Organisms associated with bacterial vaginosis in Nigerian women as determined by PCR-DGGE and 16S rRNA gene sequence.
Bibliographic record
Abstract
BACKGROUND: Bacterial vaginosis (BV) is a condition with diverse etiology. This condition predisposes women to increased susceptibility to sexually transmitted diseases, including human immunodeficiency virus (HIV) infections and preterm birth. The diagnostic methods currently adopted in the evaluation of patient samples for BV are arguably Amsel criteria, and Nugent score that require microscopy and expert interpretation. These two methods are still subjective. OBJECTIVE: The objective of this study was to determine the organisms present in the vagina of 34 HIV negative Nigerian women diagnosed as having bacterial vaginosis by using molecular techniques. METHODS: The vaginal samples were subjected to DNA extraction, and amplified with eubacterial primers via PCR. The PCR products were separated using denaturing gradient gel electrophoresis (DGGE). Bands were excised, re-amplified, purified and sequenced. Sequence identification was performed using the BLAST algorithm and Genbank data base. RESULTS: Mycoplasma hominis (12/34; 35%) was the most common isolate and 9 (26%) contained one of two clones of an unusual Rainbow Trout intestinal bacterium, while unculturable Streptococcus sp, and other bacteria made up the remaining isolates. CONCLUSIONS: The findings indicate further diversity in the etiological agents associated with BV, and raise the question as to whether diagnosis and management of this condition needs to be re-evaluated in countries like Nigeria. There is some controversy over the clinical importance of BV, as it was once regarded as a disease caused by Gardnerella and presenting as an odourous discharge condition, but is now diagnosed without necessarily the presence of these organisms or signs. With the incidence of BV aligned to an increased risk of HIV in a country ravaged by this virus, the effective eradication of BV can only be achieved if appropriate therapies are delivered.
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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 | 0.000 | 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) | 0.001 | 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".