid detection of A. baumannii infections would likely improve treatment outcomes. Recently published Acinetobacter glycoproteomic data show the prevalence of O-linked glycoproteins, suggesting the possibility for an O-glycan-based detection technology. O-glycan biosynthesis is required for protein glycosylation and capsular polysaccharide production in A. baumannii. Recent publications demonstrate key roles for protein glycosylation and capsular polysaccharide in the pathogenicity of A. baumannii. Targeted antimicrobial development against O-glycan biosynthesis may produce new effective treatment options for A. baumannii infections. Here, we discuss how the data gathered through Acinetobacter glycoproteomics can be used to develop technologies for rapid diagnosis and reveal potential antimicrobial targets. In addition, we consider the efficacy of glycoconjugate vaccine development against A. baumannii.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".