Application of Various Techniques to Gain Insights Into the Complex Urinary Tract Microbial Communities of Renal Transplant Recipients
Bibliographic record
Abstract
Background. Urinary tract infections (UTIs) are prevalent in renal transplant (RT X ) recipients and associated with worse outcomes. Early detection by sensitive diagnostic tests and appropriate treatment strategies in this cohort is therefore crucial, but evidence has shown that current methods may miss genuine infections. Research has shed light on the urinary tract microbial ecology of healthy individuals and nontransplant patients with UTI, but information on the RTx cohort is scant. We conducted a cross-sectional study to (i) compare the gold standard diagnostic culture with alternative techniques and (ii) characterize RTx patient urinary microbial communities. Methods. Midstream urine specimens were collected from 51 RTx patients attending a renal transplant clinic and 27 asymptomatic controls. Urinary microscopy, dipstick, and routine culture were performed. To improve sensitivity of microbial detection, we cultured the urinary cell sediment and performed 16S rRNA gene sequencing on urine. Uroplakin-positive urothelial cells shed in urine were analyzed by immunofluorescence staining for any bacterial association. Results. Sediment culture and 16S rRNA sequencing confirmed detection deficiencies of diagnostic culture and revealed differences in the urobiomes of RTx patients and controls. Specifically, Gardnerella , Escherichia , and Lactobacillus were most abundant in patients, whereas Lactobacillus , Streptococcus , and Gardnerella were most abundant in controls. The application of both culture and sequencing provided a more nuanced view of the urinary microbial communities. Conclusions. This study provides insight into the potential problems of diagnostic culture within RTx patients and sheds light on their urinary microbial inhabitants. Further work may identify key microbial signatures and facilitate the development of better tools for UTI detection within this cohort, which could allow targeted intervention before an infection leads to serious consequences. http://links.lww.com/TXD/A479
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".