Ultrasonic characterization of extra-cellular matrix in decellularized murine kidney and liver
Bibliographic record
Abstract
Three-dimensional scaffolds are essential to the field of tissue engineering. While novel synthetic structures are being developed, there is still a great interest in exploring natural scaffolds in tissue, the extra-cellular matrix (ECM). A recently developed technique known as “decellularizing” allows for the removal of cells from intact tissue while preserving the ECM structure. In order to exploit the uniqueness of the native ECM, a structure which varies significantly between organs, it first needs to be well studied. This study outlines the use of quantitative ultrasound as a non-destructive method to characterize the extracellular matrix of excised murine kidneys and livers. This allows for the study of both natural tissue scaffolds, as well as the contributions of the cellular and extra-cellular components to ultrasound backscatter. In this study, excised murine livers and kidneys were imaged with a VisualSonics Vevo2100 using nominal 40 MHz linear-array transducer, after being maintained in PBS. Subsequently the organs were decellularized, in this process, the ECM of the tissue is isolated from its inhabiting cells, leaving an ECM scaffold of the tissue. The remaining extracellular matrix structures were reimaged. Raw RF data was acquired and normalized by a reference phantom. Linear fits to the normalized power spectra allow for the estimation and comparison of the spectral slope and midband fit. After being decellularized, the organs were significantly smaller in volume with increased backscatter in the liver and overall decrease in the kidney. The heterogeneous structure of the kidney was apparent in parametric images, with the spectral slope and midband fit higher in the central medulla region. The ability to compare backscatter from the extracellular matrix with and without cells allows for a detailed analysis of the contribution of individual cells to the ultrasound backscatter and could be employed to evaluate scaffold structures and progress of growth on these scaffolds.
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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 | 0.000 |
| 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 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".