High resolution melting analysis and detection of Leishmania resistance: the role of multi drug resistance 1 gene
Bibliographic record
Abstract
BACKGROUND: Pentavalent antimonial compounds are currently used to treat leishmaniasis and resistance to these drugs is a serious problem. Multidrug resistance protein is an efflux pump of the cell membrane that expels foreign compounds. This study designed to evaluate the mutations in the multi-drug resistance 1 (MDR1) gene, in biopsy specimens of Leishmania tropica, with high resolution melting (HRM) method. In this experimental study, genomic DNA was extracted from 130 patients with skin leishmaniasis. Then, nucleotide changes were investigated throughout the gene using HRM and sequencing methods. The samples categorized in 5 groups by differences in the melting temperature (Tm). RESULT: The nucleotide changes analysis showed that 61% of the samples of different groups that were unresponsive to drug had mutations in the MDR1 gene, which were also confirmed by the sequencing method. These mutations can be one of the factors responsible for non-responsiveness to the treatment. CONCLUSION: According to the findings, it seems that mutation in MDR1 gene could be responsible for drug resistance to pentavalent antimonial compounds. Furthermore, HRM method can be used to diagnose drug resistance in leishmaniasis. It is also recommended that further studies be done regarding the importance of drug resistance in the leishmania affected patients.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".