Numerical Simulation of Fluid Flow and Hydrodynamic Analysis in Commonly Used Biomedical Devices in Biofilm Studies
Why this work is in the frame
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Bibliographic record
Abstract
Biofilms are microbial communities which can form on most biotic or abiotic surfaces including glass, metal, plastic, rocks, and live tissues. These colonies begin with individual planktonic bacterial cells that attach to a surface and then start to generate a sticky Extracellular Polymeric Substance (EPS). This complex polysaccharide matrix contributes to a modification of the phenotypic status of bacteria and protects them against the detrimental changes in the microenvironment surrounding the biofilms. These phenotypic changes typically confer increased resistance to antibiotics or to the host defence system in patients. This enhanced tolerance is associated with significant problems, such as hospital acquired infections, equipment damage, and energy losses In health care, biofilms are responsible for 65% of hospital acquired infections, adding more than $1 billion annually for treatment costs in United States Hospital acquired infections are the fourth leading cause of death in the U.S. accounting for 2 million death annually Almost all types of biomedical devices and tissue engineering constructs are susceptible to biofilm formation Biofilms are particularly associated with a variety of bloodstream infections related to indwelling medical devices (e.g. urinary and cardiovascular catheters, vascular and ocular prostheses, prosthetic heart valves, cardiac pacemakers, cerebrospinal fluid shunts and other types of surgical devices). They are also responsible for chronic infections and recalcitrant diseases such as cystic fibrosis and periodontal diseases (Castelli et al.
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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 | 0.001 | 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