Latent <scp>TGF</scp>‐β1 is Compartmentalized Between Blood and Seminal Plasma of <scp>HIV</scp>‐Positive Men and Its Activation in Semen is Negatively Correlated with Viral Load and Immune Activation
Bibliographic record
Abstract
PROBLEM: Semen is the primary medium for sexual transmission of HIV-1 and contains high concentrations of TGF-β1, but its role in regulating HIV-mediated immune activation is unclear. METHOD OF STUDY: TGF-β1 and sCD14 were compared in blood plasma (BP) and seminal plasma (SP) from HIV-uninfected and infected, antiretroviral therapy (ART)-naive and ART-treated men and in THP-1 cells following exposure to HIV-1. The relationship between TGF-β1 and sCD14 was determined by Spearman correlation. RESULTS: Active and latent forms of TGF-β1 were compartmentalized between BP and SP. Highest active TGF-β1 levels were present in SP of ART-naïve chronic-infected men and decreased following ART treatment. Latent TGF-β1 was upregulated in BP following HIV infection, and highest levels were observed in BP of acute-infected men. Similar expression trends were observed between latent TGF-β1 and sCD14 in BP. A significant negative correlation was observed between active TGF-β1, sCD14, and semen viral load in ART-naive men. CONCLUSION: TGF-β1 is compartmentalized between blood and semen, possibly co-expressed with sCD14 by activated monocytes/macrophages in BP as a result of HIV infection. Conversion of latent TGF-β1 into its active form could contribute to regulation of viral load and immune activation in the male genital tract, but depends on the stage of infection.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".