Developing a model for cystic fibrosis sociomicrobiology based on antibiotic and environmental stress
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cystic fibrosis (CF) infections are invariably biofilm-mediated and polymicrobial, being safe to assume that a myriad of factors affects the sociomicrobiology within the CF infection site and modulate the CF community dynamics, by shaping their social activities, overall functions, virulence, ultimately affecting disease outcome. This work aimed to assess changes in the dynamics (particularly on the microbial composition) of dual-/three-species biofilms involving CF-classical (Pseudomonas aeruginosa) and unusual species (Inquilinus limosus and Dolosigranulum pigrum), according to variable oxygen conditions and antibiotic exposure. Low fluctuations in biofilm compositions were observed across distinct oxygen environments, with dual-species biofilms exhibiting similar relative proportions and P. aeruginosa and/or D. pigrum populations dominating three-species consortia. Once exposed to antibiotics, biofilms displayed high resistance profiles, and microbial compositions, distributions, and microbial interactions significantly challenged. The antibiotic/oxygen environment supported such fluctuations, which enhanced for three-species communities. In conclusion, antibiotic therapy hugely disturbed CF communities' dynamics, inducing significant compositional changes on multispecies consortia. Clearly, multiple perturbations may disturb this dynamic, giving rise to various microbiological scenarios in vivo, and affecting disease phenotype. Therefore, an appreciation of the ecological/evolutionary nature within CF communities will be useful for the optimal use of current therapies and for newer breakthroughs on CF antibiotherapy.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it