Antibiotic Resistance and Survival in the Host
Bibliographic record
Abstract
The alarming increase in the number of multidrug-resistant microorganisms isolated in both clinical and nonclinical settings over the past decade parallels the rise in antibiotic use and exemplifies these adaptive abilities. Current antibiotic drugs target a variety of cellular processes and result in cell stasis or death through inhibition of protein, RNA or DNA synthesis, disruption of permeability barriers, or inhibition of cell wall peptidoglycan biosynthesis. Many drugs passively diffuse across the cytoplasmic membranes of both gram-positive and -negative bacteria. Bacteria possess certain intrinsic properties that provide natural resistance to some classes of antibiotics. Modifying or hydrolytic enzymes provide bacteria with a method of neutralizing certain drugs that have gained access to the cell. The nature of modern medicine dictates that, at some point, all pathogenic bacteria will encounter antimicrobials; thus, resistance will inevitably arise (possibly based on host-defense evasion mechanisms). Decreased exotoxin production may also contribute to the ability of these variants to evade host defenses and to their increased resistance to antibiotics in vivo. Biofilms are inherently resistant to both antibiotics and host defenses. Production of mature biofilms involves a complex regulatory pathway. Two-component regulatory systems provide bacteria with a way to integrate regulation of expression of virulence factors and antibiotic resistance into their general stress response pathways.
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 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".