Bacteriophages: the possible solution to treat infections caused by pathogenic bacteria
Bibliographic record
Abstract
Since their discovery in 1915, bacteriophages have been used to treat bacterial infections in animals and humans because of their unique ability to infect their specific bacterial hosts without affecting other bacterial populations. The research carried out in this field throughout the 20th century, largely in Georgia, part of USSR and Poland, led to the establishment of phage therapy protocols. However, the discovery of penicillin and sulfonamide antibiotics in the Western World during the 1930s was a setback in the advancement of phage therapy. The misuse of antibiotics has reduced their efficacy in controlling pathogens and has led to an increase in the number of antibiotic-resistant bacteria. As an alternative to antibiotics, bacteriophages have become a topic of interest with the emergence of multidrug-resistant bacteria, which are a threat to public health. Recent studies have indicated that bacteriophages can be used indirectly to detect pathogenic bacteria or directly as biocontrol agents. Moreover, they can be used to develop new molecules for clinical applications, vaccine production, drug design, and in the nanomedicine field via phage display.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".