Bibliographic record
Abstract
The gastrointestinal epithelium is known to undergo constant and rapid renewal resulting in millions of cells being shed into the fecal stream every day. The conventional wisdom was that these cells disintegrate upon exfoliation and will not survive the transit through the intestinal tract. In 1990, we (P.N.) made the discovery that a significant number of these cells remain intact and viable and that they can be isolated. The implications of this important discovery became apparent when we demonstrated that these cells are exclusively of colonic origin, are anatomically representative of the entire colon, and can be used for clinical investigations of disease processes. The term coprocytobiology (CCB) was coined to encompass the broad range of applications of this new technology. The somatic cell sampling and recovery (SCSR) process involves the isolation of exfoliated colonocytes from a small sample of stool ( approximately 1 g) collected and transported in a unique medium at ambient temperature, providing cells for the detection of a number of biomarkers of disease propensity. These exfoliated colonocytes express cytokeratins indicating epithelial lineage as well as colon-specific antigen. Over the years, the study of exfoliated colonocytes has provided striking new insights into the biology of colon cancer and inflammatory bowel disease, including detection of p53 gene mutations, reverse transcriptase polymerase chain reaction amplification, and identification of CD44 splice variants, neoplasia-associated specific binding of plant lectins, and expression of COX-2, the inducible form of cyclooxygenase. The functional diversity of cells isolated by SCSR is revealed by the demonstration of cell surface markers such as secretory component, IgA, and IgG on the one hand and the amplification and cloning of the human insulin receptor and the expression of the multidrug resistance gene mdr-1 on the other hand. This review portrays the immense potential of CCB as a powerful tool for investigating the pathophysiology of disease, identifying genetic variants in pharmacogenetics, assessment of mucosal immunity, and several other applications that use somatic cells.
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 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".