Ubisol-AquaTM: Coenzyme Q10 Prevents Antiretroviral Toxic Neuropathy in an In Vitro Model
Bibliographic record
Abstract
BACKGROUND: Peripheral neuropathy is the dose-limiting toxicity of stavudine and didanosine (nucleoside analogs used in HIV treatment) and is attributed to mitochondrial toxicity from these drugs. Acetyl L-carnitine (ALC) and co-enzyme Q(10) are proposed as neuropathy treatments, but evidence to support these is limited. METHODS: We examined ALC and a water-soluble formulation of co-enzyme Q(10) (H(Q)O) for the prevention of d4T and ddI neurotoxicity using cultured fetal rat DRG as an in vitro model. RESULTS: DdI (33microM) and d4T (50microM) caused clear toxicity (impaired neurite growth) by day 8 of DRG culture. H(Q)O at concentrations 1-100microM completely prevented the toxicity of 33microM ddI in vitro and ALC at concentrations 1-100 microM substantially (but incompletely) prevented ddI toxicity in this model. In contrast, ALC was ineffective at all concentrations tested for preventing the toxicity of 50microM d4T. H(Q)O showed dose-dependent efficacy for preventing d4T toxicity. H(Q)O (1microM) partially prevented d4T toxicity while 10 and 100microM H(Q)O completely prevented d4T toxicity in this model. CONCLUSIONS: We find H(Q)O is superior to ALC for preventing the neurotoxicity of d4T (the HIV treatment most associated with neuropathy) and ddI in vitro. Further study is needed to clarify any clinical role for co-enzyme Q(10) co-administration with d4T and ddI and to assess whether this compound may have a role in treating established cases of neuropathy.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".