Serum IgG1 and IgG3 Antibodies to <i>Chlamydia trachomatis</i> Pgp3 and Hsp60 in Men of Subfertile Couples
Bibliographic record
Abstract
BACKGROUND: Our goal was to investigate immunoglobulin G1 (IgG1) and immunoglobulin G3 (IgG3) antibody responses to Chlamydia trachomatis proteins Pgp3 and Hsp60 in males of subfertile couples and to explore the association of these antibodies with semen parameters and male factor infertility. METHODS: Serum samples were collected from 256 male partners of subfertile couples. Serum IgG1 and IgG3 antibodies to C trachomatis Pgp3 and Hsp60 were measured using enzyme immunoassays. Semen samples were analyzed for volume, sperm concentration, and motility according to World Health Organization criteria. RESULTS: Altogether, 74 (29.8%) men were seropositive to either C trachomatis Pgp3 IgG1 or IgG3, and 67 (27.0%) to either Hsp60 IgG1 or IgG3. Chlamydia trachomatis Pgp3 IgG1 and IgG3 antibodies were associated with impaired sperm motility (asthenozoospermia) (18.6% vs 6.3%, P = .006 for Pgp3 IgG1; and 21.4% vs 8.0%, P = .03 for Pgp3 IgG3). After adjusting for smoking, alcohol risk consumption, and body mass index, the association between serum C trachomatis Pgp3 IgG1 seropositivity and asthenozoospermia remained statistically significant (odds ratio, 3.0 [95% confidence interval, 1.12-8.01]; P = .03). The presence of Hsp60 IgG1 antibody was associated with a higher teratozoospermia index (1.47 ± 0.15 vs 1.39 ± 0.16; P = .001). CONCLUSIONS: Our results suggest that prior Chlamydia trachomatis infection, as indicated by Pgp3 seropositivity, may negatively impact male fertility potential by affecting sperm motility and morphology.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".