Manual polishing of 3D printed metals produced by laser powder bed fusion reduces biofilm formation
Bibliographic record
Abstract
Certain 3D printed metals and surface finishes may be better suited for canine patient specific orthopedic implants on the basis of minimizing potential bacterial biofilm growth. Thirty disks each of titanium alloy, stainless steel, and cobalt chromium alloy were 3D printed via laser powder bed fusion. Fifteen disks of each metal were subsequently polished. After incubation with a robust biofilm-forming methicillin-resistant Staphylococcus pseudintermedius isolate, disks were rinsed and sonicated to collect biofilm bacteria. Serial dilutions were plated on blood agar, and colony forming units were counted log (ln) transformed for analysis of variance. Interference microscopy quantified surface roughness for comparison to biofilm growth. Scanning electron microscopy on both pre- and post-sonicated disks confirmed biofilm presence and subsequent removal, and visualized surface features on cleaned disks. Significantly more bacteria grew on rough versus polished metal preparations (p < 0.0001). Titanium alloy had more bacterial biofilm growth compared to cobalt chromium alloy (p = 0.0001) and stainless steel (p < 0.0001). There were no significant growth differences between cobalt chromium alloy and stainless steel (p = 0.4737). Relationships between biofilm growth and surface roughness varied: positive with the rough preparations and negative with the smooth. Polished preparations had increased variance in surface roughness compared to rough preparations, and within disk variance predominated over between disk variance for all preparations with the exception of rough cobalt chromium alloy and rough stainless steel. Using scanning electron microscopy, bacterial biofilms tended to form in crevices. Overall, manual polishing of 3D printed surfaces significantly reduced biofilm growth, with preparation-specific relationships between surface roughness and biofilm growth. These results suggest that metallic implants produced by laser powder bed fusion should be polished. Further research will elucidate the optimal surface roughness per preparation to reduce potential biofilm formation and implant associated infection.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".