Performance of a fully-automated system on a WHO malaria microscopy evaluation slide set.

BACKGROUND: Manual microscopy remains a widely-used tool for malaria diagnosis and clinical studies, but it has inconsistent quality in the field due to variability in training and field practices. Automated diagnostic systems based on machine learning hold promise to improve quality and reproducibility of field microscopy. The World Health Organization (WHO) has designed a 55-slide set (WHO 55) for their External Competence Assessment of Malaria Microscopists (ECAMM) programme, which can also serve as a valuable benchmark for automated systems. The performance of a fully-automated malaria diagnostic system, EasyScan GO, on a WHO 55 slide set was evaluated. METHODS: The WHO 55 slide set is designed to evaluate microscopist competence in three areas of malaria diagnosis using Giemsa-stained blood films, focused on crucial field needs: malaria parasite detection, malaria parasite species identification (ID), and malaria parasite quantitation. The EasyScan GO is a fully-automated system that combines scanning of Giemsa-stained blood films with assessment algorithms to deliver malaria diagnoses. This system was tested on a WHO 55 slide set. RESULTS: The EasyScan GO achieved 94.3 % detection accuracy, 82.9 % species ID accuracy, and 50 % quantitation accuracy, corresponding to WHO microscopy competence Levels 1, 2, and 1, respectively. This is, to our knowledge, the best performance of a fully-automated system on a WHO 55 set. CONCLUSIONS: EasyScan GO's expert ratings in detection and quantitation on the WHO 55 slide set point towards its potential value in drug efficacy use-cases, as well as in some case management situations with less stringent species ID needs. Improved runtime may enable use in general case management settings.

Automated microscopy for routine malaria diagnosis: a field comparison on Giemsa-stained blood films in Peru.

BACKGROUND: Microscopic examination of Giemsa-stained blood films remains a major form of diagnosis in malaria case management, and is a reference standard for research. However, as with other visualization-based diagnoses, accuracy depends on individual technician performance, making standardization difficult and reliability poor. Automated image recognition based on machine-learning, utilizing convolutional neural networks, offers potential to overcome these drawbacks. A prototype digital microscope device employing an algorithm based on machine-learning, the Autoscope, was assessed for its potential in malaria microscopy. Autoscope was tested in the Iquitos region of Peru in 2016 at two peripheral health facilities, with routine microscopy and PCR as reference standards. The main outcome measures include sensitivity and specificity of diagnosis of malaria from Giemsa-stained blood films, using PCR as reference. METHODS: A cross-sectional, observational trial was conducted at two peripheral primary health facilities in Peru. 700 participants were enrolled with the criteria: (1) age between 5 and 75 years, (2) history of fever in the last 3 days or elevated temperature on admission, (3) informed consent. The main outcome measures included sensitivity and specificity of diagnosis of malaria from Giemsa-stained blood films, using PCR as reference. RESULTS: At the San Juan clinic, sensitivity of Autoscope for diagnosing malaria was 72% (95% CI 64-80%), and specificity was 85% (95% CI 79-90%). Microscopy performance was similar to Autoscope, with sensitivity 68% (95% CI 59-76%) and specificity 100% (95% CI 98-100%). At San Juan, 85% of prepared slides had a minimum of 600 WBCs imaged, thus meeting Autoscope's design assumptions. At the second clinic, Santa Clara, the sensitivity of Autoscope was 52% (95% CI 44-60%) and specificity was 70% (95% CI 64-76%). Microscopy performance at Santa Clara was 42% (95% CI 34-51) and specificity was 97% (95% CI 94-99). Only 39% of slides from Santa Clara met Autoscope's design assumptions regarding WBCs imaged. CONCLUSIONS: Autoscope's diagnostic performance was on par with routine microscopy when slides had adequate blood volume to meet its design assumptions, as represented by results from the San Juan clinic. Autoscope's diagnostic performance was poorer than routine microscopy on slides from the Santa Clara clinic, which generated slides with lower blood volumes. Results of the study reflect both the potential for artificial intelligence to perform tasks currently conducted by highly-trained experts, and the challenges of replicating the adaptiveness of human thought processes.

New vectors for simple and streamlined CRISPR-Cas9 genome editing in Saccharomyces cerevisiae.

Clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 technology is an important tool for genome editing because the Cas9 endonuclease can induce targeted DNA double-strand breaks. Targeting of the DNA break is typically controlled by a single-guide RNA (sgRNA), a chimeric RNA containing a structural segment important for Cas9 binding and a 20mer guide sequence that hybridizes to the genomic DNA target. Previous studies have demonstrated that CRISPR-Cas9 technology can be used for efficient, marker-free genome editing in Saccharomyces cerevisiae. However, introducing the 20mer guide sequence into yeast sgRNA expression vectors often requires cloning procedures that are complex, time-consuming and/or expensive. To simplify this process, we have developed a new sgRNA expression cassette with internal restriction enzyme sites that permit rapid, directional cloning of 20mer guide sequences. Here we describe a flexible set of vectors based on this design for cloning and expressing sgRNAs (and Cas9) in yeast using different selectable markers. We anticipate that the Cas9-sgRNA expression vector with the URA3 selectable marker (pML104) will be particularly useful for genome editing in yeast, since the Cas9 machinery can be easily removed by counter-selection using 5-fluoro-orotic acid (5-FOA) following successful genome editing. The availability of new vectors that simplify and streamline the technical steps required for guide sequence cloning should help accelerate the use of CRISPR-Cas9 technology in yeast genome editing.