Phytoglycogen Nanolubricants with Extended Retention Time in Joints
Bibliographic record
Abstract
Abstract Osteoarthritis is a prevalent chronic health condition that is mostly associated with the degeneration of joint cartilage due to aging or abnormal mechanical stress in the joint. Intraarticularly injected nanoparticle‐based lubricants decrease the friction between damaged cartilage surfaces, thus preventing their further degradation; however, the effectiveness of currently used nanoparticle‐based lubricants is limited by their short retention time in the joint space. To address this challenge, cationically modified biosourced phytoglycogen nanolubricants are utilized, which electrostatically bind to the cartilage components. The conjugation of the nanoparticles with red‐emissive fluorescent carbon dots enables in vivo studies of their retention in the joint. The hytoglycogen nanoconjugates exhibit high colloidal stability in physiological conditions, provide a friction coefficient of 10−3–10−2 between the sliding surfaces under physiologically relevant pressures, strongly bind to the major cartilage surface components, and show significantly prolonged retention time in the joint in vivo, with a fourfold increase in half‐life in comparison with conventionally used hyaluronic acid injectant. These properties make these functionalized phytoglycogen nanoparticles a highly promising candidate for joint lubrication.
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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".