Use of MIB-1 in the Assessment of Esophageal Biopsy Specimens from Patients with Gastroesophageal Reflux Disease in Well- and Poorly Oriented Areas
Bibliographic record
Abstract
MIB-1, a proliferation marker may be useful in the assessment of esophageal biopsy specimens for gastroesophageal reflux disease (GERD). Forty-five hematoxylin and eosin-stained esophageal biopsy specimens were histologically assessed for basal zone height, papillary length, and inflammatory cell infiltrate and classified as 10 normal and 35 esophagitis. The percentage of MIB-1-positive area (MIB-1% area) was measured on immunostained sections using image analysis (CAS 200) in the basal half of well-oriented areas and adjacent to five cross-sectioned papillae (c-pap) in poorly oriented areas. The cell layer of the MIB-1-positive cell furthest from the basal layer of the c-pap was also noted. MIB-1% area was significantly greater in both well- and poorly oriented areas of esophagitis biopsy specimens compared with normal biopsy specimens. MIB-1 positivity in the basal half and c-pap were correlated (r = 0.43, p = 0.017). MIB-1 expression correlated with basal zone height and eosinophil infiltrate (r = 0.61, p < 0.001; r = 0.32, p = 0.03, respectively). The cell layer with positive cells furthest from c-pap in normal and esophagitis biopsy specimens was two and six layers, respectively. Using 31% as a threshold to detect abnormal findings, the MIB-1 sensitivity/specificity and positive predictive value in the basal half and c-pap were 86, 70, 91% and 80, 80, 94%, respectively. In summary, MIB-1 staining correlates with basal zone hyperplasia and eosinophil infiltrate seen in GERD. MIB-1 staining can be assessed both in well- and poorly oriented areas as MIB-1% areas. Alternatively simply finding MIB-1 positive cells more than three cell layers from the basal layer is abnormal and consistent with GERD.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".