<i>Chlamydia trachomatis</i> Serovar Distribution in Patients with Follicular Conjunctivitis in Iran
Bibliographic record
Abstract
Objectives: Chlamydia trachomatis infects the urogenital tract and eyes.Anatomical tropism is correlated with serovars which are characterized according to the variation in the major outer membrane proteins encoded by the ompA gene.The aim of the present study was to determine the distribution of C. trachomatis serovars among patients with follicular conjunctivitis in Iran. Materials and Methods:A total of 68 conjunctival specimens from symptomatic adults were studied for the presence of C. trachomatis using polymerase chain reaction (PCR) analysis.Serovars were determined by Omp1 PCR-RFLP analysis.Results: C. trachomatis was detected in 38 (55.9%) of patients with follicular conjunctivitis, with higher C. trachomatis prevalence in the younger age groups.Twenty-six (38.2%) of these patients had a history of urinary tract infection.Four distinct serovars were identified in the conjunctiva samples using molecular genotyping.The most prevalent was serovar E, followed by G, I, and F. Conclusion: Our serovar distribution indicated that chlamydial follicular conjunctivitis usually has a genital source.Genital serovars may cause eye diseases, especially in sexually active adults.On the other hand, conjunctivitis might be the only sign of sexually transmitted infection.Therefore, genotyping C. trachomatis in ocular and genital specimens could be beneficial for acquiring more detailed epidemiological information about the etiology of the disease and monitoring treatment success.
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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 | 0.001 |
| 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".