An opinion on the debatable function of brain resident immune protein, T-cell receptor beta subunit in the central nervous system
Bibliographic record
Abstract
In recent years scientific research has established that the nervous and immune systems have shared molecular signaling components. Proteins native to immune cells, which are also found in the brain, have neuronal functions in the nervous system where they affect synaptic plasticity, axonal regeneration, neurogenesis, and neurotransmission. Certain native immune molecules like major histocompatibility complex I (MHC-I), paired immunoglobulin receptor B (PirB), toll-like receptor (TLR), cluster of differentiation-3 zeta (CD3ζ), CD4 co-receptor, and T-cell receptor beta (TCR-β) expression in neurons have been extensively documented. In this review, we provide our opinion and discussed the possible roles of T-cell receptor beta subunits in modulating the function of neurons in the central nervous system. Based on the previous findings of Syken and Shatz., 2003; Nishiyori et al., 2004; Rodriguez et., 1993 and Komal et., 2014; we discuss whether restrictive expression of TCR-β subunits in selected brain regions could be involved in the pathology of neurological disorders and whether their aberrant enhancement in expression may be considered as a suitable biomarker for aging or neurodegenerative diseases like Huntington's disease (HD).
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".