Galectin-3 Expression is Ubiquitous in Tumors of the Sellar Region, Nervous System, and Mimics
Bibliographic record
Abstract
Galectin-3 expression has been reported in spindle cell oncocytoma, certain pituitary adenoma subtypes, astrocytomas, oligodendrogliomas, and meningiomas. We evaluated galectin-3 protein expression by immunohistochemistry in 201 cases of a variety of nervous system and sellar tumors, as well as mRNA expression by reverse transcription-polymerase chain reaction in formalin-fixed paraffin-embedded tissue in a subset (20 cases). Immunohistochemical results were evaluated in a semiquantitative fashion on a 4-tiered scale (0 to 3). Strong (3+) immunoreactivity was seen in most of the cases (61%), followed by 2+(22%), and 1+(13%) staining. Only 4% of the lesions studied were immunonegative. Galectin-3 mRNA was present in 15 of the 18 cases (83%) in which reverse transcription-polymerase chain reaction was successful. Significant differences in protein expression were noted in the following 2 settings: specific meningioma subtypes (P=0.004, Fisher exact test) wherein clear cell meningioma demonstrated weak protein expression when compared with other meningioma variants. No significant difference was noted with respect to World Health Organization grade. Galectin-3 was also strongly expressed in benign nerve sheath tumors but only moderately expressed in malignant peripheral nerve sheath tumors (P=0.0009, Fisher exact test). Although galectin-3 positivity is a key feature of the immunophenotype of spindle cell oncocytoma, its consistent expression in other morphologically similar tumors (meningioma, pituicytoma, nerve sheath tumors, granular cell tumor, metastases) makes it of little use in the differential diagnosis of sellar region tumors, a setting in which it should be discouraged. Diagnostic uses of this marker may be limited to specific settings, including some meningioma subtypes and nerve sheath tumors.
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".