Thermophilic <i>Pseudomonas aeruginosa</i> strain Ch39 isolated from Chignahuapan hot springs in Puebla, Mexico
Bibliographic record
Abstract
Thermophilic bacteria have specific metabolic specializations to survive at high temperatures. This study focuses on Pseudomonas aeruginosa strain Ch39, a new isolate from mineral-rich thermal water from Chignahuapan, Puebla, Mexico. Biochemical testing, whole genome sequencing, and antimicrobial resistance profiling of strain Ch39 yielded significant detailed results. Genome sequencing yielded a high-quality 6.68 Mb assembly with a GC content of 66.13%, and annotation identified 4 356 protein-coding genes, including heat shock and antibiotic resistance genes. Comparative analysis of growth kinetics with the reference strains ATCC 27853 and PAO1 showed that Ch39 exhibited good growth and thermotolerance, with viability at 45°C, due to putative genetic adaptations such as heat shock proteins. Antibiotic resistance profiling showed variability in resistance profiles and the presence of resistance genes. In particular, strain Ch39 showed increased minimum inhibitory concentrations for some of the antibiotics tested, such as tetracycline (>1000 µg/mL), compared to the control strains. The observations made here emphasize the thermoadaptation of the strain and its suitability as a reservoir for antibiotic-resistance genes. This study expands our understanding of the thermophilic adaptation of P. aeruginosa and its ecological and clinical significance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".