Caracterización molecular de serovariedades de Leptospira aislados de muestras de animales y agua en Colombia
Bibliographic record
Abstract
INTRODUCTION: Leptospirosis is a bacterial disease transmitted directly or indirectly from animals to humans that may result in severe hemorrhagic, hepatic/renal and pulmonary disease. There are 20 known Leptospira species and hundreds of serovars, some of which belong to different species. It is essential to identify pathogenic Leptospira serovars and their potential reservoirs to prepare adequate control strategies. OBJECTIVE: To characterize the Leptospira serovars isolated from rodents, dogs, pigs and water samples in Colombia. MATERIALS AND METHODS: Leptospira organisms were isolated and cultured, and pathogenic strains were identified using a polymerase chain-reaction (PCR). Leptospira DNA and Salmonella Braenderup H9812 (molecular weight standard) DNA were cleaved using NotI and subjected to pulsed-field gel electrophoresis (PFGE). The PFGE patterns were analyzed based on bacterial strain-typing criteria and Dice coefficients (DCs) between these isolates and over 200 Leptospira organisms isolated from other parts of the world. RESULTS: All of the isolates were pathogenic strains, and five were genetically characterized. The P275 (84% DC) and P282 (95% DC) pig isolates were related to the Leptospira interrogans Pomona serovar; the I15 (DC: 100%) rat isolate was identical to the Leptospira interrogans Icterohameorrhagiae or Copenhageni serovars, while the C67 (64% DC) dog and A42 (60% DC) water isolates were not related (< 73.7% DC) to any of the 200 reference serovars; the closest serovars were the Leptospira noguchii Nicaragua and Orleans serovars, respectively. CONCLUSION: This was the first molecular characterization of Colombian Leptospira spp isolates; these isolates will be used to develop a Colombian diagnostic panel.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".