HIV positive men with Non‐Alcoholic Steatohepatitis (NASH) have altered hepatic fatty acid composition
Bibliographic record
Abstract
Hepatic fatty acid (FA) composition might play a role in the development of simple hepatic steatosis (SS) to NASH. Aim was to compare hepatic FA composition in HIV+ men with NASH to those with SS. METHOD: Prospective, cross‐sectional study in 19 HIV+ men (6 SS, 13 NASH). Hepatic FA composition (% of total lipid, gas chromatography) and lipid peroxidation (LPO, testkit), blood biochemistry, anthropometry, medical history and dietary intake were assessed. Mann‐Whitney‐U test was used, p<0.05 is considered significant. RESULTS: HIV‐NASH had lower arachidonic acid (AA) (mean±SEM: 1.4±0.4 vs. 3.0±0.5%) and docosahexaenoic acid (DHA) (0.37±0.86 vs. 0.86±0.10%) compared to HIV‐SS. Metabolite/precursor ratio was significantly reduced for n‐6 PUFA (AA/linoleic acid: 0.24±0.04 vs. 0.45±0.12) and a trend was seen for n‐3 ((eicosapentaenoic acid +DHA)/linolenic acid: 1.4±0.3 vs. 3.0±0.7; p=0.062). HIV‐NASH were older (47±2 vs. 40±2 y), but LPO, body mass index, diet and other variables were not different. Similar results were found when HIV‐NASH were compared to healthy controls. In addition, n6/n3 ratio was increased (HIV‐NASH: 9.1±1.4 vs. controls: 5.4±0.5). CONCLUSION: HIV+ men with NASH have reduced long‐chain PUFA and metabolite/precursor ratios in hepatic total lipids, suggesting changes in FA metabolism. Other factors do not seem to play a role. Funded by Ontario HIV Treatment Network.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".