Limited TCF7L2 Expression in MS Lesions
Bibliographic record
Abstract
Multiple sclerosis is the most frequent demyelinating disease in the human CNS characterized by inflammation, demyelination, relative axonal loss and gliosis. Remyelination occurs, but is frequently absent or restricted to a small remyelinated rim at the lesion border. Impaired differentiation of oligodendroglial precursor cells is one factor contributing to limited remyelination, especially in chronic MS. TCF7L2 is an oligodendroglial transcription factor regulating myelin gene expression during developmental myelination as well as remyelination. TCF7L2 binds to co-effectors such as β-catenin or histone deacetylases and thereby activates or inhibits the transcription of downstream genes involved in oligodendroglial differentiation. To determine whether TCF7L2 can be used as a marker for differentiating or myelinating oligodendrocytes, we analyzed the expression patterns of TCF7L2 during myelination and remyelination in human and murine CNS tissue samples. Here, we demonstrate that marked expression of TCF7L2 in oligodendrocytes is restricted to a well defined time period during developmental myelination in human and mouse CNS tissue samples. In demyelinating diseases, such as multiple sclerosis, TCF7L2 is reexpressed in oligodendrocytes in a subset of MS patients, but is also present in tissue samples from patients with non-demyelinating, inflammatory diseases. Furthermore, TCF7L2 expression was also detected in astrocytes. HDAC2, a potential binding partner of TCF7L2 that promotes oligodendroglial differentiation and myelination, is expressed in the majority of oligodendrocytes in controls and MS tissue samples. In summary, our data demonstrate that the expression of TCF7L2 in oligodendrocytes is limited to a certain differentiation stage; however the expression of TCF7L2 is neither restricted to the oligodendroglial lineage nor to (re-)myelinating conditions.
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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 | 0.000 |
| 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".