Influences of the depth-dependent material inhomogeneity of articular cartilage on the fluid pressurization in the human knee
Bibliographic record
Abstract
The material properties of articular cartilage are depth-dependent, i.e. they differ in the superficial, middle and deep zones. The role of this depth-dependent material inhomogeneity in the poromechanical response of the knee joint has not been investigated with patient-specific joint modeling. In the present study, the depth-dependent and site-specific material properties were incorporated in an anatomically accurate knee model that consisted of the distal femur, femoral cartilage, menisci, tibial cartilage and proximal tibia. The collagen fibers, proteoglycan matrix and fluid in articular cartilage and menisci were considered as distinct constituents. The fluid pressurization in the knee was determined with finite element analysis. The results demonstrated the influences of the depth-dependent inhomogeneity on the fluid pressurization, compressive stress, first principal stress and strain along the tissue depth. The depth-dependent inhomogeneity enhanced the fluid support to loading in the superficial zone by raising the fluid pressure and lowering the compressive effective stress at the same time. The depth-dependence also reduced the tensile stress and strain at the cartilage-bone interface. The present 3D modeling revealed a complex fluid pressurization and 3D stresses that depended on the mechanical contact and relaxation time, which could not be predicted by existing 2D models from the literature. The greatest fluid pressure was observed in the medial condyle, regardless of the depth-dependent inhomogeneity. The results indicated the roles of the tissue inhomogeneity in reducing deep tissue fractures, protecting the superficial tissue from excessive compressive stress and improving the lubrication in the joint.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".