In Vitro and In Vivo Evaluation of Ferric‐Hyaluronate Implants for Delivery of Amikacin Sulfate to the Tarsocrural Joint of Horses
Bibliographic record
Abstract
OBJECTIVE: To assess the antimicrobial elution characteristics, toxicity, and antimicrobial activity of amikacin-impregnated ferric-hyaluronate implants (AI-FeHAI) for amikacin delivery to the tarsocrural joint of horses. STUDY DESIGN: Experimental study. SAMPLE POPULATION: AI-FeHAI implants, equine cartilage, and synovium, and horses (n=6). METHODS: In vitro study: Five AI-FeHAI were placed in saline solution with daily replacement until implant degradation. Eluent was tested for amikacin concentration and bioactivity. Synovial and cartilage explants were incubated in the presence or absence of AI-FeHAI for 72 hours and subsequently assessed for morphology, viability, and composition. Synovial explants were incubated with Staphylococcus aureus in the presence or absence of AI-FeHAI. Spent medium was cultured daily and explants were assessed for morphology and viability after 96 hours. In vivo study: AI-FeHAI were placed in 6 tarsocrural joints. Standard cytologic analysis and amikacin concentration (SFAC) were determined in synovia obtained regularly for 28 days thereafter. Similar analyses were conducted after a single intra-articular injection of amikacin 6 months later. RESULTS: In vitro study: Amikacin concentrations exceeded 16 microg/mL and inhibited S. aureus growth for 8 days. AI-FeHAI had no effect on cartilage explants. AI-FeHAI eliminated bacteria from synovial explants. In vitro study: After AI-FeHAI placement, SFAC was highest (140.78+63.81 microg/mL) at first sampling time. By 24 hours SFAC was <16 microg/mL. After intra-articular injection, SFAC was the highest (377.91 +/- 40.15 microg/mL) at first sampling time. By 48 hours SFAC was <16 microg/mL. CONCLUSIONS: A single intra-articular amikacin injection demonstrated superior pharmacokinetics than AI-FeHAI prepared as described. CLINICAL RELEVANCE: AI-FeHAI cannot be recommended for clinical use.
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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) | 0.001 | 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".