Molecular Biology of Nucleoside Transporters and their Distributions and Functions in the Brain
Bibliographic record
Abstract
Pyrimidine and purine nucleosides and their derivatives have critical functions and pharmacological applications in the brain. Nucleosides and nucleobases are precursors of nucleotides, which serve as the energy-rich currency of intermediary metabolism and as precursors of nucleic acids. Nucleosides (e.g., adenosine) and nucleotides are key signaling molecules that modulate brain function through interaction with cell surface receptors. Brain pathologies involving nucleosides and their metabolites range from epilepsy to neurodegenerative disorders and psychiatric conditions to cerebrovascular ischemia. Nucleoside analogs are used clinically in the treatment of brain cancer and viral infections. Nucleosides are hydrophilic molecules, and transportability across cell membranes via specialized nucleoside transporter (NT) proteins is a critical determinant of their metabolism and, for nucleoside drugs, their pharmacologic actions. In mammals, there are two types of nucleoside transport process: bidirectional equilibrative processes driven by chemical gradients, and unidirectional concentrative processes driven by sodium (and proton) electrochemical gradients. In mammals, these processes, both of which are present in brain, are mediated by members of two structurally unrelated membrane protein families (ENT and CNT, respectively). In this Chapter, we review current knowledge of cellular, physiological, pathophysiological and therapeutic aspects of ENT and CNT distribution and function in the mammalian brain, including studies with NT inhibitors and new research involving NT knockout and transgenic mice. We also describe recent progress in functional and molecular studies of ENT and CNT proteins, and summarize emerging evidence of other transporter families with demonstrated or potential roles in the transport of nucleosides and their derivatives in the brain.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".