The long and sinuous road to phage-based therapy of Clostridioides difficile infections
Bibliographic record
Abstract
With the antibiotic crisis and the rise in antimicrobial resistance worldwide, new therapeutic alternatives are urgently needed. Phage therapy represents one of the most promising alternatives but for some pathogens, such as Clostridioides difficile, important challenges are being faced. The perspective of phage therapy to treat C. difficile infections is complicated by the fact that no strictly lytic phages have been identified so far, and current temperate phages generally have a narrow host range. C. difficile also harbors multiple antiphage mechanisms, and the bacterial genome is often a host of one or multiple prophages that can interfere with lytic phage infection. Nevertheless, due to recent advances in phage host receptor recognition and improvements in genetic tools to manipulate phage genomes, it is now conceivable to genetically engineer C. difficile phages to make them suitable for phage therapy. Other phage-based alternatives such as phage endolysins and phage tail-like bacteriocins (avidocins) are also being investigated but these approaches also have their own limitations and challenges. Last but not least, C. difficile produces spores that are resistant to phage attacks and all current antibiotics, and this complicates therapeutic interventions. This mini-review gives a brief historical overview of phage work that has been carried out in C. difficile, presents recent advances in the field, and addresses the most important challenges that are being faced, with potential solutions.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".