Methicillin resistant Staphylococcus aureus adhesion to human umbilical vein endothelial cells demonstrates wall shear stress dependent behaviour
Bibliographic record
Abstract
BACKGROUND: Methicillin-resistant Staphylococcus aureus (MRSA) is an increasingly prevalent pathogen capable of causing severe vascular infections. The goal of this work was to investigate the role of shear stress in early adhesion events. METHODS: Human umbilical vein endothelial cells (HUVEC) were exposed to MRSA for 15-60 minutes and shear stresses of 0-1.2 Pa in a parallel plate flow chamber system. Confocal microscopy stacks were captured and analyzed to assess the number of MRSA. Flow chamber parameters were validated using micro-particle image velocimetry (PIV) and computational fluid dynamics modelling (CFD). RESULTS: Under static conditions, MRSA adhered to, and were internalized by, more than 80% of HUVEC at 15 minutes, and almost 100% of the cells at 1 hour. At 30 minutes, there was no change in the percent HUVEC infected between static and low flow (0.24 Pa), but a 15% decrease was seen at 1.2 Pa. The average number of MRSA per HUVEC decreased 22% between static and 0.24 Pa, and 37% between 0.24 Pa and 1.2 Pa. However, when corrected for changes in bacterial concentration near the surface due to flow, bacteria per area was shown to increase at 0.24 Pa compared to static, with a subsequent decline at 1.2 Pa. CONCLUSIONS: This study demonstrates that MRSA adhesion to endothelial cells is strongly influenced by flow conditions and time, and that MSRA adhere in greater numbers to regions of low shear stress. These areas are common in arterial bifurcations, locations also susceptible to generation of atherosclerosis.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".