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Record W2167641210 · doi:10.1086/381449

Nucleoside-Related Mitochondrial Toxicity among HIV-Infected Patients Receiving Antiretroviral Therapy: Insights from the Evaluation of Venous Lactic Acid and Peripheral Blood Mitochondrial DNA

2004· article· en· W2167641210 on OpenAlexaff
Julio Montaner, Hélène C. F. Côté, Marianne Harris, Robert S. Hogg, Benita Yip, P. Richard Harrigan, Michael V. O’Shaughnessy

Bibliographic record

VenueClinical Infectious Diseases · 2004
Typearticle
Languageen
FieldMedicine
TopicHIV-related health complications and treatments
Canadian institutionsHIV Legal NetworkAIDS VancouverSt. Paul's HospitalProvidence Health Care
Fundersnot available
KeywordsMitochondrial toxicityMitochondrial DNAHyperlactatemiaNucleosideMedicineVenous bloodNuclear DNAToxicityDiscontinuationNucleoside analogueVirologyBiologyInternal medicineGeneticsGene

Abstract

fetched live from OpenAlex

Nucleoside analogues inhibit human DNA polymerase gamma. As a result, they can produce mitochondrial toxicity. We evaluated the possible role of random venous lactic-acid determinations as a screening tool for mitochondrial toxicity among patients receiving nucleoside therapy. More recently, we have developed an assay that can detect changes in mitochondrial DNA (mtDNA) levels in peripheral blood cells. Using this assay, we have characterized changes in mtDNA relative to nuclear DNA (nDNA) in peripheral blood cells from patients with symptomatic nucleoside-induced hyperlactatemia. Our results demonstrated that symptomatic hyperlactatemia was associated with markedly low mtDNA : nDNA ratios. A statistically significant increase in the mtDNA : nDNA ratio was observed after the discontinuation of antiretroviral therapy. Full validation of monitoring the mtDNA : nDNA ratio is currently under way.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.027
GPT teacher head0.329
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations45
Published2004
Admission routes1
Has abstractyes

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