Emergence of taurine as a therapeutic agent for neurological disorders
Bibliographic record
Abstract
Taurine is a sulfur-containing, semi-essential amino acid that occurs naturally in the body. It alternates between inflammation and oxidative stress-mediated injury in various disease models. As part of its limiting functions, taurine also modulates endoplasmic reticulum stress, Ca2+ homeostasis, and neuronal activity at the molecular level. Taurine effectively protects against a number of neurological disorders, including stroke, epilepsy, cerebral ischemia, memory dysfunction, and spinal cord injury. Although various therapies are available, effective management of these disorders remains a global challenge. Approximately 30 million people are affected worldwide. The design of taurine formation could lead to potential drugs/supplements for the health maintenance and treatment of central nervous system disorders. The general neuroprotective effects of taurine and the various possible underlying mechanisms are discussed in this review. This article is a good resource for understanding the general effects of taurine on various diseases. Given the strong evidence for the neuropharmacological efficacy of taurine in various experimental paradigms, it is concluded that this molecule should be considered and further investigated as a potential candidate for neurotherapeutics, with emphasis on mechanism and clinical studies to determine efficacy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".