Epidemiological study of infections causing Pseudomonas aeruginosa strains and their bacteriophages for therapeutic approach.
Bibliographic record
Abstract
L'utilisation de virus de bactéries ou bactériophages pourrait être un complément efficace à l’antibiothérapie. Mon travail a porté sur la caractérisation de bactériophages dirigés contre l’espèce Pseudomonas aeruginosa, pathogène opportuniste responsable d'infections des voies respiratoires des patients atteints de mucoviscidose.J'ai tout d'abord déterminé la sensibilité des souches mucoviscidosiques au Pyophage (un cocktail de phages thérapeutiques Géorgien) et identifié six phages lytiques de quatre genres différents. Environ 15% des souches sont résistantes au Pyophage. Ensuite, en utilisant les souches cliniques multi-résistantes aux phages comme bactérie d’enrichissement, 32 phages ont été obtenus à partir des eaux usées de France et Côte d’Ivoire. Tous les phages analysés sont caudés et distribués au sein de dix genres parmi lesquels six exclusivement lytiques. J'ai identifié des souches bactériennes qui demeurent insensibles à tous les phages. J'ai montré que le système CRISPRs-Cas n'est pas associé à la résistance des souches aux phages lytiques.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".