Real Time PCR-based diagnosis of human visceral leishmaniasis using urine samples
Copyright: © 2022 Rahim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited..
Diagnosis of visceral leishmaniasis (VL) through the detection of its causative agents namely Leishmania donovani and L. infantum is traditionally based on immunochromatographic tests, microscopy of bone marrow, spleen aspirates, liver or lymph node and differential diagnosis. While the first process has low specificity, the later one carries the risk of fatal hemorrhage. Over the last decade, multiple Polymerase Chain Reaction (PCR) based diagnosis has been developed using blood and urine sample with a varying degree of sensitivity and specificity, an issue worth improving for precision diagnosis. Earlier, we reported a PCR-based diagnosis of L. donovani in peripheral blood using a novel set of PCR primers with absolute specificity. Using the same set of primers and PCR conditions, here we describe diagnosis of L. donovani from urine, for a non-invasive, rapid and safe diagnosis. Diagnosis of VL was carried out using urine samples collected from clinically diagnosed VL patients (n = 23) of Bangladesh in Real Time PCR. Test results were validated by comparing blood samples from the same set of patients. Sensitivity and specificity of this diagnosis was analyzed using retrospective bone marrow samples, collected earlier from confirmed VL patients (n = 19). The method showed 100% sensitivity in detecting L. donovani in urine and corresponding blood and retrospective bone marrow samples, as well as 100% specificity in control groups. A Real Time PCR-based molecular detection system using urine sample is hereafter presented what could be a, non-invasive approach for VL detection with precision and perfection.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:2 |
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| Enthalten in: |
PLOS global public health - 2(2022), 12 vom: 20., Seite e0000834 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Rahim, Samiur [VerfasserIn] |
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| Anmerkungen: |
Date Revised 27.03.2023 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1371/journal.pgph.0000834 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM354678965 |
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| 520 | |a Diagnosis of visceral leishmaniasis (VL) through the detection of its causative agents namely Leishmania donovani and L. infantum is traditionally based on immunochromatographic tests, microscopy of bone marrow, spleen aspirates, liver or lymph node and differential diagnosis. While the first process has low specificity, the later one carries the risk of fatal hemorrhage. Over the last decade, multiple Polymerase Chain Reaction (PCR) based diagnosis has been developed using blood and urine sample with a varying degree of sensitivity and specificity, an issue worth improving for precision diagnosis. Earlier, we reported a PCR-based diagnosis of L. donovani in peripheral blood using a novel set of PCR primers with absolute specificity. Using the same set of primers and PCR conditions, here we describe diagnosis of L. donovani from urine, for a non-invasive, rapid and safe diagnosis. Diagnosis of VL was carried out using urine samples collected from clinically diagnosed VL patients (n = 23) of Bangladesh in Real Time PCR. Test results were validated by comparing blood samples from the same set of patients. Sensitivity and specificity of this diagnosis was analyzed using retrospective bone marrow samples, collected earlier from confirmed VL patients (n = 19). The method showed 100% sensitivity in detecting L. donovani in urine and corresponding blood and retrospective bone marrow samples, as well as 100% specificity in control groups. A Real Time PCR-based molecular detection system using urine sample is hereafter presented what could be a, non-invasive approach for VL detection with precision and perfection | ||
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| 700 | 1 | |a Rahman, Mohammad Tariqur |e verfasserin |4 aut | |
| 700 | 1 | |a Karim, Muhammad Manjurul |e verfasserin |4 aut | |
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