Virulence-associated genes and antimicrobial resistance patterns in bacteria isolated from pregnant and nonpregnant women with urinary tract infections: the risk of neonatal sepsis
Bibliographic record
Abstract
Uropathogenic Escherichia coli (UPEC) is classified as the major causative agent of urinary tract infections (UTIs). UPEC virulence and antibiotic resistance can lead to complications in pregnant women and (or) newborns. Therefore, the aim of this study was to determine the etiological agents of UTIs, as well as to identify genes related to virulence factors in bacteria isolated from pregnant and nonpregnant women. A total of 4506 urine samples were collected from pregnant and nonpregnant women. Urine cultures were performed, and PCR was used to identify phylogroups and virulence-related genes. Antibiotic resistance profiles were determined. The incidence of UTIs was 6.9% (pregnant women, n = 206 and nonpregnant women, n = 57), and UPEC belonging to phylogroup A was the most prevalent. The presence of genes related to capsular protection, adhesins, iron acquisition, and serum protection in UPEC was associated with not being pregnant, while the presence of genes related to adhesins was associated with pregnancy. Bacteria isolated from nonpregnant women were more resistant to antibiotics; 36.5% were multidrug resistant, and 34.9% were extensively drug resistant. Finally, UTIs were associated with neonatal sepsis risk, particularly in pregnant women who underwent cesarean section while having a UTI caused by E. coli. In conclusion, UPEC isolated from nonpregnant women carried more virulence factors than those isolated from pregnant women, and maternal UTIs were associated with neonatal sepsis risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.001 | 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".