Histological study of stem-like cells in human colon adenocarcinoma at different stages of the disease
Bibliographic record
Abstract
The presence of stem-like cells in tumors reflects the invasive character of the disease; however, their identification is controversial. We investigated the distribution of CD133, CD44 and CD24 using histological sections and tissue microarrays (TMAs) of human colon adenocarcinoma obtained from patients with and without lymph node metastases and/or liver metastases. Immunohistochemical staining was combined with nuclear staining and evaluated quantitatively using image analysis software. Sections of normal colon mucosa, the primary tumor, lymph node, and liver also were analyzed qualitatively and compared to the quantitative method, which was more accurate. In most tissues, the expression of CD44 and CD24 was relatively low compared to CD133, with some variations. CD133 also was expressed in the normal colon mucosa and to a lesser degree in normal hepatic parenchyma. Liver metastases exhibited significantly greater CD133 staining compared to normal colon mucosa, primary tumor and lymph node metastases. Moreover, lymph node metastases obtained from patients with liver metastases expressed significantly greater CD133 staining than those obtained from patients without liver metastasis. Our data suggest that CD133 expression in lymph node metastases may be of value for prognosis of the disease.
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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 | 0.001 | 0.001 |
| 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".