Technical note: Three-dimensional imaging of rumen tissue for morphometric analysis using micro-computed tomography
Bibliographic record
Abstract
Rumen development in calves has been evaluated by measuring papillae length, width, and density using microscopy for over 50 yr. Although common in the literature, disadvantages to this method exist, such as large variations in rumen papillae size and shape, small numbers of total papillae being measured, and the time required. The objective of this study was to develop a more effective technique for assessing rumen papillae using micro-computed tomography (micro-CT) and to compare this technique with microscopy. Rumen tissue was collected from the ventral sac of 20 postweaned bull calves at 55 d of age, immediately fixed in 10% neutral buffered formalin for 48 h, and stored in 70% ethanol at 4°C before the contrast enhancement. After evaluation of contrast-enhancement protocols, it was determined that mercury chloride provided the most pronounced contrast for accurate micro-CT imaging based on relative density of the papillae. A 1-cm(2) tissue section from the ventral sac of all bull calves was tensioned on a rapid prototyped curved plastic holder and imaged at 4 5 μm resolution for 56 min using a GE Locus Explore micro-CT (General Electric, Milwaukee, WI). MicroView V2.2 software (General Electric) was used to create a 3-dimensional virtual model of the entire sample. The length and width of papillae were measured 3-dimensionally and compared with measurements of papillae under the light microscope taken from the same region. The length and width measurements using micro-CT (2.47 ± 0.12 and 0.55 ± 0.01 mm) compared with light microscope (2.96 ± 0.03 and 0.86 ± 0.01 mm) were significantly smaller. The difference may reflect a more accurate determination in the base of the rumen tissue with micro-CT or the specificity of mercury chloride to bind only to intact rumen tissue. The mean number of papillae per centimeter squared viewed using micro-CT was 128.5 ± 33.9 with a total surface area of 681.8 ± 112.4 mm(2) and volume of 156 mm(3) per sample. Micro-CT data demonstrated that surface area and volume are positively associated and that papillae length was negatively associated with papillae per centimeter squared and positively associated with total volume of tissue section. This study represents the first time that micro-CT has been being used to assess morphology of rumen tissue. Micro-CT has the potential to improve the accuracy and efficiency of rumen tissue measurements; however, more standardization of each factor involved in tissue preparation, imaging, and location of papillae measurements is required.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".