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New diagnostic innovations for tuberculosis (TB), including point-of-care solutions, are critical to reach the goals of the End TB Strategy. However, despite decades of research, numerous reports on new biomarker candidates, and significant investment, no well-performing, simple and rapid TB diagnostic test is yet available on the market, and the search for accurate, non-DNA biomarkers remains a priority. To help overcome this 'biomarker pipeline problem', FIND and partners are working on the development of a well-curated and user-friendly TB biomarker database. The web-based database will enable the dynamic tracking of evidence surrounding biomarker candidates in relation to target product profiles (TPPs) for needed TB diagnostics. It will be able to accommodate raw datasets and facilitate the verification of promising biomarker candidates and the identification of novel biomarker combinations. As such, the database will simplify data and knowledge sharing, empower collaboration, help in the coordination of efforts and allocation of resources, streamline the verification and validation of biomarker candidates, and ultimately lead to an accelerated translation into clinically useful tools.

This work summarizes the available data on the performance of rapid diagnostic tests (RDTs) for the detection of monoinfections due to Plasmodium species P. knowlesi, P. malariae, and P. ovale and indicates low performance of RDTs to detect these infections.

Background Microscopic examination is commonly used for malaria diagnosis in the field. However, the lack of well-trained microscopists in malaria-endemic areas impacted the most by the disease is a severe problem. Besides, the examination process is time-consuming and prone to human error. Automated diagnostic systems based on machine learning offer great potential to overcome these problems. This study aims to evaluate Malaria Screener, a smartphone-based application for malaria diagnosis. Methods A total of 190 patients were recruited at two sites in rural areas near Khartoum, Sudan. The Malaria Screener mobile application was deployed to screen Giemsa-stained blood smears. Both expert microscopy and nested PCR were performed to use as reference standards. First, Malaria Screener was evaluated using the two reference standards. Then, during post-study experiments, the evaluation was repeated for a newly developed algorithm, PlasmodiumVF-Net. Results Malaria Screener reached 74.1% (95% CI 63.5–83.0) accuracy in detecting Plasmodium falciparum malaria using expert microscopy as the reference after a threshold calibration. It reached 71.8% (95% CI 61.0–81.0) accuracy when compared with PCR. The achieved accuracies meet the WHO Level 3 requirement for parasite detection. The processing time for each smear varies from 5 to 15 min, depending on the concentration of white blood cells (WBCs). In the post-study experiment, Malaria Screener reached 91.8% (95% CI 83.8–96.6) accuracy when patient-level results were calculated with a different method. This accuracy meets the WHO Level 1 requirement for parasite detection. In addition, PlasmodiumVF-Net, a newly developed algorithm, reached 83.1% (95% CI 77.0–88.1) accuracy when compared with expert microscopy and 81.0% (95% CI 74.6–86.3) accuracy when compared with PCR, reaching the WHO Level 2 requirement for detecting both Plasmodium falciparum and Plasmodium vivax malaria, without using the testing sites data for training or calibration. Results reported for both Malaria Screener and PlasmodiumVF-Net used thick smears for diagnosis. In this paper, both systems were not assessed in species identification and parasite counting, which are still under development. Conclusion Malaria Screener showed the potential to be deployed in resource-limited areas to facilitate routine malaria screening. It is the first smartphone-based system for malaria diagnosis evaluated on the patient-level in a natural field environment. Thus, the results in the field reported here can serve as a reference for future studies.

BackgroundPlasmodium knowlesi causes zoonotic malaria across Southeast Asia. First-line diagnostic microscopy cannot reliably differentiate P. knowlesi from other human malaria species. Rapid diagnostic tests (RDTs) designed for P. falciparum and P. vivax are used routinely in P. knowlesi co-endemic areas despite potential cross-reactivity for species-specific antibody targets.MethodsTen RDTs were evaluated: nine to detect clinical P. knowlesi infections from Malaysia, and nine assessing limit of detection (LoD) for P. knowlesi (PkA1-H.1) and P. falciparum (Pf3D7) cultures. Targets included Plasmodium-genus parasite lactate dehydrogenase (pan-pLDH) and P. vivax (Pv)-pLDH.ResultsSamples were collected prior to antimalarial treatment from 127 patients with microscopy-positive PCR-confirmed P. knowlesi mono-infections. Median parasitaemia was 788/µL (IQR 247-5,565/µL). Pan-pLDH sensitivities ranged from 50.6% (95% CI 39.6–61.5) (SD BIOLINE) to 87.0% (95% CI 75.1–94.6) (First Response® and CareStart™ PAN) compared to reference PCR. Pv-pLDH RDTs detected P. knowlesi with up to 92.0% (95% CI 84.3-96.7%) sensitivity (Biocredit™). For parasite counts ≥200/µL, pan-pLDH (Standard Q) and Pv-pLDH RDTs exceeded 95% sensitivity. Specificity of RDTs against 26 PCR-confirmed negative controls was 100%. Sensitivity of six highest performing RDTs were not significantly different when comparing samples taken before and after (median 3 hours) antimalarial treatment. Parasite ring stages were present in 30% of pre-treatment samples, with ring stage proportions (mean 1.9%) demonstrating inverse correlation with test positivity of Biocredit™ and two CareStart™ RDTs.For cultured P. knowlesi, CareStart™ PAN demonstrated the lowest LoD at 25 parasites/µL; LoDs of other pan-pLDH ranged from 98 to >2000 parasites/µL. Pv-pLDH LoD for P. knowlesi was 49 parasites/µL. No false-positive results were observed in either P. falciparum-pLDH or histidine-rich-protein-2 channels.ConclusionSelected RDTs demonstrate sufficient performance for detection of major human malaria species including P. knowlesi in co-endemic areas where microscopy is not available, particularly for higher parasite counts, although cannot reliably differentiate among non-falciparum malaria.