The impact of thermal cycling on Staphylococcus aureus biofilm growth on stainless steel and titanium orthopaedic plates
Bibliographic record
Abstract
BACKGROUND: Orthopaedic implant infections are difficult to eradicate because bacteria adhering to implant surfaces inhibit the ability of the immune system and antibiotics to combat these infections. Thermal cycling is a temperature modulation process that improves performance and longevity of materials through molecular structural reorientation, thereby increasing surface uniformity. Thermal cycling may change material surface properties that reduce the ability for bacteria to adhere to the surface of orthopaedic implants. This study aims to determine whether thermal cycling of orthopaedic implants can reduce bacterial growth. METHODS: colony forming units (CFU)/ml of bioluminescent Staphylococcus aureus (S. aureus)Xen29 and incubated at 37 °C for 14d. Quantity and viability of bacteria were characterized using bioluminescence imaging, live/dead staining and determination of CFUs. RESULTS: Significantly fewer CFUs grow on treated stainless steel plates compared to controls (p = 0.0088). Similar findings were seen in titanium plates (p = 0.0048) following removal of an outlier. No differences were evident in live/dead staining using confocal microscopy, or in metabolic activity determined using bioluminescence imaging (stainless steel plates: p = 0.70; titanium plates: p = 0.26). CONCLUSION: This study shows a reduction in CFUs formation on thermal cycled plates in-vitro. Further in-vivo studies are necessary to investigate the influence of thermal cycling on bacterial adhesion during bone healing. Thermal cycling has demonstrated improved wear and strength, with reductions in fatigue and load to failure. The added ability to reduce bacterial adhesions demonstrates another potential benefit of thermal cycling in orthopaedics, representing an opportunity to reduce complications following fracture fixation or arthroplasty.
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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 | 0.001 |
| 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) | 0.002 | 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 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".