Off-grid field-deployable molecular diagnostic platform for malaria surveillance

Background

Malaria, a major global health concern, continues to cause substantial morbidity and mortality, particularly in tropical regions. Traditional malaria diagnostic methods such as microscopy and quantitative polymerase chain reaction (qPCR) are effective but face challenges in field settings because of their requirement for laboratories with specialized equipment and trained personnel. This study presents the development and validation of a portable, cost-effective, field-deployable real-time qPCR platform for detecting Plasmodium species.

Methods

Field-compatible DNA isolation was performed using DNAzol, and TaqMan probes targeting 18S ribosomal RNA (rRNA) were employed to detect five Plasmodium species—P. falciparum, P. vivax, P. malariae, P. ovale, and P. knowlesi—using the bCUBE qPCR platform. In vitro-cultured P. falciparum and experimentally infected Anopheles gambiae were used to quantify P. falciparum infections, with infection prevalence compared to microscopy. The bCUBE qPCR system was also evaluated under field conditions to detect P. falciparum infections in field-collected An. gambiae mosquitoes.

Results

The bCUBE qPCR demonstrated a strong linear correlation (R2 = 0.993) with a standard laboratory qPCR machine for detecting P. falciparum infections. It successfully detected as few as 0.5 parasites/µl of blood, one oocyst in mosquito guts, and 5–10 sporozoites in salivary glands. It was also capable of discriminating between P. falciparum, P. vivax, P. malariae, P. ovale, and P. knowlesi. Field evaluations in Cameroon confirmed its accuracy in identifying P. falciparum in mosquito samples, with same-day results. The capability of the bCUBE qPCR system to detect infections in both individual and pooled mosquito surveillance further highlights its potential for in-field large-scale malaria monitoring surveillance.

Conclusions

The bCUBE qPCR system offers a portable, sensitive, and scalable solution for malaria diagnostics, enabling real-time surveillance in resource-limited settings. Its ability to provide rapid, on-site results reduces the need for centralized laboratory testing, facilitating timely decision-making in malaria control programs.