Immunometabolism in biofilm infection: lessons from cancer
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
BACKGROUND: Biofilm is a community of bacteria embedded in an extracellular matrix, which can colonize different human cells and tissues and subvert the host immune reactions by preventing immune detection and polarizing the immune reactions towards an anti-inflammatory state, promoting the persistence of biofilm-embedded bacteria in the host. MAIN BODY OF THE MANUSCRIPT: It is now well established that the function of immune cells is ultimately mediated by cellular metabolism. The immune cells are stimulated to regulate their immune functions upon sensing danger signals. Recent studies have determined that immune cells often display distinct metabolic alterations that impair their immune responses when triggered. Such metabolic reprogramming and its physiological implications are well established in cancer situations. In bacterial infections, immuno-metabolic evaluations have primarily focused on macrophages and neutrophils in the planktonic growth mode. CONCLUSION: Based on differences in inflammatory reactions of macrophages and neutrophils in planktonic- versus biofilm-associated bacterial infections, studies must also consider the metabolic functions of immune cells against biofilm infections. The profound characterization of the metabolic and immune cell reactions could offer exciting novel targets for antibiofilm therapy.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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