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.

Background: Sudan is a Sub-Saharan African country with a high prevalence rate of Tuberculosis. Natural Resistant Associated Macrophage Protein 1 (NRAMP1) plays a potential role in the development of immunity against TB, and it has a critical role in disease resistance. The aim of the study was to evaluate the association of NRAMP1 polymorphism at intron4 (INT4) region with susceptibility to TB infection. Methods: Demographic, clinical and microbiological data were collected from 150 participants and investigated using designed questionnaire. The genotyping of NRAMP1-INT4 polymorphism was performed in 60 TB-infected patients and 50 healthy control using Polymerase chain reaction and restriction fragment-length polymorphism method (PCR-RFLP). Results: Among cases (60%) were males, only (3.3%) were vaccinated. The most reported risk factors were tobacco smoking (17%), diabetes (10%), alcohol consumption (2%) and corticosteroid therapy intakes (1%). Pulmonary TB was detected in 67% of the patients, 24% had pulmonary/MDR and 9% had extrapulmonary TB. The frequency of wild G allele was significantly higher in cases compared with healthy control subjects (P-value <0.0001). Also, a significant association was observed between the heterozygosity for NRAMP1-INT4 variant and resistance to TB infection (P-value 0.001, OR= 4.83, 95%CI 1.96~11.88). Homozygotes mutant INT4 (C/C) genotype was not detected in both cases and controls. Conclusions: the NRAMP1-INT4 polymorphism may serve as marker of unidentified genetic factors that may play a critical role in host immunity to TB in the Sudanese population. Further studies with large sample size are recommended to determine population-specific genetic associations with TB susceptibility in order to guide TB therapy and prophylaxis in a population-specific manner. Keywords: M. tuberculosis, MDR, NRAMP1, SNP, Sudan.