A Modified Semi-Nested Multiplex Malaria PCR (SnM-PCR) for the Identification of the Five Human Plasmodium Species Occurring in Southeast Asia

Hong, Nguyen Van; Eede, Peter Van den; Overmeir, Chantal Van; Vythilingham, Indra; Rosanas-Urgell, Anna; Thanh, Pham Vinh; Thang, Ngo Duc; Hùng, Nguyễn Mạnh; Hưng, Lê Xuân; D’Alessandro, Umberto; Erhart, Annette

doi:10.4269/ajtmh.13-0027

Cited by 15 publications

(9 citation statements)

References 14 publications

(24 reference statements)

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“…Both methods fail to detect low-level parasitaemia, and are inferior in optimal species identification [ 4 , 5 ]. In principle, PCR can detect parasitaemia as low as one gene copy, and allows differentiation of all five Plasmodium species [ 6 , 7 ]. In point of care diagnosis, PCR cannot replace the traditional diagnostic methods, as the technique is relatively costly, resource-demanding and time-consuming.…”

Section: Introductionmentioning

confidence: 99%

A High Malaria Prevalence Identified by PCR among Patients with Acute Undifferentiated Fever in India

Haanshuus¹,

Chandy²,

Manoharan³

et al. 2016

PLoS ONE

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BackgroundApproximately one million malaria cases were reported in India in 2015, based on microscopy. This study aims to assess the malaria prevalence among hospitalised fever patients in India identified by PCR, and to evaluate the performance of routine diagnostic methods.MethodsDuring June 2011-December 2012, patients admitted with acute undifferentiated fever to seven secondary level community hospitals in Assam (Tezpur), Bihar (Raxaul), Chhattisgarh (Mungeli), Maharashtra (Ratnagiri), Andhra Pradesh (Anantapur) and Tamil Nadu (Oddanchatram and Ambur) were included. The malaria prevalence was assessed by polymerase chain reaction (PCR), routine microscopy, and a rapid diagnostic test (RDT) with PCR as a reference method.ResultsThe malaria prevalence by PCR was 19% (268/1412) ranging from 6% (Oddanchatram, South India) to 35% (Ratnagiri, West India). Among malaria positive patients P. falciparum single infection was detected in 46%, while 38% had P. vivax, 11% mixed infections with P. falciparum and P. vivax, and 5% P. malariae. Compared to PCR, microscopy had sensitivity of 29% and specificity of 98%, while the RDT had sensitivity of 24% and specificity of 99%.ConclusionsHigh malaria prevalence was identified by PCR in this cohort. Routine diagnostic methods had low sensitivity compared to PCR. The results suggest that malaria is underdiagnosed in rural India. However, low parasitaemia controlled by immunity may constitute a proportion of PCR positive cases, which calls for awareness of the fact that other pathogens could be responsible for the febrile disease in submicroscopic malaria.

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Section: Introductionmentioning

confidence: 99%

A High Malaria Prevalence Identified by PCR among Patients with Acute Undifferentiated Fever in India

Haanshuus¹,

Chandy²,

Manoharan³

et al. 2016

PLoS ONE

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“…Malaria can be diagnosed based on clinical symptoms, although the Center for Disease Control (CDC) always recommends confirming the diagnosis with a laboratory test (CDC, 2020). Laboratory tests can include the use of PCR to identify the specific strain of Plasmodium in a confirmed malaria case (Hong et al, 2013), antigen detection kits to detect Plasmodium-derived antigens (Polpanich et al, 2007;Khan et al, 2010), and serology tests such as ELISA to detect antibodies targeting malaria parasites (Murungi et al, 2019). These methods are expensive and often infeasible to implement in low-resource settings due to the required equipment and use of trained technicians (CDC, 2020).…”

Section: Malaria In Developing Countriesmentioning

confidence: 99%

Convolutional neural networks to automate the screening of malaria in low-resource countries

Zhao

Kolluri

Anand

et al. 2020

PeerJ

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Malaria is an infectious disease caused by Plasmodium parasites, transmitted through mosquito bites. Symptoms include fever, headache, and vomiting, and in severe cases, seizures and coma. The World Health Organization reports that there were 228 million cases and 405,000 deaths in 2018, with Africa representing 93% of total cases and 94% of total deaths. Rapid diagnosis and subsequent treatment are the most effective means to mitigate the progression into serious symptoms. However, many fatal cases have been attributed to poor access to healthcare resources for malaria screenings. In these low-resource settings, the use of light microscopy on a thin blood smear with Giemsa stain is used to examine the severity of infection, requiring tedious and manual counting by a trained technician. To address the malaria endemic in Africa and its coexisting socioeconomic constraints, we propose an automated, mobile phone-based screening process that takes advantage of already existing resources. Through the use of convolutional neural networks (CNNs), we utilize a SSD multibox object detection architecture that rapidly processes thin blood smears acquired via light microscopy to isolate images of individual red blood cells with 90.4% average precision. Then we implement a FSRCNN model that upscales 32 × 32 low-resolution images to 128 × 128 high-resolution images with a PSNR of 30.2, compared to a baseline PSNR of 24.2 through traditional bicubic interpolation. Lastly, we utilize a modified VGG16 CNN that classifies red blood cells as either infected or uninfected with an accuracy of 96.5% in a balanced class dataset. These sequential models create a streamlined screening platform, giving the healthcare provider the number of malaria-infected red blood cells in a given sample. Our deep learning platform is efficient enough to operate exclusively on low-tier smartphone hardware, eliminating the need for high-speed internet connection.

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“…Conventional PCR and real-time PCR methods have the ability to differentiate the mixed infections of Plasmodium species and to detect low levels of parasite copies (Mangold et al, 2005;Lee et al, 2002). A nested-PCR technique based on S18 small subunit ribosomal DNA (rDNA) can detect levels as low as fi ve parasite units per micro-liter of blood (Van Hong et al, 2013). Consequently, PCR is a reliable method of detection and can at least be used as a valuable confi rmatory technique (Johnston et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

Identifi cation of Plasmodium species from outdated blood samples by nested-PCR compared with microscopy diagnosis in Jazan region, Saudi Arabia

Dafalla¹,

Alsheikh²,

Abakar³

et al. 2017

Biosci. Biotech. Res. Comm

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Precise diagnosis of plasmodium species is essential for accurate and prompt malaria control and elimination. The present study was conducted to assess the effi ciency of malaria parasites' diagnosis by microscopy and nested-PCR techniques in Jazan region. Eight hundred eighty four samples were collected from hospitals and malaria control centers of the eleven Governates of Jazan region to confi rm their microscopy diagnosis for Plasmodium species. One hundred thirty eight (15.6%) samples were randomly selected from the saved positive microscopy confi rmed samples. The samples were re-diagnosed by microscopy for plasmodium species and found positive for two plasmodium species (128 for P. falciparum [92.8%] and 10 for P. vivax [7.2%]). But no other plasmodium species or mixed-infections were detected. On other hand, the diagnosis by nested-PCR indicated 119 (86.23%) and 6 (4.35%) mono infection by P. falciparum and P. vivax, respectively. In addition, the method detected also 13 (9.42%) mixed-infections with both P. falciparum and P. vivax. Considerable numbers of species mismatch and under-reporting of mixed infections had been noticed in the diagnosis of malaria by microscopy alone in Jazan region. The nested-PCR is valuable technique as a confi rmatory test and should be considered by reference laboratories in the region and other malaria endemic regions of the Kingdom of Saudi Arabia.

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A Modified Semi-Nested Multiplex Malaria PCR (SnM-PCR) for the Identification of the Five Human Plasmodium Species Occurring in Southeast Asia

Cited by 15 publications

References 14 publications

A High Malaria Prevalence Identified by PCR among Patients with Acute Undifferentiated Fever in India

A High Malaria Prevalence Identified by PCR among Patients with Acute Undifferentiated Fever in India

Convolutional neural networks to automate the screening of malaria in low-resource countries

Identifi cation of Plasmodium species from outdated blood samples by nested-PCR compared with microscopy diagnosis in Jazan region, Saudi Arabia

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